CN109451750A

CN109451750A - Model Predictive Control is optimized into the HVAC system being used together with distributed rudimentary air side

Info

Publication number: CN109451750A
Application number: CN201780041010.1A
Authority: CN
Inventors: 尼什·R·帕特尔; 罗伯特·D·特尼; 马修·J·埃利斯
Original assignee: Johnson Controls Technology Co
Current assignee: Johnson Controls Technology Co
Priority date: 2016-06-30
Filing date: 2017-06-21
Publication date: 2019-03-08
Also published as: JP7145766B2; JP2019527328A; WO2018005180A1

Abstract

A kind of building HVAC system includes the air side system with multiple air side sub-systems, high level model predictive controller (MPC) and multiple rudimentary air side MPC.Each air side sub-system includes being configured for providing the air side HVAC equipment being heated or cooled to the air side sub-system.The advanced MPC is configured for executing advanced optimization to generate optimum air side sub-system load distribution curve for each air side sub-system.The optimum air side sub-system load distribution optimization of profile cost of energy.Each of the rudimentary air side MPC is corresponding with one of the air side sub-system and is configured for executing rudimentary optimization to use the optimum air side sub-system load distribution curve of the respective air side sub-system being that the respective air side sub-system generates optimum air side desired temperature.Each of described rudimentary air side MPC is configured to the optimum air side desired temperature of the respective air side sub-system to operate the air side HVAC equipment of the respective air side sub-system.

Description

HVAC system using model predictive control with distributed low-level air side optimization

Cross reference to related patent applications

This application claims benefit and priority from U.S. patent application No. 15/199,909 filed on 30/6/2016 and U.S. patent application No. 15/199,910 filed on 30/6/2016. Both of these patent applications are incorporated herein by reference in their entirety.

Background

The present disclosure generally relates to a heating, ventilation and air conditioning (HVAC) system for a building. The present disclosure more particularly relates to a building HVAC system that uses Model Predictive Control (MPC) to optimize the energy costs consumed by the HVAC system.

Commercial building consumption accounts for approximately 20% of the total U.S. energy consumption, and the annual basic energy expenditure amounts to $ 2000 billion. The energy intelligence agency anticipates that commercial floor areas and basic energy consumption will continue to grow in the future. On the other hand, the average energy price is expected to remain relatively stable. As a result, the amount of energy consumed on commercial buildings will continue to increase dramatically. In view of these energy cost values and their projected growing importance, buildings have become a major goal of control strategies designed to reduce consumption or improve efficiency, particularly in the field of temperature control.

Many HVAC systems in commercial buildings and educational facilities use simple on/off controllers and proportional-integral-derivative (PID) controllers to control their equipment. The system relies on a temperature controller whose only goal is to converge to a desired temperature set point and hold it within a certain tolerance. However, a better goal is to minimize the total energy consumption or to minimize the total energy cost. In utility markets with time-varying prices, there is the potential to save costs by temporarily shifting the heating or cooling load using some form of energy storage. To achieve these savings, predictive optimization may be used with the system model to predict future loads. Load shifting relieves the power plant of the peak hours, enabling it to operate more efficiently. Furthermore, the cooler operates more efficiently during the night when the cooling water temperature is lower.

MPC has been a highly successful method of advanced process control over the last two decades. MPC uses a system model that relates inputs (control actions) to outputs (process measurements). The model is used to predict a process variable based on actions taken by the controller over a period of time called a range. At each step, the MPC uses this model to determine a sequence of control actions that achieve a goal (such as minimizing tracking error or input usage) while respecting process constraints such as plant capacity and safety margins to solve an online optimization problem. The first control action in the sequence is implemented and the optimization problem is solved again at the next step after obtaining a new measurement result. In an economic MPC, the goal of the optimization problem is to minimize the total cost.

Economically optimal control systems have not been widely deployed in the HVAC industry. One fundamental obstacle to successful deployment of MPCs in HVAC systems is the large number of building areas. To implement MPC in an HVAC system, it may be desirable to solve the optimization problem in a reasonably short time (e.g., on the order of a few minutes). Campus-wide embodiments may contain hundreds of buildings and thousands of air handler zones, each having tens of zones. A single combined control system for these applications is impractical and undesirable because the resulting single optimization problem is too large to be solved in real time.

Disclosure of Invention

One embodiment of the present disclosure is a heating, ventilation and air conditioning (HVAC) system for a building. The HVAC system includes an air-side system having a plurality of air-side subsystems, a high-level Model Predictive Controller (MPC), and a plurality of low-level air-side MPCs. Each air-side subsystem includes an air-side HVAC device configured to provide heating or cooling to the air-side subsystem. The advanced model predictive controller is configured to perform an advanced optimization to generate an optimal air-side subsystem load profile for each of the plurality of air-side subsystems. The optimal air side subsystem load profile optimizes energy costs. Each low-stage air-side MPC corresponds to one of the air-side subsystems and is configured to perform a low-stage optimization to generate an optimal air-side temperature setpoint for the respective air-side subsystem using the optimal air-side subsystem load profile for the respective air-side subsystem. Each of the low-stage air-side MPCs is configured to operate the air-side HVAC equipment of the respective air-side subsystem using the optimal air-side temperature setpoint for the respective air-side subsystem.

In some embodiments, the HVAC system comprises a waterside system having a waterside HVAC apparatus. The advanced MPC may be configured to generate an optimal waterside demand profile for the waterside system. The HVAC system may include a low-stage water-side MPC configured to perform low-stage optimization to generate an optimal water-side setpoint for the water-side system subject to demand constraints based on the optimal water-side demand profile. The low-stage water-side MPC may be configured to operate the water-side HVAC equipment using the optimal water-side setpoint.

In some embodiments, the air side subsystems represent separate buildings that are thermally decoupled from one another such that no direct heat exchange occurs between the air side subsystems.

In some embodiments, the advanced MPC is configured to generate an advanced cost function that defines the energy cost as a function of a water side demand profile. The waterside demand profile may indicate a thermal energy production of the waterside system at each of a plurality of time steps in an optimization period.

In some embodiments, the advanced MPC is configured to use a water side demand model to define the water side demand profile as a function of the plurality of air side subsystem load profiles. Each air-side subsystem load profile may indicate a thermal energy distribution to one of the air-side subsystems at each of the plurality of time steps.

In some embodiments, the advanced MPC is configured to generate an air-side subsystem temperature model for each of the plurality of air-side subsystems. Each air-side subsystem temperature model may define a relationship between the thermal energy distribution to the air-side subsystem and a temperature of the air-side subsystem.

In some embodiments, the advanced MPC is configured to optimize the energy cost and the plurality of air-side subsystem load profiles subject to constraints provided by the water-side demand model and each air-side subsystem temperature model.

In some embodiments, each air side subsystem includes multiple building zones. Each of the low-level air-side MPCs may be configured to generate an optimal air-side temperature setpoint for each of the plurality of building areas in the respective air-side subsystem.

In some embodiments, each of the low-level air-side MPCs is configured to generate a zone load profile for each of the plurality of building zones in the respective air-side subsystem. Each zone load profile may indicate a distribution of thermal energy to one of the building zones at each of a plurality of time steps in an optimization period.

In some embodiments, each of the optimal air side subsystem load profiles includes at least one of an optimal thermal energy load value for the respective air side subsystem at each of the plurality of time steps and an optimal temperature value for the respective air side subsystem at each of the plurality of time steps.

Another embodiment of the present disclosure is a method for optimizing energy costs of a building HVAC system. The building HVAC system includes an air-side system having a plurality of air-side subsystems. The method comprises the following steps: performing an advanced optimization at an advanced Model Predictive Controller (MPC) to generate an optimal air-side subsystem load profile for each of the plurality of air-side subsystems. The optimal air side subsystem load profile optimizes the energy cost. The method comprises the following steps: providing the optimal air-side subsystem load profile from the high-level MPC to a plurality of low-level air-side MPCs. Each of the low-stage air-side MPCs corresponds to one of the plurality of air-side subsystems. The method comprises the following steps: a low-level optimization is performed at each of the low-level air-side MPCs to generate optimal air-side temperature setpoints for the respective air-side subsystems. Each of the low-level optimizations is subject to a load constraint based on the optimal air-side subsystem load profile for the respective air-side subsystem. The method comprises the following steps: operating air-side HVAC equipment in each of the plurality of air-side subsystems using the optimal air-side temperature setpoint.

In some embodiments, performing the advanced optimization includes generating an optimal waterside demand profile for a waterside system. The method may further comprise: providing the optimal water side demand profile to a low grade water side MPC; performing a low-stage optimization under the low-stage water-side MPC to generate optimal water-side setpoints for the water-side system subject to demand constraints based on the optimal water-side demand profile; and operating a waterside HVAC equipment in the waterside system using the optimal waterside setpoint.

In some embodiments, performing the advanced optimization includes generating an advanced cost function that defines the energy cost as a function of a waterside demand profile. The waterside demand profile may indicate a thermal energy production of the waterside system at each of a plurality of time steps in an optimization period.

In some embodiments, performing the advanced optimization includes using a water side demand model to define the water side demand profile as a function of the plurality of air side subsystem load profiles. Each air-side subsystem load profile may indicate a thermal energy distribution to one of the air-side subsystems at each of the plurality of time steps.

In some embodiments, performing the advanced optimization includes generating an air-side subsystem temperature model for each of the plurality of air-side subsystems. Each air-side subsystem temperature model may define a relationship between the thermal energy distribution to the air-side subsystem and a temperature of the air-side subsystem.

In some embodiments, performing the advanced optimization includes optimizing the energy cost and the plurality of air-side subsystem load profiles subject to constraints provided by the water-side demand model and each air-side subsystem temperature model.

In some embodiments, each air side subsystem includes multiple building zones. Performing the low-level optimization may include generating an optimal air-side temperature set-point for each of the plurality of building areas.

In some embodiments, performing the low-level optimization includes generating a regional load profile for each of the plurality of building regions. Each zone load profile may indicate a distribution of thermal energy to one of the building zones at each of a plurality of time steps in an optimization period.

Another embodiment of the present disclosure is a method for optimizing energy costs of a building HVAC system. The building HVAC system includes an air-side system having a plurality of air-side subsystems. The method comprises the following steps: performing an advanced optimization at an advanced Model Predictive Controller (MPC) to generate an optimal air-side subsystem temperature profile for each of the plurality of air-side subsystems. The optimal air side subsystem temperature profile optimizes the energy cost. The method comprises the following steps: providing the optimal air-side subsystem temperature profile from the high-level MPC to a plurality of low-level air-side MPCs. The method comprises the following steps: performing a low-level optimization at each of the low-level air-side MPCs to generate optimal air-side temperature setpoints for the plurality of air-side subsystems. The optimal air-side temperature setpoint minimizes an error between an air-side subsystem temperature and the optimal air-side subsystem temperature profile. The method comprises the following steps: operating air-side HVAC equipment in each of the plurality of air-side subsystems using the optimal air-side temperature setpoint.

Another embodiment of the present disclosure is a heating, ventilation and air conditioning (HVAC) system for a building. The HVAC system includes an air-side system having a plurality of air-side subsystems, a water-side system, an advanced Model Predictive Controller (MPC), and a plurality of low-level air-side MPCs. Each air-side subsystem includes an air-side HVAC device configured to provide heating or cooling to the air-side subsystem. The water-side system includes a water-side HVAC device configured to generate thermal energy for use by the air-side system to provide heating or cooling. The advanced MPC is configured to perform an advanced optimization to generate an optimal air-side subsystem load profile for each of the plurality of air-side subsystems. The optimal air-side subsystem load profile optimizes total energy costs of both air-side power consumption of the air-side system and water-side power consumption of the water-side system at each of a plurality of time steps in an optimization period. Each of the low-stage air-side MPCs corresponds to one of the air-side subsystems and is configured to operate the air-side HVAC equipment of the respective air-side subsystem using the optimal air-side subsystem load profile for the respective air-side subsystem.

In some embodiments, each air-side subsystem load profile indicates a thermal energy distribution to one of the plurality of air-side subsystems at each of the plurality of time steps. The advanced MPC may be configured to use an air-side power consumption model to define the air-side power consumption of each air-side subsystem as a function of the thermal energy distribution to the air-side subsystem.

In some embodiments, the advanced MPC is configured to generate an air-side subsystem temperature model for each of the plurality of air-side subsystems. Each air-side subsystem temperature model may define a relationship between one of the air-side subsystem load profiles and a temperature of the corresponding air-side subsystem.

In some embodiments, the advanced MPC is configured to generate an optimal waterside demand profile for the waterside system. The system may include a low-stage waterside model predictive controller configured to perform low-stage optimization to generate optimal waterside setpoints for the waterside system subject to demand constraints based on the optimal waterside demand profile. The low-stage waterside model predictive controller may be configured to operate a waterside HVAC device in the waterside system using the optimal waterside setpoint.

In some embodiments, the advanced MPC is configured to perform the advanced optimization by optimizing an advanced cost function that defines the total energy cost as a function of a water side demand profile indicative of the thermal energy production of the water side system at each of the plurality of time steps in the optimization period. The advanced MPC may use a water side demand model to define the water side demand profile as a function of the plurality of air side subsystem load profiles.

In some embodiments, each of the low-stage air-side model predictive controllers is configured to perform a low-stage optimization to generate an optimal air-side temperature setpoint for the respective air-side subsystem using the optimal air-side subsystem load profile for the respective air-side subsystem. Each low-stage air-side MPC may operate the air-side HVAC equipment in the respective air-side subsystem using the optimal air-side temperature setpoint for the respective air-side subsystem.

In some embodiments, each air side subsystem includes multiple building zones. The optimal air-side temperature setpoint for each air-side subsystem may include an optimal air-side temperature setpoint for each of the plurality of building zones in the air-side subsystem.

In some embodiments, each of the low-level air-side MPCs is configured to generate a zone load profile for each of the plurality of building zones in the respective air-side subsystem. Each zone load profile may indicate a distribution of thermal energy to one of the building zones at each of the plurality of time steps in the optimization period.

Another embodiment of the present disclosure is a method for optimizing energy costs of a building HVAC system. The building HVAC system includes a water side system and an air side system having a plurality of air side subsystems. The method comprises the following steps: generating an advanced cost function defining the energy cost as a function of both the water side power consumption of the water side system and the air side power consumption of each air side subsystem at each of a plurality of time steps in an optimization period. The method comprises the following steps: performing an advanced optimization at an advanced Model Predictive Controller (MPC) to generate an optimal air-side subsystem load profile for each of the plurality of air-side subsystems. The optimal air side subsystem load profile optimizes the energy cost. The method comprises the following steps: providing the optimal air-side subsystem load profile from the high-level MPC to a plurality of low-level air-side MPCs. Each of the low-stage air-side MPCs corresponds to one of the plurality of air-side subsystems. The method comprises the following steps: operating air-side HVAC equipment in the respective air-side subsystem at each of the low-stage air-side MPCs using the optimal air-side subsystem load profile.

In some embodiments, each air-side subsystem load profile indicates a thermal energy distribution to one of the plurality of air-side subsystems at each of the plurality of time steps. The method may further comprise: using an air-side power consumption model to define the air-side power consumption of each air-side subsystem as a function of the thermal energy allocation to the air-side subsystem.

In some embodiments, the method includes generating an air-side subsystem temperature model for each of the plurality of air-side subsystems. Each air-side subsystem temperature model may define a relationship between one of the air-side subsystem load profiles and a temperature of the corresponding air-side subsystem.

In some embodiments, performing the advanced optimization includes generating an optimal waterside demand profile for the waterside system. The method may include: providing the optimal water side demand profile to a low grade water side MPC; performing a low-stage optimization under the low-stage water-side MPC to generate optimal water-side setpoints for the water-side system subject to demand constraints based on the optimal water-side demand profile; and operating a waterside HVAC equipment in the waterside system using the optimal waterside setpoint.

In some embodiments, the advanced cost function defines the energy cost as a function of a waterside demand profile indicative of thermal energy production of the waterside system at each of the plurality of time steps in the optimization period. The method may include: using a water side demand model to define the water side demand profile as a function of the plurality of air side subsystem load profiles.

In some embodiments, the method comprises: performing a low-stage optimization at each of the low-stage air-side MPCs to generate an optimal air-side temperature setpoint for the respective air-side subsystem using the optimal air-side subsystem load profile for the respective air-side subsystem. The method may include: operating the air-side HVAC equipment in the respective air-side subsystem using the optimal air-side temperature setpoint for the respective air-side subsystem.

In some embodiments, performing the low-level optimization includes generating a regional load profile for each of the plurality of building regions. Each zone load profile may indicate a distribution of thermal energy to one of the building zones at each of the plurality of time steps in the optimization period.

Another embodiment of the present disclosure is a method for optimizing energy costs of a building HVAC system. The building HVAC system includes a water side system and an air side system having a plurality of air side subsystems. The method comprises the following steps: generating an advanced cost function defining the energy cost as a function of both the water side power consumption of the water side system and the air side power consumption of each air side subsystem at each of a plurality of time steps in an optimization period. The method comprises the following steps: performing an advanced optimization at an advanced Model Predictive Controller (MPC) to generate an optimal air-side subsystem temperature profile for each of the plurality of air-side subsystems. The optimal air side subsystem temperature profile optimizes the energy cost defined with the cost function. The method comprises the following steps: providing the optimal air-side subsystem temperature profile from the high-level MPC to a plurality of low-level air-side MPCs. Each of the low-stage air-side MPCs corresponds to one of the plurality of air-side subsystems. The method comprises the following steps: operating air-side HVAC equipment in the respective air-side subsystem at each of the low-stage air-side MPCs using the optimal air-side subsystem temperature profile.

Those skilled in the art will appreciate that the summary is illustrative only and is not intended to be in any way limiting. Other aspects, inventive features, and advantages of the devices and/or processes described herein, as defined solely by the claims, will become apparent in the detailed description set forth herein when taken in conjunction with the accompanying drawings.

Drawings

FIG. 1A is a schematic diagram of a waterside system and an air-side system having multiple air-side subsystems, according to some embodiments.

Figure 1B is an illustration of a building equipped with an HVAC system having an air-side system and a water-side system, according to some embodiments.

Fig. 2 is a schematic diagram of a waterside system that may be used in the systems of fig. 1A-1B, according to some embodiments.

Fig. 3 is a block diagram of an air-side system that may be used in the systems of fig. 1A-1B, according to some embodiments.

Fig. 4 is a block diagram of a Building Management System (BMS) that may be used to monitor and control the building of fig. 1B, according to some embodiments.

Fig. 5 is a block diagram of another BMS that may be used to monitor and control the building of fig. 1B, according to some embodiments.

Fig. 6 is a block diagram of a distributed model predictive control system having an advanced model predictive controller, several low-level air-side model predictive controllers, and a low-level water-side model predictive controller, in accordance with some embodiments.

FIG. 7 is a block diagram illustrating the advanced model predictive controller of FIG. 6 in greater detail, in accordance with some embodiments.

FIG. 8 is a block diagram illustrating the low-level model predictive controller of FIG. 6 in greater detail, in accordance with some embodiments.

FIG. 9 is a graph of ambient temperature and power cost over time, both of which may be provided as inputs to the advanced model predictive controller of FIGS. 6-7, in accordance with some embodiments.

Fig. 10 is a graph of building temperature and cooling load (cooling duty) over time, both of which may be provided as a result of advanced optimization performed by the advanced model predictive controller of fig. 6-7 when the cost of air side power consumption is not included in the advanced optimization, according to some embodiments.

Fig. 11 is a graph of building temperature and cooling load over time, both of which may be provided as a result of the advanced optimization performed by the advanced model predictive controller of fig. 6-7, when the cost of air side power consumption is included in the advanced optimization, in accordance with some embodiments.

Fig. 12 is a graph of zone temperatures and zone temperature setpoints over time, both of which may be provided as a result of low-level optimization performed by the low-level model predictive controllers of fig. 6 and 8, in accordance with some embodiments.

FIG. 13 is a graph of water side demand, production, thermal energy storage, and water side equipment utilization over time illustrating performance of the water side system of FIG. 6 resulting from high level optimization and low level water side optimization, according to some embodiments.

FIG. 14 is a flow diagram of a high-level and distributed low-level model predictive control technique that may be used to optimize the energy cost of the MPC system of FIG. 6 when air-side subsystem loads are used as constraints in low-level optimization, in accordance with some embodiments.

FIG. 15 is another flow diagram of a high-level and distributed low-level model predictive control technique that may be used to optimize the energy cost of the MPC system of FIG. 6 when the low-level optimization tracks the temperature profile provided by the high-level optimization, in accordance with some embodiments.

Detailed Description

Referring generally to the drawings, a heating, ventilation and air conditioning (HVAC) system for a building is shown, according to some embodiments. The HVAC system includes a water side system and an air side system having a plurality of air side subsystems. The HVAC system uses a Model Predictive Control (MPC) system to generate optimal setpoints for the HVAC equipment of the water-side system and the HVAC equipment of each of the air-side sub-systems.

MPC is a control technique that uses a model of a controlled system to relate system inputs (e.g., control actions, set points, etc.) to system states and system outputs (e.g., measurements, process variables, etc.). The model may be used to predict system states and system outputs based on actions taken by the controller at each time step during the optimization period. At each time step, the MPC solves the online optimization problem using a system model to determine a sequence of control actions that achieve a goal (e.g., minimize tracking error, minimize energy cost, etc.) while respecting process constraints such as plant capacity and safety margins (e.g., temperature constraints, plant switching constraints, etc.). The first control action in the sequence is implemented and the optimization problem is solved again at the next time step after obtaining a new measurement result.

The HVAC systems described herein can optimize (e.g., minimize) the total cost of energy used to provide heating and/or cooling to a building or campus. A number of studies have shown that MPCs are superior to existing control systems due to their ability to predict the future and to anticipate events before they occur. By using the mass of the building for passive Thermal Energy Storage (TES), the MPC enables the energy load to be transferred from peak hours to off-peak hours. Active thermal energy storage (e.g., cold water tanks, hot water tanks, etc.) may also be used to further facilitate load transfer. By combining active and passive storage systems, energy costs can be reduced by concentrating device usage to periods of lower resource prices while maintaining comfort limits within each building.

In some embodiments, the HVAC system includes an MPC layer and a supervisory layer. The MPC layer may receive measurements from the supervisory layer and provide settings to the supervisory layer. The MPC layer may generate optimal values for various decision variables, including, for example, zone temperature setpoints, plant on/off decisions, and TES charge/discharge rates. The MPC layer may use system models, such as zone temperature and cooling/heating load models, cooling/heating load and temperature setpoint models, plant models, and active TES models, to determine the optimal values for the decision variables. The MPC layer may determine the optimal values of the decision variables by performing an optimization process subject to several constraints. The constraints may include comfort limits for zone air temperature, equipment capacity constraints, TES tank size, and rate of change limits for supervisory layer equipment.

Solving a single MPC problem to determine the optimal values for all decision variables can be difficult for large-scale applications. For example, some air side systems may include thousands of discrete zones, and some water side systems may include thousands of unique HVAC devices. Discrete decisions (e.g., turning devices on/off) can lead to mixed integer optimization problems, which further increases complexity. Due to the difficulty and computational complexity of the MPC problem, the MPC layer can decompose the entire MPC problem into multiple smaller, more manageable optimization problems.

The HVAC system can decompose the entire MPC problem into a high-level optimization problem and a low-level optimization problem. The high-level problem may be solved by a high-level model predictive controller to determine a load profile for each of the plurality of low-level air-side subsystems and a demand profile for the water-side system. In some embodiments, the high-level controller uses an active TES model and aggregates low-level models for each air-side subsystem to reduce computational complexity. The advanced controller may determine a load profile that optimizes (e.g., minimizes) the overall operating cost of the MPC system over an optimization period. Each load profile may include a load value at each time step in the optimization period. The high-level controller may provide the load profile to a plurality of low-level air-side model predictive controllers. The low-level air-side controller may use the load profile as a constraint to define a maximum allowable load value for each air-side subsystem at each time step in the optimization interval.

The low-level optimization problem may be further decomposed into a low-level water-side optimization problem and one or more low-level air-side optimization problems. Each low-level air-side problem may be addressed by one of the low-level air-side controllers to determine zone temperature setpoints for the air-side equipment of each air-side subsystem. Each low level air side controller may determine a zone temperature setpoint that optimizes (e.g., minimizes) the energy consumption of the corresponding air side subsystem while maintaining the zone temperature within defined temperature limits and without exceeding the load value provided by the high level controller. Alternatively, each low-level air-side controller may determine a temperature set point that tracks an average building temperature (e.g., a predicted building temperature state) according to a high-level optimization problem. These and other components of the HVAC system are described in greater detail below.

Building and campus HVAC system

Referring now to fig. 1A-1B, an embodiment of a heating, ventilation, and air conditioning (HVAC) system for a building or campus (i.e., a collection of buildings) is shown. FIG. 1A is a schematic diagram of a large-scale HVAC system 20 including a water side system 30 and an air side system 50. The waterside system 30 is shown as a central facility with a boiler 32, a cooler 34, a heat recovery cooler 36, a cooling tower 38, a cold Thermal Energy Storage (TES) tank 40, a hot TES tank 42, and a pump 44. The equipment of the waterside system 30 may operate to heat or cool a working fluid (e.g., water, glycol, etc.) and provide the working fluid to the air side system 50. The air side system 50 may use the heating or cooling fluid from the water side system 30 to heat or cool the airflow provided to the various building areas. Examples of water-side and air-side systems that may be used in the HVAC system 20 are described in more detail with reference to fig. 2-3.

In the campus-wide embodiment shown in fig. 1A, air side system 50 may be distributed across multiple buildings 11-17. Air side system 50 may include a plurality of Air Handling Units (AHUs) distributed across buildings 11-17. In some embodiments, the AHU is a rooftop AHU located on the roof of the buildings 11-17. In other embodiments, the AHUs may be distributed across multiple floors or areas of the buildings 11-17. Each of the buildings 11 to 17 may include one or more AHUs. For example, the building 11 is shown to include a first AHU 52 located on a first floor 53, a second AHU54 located on a second floor 55, a third AHU 56 located on a third floor 57, a fourth AHU 58 located on a fourth floor 59, and a fifth AHU 60 located on a fifth floor 61.

Each AHU of the air side system 50 may receive heating and/or cooling fluid from the waterside system 30 and may use such fluid to provide heating or cooling for various building areas. Each AHU may be configured to provide airflow to a single building area or multiple building areas. For example, AHU 52 may be configured to provide airflow to building areas 62, 64, 66, and 68. In large-scale HVAC systems, such as the HVAC system 20, the air-side system 50 may include a large number of buildings (e.g., tens, hundreds, etc.), with each building having multiple AHUs and a large number of building areas. Each building zone may be independently controlled (e.g., via dampers or Variable Air Volume (VAV) units) and may have different temperature setpoints. In some embodiments, the control objective of the HVAC system 20 is to determine the appropriate temperature setpoints for all building areas and operate the equipment of the water side system 30 and the air side system 50 to meet the respective loads.

Referring now to FIG. 1B, a perspective view of building 10 is shown. Building 10 is served by an HVAC system 100 that operates on a relatively smaller scale than HVAC system 20. HVAC system 100 may include a plurality of HVAC devices (e.g., heaters, coolers, air handling units, pumps, fans, thermal energy storage devices, etc.) configured to provide heating, cooling, ventilation, or other services to building 10. For example, the HVAC system 100 is shown as including a waterside system 120 and an air-side system 130. The waterside system 120 may provide heated or cooled fluid to the air handling unit of the air-side system 130. Air side system 130 may use heated or cooled fluid to heat or cool the airflow provided to building 10. Exemplary water side and air side systems that may be used in the HVAC system 100 are described in more detail with reference to fig. 2-3.

The HVAC system 100 is shown to include a chiller 102, a boiler 104, and a rooftop AHU 106. The waterside system 120 may use the boiler 104 and the cooler 102 to heat or cool a working fluid (e.g., water, glycol, etc.) and may circulate the working fluid to the AHU 106. In various embodiments, the HVAC devices of the waterside system 120 may be located in or around the building 10 (as shown in fig. 1B) or at an offsite location such as a central facility (as shown in fig. 1A). The working fluid may be heated in boiler 104 or cooled in cooler 102, depending on whether heating or cooling is desired in building 10. The boiler 104 may add heat to the circulating fluid, for example, by burning combustible materials (e.g., natural gas) or using an electrical heating element. The cooler 102 may place the circulating fluid in heat exchange relationship with another fluid (e.g., a refrigerant) in a heat exchanger (e.g., an evaporator) to absorb heat from the circulating fluid. The working fluid from the chiller 102 and/or boiler 104 may be delivered to the AHU 106 via conduit 108.

AHU 106 may place the working fluid in heat exchange relationship with the airflow through AHU 106 (e.g., via one or more stages of cooling coils and/or heating coils). The airflow may be, for example, outdoor air, return air from within building 10, or a combination of both. AHU 106 may transfer heat between the airflow and the working liquid to provide heating or cooling to the airflow. For example, AHU 106 may include one or more fans or blowers configured to move airflow over or through a heat exchanger containing a working fluid. The working fluid may then be returned to the cooler 102 or boiler 104 via line 110.

Air side system 130 may deliver an airflow supplied by AHU 106 (i.e., a supply airflow) to building 10 via air supply duct 112 and may provide return air from building 10 to AHU 106 via air return duct 114. In some embodiments, the air side system 130 includes a plurality of Variable Air Volume (VAV) units 116. For example, the air side system 130 is shown to include separate VAV units 116 on each floor or zone of the building 10. The VAV unit 116 may include dampers or other flow control elements that may be operated to control the amount of supply airflow provided to individual areas of the building 10. In other embodiments, the air side system 130 delivers the supply airflow into one or more areas of the building 10 (e.g., via the supply duct 112) without using the intermediate VAV unit 116 or other flow control elements. AHU 106 may include various sensors (e.g., temperature sensors, pressure sensors, etc.) configured to measure properties of the supply airflow. AHU 106 may receive input from sensors positioned within AHU 106 and/or within a building area and may adjust the flow rate, temperature, or other attributes of the supply airflow through AHU 106 to achieve setpoint conditions for the building area.

Water side system

Referring now to FIG. 2, a block diagram of a waterside system 200 according to some embodiments is shown. In some embodiments, the waterside system 200 may supplement or replace the waterside system 30 in the HVAC system 20 or the waterside system 120 in the HVAC system 100. The waterside system 200 may include some or all of the HVAC devices in the HVAC system 20 (e.g., boiler 32, chiller 34, heat recovery chiller 36, etc.) or some or all of the HVAC devices in the HVAC system 100 (e.g., boiler 104, chiller 102, pumps, valves, etc.) and may operate to supply heated or cooled fluid to the air-side system 50 or the air-side system 130. The HVAC devices of waterside system 200 may be located within building 10 (as shown in fig. 1B) or at an offsite location such as a central facility (as shown in fig. 1A).

The waterside system 200 is shown as a central facility having a plurality of sub-facilities 202 to 212. The sub-facilities 202 to 212 are shown as including: a heater sub-facility 202, a heat recovery chiller sub-facility 204, a chiller sub-facility 206, a cooling tower sub-facility 208, a Thermal Energy Storage (TES) sub-facility 210, and a cold Thermal Energy Storage (TES) sub-facility 212. The sub-facilities 202-212 consume resources (e.g., water, natural gas, electricity, etc.) from the utility to service the thermal energy load (e.g., hot water, cold water, heating, cooling, etc.) of the building or campus. For example, the heater sub-facility 202 may be configured to heat water in a hot water circuit 214 that circulates hot water between the heater sub-facility 202 and the buildings 10-17. The chiller sub-facility 206 may be configured to cool water in a cold water circuit 216 that circulates cold water between the chiller sub-facility 206 and the buildings 10-17. The heat recovery chiller sub-facility 204 may be configured to transfer heat from the cold water circuit 216 to the hot water circuit 214 to provide additional heating of the hot water and additional cooling of the cold water. The condensate water circuit 218 may absorb heat from the cold water in the chiller sub-facility 206 and reject the absorbed heat in the cooling tower sub-facility 208 or transfer the absorbed heat to the hot water circuit 214. The hot TES sub-facility 210 and cold TES sub-facility 212 may store hot and cold thermal energy, respectively, for subsequent use.

The hot water loop 214 and the cold water loop 216 may deliver heated and/or cooled water to an AHU located on the roof of the building 10 (as shown in fig. 1B) or to individual floors or areas of the buildings 11-17 (as shown in fig. 1A). The AHU pushes air through a heat exchanger (e.g., a heating coil or cooling coil) through which water flows to provide heating or cooling of the air. Heated or cooled air may be delivered to individual areas of the buildings 10-17 to service the thermal energy load of the buildings 10-17. The water is then returned to the sub-facilities 202-212 to receive further heating or cooling.

Although the sub-facilities 202-212 are shown or described as heating or cooling water for circulation to a building, it should be understood that any other type of working fluid (e.g., ethylene glycol, CO2, etc.) may be used instead of or in addition to water to service the thermal energy load. In other embodiments, the sub-facilities 202-212 may provide heating and/or cooling directly to a building or campus without the need for an intermediate heat transfer fluid. These and other variations to waterside system 200 are within the teachings of the present disclosure.

Each of the sub-facilities 202-212 may include various devices configured to facilitate the functionality of the sub-facility. For example, the heater sub-facility 202 is shown to include a plurality of heating elements 220 (e.g., boilers, electric heaters, etc.) configured to add heat to the hot water in the hot water circuit 214. The heater sub-facility 202 is also shown to include several pumps 222 and 224 configured to circulate hot water in the hot water circuit 214 and control the flow rate of the hot water through the individual heating elements 220. The chiller sub-facility 206 is shown to include a plurality of chillers 232 configured to remove heat from the cold water in the cold water circuit 216. The chiller sub-facility 206 is also shown to include several pumps 234 and 236 configured to circulate the cold water in the cold water circuit 216 and control the flow rate of the cold water through the individual chillers 232.

The heat recovery chiller sub-facility 204 is shown to include a plurality of heat recovery heat exchangers 226 (e.g., refrigeration circuits) configured to transfer heat from the cold water circuit 216 to the hot water circuit 214. The heat recovery chiller sub-facility 204 is also shown to include several pumps 228 and 230 configured to circulate hot and/or cold water through the heat recovery heat exchangers 226 and to control the flow rate of water through the individual heat recovery heat exchangers 226. The cooling tower sub-facility 208 is shown to include a plurality of cooling towers 238 configured to remove heat from the condensate in the condensate circuit 218. The cooling tower sub-facility 208 is also shown to include pumps 240 configured to circulate the condensate in the condensate loop 218 and control the flow rate of the condensate through the individual cooling towers 238.

The hot TES sub-facility 210 is shown to include a hot TES tank 242 configured to store hot water for later use. The hot TES sub-facility 210 may also include one or more pumps or valves configured to control the flow rate of hot water into or out of hot TES tank 242. The cold TES sub-facility 212 is shown to include a cold TES tank 244 configured to store cold water for later use. The cold TES sub-facility 212 may also include one or more pumps or valves configured to control the flow rate of cold water flowing into or out of cold TES tank 244.

In some embodiments, one or more of the pumps (e.g., pumps 222, 224, 228, 230, 234, 236, and/or 240) in the waterside system 200 or the pipeline in the waterside system 200 includes an isolation valve associated therewith. An isolation valve may be integrated with or positioned upstream or downstream of the pump to control fluid flow in the waterside system 200. In various embodiments, the waterside system 200 may include more, fewer, or different types of devices and/or sub-facilities based on the particular configuration of the waterside system 200 and the type of load being serviced by the waterside system 200.

Air side system

Referring now to FIG. 3, a block diagram of an air-side system 300 is shown, according to some embodiments. In some embodiments, the air-side system 300 may supplement or replace the air-side system 50 in the HVAC system 20 or the air-side system 130 in the HVAC system 100. Air-side system 300 may include some or all of the HVAC devices in HVAC system 20 (e.g., AHUs 52-60) or some or all of the HVAC devices in HVAC system 100 (e.g., AHU 106, VAV unit 116, ducts 112-114, fans, dampers, etc.) and may be positioned in or around buildings 10-17. The air side system 300 may be operable to heat or cool the airflow provided to the buildings 10-17 using heated or cooled fluid provided by the water side system 200.

Air-side system 300 is shown to include an economizer AHU 302. The economizer AHU varies the amount of outside air and return air that the air handling unit uses for heating or cooling. For example, AHU 302 may receive return air 304 from building area 306 via return air duct 308 and may deliver supply air 310 to building area 306 via supply air duct 312. In some embodiments, AHU 302 is a rooftop unit positioned on the roof of building 10 (e.g., AHU 106 as shown in fig. 1B) or otherwise positioned to receive both return air 304 and outside air 314. The AHU 302 may be configured to operate an exhaust damper 316, a mixing damper 318, and an external air damper 320 to control the amount of external air 314 and return air 304 that combine to form the supply air 310. Any return air 304 that does not pass through the mixing damper 318 may be exhausted from the AHU 302 through the exhaust damper 316 as exhaust 322.

Each of the dampers 316-320 may be operated by an actuator. For example, the exhaust damper 316 may be operated by an actuator 324, the mixing damper 318 may be operated by an actuator 326, and the external air damper 320 may be operated by an actuator 328. The actuators 324-328 may communicate with the AHU controller 330 via a communication link 332. The actuators 324-328 may receive control signals from the AHU controller 330 and may provide feedback signals to the AHU controller 330. The feedback signals may include, for example, an indication of a current actuator or damper position, an amount of torque or force applied by the actuator, diagnostic information (e.g., results of diagnostic tests performed by the actuators 324-328), status information, debug information, configuration settings, calibration data, and/or other types of information or data that may be collected, stored, or used by the actuators 324-328. The AHU controller 330 may be an economizer controller configured to control the actuators 324-328 using one or more control algorithms (e.g., a state-based algorithm, an Extremum Seeking Control (ESC) algorithm, a proportional-integral (PI) control algorithm, a proportional-integral-derivative (PID) control algorithm, a Model Predictive Control (MPC) algorithm, a feedback control algorithm, etc.).

Still referring to fig. 3, AHU 302 is shown to include cooling coil 334, heating coil 336, and fan 338 positioned within supply air duct 312. Fan 338 may be configured to push supply air 310 through cooling coil 334 and/or heating coil 336 and provide supply air 310 to building area 306. AHU controller 330 may communicate with fan 338 via communication link 340 to control the flow rate of supply air 310. In some embodiments, AHU controller 330 controls the amount of heating or cooling applied to supply air 310 by adjusting the speed of fan 338.

The cooling coil 334 may receive cooled fluid from the waterside system 200 (e.g., from the cold water circuit 216) via a line 342 and may return the cooled fluid to the waterside system 200 via a line 344. Valve 346 may be positioned along either conduit 342 or conduit 344 to control the flow rate of the cooled fluid through cooling coil 334. In some embodiments, cooling coil 334 includes multiple stages of cooling coils that can be independently activated and deactivated (e.g., by AHU controller 330, by BMS controller 366, etc.) to adjust the amount of cooling applied to supply air 310.

The heating coil 336 may receive heated liquid from the waterside system 200 (e.g., from the hot water circuit 214) via a line 348 and may return the heated liquid to the waterside system 200 via a line 350. A valve 352 may be positioned along line 348 or line 350 to control the flow rate of heated fluid through heating coil 336. In some embodiments, heating coils 336 include multiple stages of heating coils that can be independently activated and deactivated (e.g., by AHU controller 330, by BMS controller 366, etc.) to regulate the amount of heating applied to supply air 310.

Each of valves 346 and 352 may be controlled by an actuator. For example, valve 346 may be controlled by actuator 354 and valve 352 may be controlled by actuator 356. Actuators 354-356 may communicate with AHU controller 330 via communication links 358-360. Actuators 354-356 may receive control signals from AHU controller 330 and may provide feedback signals to controller 330. In some embodiments, AHU controller 330 receives measurements of the supply air temperature from a temperature sensor 362 positioned in supply air duct 312 (e.g., downstream of cooling coil 334 and/or heating coil 336). AHU controller 330 may also receive temperature measurements for building area 306 from temperature sensors 364 located in building area 306.

In some embodiments, AHU controller 330 operates valves 346 and 352 via actuators 354-356 to adjust the amount of heating or cooling provided to supply air 310 (e.g., to reach a set point temperature of supply air 310 or to maintain the temperature of supply air 310 within a set point temperature range). The position of valves 346 and 352 affects the amount of heating or cooling provided to supply air 310 by cooling coil 334 or heating coil 336 and may be related to the amount of energy consumed to achieve a desired supply air temperature. AHU controller 330 may control the temperature of supply air 310 and/or building area 306 by activating or deactivating coils 334-336, adjusting the speed of fan 338, or a combination of both.

Still referring to fig. 3, air side system 300 is shown to include a Building Management System (BMS) controller 366 and a client device 368. BMS controller 366 may include one or more computer systems (e.g., servers, supervisory controllers, subsystem controllers, etc.) that function as system level controllers, application or data servers, head nodes, or master controllers for air side system 300, water side system 200, HVAC system 100, HVAC system 20, and/or other controllable systems that serve buildings 10-17. BMS controller 366 may communicate with a plurality of downstream building systems or subsystems (e.g., HVAC systems 20 or 100, security systems, lighting systems, waterside systems 200, etc.) via communication links 370 according to similar or different protocols (e.g., LON, BACnet, etc.). In various embodiments, AHU controller 330 and BMS controller 366 may be separate (as shown in fig. 3) or integrated. In an integrated embodiment, AHU controller 330 may be a software module configured for execution by a processor of BMS controller 366.

In some embodiments, AHU controller 330 receives information (e.g., commands, set points, operational boundaries, etc.) from BMS controller 366 and provides information (e.g., temperature measurements, valve or actuator positions, operating conditions, diagnostics, etc.) to BMS controller 366. For example, AHU controller 330 may provide BMS controller 366 with temperature measurements from temperature sensors 362-364, device on/off status, device operational capabilities, and/or any other information that may be used by BMS controller 366 to monitor and control variable states or conditions within building area 306.

Client device 368 may include one or more human machine interfaces or client interfaces (e.g., graphical user interfaces, reporting interfaces, text-based computer interfaces, client-oriented web services, web servers that provide pages to web clients, etc.) for controlling, viewing, or otherwise interacting with HVAC system 20, HVAC system 100, and/or various devices thereof. Client device 368 may be a computer workstation, a client terminal, a remote or local interface, or any other type of user interface device. Client device 368 may be a fixed terminal or a mobile device. For example, client device 368 may be a desktop computer, a computer server with a user interface, a laptop computer, a tablet computer, a smart phone, a PDA, or any other type of mobile or non-mobile device. Client device 368 may communicate with BMS controller 366 and/or AHU controller 330 via communication link 372.

Building management system

Referring now to fig. 4, a block diagram of a Building Management System (BMS)400 is shown, according to an example embodiment. BMS 400 may be implemented in one or more buildings 10-17 to automatically monitor and control various building functions. BMS 400 is shown to include BMS controller 366 and a plurality of building subsystems 428. Building subsystem 428 is shown to include a building electrical subsystem 434, an Information Communication Technology (ICT) subsystem 436, a safety subsystem 438, an HVAC subsystem 440, a lighting subsystem 442, an elevator/escalator subsystem 432, and a fire safety subsystem 430. In various embodiments, building subsystems 428 may include fewer, additional, or alternative subsystems. For example, building subsystems 428 may also or alternatively include a refrigeration subsystem, an advertising or guidance indication subsystem, a cooking subsystem, a vending subsystem, a printer or copy service subsystem, or any other type of building subsystem that uses controllable devices and/or sensors to monitor or control buildings 10-17. In some embodiments, building subsystems 428 include waterside system 200 and/or airside system 300, as described with reference to fig. 2-3.

Each of building subsystems 428 can include any number of devices, controllers, and connections for performing its individual functions and control activities. As described with reference to fig. 1A-3, the HVAC subsystem 440 may include many of the same components as the HVAC system 20 or the HVAC system 100. For example, HVAC subsystem 440 may include chillers, boilers, any number of air handling units, energy conservation devices, field controllers, supervisory controllers, actuators, temperature sensors, and other devices for controlling temperature, humidity, airflow, or other variable conditions within buildings 10-17. Lighting subsystem 442 may include any number of light fixtures, ballasts, lighting sensors, dimmers, or other devices configured to controllably adjust the amount of light provided to a building space. The security subsystem 438 may include occupancy sensors, video surveillance cameras, digital video recorders, video processing servers, intrusion detection devices, access control devices, and servers or other security-related devices.

Still referring to fig. 4, BMS controller 366 is shown to include communication interface 407 and BMS interface 409. Interface 407 may facilitate communication between BMS controller 366 and external applications (e.g., monitoring and reporting application 422, enterprise control application 426, remote systems and applications 444, applications resident on user client device 448, etc.) to allow a user to control, monitor, and regulate BMS controller 366 and/or subsystems 428. Interface 407 may also facilitate communication between BMS controller 366 and client device 448. BMS interface 409 may facilitate communication (e.g., HVAC, lighting safety, elevator, power distribution, traffic, etc.) between BMS controller 366 and building subsystems 428.

Interfaces 407, 409 may be or include wired or wireless communication interfaces (e.g., sockets, antennas, transmitters, receivers, transceivers, wire terminals, etc.) for data communication with building subsystems 428 or other external systems or devices. In various embodiments, communications via interfaces 407, 409 may be direct (e.g., local wired or wireless communications) or via a communication network 446 (e.g., a WAN, the internet, a cellular network, etc.). For example, the interfaces 407, 409 may include ethernet cards and ports for sending and receiving data via an ethernet-based communication link or network. In another example, interfaces 407, 409 may include WiFi transceivers for communicating via a wireless communication network. In another example, one or more of interfaces 407, 409 may include a cellular or mobile telephone communication transceiver. In one embodiment, communication interface 407 is a power line communication interface and BMS interface 409 is an ethernet interface. In other embodiments, both communication interface 407 and BMS interface 409 are ethernet interfaces or the same ethernet interface.

Still referring to fig. 4, BMS controller 366 is shown to include processing circuitry 404 that includes a processor 406 and a memory 408. Processing circuit 404 may be communicatively connected to BMS interface 409 and/or communication interface 407 such that processing circuit 404 and its various components may send and receive data via interfaces 407, 409. Processor 406 may be implemented as a general purpose processor, an Application Specific Integrated Circuit (ASIC), one or more Field Programmable Gate Arrays (FPGAs), a set of processing components, or other suitable electronic processing components.

The memory 408 (e.g., memory unit, storage, etc.) may include one or more means (e.g., RAM, ROM, flash memory, hard disk storage, etc.) for storing data and/or computer code for performing or facilitating the various processes, layers, and modules described herein. Memory 408 can be or include volatile memory or non-volatile memory. Memory 408 may include database components, object code components, script components, or any other type of information structure for supporting the various activities and information structures described herein. According to an exemplary embodiment, memory 408 is communicatively connected to processor 406 via processing circuitry 404 and includes computer code for performing one or more processes described herein (e.g., by processing circuitry 404 and/or processor 406).

In some embodiments, BMS controller 366 is implemented within a single computer (e.g., one server, one housing, etc.). In various other embodiments, BMS controller 366 may be distributed across multiple servers or computers (e.g., which may exist in distributed locations). Further, while fig. 4 shows applications 422 and 426 as residing outside BMS controller 366, in some embodiments applications 422 and 426 may be hosted within BMS controller 366 (e.g., within memory 408).

Still referring to FIG. 4, memory 408 is shown to include an enterprise integration layer 410, an automatic measurement and verification (AM & V) layer 412, a Demand Response (DR) layer 414, a Fault Detection and Diagnosis (FDD) layer 416, an integration control layer 418, and a building subsystem integration layer 420. Layers 410-420 may be configured to receive inputs from building subsystems 428 and other data sources, determine an optimal control action for building subsystems 428 based on the inputs, generate control signals based on the optimal control action, and provide the generated control signals to building subsystems 428. The following paragraphs describe some of the general functions performed by each of the layers 410 through 420 in the BMS 400.

The enterprise integration layer 410 may be configured to serve client or local applications with information and services to support various enterprise-level applications. For example, the enterprise control application 426 may be configured to provide cross-subsystem control to a Graphical User Interface (GUI) or to any number of enterprise-level business applications (e.g., accounting systems, subscriber identification systems, etc.). Enterprise control application 426 may also or alternatively be configured to provide a configuration GUI for configuring BMS controller 366. In still other embodiments, enterprise control application 426 may work with layers 410-420 to optimize building performance (e.g., efficiency, energy usage, comfort, or safety) based on inputs received at interface 407 and/or BMS interface 409.

Building subsystem integration layer 420 may be configured to manage communications between BMS controller 366 and building subsystem 428. For example, building subsystem integration layer 420 may receive sensor data and input signals from building subsystems 428 and provide output data and control signals to building subsystems 428. Building subsystem integration layer 420 may also be configured to manage communications between building subsystems 428. The building subsystem integration layer 420 translates communications (e.g., sensor data, input signals, output signals, etc.) across multiple multi-vendor/multi-protocol systems.

Demand response layer 414 may be configured to optimize resource usage (e.g., electricity usage, natural gas usage, water usage, etc.) and/or monetary costs of such resource usage in response to satisfying the demands of buildings 10-17. Optimization may be based on time of use prices, curtailment signals, energy availability, or other data received from utility providers, distributed energy generation system 424, energy storage devices 427 (e.g., hot TES 242, cold TES 244, etc.), or other sources. Demand response layer 414 may receive inputs from other layers of BMS controller 366 (e.g., building subsystem integration layer 420, integration control layer 418, etc.). The inputs received from the other layers may include environmental or sensor inputs (e.g., temperature, carbon dioxide level, relative humidity level, air quality sensor output, occupancy sensor output, room arrangements, etc.). Inputs may also include inputs such as electrical usage from a utility (e.g., expressed in kilowatts per hour (kWh)), thermal load measurements, pricing information, projected pricing, smooth pricing, curtailment signals, and the like.

According to an exemplary embodiment, the demand response layer 414 includes control logic for responding to data and signals it receives. These responses may include communicating with control algorithms in the integrated control layer 418, changing control strategies, changing settings, or activating/deactivating building devices or subsystems in a controlled manner. The demand response layer 414 may also include control logic configured to determine when to utilize the stored energy. For example, the demand response layer 414 may determine to begin using energy from the energy storage devices 427 just before the start of peak usage hours.

In some embodiments, the demand response layer 414 includes a control module configured to proactively initiate a control action (e.g., automatically changing a set point) that minimizes energy costs based on one or more inputs indicative of or based on demand (e.g., price, curtailment signal, demand level, etc.). In some embodiments, the demand response layer 414 uses plant models to determine an optimal set of control actions. The equipment models may include, for example, thermodynamic models describing inputs, outputs, and/or functions performed by various groups of building equipment. The equipment model may represent a set of building equipment (e.g., sub-facilities, chiller arrays, etc.) or individual devices (e.g., individual chillers, heaters, pumps, etc.).

The demand response layer 414 may further include or utilize one or more demand response policy definitions (e.g., databases, XML files, etc.). The policy definition may be edited or adjusted by the user (e.g., via a graphical user interface) such that control actions initiated in response to demand input may be tailored to the user's application, desired comfort, specific building equipment, or based on other points of interest. For example, the demand response policy definition may specify, in response to a particular demand input, which devices, systems, or a piece of equipment may be turned on or off, how long, what settings may be changed, what is the allowable setting adjustment range, how long a high demand setting remains before returning to a normally scheduled setting, how close the capacity limit is, which device mode to utilize, the energy transfer rates (e.g., maximum rate, alarm rate, other rate boundary information, etc.) into and out of the energy storage device (e.g., hot storage tank, battery pack, etc.), and when to dispatch the site energy generation (e.g., via fuel cells, electric generator sets, etc.).

The integrated control layer 418 may be configured to make control decisions using data inputs or outputs of the building subsystem integration layer 420 and/or the demand response layer 414. Since subsystem integration is provided by building subsystem integration layer 420, integrated control layer 418 may integrate the control activities of subsystem 428 such that subsystem 428 appears as a single integrated super system. In an exemplary embodiment, integrated control layer 418 includes control logic that uses inputs and outputs from multiple building subsystems to provide greater comfort and energy savings relative to the comfort and energy savings that individual subsystems may provide individually. For example, integrated control layer 418 may be configured to use input from a first subsystem to make energy saving control decisions for a second subsystem. The results of these decisions may be communicated back to the building subsystem integration layer 420.

The integrated control layer 418 is shown logically below the demand response layer 414. Integrated control layer 418 may be configured to enhance the effectiveness of demand response layer 414 by enabling building subsystems 428 and their corresponding control loops to be controlled in coordination with demand response layer 414. Such a configuration may reduce disruptive demand response behavior relative to conventional systems. For example, the integrated control layer 418 may be configured to ensure that demand-responsive driven upward adjustments to the chilled water temperature setpoint (or another component that directly or indirectly affects temperature) do not result in an increase in fan energy (or other energy used to cool the space) that would result in a greater total savings in building energy usage than at the chiller.

The integrated control layer 418 may be configured to provide feedback to the demand response layer 414 so that the demand response layer 414 checks that the constraints (e.g., temperature, lighting level, etc.) are properly maintained even when the requested load shedding is being performed. Constraints may also include set points or sensed boundaries related to safety, equipment operating limits and performance, comfort, fire codes, electrical codes, energy codes, and the like. The integrated control layer 418 may also be logically below the fault detection and diagnostic layer 416 and the automatic measurement and verification layer 412. Integrated control layer 418 may be configured to provide calculated inputs (e.g., summaries) to these higher layers based on outputs from more than one building subsystem.

An automatic measurement and verification (AM & V) layer 412 may be configured to verify that the control policy commanded by the integrated control layer 418 or the demand response layer 414 is working properly (e.g., using data aggregated by the AM & V layer 412, the integrated control layer 418, the building subsystem integration layer 420, the FDD layer 416, or otherwise). The calculations made by the AM & V layer 412 may be based on building system energy models and/or equipment models for individual BMS devices or subsystems. For example, AM & V layer 412 may compare the output predicted by the model to the actual output from building subsystems 428 to determine the accuracy of the model.

Fault Detection and Diagnostic (FDD) layer 416 may be configured to provide persistent fault detection for building subsystems 428, building subsystem devices (i.e., building equipment), and control algorithms used by demand response layer 414 and integrated control layer 418. FDD layer 416 may receive data input from integrated control layer 418, directly from one or more building subsystems or devices, or from another data source. FDD layer 416 may automatically diagnose and respond to detected faults. The response to the detected or diagnosed fault may include providing an alert message to a user, a service dispatch system, or a control algorithm configured to attempt to fix the fault or resolve the fault.

FDD layer 416 may be configured to output a particular identification of the failed component or cause of failure (e.g., a loose air brake coupling) using detailed subsystem inputs available at building subsystem integration layer 420. In other exemplary embodiments, FDD layer 416 is configured to provide a "fault" event to integrated control layer 418, which executes control policies and policies in response to the received fault event. According to an example embodiment, FDD layer 416 (or policies enforced by an integrated control engine or business rules engine) may shut down the system or direct control activities around the failed device or system to reduce energy waste, extend device lifetime, or ensure proper control response.

FDD layer 416 may be configured to store or access various different system data storage devices (or data points of real-time data). FDD layer 416 may use some content of the data storage device to identify device level (e.g., a particular cooler, a particular AHU, a particular end unit, etc.) failures and other content to identify component or subsystem level failures. For example, building subsystems 428 may generate time (i.e., time series) data indicative of the performance of BMS 400 and its various components. The data generated by building subsystems 428 may include measured or calculated values that exhibit statistical properties and provide information about how the corresponding system or process (e.g., temperature control process, flow control process, etc.) performed in terms of errors from its set point. FDD layer 416 may check these processes to expose when the system begins to degrade in performance and alert the user to repair the fault before it becomes more severe.

Referring now to fig. 5, shown is a block diagram of another Building Management System (BMS)500 in accordance with some embodiments. BMS500 may be implemented in one or more buildings 10-17 and used to monitor and control HVAC systems 20 and 100, waterside system 200, airside system 300, devices of building subsystems 428, and other types of BMS devices (e.g., lighting, safety, etc.) and/or HVAC equipment.

The BMS500 provides a system architecture that facilitates automatic device discovery and device model distribution. Device discovery can occur at multiple levels of the BMS500 across multiple different communication buses (e.g., the system bus 554, the area buses 556-560 and 564, and the sensor/actuator bus 566, etc.) and across multiple different communication protocols. In some embodiments, device discovery is accomplished using an active node table that provides status information for devices connected to each communication bus. For example, each communication bus may be monitored for new devices by monitoring the respective active node table for new nodes. When a new device is detected, the BMS500 may begin interacting with the new device (e.g., sending control signals, using data from the device) without user intervention.

Some devices in the BMS500 use the equipment model to present themselves to the network. The device model defines device object properties, view definitions, schedules, trends, and associated BACnet value objects (e.g., simulated values, binary values, multi-state values, etc.) for integration with other systems. Some devices in the BMS500 store their own equipment models. Other devices in the BMS500 have equipment models stored externally (e.g., within the other devices). For example, the zone coordinator 508 may store a device model that bypasses the damper 528. In some embodiments, the zone coordinator 508 automatically creates an equipment model for the bypass dampers 528 or other devices on the zone bus 558. Other zone coordinators may also create an equipment model for devices connected to their zone bus. The equipment model for the device may be automatically created based on the type of data points exposed by the device on the regional bus, the device type, and/or other device attributes. Several examples of automatic device discovery and device model distribution are discussed in more detail below.

Still referring to fig. 5, the BMS500 is shown to include: a system manager 502; several regional coordinators 506, 508, 510, and 518; and a number of zone controllers 524, 530, 532, 536, 548 and 550. The system manager 502 may communicate with client devices 504 (e.g., user devices, desktop computers, laptop computers, mobile devices, etc.) via a data communication link 574 (e.g., BACnet IP, ethernet, wired or wireless communication, etc.). The system manager 502 may provide a user interface to the client device 504 via a data communication link 574. The user interface may allow a user to monitor and/or control the BMS500 via the client device 504.

In some embodiments, system manager 502 is connected to zone coordinators 506 to 510 and 518 via a system bus 554. The system manager 502 may be configured to communicate with the zone coordinators 506 to 510 and 518 via the system bus 554 using a master-slave token passing (MSTP) protocol or any other communication protocol. The system bus 554 may also connect the system manager 502 with other devices, such as a constant Capacity (CV) rooftop unit (RTU)512, an input/output module (IOM)514, a thermostat controller 516 (e.g., TEC5000 series thermostat controller), and a Network Automation Engine (NAE) or third party controller 520. RTU 512 may be configured for direct communication with system manager 502 and may be directly connected to system bus 554. Other RTUs may communicate with system manager 502 via an intermediary device. For example, the wired input 562 may connect the third party RTU 542 to the thermostat controller 516, which is connected to the system bus 554.

System manager 502 can provide a user interface for any device that contains an equipment model. Devices such as the zone coordinators 506 to 510 and 518 and the thermostat controller 516 may provide their equipment models to the system manager 502 via the system bus 554. In some embodiments, system manager 502 automatically creates an equipment model for a connected appliance (e.g., IOM 514, third party controller 520, etc.) that does not contain the equipment model. For example, system manager 502 may create an equipment model for any device that responds to a device tree request. The device model created by system manager 502 may be stored within system manager 502. System manager 502 may then use the equipment model created by system manager 502 to provide a user interface for devices that do not contain its own equipment model. In some embodiments, system manager 502 stores a view definition for each type of device connected via system bus 554 and uses the stored view definitions to generate a user interface for the apparatus.

Each zone coordinator 506 to 510 and 518 may be connected with one or more of the zone controllers 524, 530 to 532, 536 and 548 to 550 via a zone bus 556, 558, 560 and 564. The zone coordinators 506 to 510 and 518 may communicate with the zone controllers 524, 530 to 532, 536 and 548 to 550 via the zone buses 556 to 560 and 564 using the MSTP protocol or any other communication protocol. The zone buses 556-560 and 564 may also connect the zone coordinators 506-510 and 518 with other types of devices, such as Variable Air Volume (VAV) RTUs 522 and 540, switch bypass (COBP) RTUs 526 and 552, bypass dampers 528 and 546, and PEAK controllers 534 and 544.

The zone coordinators 506 to 510 and 518 may be configured to monitor and command various partition systems. In some embodiments, each zone coordinator 506-510 and 518 monitors and commands a separate partition system and connects to the partition system via a separate zone bus. For example, the zone coordinator 506 may be connected to the VAV RTU 522 and the zone controller 524 via a zone bus 556. The zone coordinator 508 can be connected to the COBP RTU526, the bypass damper 528, the COBP zone controller 530, and the VAV zone controller 532 via a zone bus 558. The zone coordinator 510 may be connected to the PEAK controller 534 and the VAV zone controller 536 via a zone bus 560. The zone coordinator 518 may be connected to the PEAK controller 544, the bypass damper 546, the COBP zone controller 548, and the VAV zone controller 550 via a zone bus 564.

The single model of the zone coordinators 506 to 510 and 518 may be configured to handle a variety of different types of partition systems (e.g., VAV partition systems, COBP partition systems, etc.). Each partition system may include an RTU, one or more zone controllers, and/or a bypass damper. For example, the zone coordinators 506 and 510 are shown as Verasys VAV Engines (VVEs) connected to VAV RTUs 522 and 540, respectively. The zone coordinator 506 is directly connected to the VAV RTU 522 via a zone bus 556, while the zone coordinator 510 is connected to a third party VAV RTU 540 via a wired input 568 provided to the PEAK controller 534. Region coordinators 508 and 518 are shown as Verasys COBP Engines (VCEs) connected to COBP RTUs 526 and 552, respectively. The local coordinator 508 is directly connected to the COBP RTU526 via a local bus 558, while the local coordinator 518 is connected to a third party COBP RTU 552 via a wired input 570 provided to a PEAK controller 544.

The zone controllers 524, 530 to 532, 536, and 548 to 550 may communicate with respective BMS devices (e.g., sensors, actuators, etc.) via a sensor/actuator (SA) bus. For example, the VAV zone controller 536 is shown connected to the networked sensors 538 via an SA bus 566. The zone controller 536 may communicate with the networked sensors 538 using the MSTP protocol or any other communication protocol. Although only one SA bus 566 is shown in fig. 5, it should be understood that each zone controller 524, 530 through 532, 536, and 548 through 550 may be connected to a different SA bus. Each SA bus may connect the zone controller with various sensors (e.g., temperature sensors, humidity sensors, pressure sensors, light sensors, occupancy sensors, etc.), actuators (e.g., damper actuators, valve actuators, etc.), and/or other types of controllable devices (e.g., coolers, heaters, fans, pumps, etc.).

Each zone controller 524, 530 through 532, 536, and 548 through 550 may be configured to monitor and control a different building zone. The zone controllers 524, 530 through 532, 536, and 548 through 550 may use the inputs and outputs provided via their SA buses to monitor and control the various building zones. For example, the zone controller 536 may use temperature inputs (e.g., measured temperatures of building zones) received from the networked sensors 538 via the SA bus 566 as feedback in the temperature control algorithm. The zone controllers 524, 530 through 532, 536 and 548 through 550 may use various types of control algorithms (e.g., state-based algorithms, Extremum Seeking Control (ESC) algorithms, proportional-integral (PI) control algorithms, proportional-integral-derivative (PID) control algorithms, model predictive control algorithms, feedback control algorithms, etc.) to control variable states or conditions (e.g., temperature, humidity, airflow, lighting, etc.) in or around the building 10.

Distributed model predictive control system

Referring now to FIG. 6, a block diagram of a distributed Model Predictive Control (MPC) system 600 is shown, in accordance with some embodiments. The MPC system 600 uses MPC techniques to determine the optimal set points of the air side system and the water side system devices over a time horizon. As described with reference to fig. 1A-5, MPC system 600 may be used in combination with HVAC system 20, waterside system 20, airside system 50, HVAC system 100, waterside system 120, airside system 130, waterside system 200, airside system 300, BMS 400, and/or BMS 500. For example, MPC system 600 may determine an optimal temperature set point for air-side devices 622-626 of air-side system 50 and/or air-side system 300. Similarly, the MPC system 600 may determine the optimal set points for the waterside devices 628 of the waterside system 30 and/or the waterside system 200.

In an economic MPC, the goal of the optimization problem is typically to minimize the total cost as defined by the cost function. A number of studies have shown that MPCs are superior to existing control systems due to their ability to predict the future and to anticipate events before they occur. By using the mass of the building for passive Thermal Energy Storage (TES), the MPC enables the energy load to be transferred from peak hours to off-peak hours. Active thermal energy storage (e.g., cold water tanks, hot water tanks, etc.) may also be used to further facilitate load transfer. By combining active and passive storage systems, energy costs can be reduced by concentrating device usage to periods of lower resource prices while maintaining comfort limits within each building.

Still referring to fig. 6, distributed MPC system 600 is shown as including MPC layer 610 and supervisory layer 620. The MPC layer 610 is shown to include a high-level model predictive controller 608 and several low-level model predictive controllers 612 through 618. Controllers 612, 614, and 616 are shown as low-stage air-side model predictive controllers, while controller 618 is shown as a low-stage water-side model predictive controller. The MPC layer 610 may be configured to determine and provide optimal temperature set points and device operation set points to the devices of the supervisory layer 620. In some embodiments, the MPC layer 610 can be retrofitted to any existing BMS to provide set point optimization for the air side equipment and the water side equipment of the BMS.

The supervisory layer 620 is shown to include air side devices 622-626 and a water side device 628. As described with reference to fig. 1A-1B and 3, air-side equipment 622-626 may include some or all of air-side system 50, air-side system 130, and/or air-side system 300. As described with reference to fig. 1A-2, waterside device 628 may include some or all of waterside system 30, waterside system 120, and/or waterside system 200. In some embodiments, as described with reference to fig. 4-5, supervisory layer 620 includes some or all of the devices of BMS 400 and/or BMS 500. For example, supervisory layer 620 may include PID controllers, operational equipment (e.g., coolers, boilers, air handling units, etc.), and/or other systems or devices configured to control a process variable to a set point.

In some embodiments, distributed MPC system 600 includes a load/rate predictor 602. The load/rate predictor 602 may provide load and rate predictions to the MPC layer 610 including, for example, interference predictions, electricity rates, demand electricity rate prices, and outside air temperatures. Load/rate predictor 602 is shown receiving weather forecasts from weather service 604. In some embodiments, load/rate predictor 602 generates an interference prediction based on weather forecasts. In some embodiments, load/rate predictor 602 uses feedback from supervisory layer 620 to generate an interference prediction. Feedback from supervisor layer 620 may include various types of sensor inputs (e.g., temperature, flow, humidity, enthalpy, etc.) or other data related to the controlled building or campus (e.g., building occupancy data, building electrical loads, etc.). In some embodiments, load/rate predictor 602 generates an interference prediction that includes a predicted interference value for each time step within the optimization period.

In some embodiments, load/rate predictor 602 generates the interference prediction using a deterministic plus stochastic model trained from historical load data. The load/rate predictor 602 may use any of a variety of prediction methods to generate the interference predictions (e.g., linear regression for deterministic portions and autoregressive models for stochastic portions). Load/rate predictor 602 may predict one or more different types of interference for a building or campus. For example, load/rate predictor 602 may predict the thermal load generated by heat transfer between air within a building and outside air through the building walls. Load/rate predictor 602 may predict the thermal load generated by internal heating within the building (e.g., heat generated by electronics in the building, heat generated by a building occupant, etc.). In some embodiments, load/rate predictor 602 performs load/rate prediction using the techniques described in U.S. patent application No. 14/717,593, filed 5/20/2015, entitled "Building Management System for calculating Time Series Values of Building Variables," the entire disclosure of which is incorporated herein by reference.

Load/rate predictor 602 is shown receiving utility rates from utility 606. The utility rate may indicate a cost or price per unit of resource (e.g., electricity, natural gas, water, etc.) provided by utility 606 at each time step in the optimization period. In some embodiments, the utility rate is a time-varying rate. For example, the price of electricity is high at certain times of day or days of the week (e.g., during high demand periods) and is low at other times of day or days of the week (e.g., during low demand periods). Utility rates may define various time periods and costs per unit of resource during each time period. The utility rates may be actual rates received from utility 606 or predicted utility rates estimated by load/rate predictor 602.

In some embodiments, the utility rates include demand charges for electricity for one or more resources provided by utility 606. The demand electricity rate may define a separate cost imposed by utility 606 based on a maximum usage of a particular resource (e.g., a maximum energy consumption) during the demand electricity rate period. The utility rates may define various demand electricity rate periods and one or more demand electricity rates associated with each demand electricity rate period. In some instances, the demand electricity charge periods may partially or completely overlap with each other and/or with the optimization period. Advantageously, the MPC layer 610 may take into account demand charges in the advanced optimization process performed by the advanced model predictive controller 608. Utility 606 may be defined by a time-varying (e.g., hourly) price, a maximum service level (e.g., a maximum consumption rate allowed by a physical infrastructure or by a contract), and (in the case of electricity) a demand charge or a charge for a peak consumption rate over a period of time. Load/rate predictor 602 may store the predicted load and utility rates in memory and/or provide the predicted load and utility rates to the advanced MPC 608.

The MPC layer 610 may receive measurements from the supervisory layer 620 and provide set values to the supervisory layer 620. The MPC layer 610 may generate optimal values for various decision variables including, for example, zone temperature setpoints, plant on/off decisions, and TES charge/discharge rates. The MPC layer 610 may determine the optimal values of the decision variables using system models such as a zone temperature and cooling/heating load model, a cooling/heating load and temperature setpoint model, a plant model, and an active TES model. The MPC layer 610 may determine the optimal values of the decision variables by performing an optimization process subject to several constraints. The constraints may include comfort limits for zone air temperature, equipment capacity constraints, TES canister size, and rate of change limits for the equipment of supervisory layer 620.

As discussed above, solving a single MPC problem to determine the optimal values of decision variables may be difficult for large-scale applications. For example, the air side system 50 may include thousands of discrete zones, and the water side system 30 may include thousands of unique HVAC devices. Discrete decisions (e.g., turning devices on/off) can lead to mixed integer optimization problems, which further increases complexity. Because of the difficulty and computational complexity of the MPC problem, MPC layer 610 can decompose the entire MPC problem into multiple smaller, more manageable optimization problems.

As shown in fig. 6, a distributed MPC system 600 may decompose an entire MPC problem into a high-level optimization problem and a low-level optimization problem. The high level issue may be addressed by the high level controller 608 to determine the load profile of each of the low level air side subsystems 632-636 and the demand profile of the water side system 30. In some embodiments, the high-level controller 608 uses an active TES model and aggregates the low-level models of each air-side subsystem 632-636 to reduce computational complexity. The high-level controller 608 may determine a load profile that optimizes (e.g., minimizes) the overall operating cost of the MPC system 600 over an optimization period. Each load profile may include a load value at each time step in the optimization period. The low-level air-side controllers 612 to 616 may use the load profiles as constraints to define a maximum allowable load value for each air-side subsystem 632 to 636 at each time step in the optimization period. The high level controller 608 may provide a load profile to each of the low level air side controllers 612 through 616. The high-level optimization performed by the high-level controller 608 is described in more detail with reference to FIG. 7.

The low-level optimization problem may be further decomposed into a low-level water-side optimization problem and one or more low-level air-side optimization problems. Each low-level air-side problem may be addressed by one of the low-level air-side controllers 612 through 616 to determine the zone temperature set points for the air-side devices 622 through 626 of each air-side subsystem 632 through 636. Each low level air side controller 612 to 616 may determine a zone temperature set point that optimizes (e.g., minimizes) the energy consumption of the respective air side subsystem 632 to 636 while maintaining the zone temperature within defined temperature limits and without exceeding the load value provided by the high level controller 608. Alternatively, each low-level air-side controller 612 to 616 may determine a temperature set point that tracks an average building temperature (e.g., a predicted building temperature state) according to a high-level optimization problem. The low-level optimizations performed by the low-level controllers 612 to 616 are described in more detail with reference to fig. 8.

The low stage water side problem may be addressed by the low stage water side controller 618. In some embodiments, the low-level waterside problem is a mixed integer linear program. The low level waterside controller 618 may determine the optimal settings for the waterside devices 628 that minimize operating costs while meeting the demand profile from the high level controller 608. Decision variables optimized by the low-stage water-side controller 618 may include, for example, plant on/off status, heat load of the chiller, flow rate of the pump, set points for other auxiliary water-side plants, and TES charging/discharging rate. The low-stage waterside controller 618 may use the demand profile from the high-stage controller 608 as an input to the total demand met by the waterside system 30 at each time step of the optimization period.

In some embodiments, the low-stage waterside controller 628 decomposes the low-stage waterside optimization problem into a first waterside optimization problem and a second waterside optimization problem. The first waterside optimization problem may distribute demand indicated by the high level controller 608 across multiple sub-facilities of the waterside system (e.g., heater sub-facility 202, heat recovery chiller sub-facility 204, chiller sub-facility 206, cooling tower sub-facility 208, hot TES tank 210, and cold TES tank 212). The second waterside optimization problem can be decomposed as a mixed integer optimization problem such that each sub-facility determines the optimal device on/off status and device settings for the waterside device 628. An example of a waterside optimization technique that may be used by the low-grade waterside controller 628 is described in detail in U.S. patent application No. 14/634,609, filed on 27/2/2015, the entire disclosure of which is incorporated herein by reference.

As shown in fig. 6, each low-level air-side model predictive controller 612 to 616 may control subsystems 632 to 636 of the overall air-side system 50. Each low-level air-side controller 612 to 616 may perform a separate air-side optimization process to determine the optimal temperature set-points for the air-side devices 622 to 626 of the respective air-side subsystems 632 to 636. Each air side subsystem 632-636 (e.g., each building) may include multiple AHUs, each of which may be configured to deliver air to multiple building zones. Thus, each air side system 632-636 may include a number of building areas.

In some embodiments, the high level controller 608 uses an aggregate model of each air side subsystem 632-636 and distributes a thermal energy load to each air side subsystem 632-636. The low-level air-side controllers 612 through 626 may use a more detailed zone-level model to determine the optimal temperature setpoints for each building zone of the respective air-side subsystem during the low-level optimization process. The decomposition of the air-side system 50 into separate air-side subsystems 632-636 may improve computational performance and significantly reduce the amount of time required to solve the low-level MPC problem. For example, all low-level MPC problems can be solved in a matter of minutes.

In some embodiments, each air side subsystem 632-636 represents a separate building. Significant coupling between the air-side subsystems 632-636 (e.g., heat exchange between the subsystems 632-636) may impact performance because the low-level controllers 612-616 are not required to coordinate their solutions. One way to break down the air side system 50 to ensure that there is no coupling between the subsystems 632-636 is by building breaking down, as the individual buildings cannot exchange heat with each other. For this reason, it may be desirable to select the air side subsystems 632-636 such that each air side subsystem 632-636 represents a separate building. In other embodiments, each air side subsystem 632-636 may represent a single building area, a collection of areas within a building, or even multiple buildings.

In the MPC system 600, the advanced model predictive controller 608 determines the thermal energy load to be allocated to each air-side subsystem 632-636 (e.g., each building) and the demand profile of the water-side system 30. Each air-side subsystem 632-636 may include a separate low-level air-side controller 612-616 that calculates a temperature set-point for each zone in this air-side subsystem 632-636 (e.g., for each zone in a building). If each low-stage air-side subsystem 632-636 represents a separate building, the low-stage air-side problem may be solved in a distributed manner because there is no heat transfer between the separate buildings. The low-level air-side problem can be easily extended for handling large industry and campus-wide implementations without increasing computational complexity.

Distributed MPC system 600 provides several advantages over alternative control strategies. For example, the high-level controller 608 may coordinate the operation of the low-level air-side subsystems 632-636 via a load profile provided to each of the low-level controllers 612-616. By including the demand charges in the high-level objective function, the high-level controller 608 may generate a load profile that presents the operation of the low-level air-side subsystems 632-636. In other words, the high-level controller 608 may generate a load profile that ensures that the low-level air-side subsystems 632-636 do not all consume power simultaneously. This allows the high-level controller 608 to coordinate the operation of the low-level air-side subsystems 632-636 and account for demand charges of electricity without requiring communication between the low-level air-side controllers 612-616. The coupling to all air side subsystems 632-636 caused by the presence of a single water side system 30 is also addressed by the high level controller 608. Thus, the low-level control issues are completely decoupled, such that no iterations and communications between the low-level controllers 612 to 616 are required.

Data communication between the MPC layer 610 and the supervisory layer 620 may also be significantly reduced. For example, as shown in fig. 6, data communication between the MPC layer 610 and the supervisory layer 620 may be limited to measurements and settings. This allows MPC system 600 to be integrated with any existing BMS. The high-level controller 608 may use an aggregate model of each of the air-side subsystems 632-636 and the water-side system 30 to reduce computational complexity during high-level optimization. Each low-level air-side controller 612 to 616 may provide the high-level controller 608 with an aggregate disturbance estimate, aggregate building temperature, and aggregate system parameters (e.g., heat capacity, heat transfer coefficient, etc.) for the respective air-side subsystems 632 to 636.

Distributed MPC system 600 can be used to control a variety of different systems including, for example, chiller plants, air handling units, rooftop units, variable refrigerant flow systems, air side systems, water side systems, building management systems, and/or other types of systems for which power consumption or thermal energy loads can be assigned to different subsystems. Most building temperature regulation methods do not consider detailed models of the waterside devices 628 or integer decision variables, which reduces the accuracy of the energy cost calculations. However, the MPC system 600 may include integer variables in the water-side optimization problem to determine when to turn on and off the plant rather than relying on heuristics. Active TES can also be used in the waterside optimization problem, which allows for maximum potential for load transfer and cost savings. However, MPC system 600 may still be universally applicable regardless of whether active TES is available.

Advanced model predictive controller

Referring now to FIG. 7, a block diagram illustrating an advanced Model Predictive Controller (MPC)608 in greater detail in accordance with some embodiments is shown. The advanced MPC608 is shown to include a communication interface 702 and processing circuitry 704. The communication interface 702 may include a wired or wireless interface (e.g., receptacle, antenna, transmitter, receiver, transceiver, wire terminal, etc.) for data communication with various systems, devices, or networks. For example, the communication interface 702 may include an ethernet card and port for sending and receiving data via an ethernet-based communication network and/or a WiFi transceiver for communicating via a wireless communication network. Communication interface 702 may be configured to communicate via a local or wide area network (e.g., the internet, a building WAN, etc.) and may use various communication protocols (e.g., BACnet, IP, LON, etc.).

The communication interface 702 may be a network interface configured to facilitate electronic data communication between the high-level MPC608 and various external systems or devices (e.g., weather service 604, utility 606, low-level controllers 612-618, BMS devices, etc.). For example, the advanced MPC608 may receive weather forecasts from the weather service 604, utility rates from the utility 606, and/or load and rate predictions from the load/rate predictor 602. The advanced MPC608 may receive measurements from the BMS indicative of one or more measured states of the controlled building or campus (e.g., temperature, humidity, electrical load, etc.) and one or more states of the waterside system 30 (e.g., equipment status, power consumption, equipment availability, etc.). In some embodiments, the advanced MPC608 receives measurements of TES status from the waterside system 30 that indicate the current fill level of the TES tank.

The high-level MPC608 may receive building disturbance estimates from each of the low-level air-side controllers 612 through 616. The building disturbance estimate may indicate an estimated thermal energy load for each air side subsystem 632-636. The high-level MPC608 may receive the aggregate system curve, aggregate building parameters, and/or performance coefficients from each of the low-level controllers 612-618. The high-level MPC608 may use the information received at the communication interface 702 to generate a load profile for each low-level air-side subsystem 632-636 and a demand profile for the water-side system 30. The high-level MPC608 may provide the load profile and the demand profile to the low-level controllers 612 to 618.

The processing circuitry 704 is shown to include a processor 706 and a memory 708. Processor 706 may be a general or special purpose processor, an Application Specific Integrated Circuit (ASIC), one or more Field Programmable Gate Arrays (FPGAs), a set of processing components, or other suitable processing components. The processor 706 can be configured to execute computer code or instructions stored in the memory 708 or received from other computer-readable media (e.g., CDROM, network storage device, remote server, etc.).

Memory 708 may include one or more devices (e.g., memory units, memory devices, storage devices, etc.) for storing data and/or computer code for accomplishing and/or facilitating the various processes described in this disclosure. Memory 708 may include Random Access Memory (RAM), Read Only Memory (ROM), hard drive storage, temporary storage, non-volatile memory, flash memory, optical storage, or any other suitable memory for storing software objects and/or computer instructions. Memory 708 may include database components, object code components, script components, or any other type of information structure for supporting the various activities and information structures described in this disclosure. Memory 708 may be communicatively connected to processor 706 via processing circuitry 704 and may include computer code for executing (e.g., by processor 706) one or more processes described herein. When the processor 706 executes instructions stored in the memory 708, the processor 706 typically configures the advanced MPC608 (and more particularly the processing circuit 704) to perform such activities.

Still referring to FIG. 7, an advanced MPC608 is shown as including a building temperature modeler 714. The building temperature modeler 714 may generate a temperature model for each air-side subsystem 632-636. The temperature model generated by the building temperature modeler 714 is referred to as a building temperature model under the assumption that each air side subsystem 632-636 represents a separate building. However, if the air side subsystems 632-636 represent other types of spaces, the temperature model generated by the building temperature modeler 714 may be a temperature model of other types of subsystems. Building temperature modeler 714 may generate a temperature model for each building. Although only three air side subsystems 632-636 are shown in FIG. 6, it should be understood that any number of buildings and air side subsystems 632-636 may be present. In general, building temperature modeler 714 may generate n_bBuilding temperature model, wherein n_bIs the total number of buildings and/or air side subsystems 632 through 636.

In some embodiments, building temperature modeler 714 uses a building heat transfer model to model the temperature of each building. The dynamics of heating or cooling individual building areas are described by energy balance:

where C is the heat capacity of the building area, H is the ambient heat transfer coefficient of the building area, T is the temperature of the building area, T is the heat transfer coefficient of the building area_aIs the ambient temperature outside the building area (e.g., outside air temperature),is the amount of cooling (i.e., cooling load) applied to a building area, andis an external load, radiation, or other disturbance experienced by a building area. In the former equation, the first and second equations,represents the heat transferred out of a building area by the HVAC system (i.e., cooling) and thus has a negative sign. However, if heating is applied to the building area instead of cooling itThe sign of (A) can be switched to positive sign so thatRepresenting the amount of heating (i.e., the heating load) applied to a zone of a building by an HVAC system.

The former equation combines all mass and air characteristics of a building zone into a single zone temperature. Other heat transfer models that may be used by the building temperature modeler 714 include the following air and mass area models:

wherein, C_zAnd T_zIs the heat capacity and temperature, T, of the air in the building area_aIs the ambient air temperature, H_azIs the heat transfer coefficient between the air of the building area and the ambient air outside the building area (e.g. through the outer walls of the building area), C_mAnd T_mIs the heat capacity and temperature of the non-air mass in the building area, and H_mzIs the heat transfer coefficient between the air and non-air masses of a building area.

The former equation combines all the mass characteristics of a building area into a single area mass. Other heat transfer models that may be used by the building temperature modeler 714 include the following air, shallow mass, and deep mass area models:

wherein, C_zAnd T_zIs the heat capacity and temperature, T, of the air in the building area_aIs the ambient air temperature, H_azIs the heat transfer coefficient between the air of the building area and the ambient air outside the building area (e.g. through the outer walls of the building area), C_sAnd T_sHeat capacity and temperature of superficial mass in a building area, H_szIs the heat transfer coefficient between the air and the superficial mass of the building area, C_dAnd T_dIs the heat capacity and temperature of the mass of the deep layers in the building area, and H_dsIs the heat transfer coefficient between the shallow mass and the deep mass.

In some embodiments, building temperature modeler 714 models the temperature of each building using the following building temperature model:

wherein, C_bAnd T_bIs the heat capacity and temperature, T, of the building specified by the building index b_aIs the ambient air temperature outside building b (e.g., outside air temperature), H_bIs the heat transfer coefficient between building b and the ambient air,is the amount of cooling applied to the building (i.e., the amount of heat removed from the building) by MPC system 600, andis the external load, radiation or interference experienced by building b. If heating is provided to the building instead of coolingThe sign of (b) may be switched from negative to positive.

Building temperature modeler 714 may use the building interference estimates received from low-grade air-side controllers 612 through 616 to identify external interference for each building b at each time step of the optimization periodIs a suitable value of. In some embodiments, building temperature modeler 714 uses the weather forecast from weather service 604 and/or the load and rate forecast provided by load/rate predictor 602 to determine the ambient air temperature T for each building b at each time step of the optimization period_aAnd/orExternal interferenceIs a suitable value of. C_bAnd H_bMay be specified as a parameter of building b, received from a low-level air-side controller of building b, received from a user, retrieved from memory 708, or otherwise provided to building temperature modeler 714. Building temperature modeler 714 may generate a building temperature model for each building b, where b is 1 … n_bAnd n is_bIs the total number of buildings.

Still referring to FIG. 7, the advanced MPC608 is shown as including a Thermal Energy Storage (TES) modeler 724. TES modeler 724 may generate a model for active thermal energy storage provided by TES tanks 42 and 40. In some embodiments, TES modeler 724 models active thermal energy storage using the following equation:

where s is the amount of thermal energy stored in a given TES tank (e.g., heating potential or cooling potential), σ is the decay constant of the TES tank, andis the rate at which thermal energy is stored in the TES canister. TES modeler 724 may generate an active TES model for each TES canister.

The advanced MPC608 is shown as including a water side demand modeler 722. Water side demand modeler 722 may generate a model representing demand for water side system 30 as the thermal energy load assigned to each building b at each time step of an optimization periodAnd thermal energy storageAmount ofA function. In some embodiments, the waterside demand modeler 722 models the waterside demand using the following equation:

wherein,is the waterside demand at time step k (e.g., thermal energy generation of the waterside system 30 at time step k),is the thermal energy load assigned to building b at time step k, andis the amount of thermal energy stored in the TES canister during time step k. The former equation indicates the total demand on the waterside system 30Is the thermal energy load assigned to each building bWith thermal energy stored in the TES tankThe sum of (a) and (b). This equation may be used by the advanced MPC608 as an energy balance constraint to ensure that the waterside system 30 generates enough thermal energy to cover the building load and thermal energy storage at each time step k.

The advanced MPC608 is shown as including a constraint modeler 710. Constraint modeler 710 may generate optimization constraints and impose the optimization constraints on an optimizer executed by advanced optimizer 712. Constraints imposed by the constraint modeler 710 may include, for example, equipment capacity constraints and building temperature constraints. In some embodiments, the constraint modeler 710 imposes the following constraints:

ensuring water side demand at each time step kLess than or equal to the maximum capacity of the waterside system 30Similarly, the constraint modeler 710 may impose the following constraints:

0≤s_k≤s_{maximum of}

Ensuring that the amount of thermal energy stored in the TES tank at any time step k is at the minimum possible level of the TES tank (i.e., zero) and the maximum possible level of the TES tank (i.e., s)_{Maximum of}) In the meantime.

In some embodiments, constraint modeler 710 is operable to model building temperature T_bConstraints are imposed. For example, the constraint modeler 710 may constrain the minimum temperature T as shown in the following equation_{Minimum size}And maximum temperature T_{Maximum of}Building temperature T between_b：

T_{Minimum size}≤T_b≤T_{Maximum of}

Wherein, T_{Minimum size}And T_{Maximum of}May be adjusted based on the temperature set point of the building. In some embodiments, the constraint modeler 710 automatically adjusts T based on information received from the low-level air side controller and/or the building's BMS_{Minimum size}And T_{Maximum of}The value of (c). For example, the constraint modeler 710 may use a building's temperature set point schedule and/or occupancy schedule to automatically adjust T at each time step k_{Minimum size}And T_{Maximum of}The value of (c). This allows the constraint modeler 710 to use temperature limits based on the time varying set point temperature range of the building,so that the temperature T of the building_bMaintained at a suitable temperature limit T_{Minimum size}And T_{Maximum of}And (4) the following steps.

Still referring to FIG. 7, the advanced MPC608 is shown to include an energy cost modeler 720 and a demand electricity rate modeler 718. The energy cost modeler 720 may generate an energy cost model representing the cost of energy consumed by the MPC system 600. The energy costs may include the cost per unit energy resource (e.g., electricity, water, natural gas, etc.) consumed by the MPC system 600 during the optimization period and a demand charge based on the maximum power consumption. The demand electricity component of the energy cost model may be modeled by the demand electricity modeler 718 and executed via the demand electricity constraint. In some embodiments, the energy cost model only considers the energy resources consumed by the waterside system 30. In other embodiments, the energy cost model also takes into account the power consumption of the air-side system 50, which may be modeled by the air-side power consumption modeler 716. Examples of these two scenarios are described below.

Example 1: energy cost model without air side power consumption

In some embodiments, the energy cost modeler 720 generates an energy cost model that takes into account the energy consumption of the water side system 30 and does not include the air side power consumption. For example, energy cost modeler 720 may model the total energy cost during the optimization period using the following equation:

the first term of the energy cost model considers the cost per unit of energy consumed during each time step k of the optimization period (e.g., $/kWh). In some embodiments, c_kIs consumed at time step k to meet the total water side demand at time step kCost per unit energy ofParameter η_totIs the reciprocal of the coefficient of performance of the polymerization air-side/water-side system (e.g., 0.1 ≦ η_tot≦ 0.25), and Δ is the duration of the time step k. Thus, itemRepresenting consumption during time step k to meet water side demandTotal amount of energy (e.g., kWh). Multiplied by the cost of energy consumed per unit c_k(e.g., $/kWh) yields the total cost of the energy consumed during time step k (e.g., $). The energy cost model may include a sum of the energy costs during each time step k to determine a total cost of energy consumption over the optimization period.

The second term of the energy cost model takes into account demand charges. In some embodiments, c_{Peak value}Is a demand electricity rate (e.g., $/kW),is the peak water demand during the demand electricity period (e.g.,and η) and η_totIs the inverse of the coefficient of performance of the polymerization air side/water side system. Thus, itemIndicating peak water side demand is metPeak power consumption. Multiplying by the demand charge rate c_{Peak value}Resulting in a total cost (e.g., $) of the demand electricity fee.

In some embodiments, the demand electricity rate modeler 718 generates a demand electricity rate constraint to ensureWith appropriate values. If the demand electricity rate period is completely contained within the optimization period (e.g., between time steps k-0 and k-N-1), thenIs simply the maximum value during the optimization periodThe demand electricity rate modeler 718 may implement the following demand electricity rate constraint:

ensuring peak water side demandAlways greater than or equal to the water side demand at each time stepThis forces peak water side demandAt least as large as the maximum waterside demand during the optimization period.

During the demand electricity rate period if the demand electricity rate period starts before the optimization periodMay occur before the start of the optimization period. The demand electricity rate modeler 718 may implement the following demand electricity rate constraint:

ensuring that: even if the maximum waterside demand is before the beginning of the current optimization periodOccurrence of peak water side demandIs always greater than or equal to the maximum water side demand that occurs during the same demand electricity rate periodIn some embodiments, the demand charges modeler 718 updates whenever a new maximum waterside demand is setTo ensure that the energy cost model accurately represents the demand charges imposed by the electric utility.

The advanced optimizer 712 may use an energy cost model, a demand charges model, a building temperature model, a thermal energy storage model, a water side demand model, and optimization constraints in order to formulate an optimization problem. In some embodiments, the advanced optimizer 712 seeks to minimize the total cost of energy (i.e., energy cost and demand electricity charges) consumed by the waterside system 30 subject to building temperature constraints and other constraints provided by the advanced models described herein. For example, advanced optimizer 712 may formulate the advanced optimization problem as:

subject to the following constraints:

0≤s_k≤s_{maximum of}

T_{Minimum size}≤T_b≤T_{Maximum of}

In some embodiments, advanced optimizer 712 converts one or more of the above identified models and/or constraints into a state space form for use in an advanced optimization problem. For example, the advanced optimizer 712 can convert the aforementioned equation into a discretized state space model of the form:

x_k+1＝Ax_k+Bu_k+B_dd_k

y_k＝Cx_k+Du_k

wherein x is_kIs the system state vector at time step k, u_kIs the system input vector at time step k, y_kIs the measurement or system output vector at time step k, d_kIs the interference vector at time step k, and x_k+1Is the (predicted) system state vector at time step k + 1. Table 1 illustrates the variables that may be included in each of these vectors:

table 1: variables in advanced optimization

As shown in Table 1, the system state vector x includes the building temperature T_bAnd TES storage class s. In some embodiments, the system state vector x includes n_bBuilding temperature T of each of the individual buildings_bAnd TES storage level s such that the total number of variables n in system state vector x equals n_b+1. Input vector u may include the thermal energy load assigned to each building bAnd a thermal energy load assigned to the thermal energy storage deviceIn some embodiments, input vector u comprises n_bThermal energy load of each of the individual buildingsAnd TES loadSo that the total number of variables m in the input vector u equals n_b+1. The disturbance vector d may include the ambient air temperature T of each building_aAnd estimating interferenceIn some embodiments, interference vector d comprises n_bEstimated interference for each of the individual buildingsAnd a single ambient air temperature T_aSo that the total number of variables n in the interference vector d_dIs equal to n_b+1。

In some embodiments, the measurement vector y is the same as the system state vector x. This indicates that all system states are directly measured (i.e., y)_k＝x_k) And the values of the C and D metrics in the state space model are C ═ I and D ═ 0. In other embodiments, the system state x may be constructed or predicted from the measurement y. For example, the advanced MPC608 may use kalman filters or other prediction techniques toThe system state x is constructed from the measurements y. Thus, can utilizeAndinstead of system state x, where the cap symbol indicates that this state is predicted. A, B, C in the state space expression, and the value of the D metric may be identified using system identification techniques. An example of a state prediction and System Identification technique that may be used by the advanced MPC608 is described in detail in U.S. patent No. 9,235,657 entitled "System Identification and Model Development" and filed 3/13/2013, the entire disclosure of which is incorporated herein by reference.

Example 2: energy cost model with air side power consumption

In some embodiments, the energy cost modeler 720 generates an energy cost model that considers both the energy consumption of the water side system 30 and the energy consumption of the air side system 50. For example, the energy cost model may take into account the heat energy load consumed by fans and other types of air side equipment 622 to deliver the distributed heat energy load to the buildingOf (2) isIn some embodiments, the power consumption of each air-side subsystem 632-636Is the thermal energy load assigned to this air side subsystemAs a function of (c).

The air side power consumption modeler 716 may generateTo make air side consumeAnd the load of heat energyA related air side power consumption model. In some embodiments, the air side power consumption modeler 716 models the air side power consumption using the following equation:

wherein,is consumed by air side equipment 622 of building b to deliver a thermal energy loadPower amount of conversion factor η_{Air (a)}May be a function of the coefficient of performance of the air-side equipment 622 (e.g., the inverse of the coefficient of performance)_{Air (a)}Is constant, which indicates air side power consumptionAnd the load of heat energyIn other embodiments, the operational data may be used by the air-side power consumption modeler 716 to calculate η_{Air (a)}As a non-linear function of load and other parameters.

In some embodiments, the air side power consumption modeler 716 calculates the conversion factor η_{Air (a)}As a function of various air side system parameters such as type of AHU, time of day, comfort limits, ambient conditions, chilled water supply temperature, chilled water supply flow rate, and/orFor example, air side power consumption modeler 716 may collect operational data from air side device 622 and/or low level air side controller 612 and use the operational data to determine η_{Air (a)}Is a suitable value of.

In some embodiments, the air side power consumption modeler 716 depends on thermal energy loadAnd each fan power model to calculate η_{Air (a)}. For example, air at 20 ℃ may have a density ρ as shown in the following equation_{Air (a)}And heat capacity C_{p, air}：

Thermal energy load provided by air flowThe following model can be used for representation:

wherein,is the volumetric flow rate, T, of the supply air into the building area_RoomIs the temperature of the building area, and T_{Supply of}Is the temperature of the supply air. Assuming supply air temperature T_{Supply of}About 55 ° F and room air temperature T_RoomApproximately 72 ° F, the air side power consumption modeler 716 may calculate the thermal energy load per unit volume of airflow (e.g., the cooling capacity of the air) as follows:

this cooling capacity value may be used by the air side power consumption modeler 714And estimated power consumption per unit volume of a typical air side fan η_{Air (a)}The value of (c). For example, a typical HVAC fan consumes approximately 1 horsepower (hp) to provide an airflow between 1000 cubic feet per minute (CFM) and 1500 CFM. These values may be converted to metric values as follows:

substituting these values into the air side power consumption model yields:

η_{air (a)}＝0.091-0.14

This indicates the air-side power consumption of each air-side subsystem 622-626About the thermal energy load delivered by the air side subsystem10% of the total.

Assuming that the air side can be power-consumedIs modeled asThe energy cost modeler 720 may model the total energy cost during the optimization period using the following equation:

the first part of the energy cost model takes into account the per unit cost of energy consumed by the waterside system 30 during each time step k of the optimization period (e.g., $/kWh). In some embodiments, c_kIs the cost per unit energy consumed at time step k, Δ is the duration of time step k, and η_HVACIs the inverse of the coefficient of performance of the waterside system 30 (e.g., η)_HVAC0.2). Item(s)Indicating the water side system 30 during time step k to meet the water side demandAmount of power consumption (e.g., kW). Multiplied by the cost of energy consumed per unit c_k(e.g., $/kWh) and duration Δ (e.g., hours) yield the total cost (e.g., $) of the energy consumed by the waterside system 30 during time step k. Energy cost model that can span all time steps k 0 … N-1 of the optimization periodThe first part is summed to determine the total energy consumed by the waterside system 30 over the duration of the optimization period.

The second part of the energy cost model considers the cost per unit of energy (e.g., $/kWh) consumed by each air-side subsystem (e.g., each building b) during each time step k of the optimization period, as described above, η_{Air (a)}Is the inverse of the coefficient of performance of the air-side subsystem (e.g., η)_{Air (a)}About 0.1), andis the thermal energy load delivered by the air side subsystem of building b at time step k. Item(s)Representing power consumption of air-side equipment of building bCan span all buildings b-1 … n_bAnd summing the second portion of the energy cost model across all time steps k 0 … N-1 to determine the total power consumption of all air-side subsystems over the duration of the optimization period. Multiplied by the cost of energy consumed per unit c_k(e.g., $/kWh) and duration Δ (e.g., hours) yield the total cost (e.g., $) of energy consumed by the air-side subsystem over the duration of the time step.

The third part of the energy cost model takes into account demand charges. In some embodiments, c_{Peak value}Is a demand electric charge rate (e.g., $/kW), andis the peak aggregate air-side and water-side power consumption during the applicable demand electricity charge period. Multiplying by the demand charge rate c_{Peak value}Resulting in a total cost (e.g., $) of the demand electricity fee. In some embodiments, the demand electricity rate modeler 718 generates a demand electricity rate constraint to ensureWith appropriate values. If the demand electricity rate period is completely contained within the optimization period (e.g., between time steps k-0 and k-N-1), thenIs the maximum value of the combined water side/air side power consumption at any time step k during the optimization period. The demand electricity rate modeler 718 may implement the following demand electricity rate constraint:

ensuring peak power consumptionAlways greater than or equal to the water side power consumption at each time stepPower consumption on the air sideAnd (4) summing. This forces peak power consumptionAt least as large as the maximum combined air side/water side demand during the optimization period.

If the demand electricity rate period begins before the optimization period, the maximum peak power consumption during the demand electricity rate period may occur before the optimization period begins. The demand electricity rate modeler 718 may implement the following demand electricity rate constraint:

ensuring that: even if maximum power consumption occurs before the start of the current optimization period, peakValue power consumptionIs always greater than or equal to the maximum power consumption occurring during the same demand electricity rate periodIn some embodiments, the demand electricity rate modeler 718 updates each time a new maximum power consumption is setTo ensure that the energy cost model accurately represents the demand charges imposed by the electric utility.

The advanced optimizer 712 may use an energy cost model, an air side power consumption model, a demand electricity rate model, a building temperature model, a thermal energy storage model, a water side demand model, and optimization constraints in order to formulate an optimization problem. In some embodiments, the advanced optimizer 712 seeks to minimize the total cost of energy consumed by the aggregated air-side/water-side system subject to building temperature constraints and other constraints provided by the advanced models described herein. For example, advanced optimizer 712 may formulate the advanced optimization problem as:

subject to the following constraints:

0≤s_k≤s_{maximum of}

T_{Minimum size}≤T_b≤T_{Maximum of}

x_k+1＝Ax_k+Bu_k+B_dd_k

y_k＝Cx_k+Du_k

wherein x is_kIs the system state vector at time step k, u_kIs the system input vector at time step k, y_kIs the measurement or system output vector at time step k, d_kIs the interference vector at time step k, and x_k+1Is the (predicted) system state vector at time step k + 1. The variables included in each vector may be the same as shown in table 1 above.

The high-level optimizer 712 may execute an optimization procedure to determine the optimal value of each of the input variables in the vector u at each time step k of the optimization period. For example, the advanced optimizer 712 may determine at each timeThermal energy load assigned to each building b at interval kAnd the thermal energy load assigned to the thermal energy storage device at each time step kThe optimum value of (2). Each group of thermal energy loads having the same building index bA load profile for a particular air-side subsystem is formed and includes a load value at each time step k in the optimization period. Similarly, the set of TES loadsThe load profile of the TES canister is formed and includes the load value at each time step k in the optimization period. The high-level optimizer 712 may provide an air-side subsystem load profile to the low-level air-side controllers 612-616 and may provide a TES load profile to the low-level water-side controller 618.

In some embodiments, the high-level optimizer 712 generates a predicted temperature state vector for each of the low-level air-side subsystems 632-636Each predicted temperature state vectorMay include predicting the building temperature state at each time step k during the optimization periodThe temperature state may be predicted using any of a variety of prediction techniquesThe prediction technique includes, for example, a kalman filter as described in U.S. patent No. 9,235,657. The high-level optimizer 712 may provide the predicted temperature state to the low-level air-side controllers 612-616 of the respective low-level air-side subsystems 632-636Each vector of (a). In some embodiments, the low-stage air-side controllers 612 to 616 use predicted temperature statesGenerating tracking predicted temperature states at each time step kThe zone temperature set point.

In some embodiments, the advanced optimizer 712 calculates the total demand on the waterside system 30 at each time step kAs the building thermal energy load at time step kWith TES loadThe sum of (a) and (b). The set of waterside demand values forms a demand profile for the waterside system 30 and includes the demand values at each time step k in the optimization period. Demand value at a specific time step kRepresenting the total demand that the waterside system 30 must meet at this time step k. The high-stage optimizer 712 may provide the water-side demand profile to the low-stage water-side controller 618.

Low-level air side model predictive controller

Referring now to FIG. 8, a block diagram illustrating a low-level Model Predictive Controller (MPC)612 in greater detail in accordance with some embodiments is shown. Although only one low-level air-side MPC612 is shown in detail, it should be understood that any other low-level air-side MPC (e.g., low-level air-side MPCs 614-616) in the control system 600 may include some or all of the same components as the low-level air-side MPC 612. The control system 600 may include any number of low-stage air-side MPCs, where each low-stage air-side MPC may be independently operated to monitor and control a separate low-stage air-side subsystem (e.g., air-side subsystems 632-636).

The low-level air-side MPC612 is shown to include a communication interface 802 and a processing circuit 804. The communication interface 802 may include a wired or wireless interface (e.g., receptacle, antenna, transmitter, receiver, transceiver, wire terminal, etc.) for data communication with various systems, devices, or networks. For example, the communication interface 802 may include an ethernet card and port for sending and receiving data via an ethernet-based communication network and/or a WiFi transceiver for communicating via a wireless communication network. Communication interface 802 may be configured for communication via a local or wide area network (e.g., the internet, a building WAN, etc.) and may use various communication protocols (e.g., BACnet, IP, LON, etc.).

The communication interface 802 may be a network interface configured to facilitate electronic data communication between the low-level air-side MPC612 and various external systems or devices (e.g., the weather service 604, the high-level MPC608, the air-side devices 622, etc.). For example, the low-level air-side MPC612 may receive weather forecasts from the weather service 604 and/or load predictions from the load/rate predictor 602. The low-level air-side MPC612 may receive measurements from the BMS indicative of one or more measured conditions (e.g., temperature, humidity, electrical load, etc.) of the controlled building or campus and one or more conditions (e.g., equipment status, power consumption, equipment availability, etc.) of the air-side subsystems 632. The low-level air-side MPC612 may receive the predicted temperature state and/or load profile from the high-level MPC 608. The low-level air-side MPC612 may use the information received at the communication interface 802 to generate zone temperature set points for each zone of the low-level air-side subsystem 632. The low-level air-side MPC612 may provide the zone temperature set point to the air-side device 622.

The processing circuit 804 is shown as including a processor 806 and a memory 808. Processor 806 may be a general or special purpose processor, an Application Specific Integrated Circuit (ASIC), one or more Field Programmable Gate Arrays (FPGAs), a set of processing components, or other suitable processing components. The processor 806 may be configured to execute computer code or instructions stored in the memory 808 or received from other computer-readable media (e.g., CDROM, network storage device, remote server, etc.).

Memory 808 may include one or more devices (e.g., memory units, memory devices, storage devices, etc.) for storing data and/or computer code for accomplishing and/or facilitating the various processes described in this disclosure. Memory 808 may include Random Access Memory (RAM), Read Only Memory (ROM), hard drive storage, temporary storage, non-volatile memory, flash memory, optical storage, or any other suitable memory for storing software objects and/or computer instructions. Memory 808 may include database components, object code components, script components, or any other type of information structure for supporting the various activities and information structures described in this disclosure. Memory 808 may be communicatively connected to processor 806 via processing circuitry 804 and may include computer code for executing (e.g., by processor 806) one or more processes described herein. When the processor 806 executes instructions stored in the memory 808, the processor 806 typically configures the low-level air-side MPC612 (and more particularly the processing circuit 804) to accomplish such activities.

Still referring to fig. 8, the low-level air-side MPC612 is shown to include a regional interference predictor 824. The regional interference predictor 824 may generate an interference prediction for each region i of the air side subsystem 632. Throughout this disclosure, the index i is used to represent each region, where i ═ i1…n_zAnd n is_zIs the total number of zones in a given air side subsystem. The interference prediction for region i may comprise a vector of interference valuesWherein the vectorComprises a predicted interference value at a particular time step k of the optimization period

The regional disturbance predictor 824 is shown as receiving weather forecasts from the weather service 604. In some embodiments, the regional disturbance predictor 824 generates a disturbance prediction from weather forecasts. In some embodiments, regional interference predictor 824 uses feedback from supervisory layer 620 to generate an interference prediction. Feedback from supervisor layer 620 may include various types of sensor inputs (e.g., temperature, flow, humidity, enthalpy, etc.) or other data related to the controlled building or campus (e.g., building occupancy data, building electrical loads, etc.). In some embodiments, the regional interference predictor 824 generates interference predictions using deterministic plus stochastic models trained from historical load data. The regional interference predictor 824 may use any of a variety of prediction methods to generate the interference predictions (e.g., linear regression for deterministic portions and autoregressive models for random portions).

The area disturbance predictor 824 may predict one or more different types of disturbance for each building area i. For example, the area disturbance predictor 824 may predict the heat load generated by heat transfer between the air within a building area i and the outside air through the building wall. The region disturbance predictor 824 may predict a thermal load (e.g., heat generated by electronics in the building region, heat generated by a region occupant, etc.) resulting from internal heating within the building region. In some embodiments, the regional interference predictor 824 uses prediction techniques described in U.S. patent application No. 14/717,593 for interference prediction.

Still referring to FIG. 8, the low-level air-side MPC612 is shown to include a zone temperature modeler 814. The zone temperature modeler 814 may generate a temperature model for each building zone i of the air side subsystem 632. The air side subsystem 632 may have any number of zones. In some embodiments, the temperature of each zone may be independently controlled and/or adjusted. Some building areas may exchange heat with each other (e.g., if the building areas are adjacent to each other), while other building areas may not exchange energy directly. In general, the zone temperature modeler 814 may generate n_zA zone temperature model, wherein n_zIs the total number of zones in the air side subsystem 632.

In some embodiments, zone temperature modeler 814 uses a zone heat transfer model to model the temperature of each building zone. The dynamics of heating or cooling individual building areas are described by energy balance:

where C is the heat capacity of the building area, H is the ambient heat transfer coefficient of the building area, T is the temperature of the building area, T is the heat transfer coefficient of the building area_aIs the ambient temperature outside the building area (e.g., outside air temperature),is the amount of cooling (i.e., cooling load) applied to a building area, andis an external load, radiation, or other disturbance experienced by a building area. In the former equation, the first and second equations,representation delivered by HVAC systemHeat (i.e., cooling) of the building area and thus has a negative sign. However, if heating is applied to the building area instead of cooling itThe sign of (A) can be switched to positive sign so thatRepresenting the amount of heating (i.e., the heating load) applied to a zone of a building by an HVAC system.

The former equation combines all mass and air characteristics of a building zone into a single zone temperature. Other heat transfer models that may be used by the zone temperature modeler 814 include the following air and mass zone models:

The former equation combines all the mass characteristics of a building area into a single area mass. Other heat transfer models that may be used by the zone temperature modeler 814 include the following air, shallow mass, and deep mass zone models:

In some embodiments, the zone temperature modeler 814 models the temperature of each building using the following zone temperature model:

wherein, C_iAnd T_iIs the heat capacity and temperature, T, of the building area specified by the area index i_aIs the ambient air temperature outside zone i (e.g., outside air temperature), H_iIs the heat transfer coefficient between zone i and the ambient air,is the amount of cooling (i.e., the amount of heat removed from the area) applied to area i of the building by MPC system 600, andis the external load, radiation or interference experienced by the zone i. If heating is provided to the zone instead of coolingThe sign of (b) may be switched from negative to positive.

Parameter β_ijCharacterizing the degree of coupling between an area i and another area j (e.g., a building area adjacent to area i.) if areas i and j are not adjacent and/or cannot directly exchange heat with each other, area temperature modeler 814 may set β_ijIs equal to zero. The zone temperature model may include a sum of heat transfers between building zone i and each other building zone j ≠ i as zone temperature T_iAnd T_jAnd coefficient of coupling β_ijAs a function of (c). In other embodiments, external interference estimates may be usedTo account for heat transfer between the zones.

The regional temperature modeler 814 may use the regional interference estimates received from the regional interference predictor 824 to identify the external interference for each region i at each time step of the optimization periodIs a suitable value of. In some embodiments, area temperature modeler 824 uses the weather forecast from weather service 604 and/or the load and rate forecast provided by load/rate predictor 602 to determine the ambient air temperature T for each area i at each time step of the optimization period_aAnd/or external interferenceIs a suitable value of. C_iAnd H_iCan be specified as a parameter i of the area, received from a user from a BMS managing the area i of the building, received from a user, retrieved from memory 808, orIs otherwise provided to the zone temperature modeler 814. The zone temperature modeler 714 may generate a zone temperature model for each zone i, where i is 1 … n_zAnd n is_zIs the total number of buildings.

Still referring to FIG. 8, the low-level air-side MPC612 is shown to include a building load modeler 816. Building load modeler 816 may generate a total amount of thermal energy Q delivered to the air side subsystem_{General assembly}(e.g., total heating or cooling delivered to a building) as a function of individual zone loadingThe model of (1). In some embodiments, the building load modeler 816 models the total building load using the following equation:

wherein Q is_{General assembly}Is the total amount of thermal energy (e.g., heating or cooling) delivered to the air-side subsystem, andis the rate of thermal energy delivered (in power) for a particular zone i. The building load model may provide thermal energy loading for each building zoneSumming to calculate total air side subsystem thermal energy loadThe total air-side subsystem thermal energy load is the total amount of thermal energy Q delivered to the air-side subsystem_{General assembly}The derivative of (c).

The low-level air-side MPC612 is shown as including a cooling/heating load modeler 820. The cooling/heating load modeler 820 may generate one or more models that are to be applied per modelThermal energy loading of individual building areasDefined as the zone temperature T as shown in the following equation_iAnd zone temperature setpoint T_sp,iFunction of (c):

the model generated by the cooling/heating load modeler 820 may be used as an optimization constraint to ensure thermal energy loadWill not drop to the zone temperature T_iValues deviating from the acceptable or more comfortable temperature range.

In some embodiments, cooling/heating load modeler 820 uses multiple models to thermally load zonesAnd zone temperature T_iAnd zone temperature setpoint T_sp,iAnd (4) correlating. For example, the cooling/heating load modeler 820 may use a model of a zone supervisory controller to determine a control action performed by the controller as a zone temperature T_iAnd zone temperature setpoint T_sp,iAs a function of (c). An example of such a regional supervisory controller model is shown in the following equations:

v_{air, i}＝f₁(T_i,T_sp,i)

Wherein v is_{Air, i}Is the rate of airflow to building zone i (i.e., the control action). Function f₁Can be identified from the data. For example, the cooling/heating load modeler 820 may collect v_{Air, i}And T_iAnd identifying T_sp,iTo the corresponding value of (c). The cooling/heating load modeler 820 may use the collected v_{Air, i}、T_iAnd T_sp,iAs training data to perform a system identification process to determine a function f defining the relationship between such variables₁。

The cooling/heating load modeler 820 may use the control action v_{Air, i}And regional thermal energy loadA relevant energy balance model, as shown in the following equation:

wherein the function f₂Can be identified from the training data. The cooling/heating load modeler 820 may use the collected v_{Air, i}Andto perform a system identification process to determine a function f defining the relationship between such variables₂。

In some embodiments of the present invention, the,and v_{Air, i}There is a linear relationship between them. Assuming an ideal Proportional Integral (PI) controller andand v_air,iLinear relationship between, thermal energy loading of each building zone can be carried out using a simplified linear controller modelIs defined as the zone temperature T_iAnd zone temperature setpoint T_sp,iAs a function of (c). An example of such a model is shown in the following equation:

ε_i＝T_sp,i-T_i

wherein,is the steady state heating rate or cooling rate, K_c,iIs the proportional gain, tau, of the PI controller in the scaling region_I,iIs the area PI controller integration time, and ε_iIs the setpoint error (i.e., the zone temperature setpoint T)_i,spAnd zone temperature T_spThe difference therebetween). The saturation can be passed throughIs represented by the constraint of (a). If the linear model is not sufficient to accurately model the heat transfer in the PI controller and AHU of zone i, a non-linear heating/cooling load model may be used instead.

Advantageously, modeling the supervisory controller (e.g., a zone PI controller) in a low-level air-side optimization problem allows the low-level air-side MPC612 to use the dynamics of the supervisory controller in determining the optimal temperature set point. In some embodiments, the response of the supervisory controller may be slow. For example, some zones may take up to an hour to reach the new temperature set point. Using the dynamics of the supervisory controller in the low-level MPC problem allows the low-level air-side MPC612 to take into account the time between control actions and effects so that the optimal temperature set point can be selected based on the time-varying energy prices.

Still referring to FIG. 8, the low-level air-side MPC612 is shown to include a constraint modeler 810. Constraint modeler 810 may generate optimization constraints and apply the optimization constraints to an optimizer executed by low-level optimizer 812. Constraints imposed by constraint modeler 810 may include, for example, plant capacity constraints and regional temperature constraints. In some embodiments, constraint modeler 810 operates on a zone temperature T_iConstraints are imposed. For example, constraint modeler 810 may constrainMinimum temperature T as shown in the following equation_{Minimum size}And maximum temperature T_{Maximum of}Zone temperature T in between_i：

T_{Minimum size}≤T_i≤T_{Maximum of}

Wherein, T_{Minimum size}And T_{Maximum of}May be adjusted based on the temperature set point of the building.

In some embodiments, constraint modeler 810 automatically adjusts T based on information received from the BMS for the building area_{Minimum size}And T_{Maximum of}The value of (c). For example, constraint modeler 810 may use a temperature setpoint schedule and/or an occupancy schedule for a building zone to automatically adjust T at each time step k_{Minimum size}And T_{Maximum of}The value of (c). This allows the constraint modeler 810 to use the temperature limits of the time-varying setpoint temperature range for a zone such that the zone temperature T_iMaintained at a suitable temperature limit T_{Minimum size}And T_{Maximum of}And (4) the following steps.

In some embodiments, the constraint modeler 810 imposes constraints to ensure that the total air-side subsystem load during any time step k is not greater than the thermal energy load assigned to the air-side subsystem by the advanced MPC 608. For example, constraint modeler 810 may impose the following constraints:

wherein Q is_{Total, k +1}Is the total air-side subsystem energy, Q, consumed at time step k +1_{Total, k}Is the total air side subsystem energy consumed at time step k,is the thermal energy load assigned to the air-side subsystem b by the advanced MPC608, and Δ is the duration of each time step. The left side of the equation represents the air side subsystem thermal energy load (i.e., the heat energy load) during time step kThe change in total thermal energy delivered between consecutive time steps divided by the time step duration), while the right side of the equation represents the thermal energy load distributed by the advanced MPC608 to the air-side subsystem b during the time step k.

In some embodiments, constraint modeler 810 imposes additional constraints to ensure that the total amount of thermal energy delivered does not decrease between successive time steps, as shown in the following equation:

Q_{total, k +1}-Q_{Total, k}≥0

Due to Q_{Total, k +1}Is the sum of the amount of thermal energy delivered up to time step k +1, so this constraint prevents the low-level optimizer 812 from selecting the thermal energy load at time step kNegative values of (c). In other words, the rate at which thermal energy is delivered (i.e.,) The total amount of thermal energy delivered over the optimization period may be added, but not subtracted.

The low-level optimizer 812 may use zone temperature models, building load models, cooling/heating load models, and optimization constraints in order to formulate an optimization problem. In some embodiments, the advanced optimization problem seeks to enable the thermal energy Q used by the air-side subsystem 632 over an optimization period subject to zone temperature constraints and other constraints provided by the low-grade air-side model described herein_total,NThe total amount is minimized. For example, the low-level optimizer 812 may formulate a low-level optimization problem as:

subject to the following constraints:

T_{minimum size}≤T_i≤T_{Maximum of}

Q_{Total, k +1}-Q_{Total, k}≥0

Wherein the function f is according toT_iAnd T_i,spDefined by the relation between

ε_i＝T_sp,i-T_i

In some embodiments, low-level optimizer 812 converts one or more of the above identified models and/or constraints into a state space form for use in a low-level optimization problem. For example, the low-level optimizer 812 can convert the foregoing equation into a discretized state-space model of the form:

x_k+1＝Ax_k+Bu_k+B_dd_k

y_k＝Cx_k+Du_k

wherein x is_kIs the system state vector at time step k, u_kIs the system input vector at time step k, y_kIs the measurement or system output vector at time step k, d_kIs the interference vector at time step k, and x_k+1Is the (predicted) system state vector at time step k + 1. Table 2 illustrates the variables that may be included in each of these vectors:

table 2: variables in Low level optimization

As shown in Table 2, the system state vector x includes the zone temperature T_iIntegration of area tracking errorAnd the total thermal energy delivered to the air side subsystem. In some embodiments, the system state vector x includes n_zZone temperature T of each of the zones_iAnd integrated area tracking errorAnd a single total heat energy value such that the total number of variables n in the system state vector x equals 2n_z+1. The input vector u may comprise a temperature set point T_sp,i. In some embodiments, input vector u comprises n_zTemperature set point T of each of the zones_sp,iSo that the total number of variables m in the input vector u equals n_z。

In some embodiments, the measurement vector y is the same as the system state vector x, but does not have an integrating area tracking errorThis indicates the zone temperature T_iAnd total amount of thermal energy Q delivered_{General assembly}Are measured directly. Can be according to T_i,spAnd T_iDifference therebetweenTo calculate an integral area tracking errorThe value of (c). The disturbance vector d may comprise the ambient air temperature T_aEstimated interference per areaAnd steady state heating/cooling rates for each zoneIn some embodiments, interference vector d comprises n_zEstimated interference for each of the individual buildingsAnd steady state heating/cooling ratesAnd a single ambient air temperature T_aSo that the total number of variables n in the interference vector d_dIs equal to n_z+1。

In some embodiments, the system state x may be constructed or predicted from the measurement y. For example, the low-level air-side MPC612 may use a kalman filter or other predictive techniques to construct the system state x from the measurement y. A, B, C in the state space expression, and the value of the D metric may be identified using system identification techniques. An example of state prediction and system identification techniques that may be used by the low-level MPC612 is described in detail in U.S. patent No. 9,235,657.

Still referring to fig. 8, the low-level air-side MPC612 is shown as including a model aggregator 818. Model aggregator 818 may generate aggregated values for various building parameters and/or variables used in low-level optimization. For example, model aggregator 818 may determine the temperature T of each zone in the building by a separate zone temperature T for each zone_iConducting polymerization to generate a polymerization building temperature T for a low-grade air side subsystem_b. In some embodiments, model aggregator 818The polymeric building temperature T is generated using the following equation_b：

Wherein, C_iIs the heat capacity of zone i, and T_iIs the temperature of zone i. The numerator of the former equation represents the total heat in the building and the denominator represents the total heat capacity of the building. Summing these two quantities across all building areas i 1 … n_z. Model aggregator 818 may divide the total heat by the total heat capacity to estimate the average building temperature T_b. Model aggregator 818 can calculate the aggregate building temperature T at each time step k of the optimization period_b,k。

Model aggregator 818 can calculate heat capacity C of a building, for example_bHeat transfer coefficient of building H_bAnd estimating building interferenceAnd the aggregate value of other building parameters or variables. In some embodiments, model aggregator 818 calculates the aggregate values of these variables and parameters using the following equations:

wherein the heat capacity of the building C_bIs the zone heat capacity C of each building zone_iSum of values, building heat transfer coefficient H_bIs the regional heat transfer coefficient H of each building area_iSum of values, and estimate building interferenceEstimated building disturbance for each building areaThe sum of (a) and (b). DieThe type aggregator 818 can calculate C at each time step k of the optimization period_b,k、H_b,kAndthe polymerization value of (a).

In some embodiments, model aggregator 818 will aggregate building parameters and variables T_b、C_b、H_bAndprovided to the advanced MPC 608. The advanced MPC608 may use such values as inputs to advanced models, constraints, and optimization functions used in advanced optimization. Advantageously, the model aggregation performed by the model aggregator 818 helps to reduce the amount of information exchanged between each low-level air-side MPC612 through 616 and the high-level MPC 608. For example, each low-level MPC612 to 616 may provide the above-described aggregate values to high-level MPC608, rather than individual values for such variables and parameters for each building area.

Still referring to fig. 8, the low-level air-side MPC612 is shown as including a temperature tracker 822. In some embodiments, the temperature tracker 822 performs an alternative optimization that may supplement or replace the low-level optimization performed by the low-level optimizer 812. Temperature tracker 822 does not enable total thermal energy Q to be used by the low-grade air side subsystem_{Total, N}Minimization, but tracking of predicted building temperature states generated by the advanced MPC608 from advanced optimization can be generatedZone temperature set point T_sp,i. For example, the high-level MPC608 may calculate the building temperature state for each of the low-level air-side subsystems 632-636 that is predicted to produce the load profile generated by the high-level MPC608As described above, the predicted temperature may be calculated using a Kalman filter or any other type of state prediction techniqueDegree stateThe advanced MPC608 may predict temperature statesIs provided to each low-level air-side MPC612 through 616 for use in the temperature tracking process.

Temperature tracker 822 is shown receiving a predicted temperature state via communication interface 802The temperature tracker 822 may also receive the aggregate building temperature T generated by the model aggregator 818_b,k. Temperature tracker 822 may seek to cause polymerization of building temperature T_b,kAnd predicting the temperature stateThe objective function of error minimization between them is formulated as shown in the following equation:

where μ is the total amount of thermal energy Q applied to be used by the air-side subsystem over the optimized time period_{Total, N}Smaller penalty factor. The value of μ can be adjusted to increase or decrease the total amount of heat energy Q allocated relative to the temperature tracking error_{Total, N}The weight of (c).

The temperature tracker 822 can use the previous equation in the optimization routine as an objective function to determine the zone temperature setpoint T_sp,iThe optimum value of (2). The optimization performed by the temperature tracker 822 may be similar to the optimization performed by the low-level optimizer 812, except that the temperature tracker 822 is not constrained by the load profile provided by the high-level MPC608 and seeks to optimize a different objective function. For example, the temperature tracker 822 may minimize the objective function:

subject to the following constraints:

T_{minimum size}≤T_i≤T_{Maximum of}

Q_{Total, k +1}-Q_{Total, k}≥0

ε_i＝T_sp,i-T_i

In some embodiments, the temperature tracker 822 converts one or more of the above identified models and/or constraints into a state space form for use in a low-level optimization problem. For example, the temperature tracker 822 can convert the foregoing equation into a discretized state-space model of the form:

x_k+1＝Ax_k+Bu_k+B_dd_k

y_k＝Cx_k+Du_k

wherein x is_kIs the system state vector at time step k, u_kIs the system input vector at time step k, y_kIs the measurement or system output vector at time step k, d_kIs the interference vector at time step k, and x_k+1Is the (predicted) system state vector at time step k + 1. The variables included in each vector may be the same as shown in table 2 above.

Simulation study

Referring now to fig. 9-13, several graphs 900-1350 illustrating results of simulation studies are shown, according to some embodiments. The simulation study involves using model predictive control system 600 to monitor and control a five-zone building with large active TES tanks. Each of the graphs 900-1350 shows a set of values for a particular variable or parameter over time. Some variables or parameters may be received as input data to the control system 600 (e.g., weather data, utility rates, etc.), while other variables or parameters may be calculated and/or optimized by the control system 600 as previously described.

FIG. 9 shows weather data and electricity pricing data over the duration of an optimization period. Graph 900 shows ambient temperature 902 over time. Ambient temperature 902 may be received as a weather forecast from weather service 604 and used to determine variable T_aAppropriate values in the high-level optimization and the low-level optimization. The graph 950 shows the power pricing 952 over time. Power pricing 954 may be received as input from utility 606 and/or predicted by load/rate predictor 602.

FIG. 10 shows the results of an advanced optimization performed by the advanced MPC608 when the cost of air side power consumption is not included in the advanced optimization function. Graph 1000 illustrates a building temperature 1002 (e.g., building temperature T) over time_bBuilding, buildingTemperature of building). The building temperature 1002 may be measured as an output of the building (e.g., using equipment of the air side subsystem 632) and/or predicted as a result of advanced optimization. When air side power is not included, the advanced MPC608 may choose to maintain the building temperature at the upper bound of the comfort zone (i.e., T) at all times_{Maximum of})。

Graph 1050 shows the air side subsystem 632 (e.g.,) The cooling load 1052 varies over time. The cooling load 1052 is an example of a load profile that may be optimized by the high-level MPC608 for each air-side subsystem and provided to the low-level air-side MPCs 612 to 616 for that air-side subsystem. The cooling load 1052 may be calculated by the advanced MPC608 as a result of the optimal building temperature 1002. In some embodiments, when air-side power is not included, the advanced MPC608 does not utilize a passive thermal energy storage device, but rather only uses active TES (e.g., TES tanks) to transfer water-side power loads.

FIG. 11 illustrates the results of an advanced optimization performed by the advanced MPC608 when the cost of air side power consumption is included in the advanced optimization function. Graph 1100 illustrates a building temperature 1102 (e.g., building temperature T) over time_bBuilding temperature). The building temperature 1102 may be measured as an output of the building (e.g., using equipment of the air side subsystem 632) and/or predicted as a result of advanced optimization. When air side power consumption is included, the advanced MPC608 may choose to pre-cool the building using passive TES before peak hours when electricity prices are highest.

Graph 1150 shows that the air side subsystem 632 (e.g.,) Cooling load 1152 over time. The cooling load 1152 is an example of a load profile that may be optimized by the high-level MPC608 for each air-side subsystem and provided to the low-level air-side MPCs 612 to 616 for that air-side subsystem. The cooling load 1152 may be calculated by the advanced MPC608 as a result of the optimal building temperature 1102. In some embodiments, when air side power is involved, the advanced MPC608 uses a passive thermal energy storage device that allows the advanced MPC608 to shift the load from peak periods to off-peak periods. The flat portion 1154 of the cooling load profile may result from the advanced MPC608 attempting to smooth the cooling load 1152 to reduce the peak demand electricity rate.

Fig. 12 shows the results of the low-level air-side optimization performed by the low-level air-side MPC 612. Graph 1200 shows zone temperatures 1202, 1204, 1206, 1208, and 1210 (e.g., T) for several building zones over time_i). The zone temperatures 1202-1210 may be measured as the output of the zone (e.g., the equipment using the air side subsystem 632) and/or predicted as a result of low-level optimization. When pre-cooling occurs, the zone temperatures 1202-1210 begin to decrease at different (e.g., staggered or offset) times to reduce the overall building load at any given time. This is a result of the low-stage air-side MPC612 meeting the cooling load limit (e.g., the value of the cooling load 1102 or 1152) received from the high-stage MPC 608.

Graph 1250 illustrates temperature setpoints 1252, 1254, 1256, 1258, and 1260 (e.g., T) for several building zones over time_sp,i). The zone temperature setpoints 1252 through 1260 may be calculated by the low-level air-side MPC612 as a result of the low-level air-side optimization. For example, the low-level air-side MPC612 may use definitionsT_sp,iAnd T_iCooling load model of the relationship betweenTo determine the appropriate temperature setpoint T for each building zone_sp,i. In some embodiments, the low-level air-side MPC612 causes the zone temperature setpoints 1252-1260 to begin to decrease at different (e.g., staggered or offset) times to reduce the overall building load at any given time and/or to reach the temperature T shown in the graph 1200_i。

Fig. 13 shows the results of the low-stage waterside optimization performed by the low-stage waterside MPC 618. Graph 1300 illustrates how the combination of thermal energy generation 1304 from a chiller and thermal energy storage 1306 from a TES tank can be used to meet demand 1302 from advanced optimization. The thermal energy storage 1306 may be positive to indicate that the TES tank is releasing stored thermal energy or may be negative to indicate that the TES tank is being loaded (e.g., filled) with a portion of the thermal energy generation 1304. The unsatisfied load 1308 represents an amount of the demand profile 1302 that is not satisfied by the generation 1304 or storage 1306. A penalty may be assigned to the unsatisfied load 1308 in the waterside optimization function to ensure that the unsatisfied load does not occur unless the waterside device 628 fails to meet the total demand 1302.

Graph 1350 shows plant operating schedules 1352 and 1354 for two chillers that meet thermal energy generation 1304. The area in box 1356 indicates that the corresponding cooler is active, while the non-boxed area 1358 indicates that the cooler is not active. The loading fraction (e.g., between zero and one) of each cooler is indicated by load lines 1360 and 1362. Where active TES is available, the chiller may produce more chilled water to load into the TES tank when the energy price is lower (e.g., during the night). The TES canister may then be released when the energy price is high (e.g., during the day) to reduce the overall electricity consumption cost.

Flow chart

Referring now to FIG. 14, a flow diagram 1400 is shown illustrating a model predictive control technique that may be performed by the MPC system 600, in accordance with some embodiments. Flowchart 1400 is shown as including: performing advanced optimization at an advanced Model Predictive Controller (MPC) to account for multiple air sidesEach of the subsystems generates an optimal load profile (block 1402). In some embodiments, the high-level optimization is performed by the high-level MPC608, as described with reference to fig. 6-7. For example, advanced optimization may include generating an advanced energy cost function that defines energy costs as waterside demand profilesAs a function of (c). In some embodiments, the water side demand profileIndicating the thermal energy production of the water side system at each of a plurality of time steps k in an optimization period.

In some embodiments, the advanced MPC608 generates an energy cost function that takes into account the cost per unit of energy consumption and demand electricity rate of the water side system 30 but does not take into account the energy consumption of the air side system 50. An example of such an energy cost function is shown in the following equation:

the first term of the energy cost function takes into account the cost per unit of energy consumed during each time step k of the optimization period (e.g., $/kWh). In some embodiments, c_kIs consumed at time step k to meet the total water side demand at time step kCost per unit energy, parameter η_totIs the reciprocal of the coefficient of performance of the polymerization air-side/water-side system (e.g., 0.1 ≦ η_tot≦ 0.25), and Δ is the duration of the time step k. Thus, itemΔ represents the consumption during time step k to satisfy the watersideDemand forTotal amount of energy (e.g., kWh). Multiplied by the cost of energy consumed per unit c_k(e.g., $/kWh) yields the total cost of the energy consumed during time step k (e.g., $). The energy cost function may include a sum of the energy costs during each time step k to determine a total cost of energy consumption over the optimization period.

The second term of the energy cost function takes into account the demand electricity charge. In some embodiments, c_{Peak value}Is a demand electricity rate (e.g., $/kW),is the peak water demand during the demand electricity period (e.g.,and η) and η_totIs the inverse of the coefficient of performance of the polymerization air side/water side system. Thus, itemIndicating peak water side demand is metPeak power consumption. Multiplying by the demand charge rate c_{Peak value}Resulting in a total cost (e.g., $) of the demand electricity fee.

In some embodiments, the advanced MPC608 generates an energy cost function that takes into account the cost per unit of energy consumption of the water side system 30, the electricity demand charge, and the energy consumption of the air side system 50. An example of such an energy cost function is shown in the following equation:

the first part of the energy cost function takes into account the water side systemThe system 30 consumes energy per unit cost (e.g., $/kWh) during each time step k of the optimization period. In some embodiments, c_kIs the cost per unit energy consumed at time step k, Δ is the duration of time step k, and η_HVACIs the inverse of the coefficient of performance of the waterside system 30 (e.g., η)_HVAC0.2). Item(s)Indicating the water side system 30 during time step k to meet the water side demandAmount of power consumption (e.g., kW). Multiplied by the cost of energy consumed per unit c_k(e.g., $/kWh) and duration Δ (e.g., hours) yield the total cost (e.g., $) of the energy consumed by the waterside system 30 during time step k. The first portion of the energy cost function may be summed across all time steps k 0 … N-1 of the optimization period to determine the total energy consumed by the waterside system 30 over the duration of the optimization period.

The second part of the energy cost function considers the cost per unit (e.g., $/kWh) of energy consumed by each air-side subsystem (e.g., each building b) during each time step k of the optimization period, as described above, η_{Air (a)}Is the inverse of the coefficient of performance of the air-side subsystem (e.g., η)_{Air (a)}About 0.1), andis the thermal energy load delivered by the air side subsystem of building b at time step k. Item(s)Representing power consumption of air-side equipment of building bCan span all buildings b-1 … n_bAnd a pair of energy costs across all time steps k 0 … N-1The second part of the function is summed to determine the total power consumption of all air side subsystems over the duration of the optimization period. Multiplied by the cost of energy consumed per unit c_k(e.g., $/kWh) and duration Δ (e.g., hours) yield the total cost (e.g., $) of energy consumed by the air-side subsystem over the duration of the time step.

The third part of the energy cost function takes into account the demand electricity charge. In some embodiments, c_{Peak value}Is a demand electric charge rate (e.g., $/kW), andis the peak aggregate air-side and water-side power consumption during the applicable demand electricity charge period. Multiplying by the demand charge rate c_{Peak value}Resulting in a total cost (e.g., $) of the demand electricity fee.

In some embodiments, performing advanced optimization in block 1402 includes using a waterside demand model to profile the waterside demand profileDefined as the plurality of air side subsystem load profilesAs a function of (c). Load distribution curve of each air side subsystemA distribution of thermal energy to one of the air-side subsystems at each of the plurality of time steps may be indicated.

In some embodiments, the water-side demand model represents the demand on the water-side system 30 as the thermal energy load assigned to each air-side subsystem at each time step of the optimization periodAnd thermal energy storageA function of the amount. An example of such a water side demand model is shown in the following equation:

In some embodiments, the advanced optimization in block 1402 includes generating an air-side subsystem temperature model for each of the plurality of air-side subsystems. Each air-side subsystem temperature model may define the thermal energy distribution to the air-side subsystemTemperature T of the air side subsystem_bThe relationship between them. An example of such an air side subsystem temperature model is shown in the following equation:

wherein, C_bAnd T_bIs the heat capacity and temperature, T, of the air-side subsystem, designated by the index b_aIs the ambient air temperature outside the air-side subsystem b (e.g., outside air temperature), H_bIs the heat transfer coefficient between the air-side subsystem b and the ambient air,is the amount of cooling applied to the air-side subsystem (i.e., the amount of heat removed from the air-side subsystem) by the MPC system 600, andis the external load, radiation or interference experienced by the air-side subsystem b. If heating is provided to the air side subsystem instead of coolingThe sign of (b) may be switched from negative to positive.

The high-level optimization may use the building interference estimates received from the low-level air-side controllers 612 through 616 to identify external interference for each air-side subsystem b at each time step of the optimization periodIs a suitable value of. In some embodiments, advanced optimization uses weather forecasts from weather service 604 and/or load and rate predictions provided by load/rate predictor 602 to determine ambient air for each air-side subsystem b at each time step of the optimization sessionTemperature T_aAnd/or external interferenceIs a suitable value of. C_bAnd H_bMay be specified as a parameter of the air-side subsystem b, received from a low-level air-side controller of the air-side subsystem b, received from a user, retrieved from memory, or otherwise provided to the high-level MPC 608. Advanced optimization may include generating a temperature model for each air-side subsystem b, where b is 1 … n_bAnd n is_bIs the total number of air side subsystems.

In some embodiments, performing the advanced optimization in step 1402 includes optimizing the energy cost and the plurality of air-side subsystem load profiles subject to constraints provided by the water-side demand model and each air-side subsystem temperature model. Advanced optimization may use an energy cost model, a demand electricity rate model, a building temperature model, a thermal energy storage model, a water side demand model, and optimization constraints to formulate an optimization problem.

In some embodiments, advanced optimization seeks to minimize the total cost of energy (i.e., energy cost and demand electricity charges) consumed by the waterside system 30 subject to building temperature constraints and other constraints provided by the advanced models described herein. For example, advanced optimization may formulate an advanced optimization problem as:

subject to the following constraints:

0≤s_k≤s_{maximum of}

T_{Minimum size}≤T_b≤T_{Maximum of}

In some embodiments, advanced optimization seeks to minimize the total cost of energy consumed by the aggregated air-side/water-side system subject to building temperature constraints and other constraints provided by the advanced models described herein. For example, advanced optimization may formulate an advanced optimization problem as:

subject to the following constraints:

0≤s_k≤s_{maximum of}

T_{Minimum size}≤T_b≤T_{Maximum of}

Still referring to FIG. 14, a flow chart 1400 is shown that includes assigning an optimal air side subsystem load profileFrom the high-level MPC, to multiple low-level MPCs (block 1404). Each load distribution curveMay be sent from the high-level MPC608 to one of the low-level air-side MPCs 612 through 616, as shown in fig. 6. In some embodiments, each low-stage air-side MPC is configured to monitor and control a particular air-side subsystem. Each low-stage air-side MPC may receive an optimal subsystem load profile for the air-side subsystem monitored and controlled by the low-stage air-side MPC.

Flowchart 1400 is shown as including: a low-level optimization is performed at each of the low-level MPCs to generate optimal temperature setpoints for each of the air-side subsystems (block 1406). In some embodiments, the low-level optimization in block 1406 includes generating a zone temperature model for each zone i of each air side subsystem. The zone temperature model may define the temperature T of the zone_iThermal energy load profile versus zoneIn betweenAnd (4) relationship. An example of such a zone temperature model is shown in the following equation:

The low-level optimization may include using interference from a regionThe regional interference estimates received by predictor 824 identify the external interference for each region i at each time step of the optimization intervalIs a suitable value of. In some embodiments, the low-level optimization uses the weather forecast from weather service 604 and/or the load and rate forecast provided by load/rate predictor 602 to determine the ambient air temperature T for each region i at each time step of the optimization period_aAnd/or external interferenceIs a suitable value of. C_iAnd H_iMay be specified as a parameter i of the area, received from a user from a BMS managing the building area i, received from a user, retrieved from memory 808, or otherwise provided to the area temperature modeler 814.

In some embodiments, the low-level optimization in block 1406 includes generating a total amount of thermal energy Q delivered to the air side subsystem_total(e.g., total heating or cooling delivered to a building) as individual zone loadsA model of the function of (a). In some embodiments, the low-level optimization includes modeling the total building load using the following equations:

In some embodiments, the low-level optimization includes generating one or more models that load thermal energy of each building zoneDefined as the zone temperature T as shown in the following equation_iAnd zone temperature setpoint T_sp,iFunction of (c):

models generated by low-level optimization may be used as optimization constraints to ensure thermal energy loadWill not drop to the zone temperature T_iValues deviating from the acceptable or more comfortable temperature range.

In some embodiments, the low-level optimization in block 1406 seeks to cause thermal energy Q used by the air-side subsystem subject to zone temperature constraints and other constraints provided by the low-level air-side model described herein over an optimization period_total,NThe total amount is minimized. For example, the low-level optimization may formulate a low-level optimization problem as:

subject to the following constraints:

T_{minimum size}≤T_i≤T_{Maximum of}

Q_{Total, k +1}-Q_{Total, k}≥0

ε_i＝T_sp,i-T_i

Still referring to fig. 14, the flowchart 1400 is shown including operating HVAC equipment in each of the air-side sub-systems using the optimal temperature set point (block 1408). For example, each low-stage air-side MPC612 to 616 may operate the air-side HVAC equipment 622 to 626 of the respective air-side subsystem 632 to 636. As described with reference to fig. 1A-1B and 3, air-side equipment 622-626 may include some or all of air-side system 50, air-side system 130, and/or air-side system 300. Operating the air side devices 622-626 may include activating or deactivating the devices, adjusting operating settings, or otherwise controlling the air side devices.

Referring now to FIG. 15, a flow diagram 1500 is shown illustrating a model predictive control technique that may be performed by the MPC system 600, in accordance with some embodiments. Flowchart 1500 is shown as including: an advanced optimization is performed at an advanced Model Predictive Controller (MPC) to generate an optimal temperature profile for each of a plurality of air-side subsystems (block 1502). In some embodiments, the high-level optimization is performed by the high-level MPC608, as described with reference to fig. 6-7. The advanced optimization may be the same as or similar to the advanced optimization described with reference to fig. 14.

The advanced optimization in block 1502 may generate an optimal temperature profile for each air-side subsystemOptimum temperature profileThe predicted temperature state vector for each low-stage air-side subsystem 632-636 may be includedEach predicted temperature state vectorMay include predicting the building temperature state at each time step k during the optimization periodThe temperature state may be predicted using any of a variety of prediction techniquesThe prediction technique includes, for example, a kalman filter as described in U.S. patent No. 9,235,657.

Flow chart 1500 is shown as including assigning the best nullTemperature profile of gas side subsystemA plurality of low-level MPCs are provided from the high-level MPCs (block 1504). Temperature profile of each subsystemMay be sent from the high-level MPC608 to one of the low-level air-side MPCs 612 through 616. In some embodiments, each low-stage air-side MPC is configured to monitor and control a particular air-side subsystem. Each low-level air-side MPC may receive an optimal subsystem temperature profile for the air-side subsystem monitored and controlled by that low-level air-side MPC

Flowchart 1500 is shown as including: a low-level optimization is performed at each of the low-level MPCs to generate an optimal temperature set-point for each of the air-side subsystems (block 1506). The low-level optimization in block 1506 may be similar to the low-level optimization in block 1406. However, rather than minimizing the total amount of thermal energy used, the low-level optimization in block 1506 may formulate a low-level optimization problem to track the optimal temperature generated by the high-level optimization in block 1502.

In some embodiments, the low-level optimization in block 1506 may be formulated as:

subject to the following constraints:

T_{minimum size}≤T_i≤T_{Maximum of}

Q_{Total, k +1}-Q_{Total, k}≥0

ε_i＝T_sp,i-T_i

Still referring to FIG. 15, a flowchart 1500 is shown that includes operating HVAC equipment in each of the air-side sub-systems using the optimal temperature setpoint (block 1508). For example, each low-stage air-side MPC612 to 616 may operate the air-side HVAC equipment 622 to 626 of the respective air-side subsystem 632 to 636. As described with reference to fig. 1A-1B and 3, air-side equipment 622-626 may include some or all of air-side system 50, air-side system 130, and/or air-side system 300. Operating the air side devices 622-626 may include activating or deactivating the devices, adjusting operating settings, or otherwise controlling the air side devices.

Configuration of the exemplary embodiment

The construction and arrangement of the systems and methods as shown in the various exemplary embodiments are illustrative only. Although only a few embodiments have been described in detail in this disclosure, many modifications are possible (e.g., variations in sizes, dimensions, structures, shapes and proportions of the various elements, values of parameters, mounting arrangements, use of materials, colors, orientations, etc.). For example, the position of elements may be reversed or otherwise varied, and the nature or number of discrete elements or positions may be altered or varied. Accordingly, all such modifications are intended to be included within the scope of this disclosure. The order or sequence of any process or method steps may be varied or re-sequenced according to alternative embodiments. Other substitutions, modifications, changes, and omissions may be made in the design, operating conditions, and arrangement of the exemplary embodiments without departing from the scope of the present disclosure.

The present disclosure contemplates methods, systems, and program products on any machine-readable media for performing various operations. Embodiments of the present disclosure may be implemented using an existing computer processor, or by a special purpose computer processor in conjunction with a suitable system for this or another purpose, or by a hardwired system. Embodiments within the scope of the present disclosure include program products comprising machine-readable media for carrying or having machine-executable instructions or data structures stored thereon. Such machine-readable media can be any available media that can be accessed by a general purpose or special purpose computer or other machine with a processor. By way of example, such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, etc., or any other medium which can be used to carry or store desired program code in the form of machine-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer or other machine with a processor. Combinations of the above are also included within the scope of machine-readable media. Machine-executable instructions comprise, for example, instructions and data which cause a general purpose computer, special purpose computer, or special purpose processing machines to perform a certain function or group of functions.

Although the figures show a specific order of method steps, the order of the steps may differ from that depicted. Two or more steps may also be performed simultaneously or partially simultaneously. Such variations will depend on the software and hardware systems chosen and on designer choice. All such variations are within the scope of the present disclosure. Likewise, software implementations could be accomplished with standard programming techniques with rule based logic and other logic to accomplish the various connection steps, processing steps, comparison steps and decision steps.

Claims

1. A heating, ventilation and air conditioning (HVAC) system for a building, the HVAC system comprising:

an air-side system having a plurality of air-side subsystems, each air-side subsystem including an air-side HVAC plant configured to provide heating or cooling to the air-side subsystem;

an advanced model predictive controller configured to perform an advanced optimization to generate an optimal air-side subsystem load profile for each of the plurality of air-side subsystems, wherein the optimal air-side subsystem load profile optimizes energy costs;

a plurality of low-stage air-side model predictive controllers, each low-stage air-side model predictive controller corresponding to one of the air-side subsystems and configured to perform low-stage optimization to generate an optimal air-side temperature setpoint for the respective air-side subsystem using the optimal air-side subsystem load profile for the respective air-side subsystem;

wherein each of the plurality of low-stage air-side model predictive controllers is configured to operate the air-side HVAC equipment of the respective air-side subsystem using the optimal air-side temperature setpoint for the respective air-side subsystem.

2. The HVAC system of claim 1, further comprising:

a waterside system comprising a waterside HVAC plant, wherein the advanced model predictive controller is configured to generate an optimal waterside demand profile for the waterside system; and

a low-stage waterside model predictive controller configured to perform low-stage optimization to generate optimal waterside setpoints for the waterside system subject to demand constraints based on the optimal waterside demand profile;

wherein the low-stage water-side model predictive controller is configured to operate the water-side HVAC plant using the optimal water-side setpoint.

3. The HVAC system of claim 1, wherein the air-side subsystems represent separate buildings that are thermally decoupled from one another such that no direct heat exchange occurs between the air-side subsystems.

4. The HVAC system of claim 1, wherein the advanced model predictive controller is configured to generate an advanced cost function defining the energy cost as a function of a water side demand profile indicative of thermal energy production of the water side system at each of a plurality of time steps in an optimization period.

5. The HVAC system of claim 4, wherein the advanced model predictive controller is configured to use a water side demand model to define the water side demand profile as a function of the plurality of air side subsystem load profiles, each air side subsystem load profile indicating a thermal energy distribution to one of the air side subsystems at each of the plurality of time steps.

6. The HVAC system of claim 5, wherein the advanced model predictive controller is configured to generate an air-side subsystem temperature model for each of the plurality of air-side subsystems, each air-side subsystem temperature model defining a relationship between the thermal energy distribution to an air-side subsystem and a temperature of the air-side subsystem.

7. The HVAC system of claim 6, wherein the advanced model predictive controller is configured for optimizing the energy cost and the plurality of air side subsystem load profiles subject to constraints provided by the water side demand model and each air side subsystem temperature model.

8. The HVAC system of claim 1, wherein:

each air side subsystem includes a plurality of building zones; and is

Each of the low-level air-side model predictive controllers is configured to generate an optimal air-side temperature setpoint for each of the plurality of building areas in the respective air-side subsystem.

9. The HVAC system of claim 8, wherein each of the low-grade air-side model predictive controllers is configured to generate a zone load profile for each of the plurality of building zones in the respective air-side subsystem, each zone load profile indicating a distribution of thermal energy to one of the building zones at each of a plurality of time steps in an optimization period.

10. The HVAC system of claim 1, wherein each of the optimal air side subsystem load profiles comprises at least one of:

an optimal thermal energy load value for the respective air-side subsystem at each of a plurality of time steps in an optimization period; and

an optimal temperature value for the respective air-side subsystem at each of the plurality of time steps in the optimization period.

11. A method for optimizing energy costs of a building HVAC system including an air-side system having a plurality of air-side subsystems, the method comprising:

performing an advanced optimization at an advanced model predictive controller to generate an optimal air-side subsystem load profile for each of the plurality of air-side subsystems, wherein the optimal air-side subsystem load profile optimizes the energy cost;

providing the optimal air side subsystem load profile from the high level model predictive controller to a plurality of low level air side model predictive controllers, each of the low level air side model predictive controllers corresponding to one of the plurality of air side subsystems;

performing a low-level optimization at each of the low-level air-side model predictive controllers to generate optimal air-side temperature setpoints for the respective air-side subsystem subject to load constraints based on the optimal air-side subsystem load profile for the respective air-side subsystem; and

operating air-side HVAC equipment in each of the plurality of air-side subsystems using the optimal air-side temperature setpoint.

12. The method of claim 11, wherein the air-side subsystems represent separate buildings that are thermally decoupled from one another such that no direct heat exchange occurs between the air-side subsystems.

13. The method of claim 11, wherein performing the advanced optimization comprises generating an optimal waterside demand profile for a waterside system, the method further comprising:

providing the optimal water side demand profile to a low-grade water side model predictive controller;

performing a low-stage optimization at the low-stage waterside model predictive controller to generate optimal waterside setpoints for the waterside system subject to demand constraints based on the optimal waterside demand profile; and

operating a water side HVAC plant in the water side system using the optimal water side setpoint.

14. The method of claim 11, wherein performing the advanced optimization comprises generating an advanced cost function defining the energy cost as a function of a water side demand profile indicative of thermal energy production by the water side system at each of a plurality of time steps in an optimization period.

15. The method of claim 14, wherein performing the advanced optimization comprises using a water side demand model to define the water side demand profile as a function of the plurality of air side subsystem load profiles, each air side subsystem load profile indicating a thermal energy distribution to one of the air side subsystems at each of the plurality of time steps.

16. The method of claim 15, wherein performing the advanced optimization comprises generating an air-side subsystem temperature model for each of the plurality of air-side subsystems, each air-side subsystem temperature model defining a relationship between the thermal energy distribution to an air-side subsystem and a temperature of the air-side subsystem.

17. The method of claim 16, wherein performing the advanced optimization comprises optimizing the energy cost and the plurality of air-side subsystem load profiles subject to constraints provided by the water-side demand model and each air-side subsystem temperature model.

18. The method of claim 11, wherein:

each air side subsystem includes a plurality of building zones; and is

Performing the low-level optimization includes generating an optimal air side temperature setpoint for each of the plurality of building zones.

19. The method of claim 18, wherein performing the low-level optimization comprises generating a zone load profile for each of the plurality of building zones, each zone load profile indicating a distribution of thermal energy to one of the building zones at each of a plurality of time steps in an optimization period.

20. A method for optimizing energy costs of a building HVAC system including an air-side system having a plurality of air-side subsystems, the method comprising:

performing an advanced optimization at an advanced model predictive controller to generate an optimal air-side subsystem temperature profile for each of the plurality of air-side subsystems, wherein the optimal air-side subsystem temperature profile optimizes the energy cost;

providing the optimal air-side subsystem temperature profile from the high-level model predictive controller to a plurality of low-level air-side model predictive controllers;

performing a low-level optimization at each of the low-level air-side model predictive controllers to generate optimal air-side temperature setpoints for the plurality of air-side subsystems, wherein the optimal air-side temperature setpoints minimize an error between air-side subsystem temperatures and the optimal air-side subsystem temperature profile; and

21. A heating, ventilation and air conditioning (HVAC) system for a building, the HVAC system comprising:

a water side system comprising a water side HVAC plant configured to generate thermal energy used by the air side system to provide heating or cooling;

an advanced model predictive controller configured to perform an advanced optimization to generate an optimal air-side subsystem load profile for each of the plurality of air-side subsystems, wherein the optimal air-side subsystem load profile optimizes a total energy cost of both air-side power consumption of the air-side system and water-side power consumption of the water-side system at each of a plurality of time steps in an optimization period; and

a plurality of low-stage air-side model predictive controllers, each low-stage air-side model predictive controller corresponding to one of the air-side subsystems and configured to operate the air-side HVAC equipment of the respective air-side subsystem using the optimal air-side subsystem load profile for the respective air-side subsystem.

22. The HVAC system of claim 21, wherein the air-side subsystems represent separate buildings that are thermally decoupled from one another such that no direct heat exchange occurs between the air-side subsystems.

23. The HVAC system of claim 21, wherein:

each air-side subsystem load profile indicating a distribution of thermal energy to one of the plurality of air-side subsystems at each of the plurality of time steps; and is

The advanced model predictive controller is configured to use an air-side power consumption model to define the air-side power consumption of each air-side subsystem as a function of the thermal energy distribution to the air-side subsystem.

24. The HVAC system of claim 21, wherein the advanced model predictive controller is configured to generate an air-side subsystem temperature model for each of the plurality of air-side subsystems, each air-side subsystem temperature model defining a relationship between one of the air-side subsystem load profiles and a temperature of the respective air-side subsystem.

25. The HVAC system of claim 21, wherein the advanced model predictive controller is configured to generate an optimal waterside demand profile for the waterside system;

the system further includes a low-stage waterside model predictive controller configured to perform a low-stage optimization to generate optimal waterside setpoints for the waterside system subject to demand constraints based on the optimal waterside demand profile;

wherein the low-stage waterside model predictive controller is configured to operate waterside HVAC equipment in the waterside system using the optimal waterside setpoint.

26. The HVAC system of claim 21, wherein the advanced model predictive controller is configured to:

performing the advanced optimization by optimizing an advanced cost function that defines the total energy cost as a function of a water side demand profile indicative of thermal energy production by the water side system at each of the plurality of time steps in the optimization period; and

using a water side demand model to define the water side demand profile as a function of the plurality of air side subsystem load profiles.

27. The HVAC system of claim 21, wherein each of the low-stage air-side model predictive controllers is configured to:

performing a low-level optimization to generate optimal air-side temperature setpoints for the respective air-side subsystem using the optimal air-side subsystem load profile for the respective air-side subsystem; and

operating the air-side HVAC equipment in the respective air-side subsystem using the optimal air-side temperature setpoint for the respective air-side subsystem.

28. The HVAC system of claim 27, wherein:

each air side subsystem includes a plurality of building zones; and is

The optimal air-side temperature setpoint for each air-side subsystem includes an optimal air-side temperature setpoint for each of the plurality of building zones in the air-side subsystem.

29. The HVAC system of claim 27, wherein each of the low-grade air-side model predictive controllers is configured to generate a zone load profile for each of the plurality of building zones in the respective air-side subsystem, each zone load profile indicating a distribution of thermal energy to one of the building zones at each of the plurality of time steps in the optimization period.

30. The HVAC system of claim 21, wherein each of the optimal air side subsystem load profiles comprises at least one of:

an optimal thermal energy load value for the respective air-side subsystem at each of the plurality of time steps; and

an optimal temperature value for the respective air-side subsystem at each of the plurality of time steps.

31. A method for optimizing energy costs of a building HVAC system including a water-side system and an air-side system having a plurality of air-side subsystems, the method comprising:

generating an advanced cost function defining the energy cost as a function of both the water side power consumption of the water side system and the air side power consumption of each air side subsystem at each of a plurality of time steps in an optimization period;

operating air-side HVAC equipment in the respective air-side subsystem at each of the low-level air-side model predictive controllers using the optimal air-side subsystem load profile.

32. The method of claim 31, wherein the air-side subsystems represent separate buildings that are thermally decoupled from one another such that no direct heat exchange occurs between the air-side subsystems.

33. The method of claim 31, wherein each air-side subsystem load profile indicates a thermal energy distribution to one of the plurality of air-side subsystems at each of the plurality of time steps;

the method further includes defining the air-side power consumption of each air-side subsystem as a function of the thermal energy allocation to the air-side subsystem using an air-side power consumption model.

34. The method of claim 31, further comprising: generating an air-side subsystem temperature model for each of the plurality of air-side subsystems, each air-side subsystem temperature model defining a relationship between one of the air-side subsystem load profiles and a temperature of the respective air-side subsystem.

35. The method of claim 31, wherein performing the advanced optimization comprises generating an optimal waterside demand profile for the waterside system, the method further comprising:

36. The method of claim 31, wherein the advanced cost function defines the energy cost as a function of a waterside demand profile indicative of thermal energy production by the waterside system at each of the plurality of time steps in the optimization period;

the method further includes defining the water side demand profile as a function of the plurality of air side subsystem load profiles using a water side demand model.

37. The method of claim 31, further comprising:

performing a low-level optimization at each of the low-level air-side model predictive controllers to generate optimal air-side temperature setpoints for the respective air-side subsystem using the optimal air-side subsystem load profiles for the respective air-side subsystem; and

38. The method of claim 37, wherein:

each air side subsystem includes a plurality of building zones; and is

39. The method of claim 38, wherein performing the low-level optimization comprises generating a zone load profile for each of the plurality of building zones, each zone load profile indicating a distribution of thermal energy to one of the building zones at each of the plurality of time steps in the optimization period.

40. A method for optimizing energy costs of a building HVAC system including a water-side system and an air-side system having a plurality of air-side subsystems, the method comprising:

performing an advanced optimization at an advanced model predictive controller to generate an optimal air-side subsystem temperature profile for each of the plurality of air-side subsystems, wherein the optimal air-side subsystem temperature profile optimizes the energy cost defined by the cost function;

providing the optimal air side subsystem temperature profile from the high-level model predictive controller to a plurality of low-level air side model predictive controllers, each of the low-level air side model predictive controllers corresponding to one of the plurality of air side subsystems; and

operating air-side HVAC equipment in the respective air-side subsystem at each of the low-stage air-side model predictive controllers using the optimal air-side subsystem temperature profile.