CN115459244A - Building micro-grid control method and system based on time sequence zero-carbon balance - Google Patents

Abstract

The invention provides a building microgrid control method and system based on time sequence zero-carbon balance, comprising the following steps: forecasting the operation daily load and the distributed energy output according to the building load data and the meteorological data, dividing the operation day into at least 2 time periods, setting the time scale as T, and forecasting the operation daily load and the distributed energy output to obtain a long-time scale forecasting result; during actual operation, the power load prediction mean value and the distributed energy output in each time period are predicted in a short time period, so that a short time scale prediction result is obtained; and judging the energy storage charging and discharging state mode of the energy storage battery according to the long-time scale prediction result, maintaining the charging and discharging state of the energy storage battery in a long time period, and processing the short-time scale prediction result to make a time sequence zero carbon balance control strategy so as to realize zero carbon. The invention solves the technical problems of potential safety hazard caused by intermittence and fluctuation, frequent charging and discharging of the storage battery, cycle life attenuation and poor distributed cooperative balance.

Description

A building microgrid control method and system based on time series zero carbon balance

technical field

The invention relates to the field of energy control, in particular to a building micro-grid control method and system based on time-sequence zero-carbon balance.

Background technique

Carbon emission reduction has aroused widespread concern and in-depth research in the society. At present, carbon emissions generated by the construction industry account for a relatively high proportion of the total CO ₂ emissions. Therefore, building carbon reduction is the most potential, most direct and effective way of energy saving and emission reduction. Low-carbon building has gradually become the future development trend and practice standard. The realization of zero-carbon buildings mainly includes the use of renewable energy technologies and the improvement of building energy efficiency. Renewable energy generation technologies such as photovoltaic power generation and wind power generation are the main zero-carbon energy technologies because they do not produce CO ₂ . By rationally deploying photovoltaic power generation, wind power generation and energy storage in building power systems, carbon emissions from building energy use can be reduced.

Due to the intermittent and fluctuating characteristics of renewable energy power generation technologies such as photovoltaics and wind power, the existing zero-carbon buildings will fail to achieve the zero-carbon goal because the supply of renewable energy cannot meet the demand during actual operation.

The existing patent application document with the publication number CN113937796A "A Multi-time Scale Optimization Method Including Wind, Light, Storage and Storage Combined System" consists of 24 hours in advance, 1 hour in advance, and 15 minutes in advance. Based on the output of the thermal power unit determined on an hourly scale, the optimal output of the pumped storage unit is determined 1 hour in advance. The advantage of fast response speed of the battery is to obtain the best charging and discharging power of the battery pack in the next 15 minutes, and the objective function is to minimize the operating cost of the system. The technical solution disclosed in this existing document determines the output of various energy sources on a preset time scale, and predicts the optimal charge and discharge rate of the battery pack to optimize the output smoothness of the battery pack. It can be seen that this existing solution cannot control the battery pack. The specific output and the number of charging and discharging are not conducive to the safety of power grid output and power storage.

The existing patent application document with the publication number CN114492209A "A Two-Level Scheduling Control Method for Microgrid Considering the State of Charge of Hybrid Energy Storage" includes: obtaining historical data of photovoltaic, wind power generation power, and load power demand, hybrid energy storage power limit value, hybrid energy storage state of charge limit; the upper layer control adopts gray GM (1, N) and BP neural network combination prediction method to establish the upper layer MPC model of microgrid; estimate the initial value of hybrid energy storage state of charge under day-ahead scheduling, It is used in the lower-level control; the dynamic programming algorithm is used in the lower-level control to optimize the hybrid energy storage charge and discharge power control, and the charge state of the hybrid energy storage is output to the upper-level control as a state variable; according to the objective function and constraints, the micro The upper layer MPC model of the power grid. The time scales of each layer of this prior art are different, but the upper layer MPC model of the microgrid used in this technology still guides the hybrid energy storage charge and discharge power control in the lower layer control according to the HESS total power PC(k), and the layered optimization and synthesis There are limitations in the effect of allocating loads and energy, which restricts the coordination of distributed energy, storage batteries and controllable loads at all levels.

To sum up, the existing technology has technical problems such as intermittent and fluctuations, frequent charging and discharging of batteries causing potential safety hazards, cycle life attenuation, and poor balance of distributed coordination.

Contents of the invention

The technical problem to be solved by the present invention is how to solve the technical problems in the prior art of intermittent and fluctuations, potential safety hazards caused by frequent charging and discharging of batteries, cycle life decay, and poor balance of distributed coordination.

The present invention solves the above-mentioned technical problems by adopting the following technical solutions: a building micro-grid control method based on time-sequence zero-carbon balance includes:

S1. Collect historical load data on the user side of the building and data from nearby weather stations to predict the daily operating load and distributed energy output. Divide the operating day into no less than 2 time periods, set the time scale as T, and predict the operation accordingly. Daily load and distributed energy output to obtain the average power load forecast P'ld(i) and new energy output forecast data P'dg(i) in each time period of the operation day;

S2. Taking the average value of electricity load forecast P'ld(i) and new energy output forecast data P'dg(i) in each time period of the operation day as the long-term scale forecast result;

S3. During the actual operation on the operation day, the short-term prediction average value of electricity load P'ld(i) and the distributed energy output in each time period are predicted, and the short-time scale prediction results are obtained based on this;

S4. Judging the energy storage charge and discharge state mode of the energy storage battery according to the long-term prediction results, so as to maintain the charge and discharge state of the energy storage battery in a long period of time, and process the short-time scale prediction results to formulate a time-series zero-carbon balance Control strategy, wherein, step S4 comprises:

S41. When the energy storage battery is identified as the state of charge mode, if the short-term electricity load forecast Pld(j) is greater than the short-term new energy output forecast Pdg(j), the controllable load reduction, the energy storage battery participating in the discharge, and the utility power output to balance the power of the microgrid;

S42. If the short-term power load forecast Pld(j) is smaller than the short-term new energy output forecast Pdg(j), sequentially perform energy storage battery charging, adjust energy storage charging output, increase controllable load, reduce photovoltaic output, and reduce wind output;

S43. When the energy storage battery is identified as the discharge state mode, if the short-term electricity load forecast Pld(j) is greater than the short-term new energy output forecast Pdg(j), the energy storage battery participates in the discharge, adjusts the energy storage discharge output, and lowers it in sequence. Controllable load and mains output;

S44. If the short-term electricity load forecast Pld(j) is smaller than the short-term new energy output forecast Pdg(j), the controllable load increase, the photovoltaic output reduction, and the wind power output are successively reduced, so as to reduce the charging and discharging switching times of the energy storage battery.

Based on the load and output prediction of different time scales, the present invention stratifies and optimizes the allocation of each load and energy unit. Through this method, the coordination of distributed energy, energy storage devices and controllable loads can be effectively guided, the impact of renewable energy can be stabilized, and the supply can be improved. side and demand side responses, and can give the realization path of zero-carbon buildings based on historical results;

S5. Obtain the building micro-grid operating data results according to the time-series zero-carbon balance control strategy, process and obtain the mains power usage period and the mains power consumption, and quantify the building micro-grid operating data results to iteratively obtain no less than 2 types Zero-carbon building realization path, optimize building micro-grid according to the zero-carbon building realization path, and realize zero-carbon.

In a more specific technical solution, step S2 includes:

S21. When the predicted average value of electricity load P'ld(i) in each time period is less than the new energy output forecast data P'dg(i), the energy storage battery is identified as the state of charge mode, and the energy storage battery is charged accordingly until The energy storage battery reaches the preset SOC charging upper limit;

S22. When the predicted average value of electricity load P'ld(i) in each time period is less than the new energy output forecast data P'dg(i), identify the energy storage battery as the discharge state mode to reduce the charging and discharging switching of the energy storage battery frequency.

The micro-grid balancing strategy adopted in the present invention ensures that the battery is charged as far as possible until the set SOC charging upper limit. When P'ld(i) is less than P'dg(i), the energy storage battery is identified as the discharge state mode, and the battery should be charged during discharge. Minimize the number of charging and discharging switching to protect the energy storage battery.

In a more specific technical solution, step S3 includes:

S31. Divide the i-th time period in the operation day into m small time periods;

S32. Set the short-term prediction time scale as t;

S33. Process the building user-side historical load data corresponding to the small time period and the short-time forecast time scale t and the data of nearby weather stations, so as to obtain a short-time scale forecast result.

The short-time scale control of the present invention is mainly based on the optimization control of the control layer, and the timing adjustment constraints of each unit. On this basis, a reasonable control strategy is adopted to realize system energy management through the coordination of distributed power sources, energy storage systems, and controllable loads. with balance.

In a more specific technical solution, the short-term prediction time scale in step S32 includes: a minute-level scale.

In a more specific technical solution, the short-time scale prediction results in step S33 include short-term electricity load prediction Pld(j) and short-term new energy output prediction Pdg(j).

In a more specific technical solution, step S41 includes:

S411. When the down-regulation condition is satisfied, the down-regulation load is obtained through the following logic processing:

ΔPc=min(Pld-Pdg,ΔPc1)

Among them, ΔPc1 is the maximum down-regulation load;

S412. When the load ΔPc output is lowered to ΔPc1, enable the energy storage battery to participate in the discharge;

S413. When the energy storage state of charge SOC is greater than the minimum allowable state of charge SOCmin of the energy storage, control the energy storage battery to adjust the discharge output with the following logic:

PESS=min(Pld-Pdg-ΔPc, PESSd)

Among them, PESSd is the maximum discharge output;

S414. When the output energy storage discharge output PESS is PESSd, control the mains output with the following logic:

Pg=Pld-Pdg-PESSd-ΔPc,

And output the mains-time curve to the database.

In a more specific technical solution, step S42 includes:

S421. Charging the energy storage battery, when the energy storage state of charge SOC is less than the maximum allowable state of charge SOCmax of the energy storage, control the energy storage battery to adjust the charging output with the following logic:

PESS=min(Pdg-Pld, PESSc)

Among them, PESSc is the maximum charging output of energy storage;

S422. When the output energy storage charging output PESS is the maximum energy storage charging output PESSc, use the following logic to increase the controllable load:

ΔPc=min(Pdg-Pld-PESSc,ΔPc2)

Among them, ΔPc2 is the maximum load adjustment;

S423. When the output of the up-regulated controllable load ΔPc is the maximum up-regulated load ΔPc2, reduce the output of new energy, wherein the output of new energy from photovoltaics and wind turbines is reduced in turn, and the output of photovoltaics is lowered according to the following logic:

ΔPpv=min(Pdg-Pld-PESSc-ΔPc,Ppv)

Among them, Ppv is the actual photovoltaic output;

S424. When the output ΔPpv of the photovoltaic output is the actual photovoltaic output Ppv, reduce the wind power output with the following logic:

ΔPw=Pdg-Pld-PESSc-ΔPc-ΔPpv.

Aiming at the uncertainty of wind power, photovoltaic output, etc., the present invention reduces the uncertainty of intermittent energy sources to a certain extent through the prediction of wind and light power. Based on the optimization management at the global level, considering the time transfer characteristics of the energy of the energy storage equipment, the reasonable distribution of the overall charge and discharge of the energy storage is ensured, and the prediction error of intermittent energy generation is reduced.

In a more specific technical solution, step S43 includes:

S431. When the energy storage state of charge SOC is greater than the minimum allowable state of charge SOCmin of the energy storage, determine that the energy storage battery is capable of discharging, and control the energy storage battery to adjust the discharge output with the following logic:

PESS = min(Pld-Pdg, PESSd);

S432. When the discharge output PESS of the energy storage battery is PESSd, lower the controllable load with the following logic:

ΔPc=min(Pld-Pdg-PESS,ΔPc1);

S432. When the output of the energy storage load ΔPc is lowered to ΔPc1, the following logic is used to control the output of the mains power

Pg=Pld-Pdg-PESSd-ΔPc.

In a more specific technical solution, step S44 includes:

S441. Use the following logic to increase the controllable load:

ΔPc=min(Pdg-Pld,ΔPc2);

S442. When the output of the controllable load ΔPc is increased to ΔPc2, the photovoltaic output is lowered with the following logic:

ΔPpv=min(Pdg-Pld-PESSc-ΔPc,Ppv)

Among them, Ppv is the actual photovoltaic output;

S443. When the output ΔPpv of the down-regulated photovoltaic output is the actual photovoltaic output Ppv, the following logic is used to down-regulate the wind power output:

ΔPw=Pdg-Pld-ΔPc-ΔPpv.

In a more specific technical solution, a building microgrid control system based on time sequence zero carbon balance includes:

Run the daily load and new energy output forecasting module, which is used to collect historical load data on the user side of the building and data from nearby weather stations to predict the daily load and distributed energy output, and divide the operating day into no less than 2 time periods , set the time scale as T, and use it to predict the daily operating load and distributed energy output, so as to obtain the average power load forecast P'ld(i) and new energy output forecast data P'dg(i) in each period of the operating day ;

The long-term scale prediction result module is used to use the average power load forecast P'ld(i) and the new energy output forecast data P'dg(i) in each period of the operation day as the long-term scale forecast results, and the long-term scale forecast The result module is connected with the operation daily load and new energy output prediction module;

The short-time-scale prediction module is used to predict the average value of electricity load P'ld(i) and distributed energy output in each time period in a short-term during the actual operation of the operation day, so as to obtain short-time-scale prediction results;

Time-series zero-carbon balance control strategy formulation module, which is used to judge the energy storage charge and discharge state mode of the energy storage battery according to the long-term scale prediction results, so as to maintain the charge and discharge state of the energy storage battery in a long period of time, and deal with the short-time scale prediction As a result, to formulate a time-series zero-carbon balance control strategy, the time-series zero-carbon balance control strategy formulation module is connected with the long-term scale prediction result module and the short-time scale prediction module, wherein the time-series zero-carbon balance control strategy formulation module includes:

The microgrid balance module is used to perform controllable load reduction and energy storage battery in sequence if the short-term electricity load forecast Pld(j) is greater than the short-term new energy output forecast Pdg(j) when the energy storage battery is identified as the state of charge mode. Participate in discharge and mains output to balance microgrid power;

Photovoltaic wind power output reduction module, used to sequentially charge the energy storage battery, adjust the energy storage charging output, controllable load increase, and reduce the photovoltaic wind power when the short-term electricity load forecast Pld(j) is less than the short-term new energy output forecast Pdg(j). output and reduce wind output;

The controllable load and mains power output down-regulation module is used for when the energy storage battery is identified as the discharge state mode, if the short-term power load forecast Pld(j) is greater than the short-term new energy output forecast Pdg(j), the energy storage battery Participate in discharge, adjust energy storage discharge output, reduce controllable load and mains output;

The energy storage charging and discharging frequency reduction module is used to sequentially increase the controllable load, reduce the photovoltaic output and reduce the wind output when the short-term electricity load forecast Pld(j) is less than the short-term new energy output forecast Pdg(j), so as to reduce The charging and discharging switching times of the energy storage battery;

The building micro-grid zero-carbon realization module is used to obtain the building micro-grid operation data results according to the time-series zero-carbon balance control strategy, according to which the mains power usage period and the mains power consumption are obtained, and the building micro-grid operation data results are quantitatively processed to Iteratively obtain no less than two zero-carbon building realization paths, optimize the building micro-grid according to the zero-carbon building realization path, and realize zero-carbon accordingly, the zero-carbon realization module of the building micro-grid and the time-sequential zero-carbon balance control Policy making module connection.

Compared with the prior art, the present invention has the following advantages: the present invention is based on the load and output prediction of different time scales, and the load and energy units are allocated hierarchically and optimally. Through this method, distributed energy, energy storage devices and controllable loads can be effectively guided The synergy of renewable energy can stabilize the impact of renewable energy, improve supply-side and demand-side responses, and provide a path to realize zero-carbon buildings based on historical results.

The micro-grid balancing strategy adopted in the present invention ensures that the battery is charged as far as possible until the set SOC charging upper limit. When P'ld(i) is less than P'dg(i), the energy storage battery is identified as the discharge state mode, and the battery should be charged during discharge. Minimize the number of charging and discharging switching to protect the energy storage battery.

The short-time scale control of the present invention is mainly based on the optimization control of the control layer, and the timing adjustment constraints of each unit. On this basis, a reasonable control strategy is adopted to realize system energy management through the coordination of distributed power sources, energy storage systems, and controllable loads. with balance.

Aiming at the uncertainty of wind power, photovoltaic output, etc., the present invention reduces the uncertainty of intermittent energy sources to a certain extent through the prediction of wind and light power. Based on the optimization management at the global level, considering the time transfer characteristics of the energy of the energy storage equipment, the reasonable distribution of the overall charge and discharge of the energy storage is ensured, and the prediction error of intermittent energy generation is reduced. The invention solves the technical problems in the prior art of intermittent and fluctuating, potential safety hazards caused by frequent charging and discharging of storage batteries, cycle life attenuation and poor balance of distributed coordination.

Description of drawings

FIG. 1 is a schematic diagram of basic steps of a building microgrid control method based on time-sequence zero-carbon balance according to Embodiment 1 of the present invention;

Fig. 2 is a schematic diagram of the first formulation flow chart of the time-series zero-carbon balance control strategy in Embodiment 1 of the present invention;

Fig. 3 is a schematic diagram of the second formulation process of the time-series zero-carbon balance control strategy in Embodiment 1 of the present invention;

Fig. 4 is a schematic diagram of a building micro-grid control system based on time-sequence zero-carbon balance connected to a micro-grid according to Embodiment 2 of the present invention.

detailed description

In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are part of the present invention Examples, not all examples. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

Example 1

As shown in Figure 1, a time-series zero-carbon balance-based building microgrid control method provided by the present invention includes the following steps:

S1. Obtain historical database and weather station data;

S2. Long-term load forecasting, to obtain the average electricity load forecast P'ld(i), new energy processing forecast P'dg(i), and time scale T within n periods of operation day;

S3. Execute the loop operation for i=1 to n step T according to the time scale;

S4. Determine whether the average electricity load forecast P'ld(i) is greater than or equal to the new energy processing forecast P'dg(i);

S5. Determine that the energy storage battery is in the charging and charging mode;

S5. Determine that the energy storage battery is in the charging and discharging mode;

S6. Short-term load forecasting in the i-th time period, electricity load forecasting Pld(j), new energy output forecasting Pdg(j), time scale t;

S7. Execute the loop operation for j=1 to m step t according to the time scale;

S8. Judging whether the electricity load forecast Pld(j) is equal to the new energy output forecast Pdg(j);

S9. Determine whether the electricity load forecast Pld(j) is greater than the new energy output forecast Pdg(j), so as to formulate a time-sequential zero-carbon balance control strategy.

As shown in Figure 2, the formulation process of time-series zero-carbon balance control strategy includes:

S101. When the electricity load forecast Pld(j) is not equal to the new energy output forecast Pdg(j), determine whether the electricity load forecast Pld(j) is greater than the new energy output forecast Pdg(j);

S102. If the electric load forecast Pld(j) is greater than the new energy output forecast Pdg(j), determine whether the energy storage state of charge SOC is greater than the minimum allowable state of charge SOCmin of the energy storage;

S103. If the energy storage state of charge SOC is greater than the minimum allowable energy storage state of charge SOCmin, use the logic: PESS=min(Pld-Pdg-ΔPc, PESSd) to process the maximum discharge output PESSd to obtain the energy storage discharge output PESS;

S104. If the energy storage state of charge SOC is less than or equal to the minimum allowable energy storage state of charge SOCmin, adjust the energy storage discharge output PESS=0;

S105. Determine whether the output energy storage discharge output PESS is equal to the maximum discharge output PESSd;

S106. If the output energy storage discharge output PESS is equal to the maximum discharge output PESSd, then judge whether the maximum down-regulated load ΔPc1 is 0;

S107. If the maximum down-regulated load amount ΔPc1 is 0, make the down-regulated load ΔPc=0;

S108. The maximum down-regulated load ΔPc1 is not 0, then the load is down-regulated according to this logic: down-regulated load ΔPc=min(Pld-Pdg-PESS,ΔPc1);

S109. Judging whether the output of the down-regulated load ΔPc is the maximum down-regulated load amount ΔPc1;

S1010. If the output of the down-regulated load ΔPc is the maximum down-regulated load amount ΔPc1, use this logic to output the utility power: Pg=Pld-Pdg-PESSd-ΔPc, and jump to step S1018;

S1011. If the electricity load forecast Pld(j) is less than or equal to the new energy output forecast Pdg(j), then judge whether the maximum load increase ΔPc2 is 0;

S1012. If the maximum up-regulated load ΔPc2 is 0, make the up-regulated load ΔPc=0;

S1013. If the maximum load increase ΔPc2 is not 0, then increase the load with this logic: ΔPc=min(Pdg-Pld, ΔPc2);

S1014, judging whether the up-regulated load ΔPc is equal to the maximum up-regulated load ΔPc2;

S1015. If the up-regulated load ΔPc is equal to the maximum up-regulated load ΔPc2, obtain the down-regulated photovoltaic output according to the following logic: ΔPpv=min(Pdg-Pld-ΔPc,Ppv), otherwise skip to step S1018;

S1016. Determine whether the photovoltaic output reduction amount ΔPpv is the actual photovoltaic output Ppv;

S1017. If the photovoltaic output reduction ΔPpv is the actual photovoltaic output Ppv, then obtain the wind power output reduction ΔPw=Pdg-Pld-ΔPc-ΔPpv with the following logic;

S1018. Determine whether the short-scale cyclic variable j is equal to m, and if so, end the short-time-scale processing step;

S1019. Determine whether the long-scale cycle variable i is equal to n, and if so, end the long-time scale processing step.

As shown in Figure 3, the formulation process of the time-series zero-carbon balance control strategy also includes:

S101', when the electric load forecast Pld(j) is not equal to the new energy output forecast Pdg(j), determine whether the electric load forecast Pld(j) is greater than the new energy output forecast Pdg(j);

S102', if the electricity load forecast Pld(j) is greater than the new energy output forecast Pdg(j), then judge whether the maximum down-regulated load ΔPc1 is 0;

S103', if so, make the down-regulation load ΔPc=0;

S104', if not, lower the load with the following logic: ΔPc=min(Pld-Pdg, ΔPc1);

S105', judging whether the down-regulated load ΔPc is the maximum down-regulated load ΔPc1;

S106', if yes, judge whether the energy storage state of charge SOC is greater than the minimum allowable state of charge SOCmin of the energy storage, if not, then jump to step S1022';

S107', if the energy storage state of charge SOC is less than or equal to the minimum allowable energy storage state of charge SOCmin, then adjust the energy storage discharge output PESS=0;

S108', the SOC of the energy storage state of charge is greater than the minimum allowable state of charge of the energy storage SOCmin, then use the logic: PESS=min(Pld-Pdg-ΔPc, PESSd) to process the maximum discharge output PESSd to adjust the energy storage discharge output PESS;

S109', judging whether the energy storage discharge output PESS is equal to the maximum discharge output PESSd;

S1010', if yes, use this logic to output power from the mains: Pg=Pld-Pdg-PESSd-ΔPc, if not, skip to step S1022';

S1011', if the electric load forecast Pld(j) is less than or equal to the new energy output forecast Pdg(j), then determine whether the energy storage state of charge SOC is less than the maximum allowable state of charge SOCmax of the energy storage;

S1012', if so, process the maximum charging output PESSc of the energy storage according to the following logic: PESS=min(Pdg-Pld, PESSc) to adjust and obtain the charging output PESS of the energy storage;

S1013', making the energy storage discharge output PESS=0;

S1014', judging whether the output energy storage charging output PESS is equal to the maximum energy storage charging output PESSc;

S1015', if so, then judge whether the maximum upward adjustment load ΔPc2 is 0;

S1016', if so, make the down-regulation load ΔPc=0;

S1017', if not, then use the logic to increase the load ΔPc=min(Pdg-Pld-PESSc, ΔPc2);

S1018', judging whether the increased load ΔPc is equal to the maximum increased load ΔPc2;

S1019', if yes, then use the following logic to obtain the photovoltaic output reduction amount: ΔPpv=min(Pdg-Pld-PESSc-ΔPc,Ppv), if not, then jump to step 1022';

S1020', judging whether the photovoltaic output reduction amount ΔPpv is equal to the actual photovoltaic output Ppv;

S1021', if so, obtain the wind power output reduction amount with the following logic: ΔPw=Pdg-Pld-PESSc-ΔPc-ΔPpv;

S1022', if not, then judge whether the short-scale circular variable j is equal to m, if so, then end the short-time-scale strategy formulation step;

S1023'. Determine whether the long-scale circular variable i is equal to n, and if so, end the long-term scale strategy formulation step.

In this embodiment, the daily operating load and distributed energy output are predicted based on the historical load data on the user side of the building and the data of nearby weather stations, and the operating day is divided into n time periods, and the time scale is T, which is a long-term scale prediction , taking into account the wind power forecasting system and forecasting accuracy, which is generally hourly, the average power load forecast P'ld(i) and new energy output forecast P'dg(i) in each period of the operation day are obtained.

Due to the high cost of energy storage batteries at present, in order to ensure the life of energy storage batteries, the number of frequent charging and discharging of batteries should be reduced as much as possible. Therefore, according to the long-term scale prediction results, when P'ld(i) is less than P'dg(i), the energy storage battery is considered to be in the charging state mode, and the strategy should ensure that the battery is charged as much as possible until the set SOC charging upper limit. When P'ld(i) is less than P'dg(i), the energy storage battery is considered to be in the discharge state mode, and the number of charge-discharge switching should be minimized during discharge to protect the energy storage battery.

During the actual operation on the operation day, short-term forecasts are made on the load and distributed energy output, and the i-th time period is divided into m small time periods, and the time scale is t. Load forecast Pld(j), new energy output forecast Pdg(j), time scale t.

In this embodiment, according to the long-term prediction, the energy storage charging and discharging state mode is judged, and the charging (discharging) state should be kept as far as possible in a long period of time.

1) When the energy storage is identified as the state of charge mode, if Pld(j)>Pdg(j), in order to ensure power balance, the controllable load down-regulation is considered first in turn, and if the down-regulation condition is met, the down-regulated load ΔPc=min(Pld-Pdg, ΔPc1), ΔPc1 is the maximum down-regulated load; when ΔPc output is ΔPc1, it means that Pld(j) is still greater than or equal to Pdg(j) at this time, and energy storage is considered to participate in discharge, when energy storage state of charge SOC > energy storage minimum When the allowable state of charge is SOCmin, the energy storage has discharge conditions. Adjust the energy storage discharge output PESS=min(Pld-Pdg-ΔPc, PESSd), and PESSd is the maximum discharge output. When the output energy storage discharge output PESS is PESSd, it should be considered Utilize the mains power, use the mains power output Pg=Pld-Pdg-PESSd-ΔPc, and output the mains power-time curve to the database.

If Pld(j)<Pdg(j), priority is given to charging the energy storage battery. When the state of charge of the energy storage SOC<the maximum allowable state of charge of the energy storage SOCmax, the energy storage has the charging conditions, and the charging output of the energy storage is adjusted to PESS= min(Pdg-Pld, PESSc), PESSc is the maximum charging output of energy storage. When the output energy storage charging output PESS is PESSc, the controllable load increase should be considered. If the increase condition is met, increase the load ΔPc=min(Pdg-Pld- PESSc,ΔPc2), ΔPc2 is the maximum load increase; when ΔPc output is ΔPc2, the output of new energy should be considered to be reduced, and the order of photovoltaic power generation is prior to that of wind turbines; photovoltaic power generation has a simple structure and fast start-stop speed, and wind power generation has rotating devices, so start-stop It takes a long time, so it should be given priority to reduce the photovoltaic output. The photovoltaic output reduction ΔPpv=min(Pdg-Pld-PESSc-ΔPc,Ppv), Ppv is the actual photovoltaic output. When the output ΔPpv is Ppv, the wind power output should be reduced. Wind power Output down-regulation ΔPw=Pdg-Pld-PESSc-ΔPc-ΔPpv.

When the energy storage is identified as the discharge state mode, if Pld(j)>Pdg(j), the energy storage is considered to participate in the discharge first, and when the energy storage state of charge SOC>the minimum allowable state of charge SOCmin of the energy storage, the energy storage is capable of discharging Conditions, adjust the energy storage discharge output PESS=min(Pld-Pdg, PESSd), when the output energy storage discharge output PESS is PESSd, it should be considered to lower the controllable load, if the lower adjustment condition is met, the lower load ΔPc=min(Pld-Pdg -PESS,ΔPc1), when the ΔPc output is ΔPc1, the utility power should be considered, and the utility power output Pg=Pld-Pdg-PESSd-ΔPc.

If Pld(j)<Pdg(j), in order to avoid frequent switching between charging and discharging of energy storage batteries, the controllable load increase should be considered first in turn. If the increase condition is met, the increase load ΔPc=min(Pdg-Pld,ΔPc2), when ΔPc output When ΔPc2 is ΔPc2, it should be considered to reduce the photovoltaic output. The downward adjustment of photovoltaic output ΔPpv=min(Pdg-Pld-PESSc-ΔPc,Ppv), Ppv is the actual photovoltaic output. When the output ΔPpv is Ppv, the wind power output should be reduced, and the wind power output should be lowered Quantity ΔPw=Pdg-Pld-ΔPc-ΔPpv.

Example 2

As shown in Fig. 4, the building micro-grid control system based on time-sequence zero-carbon balance provided by the present invention includes: a micro-grid energy management system 1, including: a micro-grid controller 101, an AC/DC energy router 102, wherein the micro-grid control The controller 101 is connected with the AC/DC energy router 102 for two-way interaction of monitoring data and microgrid control data;

In this embodiment, the microgrid energy management system 1 is connected to the weather station 2 to obtain climate and environment data collected by the weather station 2; the inverter 3, the converter 5 and the bidirectional converter 7 are respectively connected to the AC/DC energy router 102 is connected to upload the real-time data of the power grid, and receive the micro-grid control signal sent by the micro-grid controller 101 through the AC-DC energy router;

In this embodiment, the distributed photovoltaic modules 4 are connected to the inverter 3, the wind turbine 6 is connected to the converter 5, and the energy storage battery 8 is connected to the bidirectional converter 7, so as to use electricity according to the control signal of the microgrid. output and change the state of charge and discharge. In this embodiment, the AC/DC energy router 102 realizes the working state of source storage and load and power control by respectively connecting photovoltaic power, wind power, energy storage, and controllable loads. In this embodiment, the microgrid controller 101 mainly collects Unit actual data, control AC and DC energy router 102 and two-way communication.

The system classifies loads into controllable loads 10 and uncontrollable loads 11. Controllable loads 10 can be adjusted and controlled according to system strategies as a means of optimizing power balance. The load forecast for the building will be based on the load history data of the previous day or a similar day.

In summary, the present invention is based on load and output predictions at different time scales, and optimizes the allocation of each load and energy unit in layers. Through this method, it can effectively guide the coordination of distributed energy, energy storage devices, and controllable loads, and stabilize the impact of renewable energy. , Improve supply-side and demand-side responses, and provide a path to realize zero-carbon buildings based on historical results.

The micro-grid balancing strategy adopted in the present invention ensures that the battery is charged as far as possible until the set SOC charging upper limit. When P'ld(i) is less than P'dg(i), the energy storage battery is identified as the discharge state mode, and the battery should be charged during discharge. Minimize the number of charging and discharging switching to protect the energy storage battery.

The short-time scale control of the present invention is mainly based on the optimization control of the control layer, and the timing adjustment constraints of each unit. On this basis, a reasonable control strategy is adopted to realize system energy management through the coordination of distributed power sources, energy storage systems, and controllable loads. with balance.

Aiming at the uncertainty of wind power, photovoltaic output, etc., the present invention reduces the uncertainty of intermittent energy sources to a certain extent through the prediction of wind and light power. Based on the optimization management at the global level, considering the time transfer characteristics of the energy of the energy storage equipment, the reasonable distribution of the overall charge and discharge of the energy storage is ensured, and the prediction error of intermittent energy generation is reduced. The invention solves the technical problems in the prior art of intermittent and fluctuating, potential safety hazards caused by frequent charging and discharging of storage batteries, cycle life attenuation and poor balance of distributed coordination.

The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: it can still be described in the foregoing embodiments Modifications are made to the recorded technical solutions, or equivalent replacements are made to some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (10)

1. A building microgrid control method based on time sequence zero-carbon balance is characterized by comprising the following steps:

s1, acquiring historical load data of a building user side and data of a nearby meteorological station to predict operation day load and distributed energy output, dividing an operation day into at least 2 time periods, setting a time scale as T, predicting the operation day load and the distributed energy output to obtain an electricity load prediction mean value P 'ld (i) and new energy output prediction data P' dg (i) in each time period of the operation day;

s2, taking the predicted mean value P 'ld (i) of the electric load in each time period of the operation day and the predicted data P' dg (i) of the new energy output as long-time scale prediction results;

s3, when the operation is actually carried out on the operation day, the electric load prediction mean value P' ld (i) and the distributed energy output in each time period are predicted in a short time period, and a short-time scale prediction result is obtained;

s4, judging an energy storage charging and discharging state mode of the energy storage battery according to the long-time scale prediction result, maintaining the charging and discharging state of the energy storage battery in a long time period, and processing the short-time scale prediction result to make a time sequence zero carbon balance control strategy, wherein the step S4 comprises the following steps:

s41, when the energy storage battery is determined to be in the charging state mode, if the short-term power utilization load prediction Pld (j) is larger than the short-term new energy output prediction Pdg (j), sequentially performing controllable load down regulation, energy storage battery participating discharge and commercial power output so as to balance the power of a microgrid;

s42, if the short-term power load forecast Pld (j) is smaller than the short-term new energy output forecast Pdg (j), sequentially charging an energy storage battery, adjusting energy storage charging output, adjusting controllable load up, reducing photovoltaic output and reducing wind power output;

s43, when the energy storage battery is determined to be in the discharge state mode, if the short-term power utilization load prediction Pld (j) is larger than the short-term new energy output prediction Pdg (j), the energy storage battery is subjected to discharge, the energy storage discharge output is adjusted, the controllable load is adjusted downwards, and the commercial power output is adjusted in sequence;

s44, if the short-term power load forecast Pld (j) is smaller than the short-term new energy output forecast Pdg (j), sequentially performing controllable load up-regulation, photovoltaic output reduction and wind power output reduction so as to reduce the charging and discharging switching times of the energy storage battery;

and S5, acquiring a building microgrid operation data result according to the time sequence zero-carbon balance control strategy, processing to obtain a commercial power use time interval and commercial power use electric quantity, quantizing the building microgrid operation data result, iteratively acquiring no less than 2 zero-carbon building realization paths, and optimizing the building microgrid according to the zero-carbon building realization paths to realize zero-carbon.

2. The building microgrid control method based on time-sequence zero-carbon balance of claim 1, wherein the step S2 comprises:

s21, when the predicted average value P 'ld (i) of the electric load in each time interval is smaller than the predicted data P' dg (i) of the new energy output, the energy storage battery is determined as a charging state mode, and the energy storage battery is charged according to the charging state mode until the energy storage battery reaches a preset SOC upper charging limit;

and S22, when the predicted mean value P 'ld (i) of the electric load in each time period is smaller than the predicted data P' dg (i) of the new energy output, the energy storage battery is regarded as a discharge state mode, and the charging and discharging switching times of the energy storage battery are reduced.

3. The method for building microgrid control based on sequential zero-carbon balance of claim 1, wherein the step S3 comprises:

s31, dividing the ith time period in the operating day into m small time periods;

s32, setting a short-time prediction time scale as t;

and S33, processing the historical load data of the building user side and the data of the nearby meteorological stations corresponding to the small time period and the short-time prediction time scale t to obtain a short-time scale prediction result.

4. The method as claimed in claim 3, wherein the short-time prediction time scale in step S32 comprises: the minute scale.

5. The method as claimed in claim 3, wherein the short-time-scale prediction results in step S33 include short-term electricity load prediction Pld (j) and short-term new energy output prediction Pdg (j).

6. The method as claimed in claim 1, wherein the step S41 includes:

s411, when the down regulation condition is met, obtaining a down regulation load through the following logic processing:

ΔPc＝min(Pld-Pdg,ΔPc1)

wherein, Δ Pc1 is the maximum turndown load amount;

s412, when the output of the down-regulated load Δ Pc is Δ Pc1, enabling the energy storage battery to participate in discharging;

s413, when the energy storage SOC is greater than the energy storage minimum allowable SOC SOCmin, controlling the energy storage battery to adjust the discharge output according to the following logic:

PESS＝min(Pld-Pdg-ΔPc，PESSd)

wherein, PESSd is the maximum discharge output;

and S414, when the output energy storage discharge output PESS is PESSd, controlling the output of the commercial power according to the following logic:

Pg＝Pld-Pdg-PESSd-ΔPc，

and outputs the mains supply-time curve to the database.

7. The method as claimed in claim 1, wherein the step S42 includes:

s421, charging the energy storage battery, and when the SOC of the energy storage state of charge is smaller than the SOCmax of the maximum allowed SOC of the energy storage, controlling the energy storage battery to adjust the charging output according to the following logic:

PESS＝min(Pdg-Pld，PESSc)

wherein, the PESSc is the maximum charging output of the stored energy;

s422, when the output energy storage charging output power PESS is the energy storage maximum charging output power PESSc, the following logic is used for up-regulating the controllable load:

ΔPc＝min(Pdg-Pld-PESSc,ΔPc2)

wherein, Δ Pc2 is the maximum load amount of the up-regulation;

s423, when the up-regulation controllable load Δ Pc is output as the maximum up-regulation load Δ Pc2, reducing the new energy output, wherein the new energy output of the photovoltaic and the fan is reduced in sequence, and the photovoltaic output is reduced according to the following logic:

ΔPpv＝min(Pdg-Pld-PESSc-ΔPc,Ppv)

wherein Ppv is the actual photovoltaic output;

s424, when the output delta Ppv of the photovoltaic output is the actual photovoltaic output Ppv, reducing the wind power output according to the following logic:

ΔPw＝Pdg-Pld-PESSc-ΔPc-ΔPpv。

8. the building microgrid control method based on time-series zero-carbon balance of claim 1, wherein the step S43 comprises:

s431, when the SOC of the energy storage SOC is larger than the SOCmin of the energy storage minimum allowable SOC, judging that the energy storage battery has a discharging condition, and controlling the discharging output of the energy storage battery according to the following logic:

PESS＝min(Pld-Pdg，PESSd)；

s432, when the discharge output PESS of the energy storage battery is PESSd, the controllable load is adjusted down according to the following logic:

ΔPc＝min(Pld-Pdg-PESS,ΔPc1)；

and S432, when the output of the down-regulation energy storage load delta Pc is delta Pc1, controlling the output Pg = Pld-Pdg-PESSd-delta Pc of the commercial power according to the following logic.

9. The method as claimed in claim 1, wherein the step S44 includes:

s441, the controllable load is adjusted up using the following logic:

ΔPc＝min(Pdg-Pld,ΔPc2)；

s442, when the output of the up-regulation controllable load Δ Pc is Δ Pc2, the photovoltaic output is down-regulated according to the following logic:

ΔPpv＝min(Pdg-Pld-PESSc-ΔPc,Ppv)

wherein Ppv is the actual photovoltaic output;

s443, when the output delta Ppv of the down-regulated photovoltaic output is the actual photovoltaic output Ppv, the wind power output is down-regulated according to the following logic:

ΔPw＝Pdg-Pld-ΔPc-ΔPpv。

10. a building microgrid control system based on time-sequential zero-carbon balancing, the system comprising:

the system comprises a daily operation electric load and new energy output prediction module, a daily operation electric load and new energy output prediction module and a weather station data acquisition module, wherein the daily operation electric load and new energy output prediction module is used for acquiring historical load data of a building user side and data of a nearby weather station to predict daily operation load and distributed energy output, dividing an operation day into at least 2 time periods, and setting a time scale as T so as to predict daily operation load and distributed energy output and obtain a predicted average value P 'ld (i) of electric loads and predicted data P' dg (i) of new energy output in each time period of the operation day;

the long-time scale prediction result module is used for taking the predicted average value P 'ld (i) of the electric load in each time period of the operating day and the predicted data P' dg (i) of the new energy output as a long-time scale prediction result, and the long-time scale prediction result module is connected with the operating day electric load and new energy output prediction module;

the short-time scale prediction module is used for predicting the predicted mean value P' ld (i) of the electric load and the output of the distributed energy in each time interval in a short time during actual operation of the operation day so as to obtain a short-time scale prediction result;

the time sequence zero-carbon balance control strategy making module is used for judging an energy storage charging and discharging state mode of the energy storage battery according to the long-time scale prediction result, maintaining the charging and discharging state of the energy storage battery in a long time period according to the energy storage charging and discharging state mode, processing the short-time scale prediction result and making a time sequence zero-carbon balance control strategy, and the time sequence zero-carbon balance control strategy making module is connected with the long-time scale prediction result module and the short-time scale prediction module, wherein the time sequence zero-carbon balance control strategy making module comprises:

a microgrid balancing module, configured to, when the energy storage battery is determined to be in the charging state mode, if the short-term power load prediction Pld (j) is greater than the short-term new energy output prediction Pdg (j), sequentially perform controllable load down-regulation, energy storage battery participation discharge, and utility power output, so as to balance microgrid power;

the photovoltaic wind power output reducing module is used for sequentially charging the energy storage battery, adjusting the energy storage charging output, increasing the controllable load, reducing the photovoltaic output and reducing the wind power output when the short-term power load prediction Pld (j) is smaller than the short-term new energy output prediction Pdg (j);

the controllable load and commercial power output down-regulation module is used for sequentially performing the participation of the energy storage battery in discharging, adjusting the energy storage discharging output, down-regulating the controllable load and the commercial power output if the short-term power load prediction Pld (j) is greater than the short-term new energy output prediction Pdg (j) when the energy storage battery is determined to be in the discharging state mode;

the energy storage charging and discharging frequency reducing module is used for sequentially carrying out controllable load up-regulation, photovoltaic output reduction and wind power output reduction when the short-term power load prediction Pld (j) is smaller than the short-term new energy output prediction Pdg (j), so that the charging and discharging switching frequency of the energy storage battery is reduced;

building microgrid zero-carbon implementation module is used for according to the zero-carbon balance control strategy of chronogenesis acquires building microgrid operation data result, obtains commercial power service period and commercial power use electric quantity according to handling, and the quantization processing building microgrid operation data result to the iteration acquires the zero-carbon building realization route of being no less than 2 kinds, according to zero-carbon building realizes the route optimization building microgrid according to realizing zero-carbon, building microgrid zero-carbon implementation module with the zero-carbon balance control strategy of chronogenesis makes the module and connects.

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