CN117421885B - Chilled water system optimal control method based on event-driven mechanism - Google Patents
Chilled water system optimal control method based on event-driven mechanism Download PDFInfo
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Abstract
The invention relates to the technical field of air conditioner operation regulation and control, in particular to a chilled water system optimization control method based on an event driving mechanism, which comprises the steps of firstly collecting historical operation data of a water chilling unit system and carrying out data detection and processing on the historical data; fitting a data model of each device in the water chilling unit system, wherein the data model comprises a water chilling unit mathematical model, a water pump mathematical model and a cooling tower mathematical model; dividing a water chilling unit system into a freezing side subsystem and a cooling side subsystem, and constructing mathematical models of the freezing side subsystem and the cooling side subsystem; classifying working conditions according to the actual operation working conditions of the refrigerating unit system, and determining event types; and (3) formulating a corresponding execution strategy according to the event type, and triggering the corresponding execution strategy to optimally control the chilled water system based on the event type. The invention can reduce the system to sink into the local system power minimum value due to the deviation of the sensor based on the event trigger driving strategy; meanwhile, the system can be quickly optimized to an optimal running state.
Description
Technical Field
The invention relates to the technical field of air conditioner operation regulation and control, in particular to a chilled water system optimal control method based on an event driving mechanism.
Background
Under the condition of each load demand, the water chilling unit has an optimal running state, the running efficiency of the water chilling unit reaches the highest at the moment, and an image drawn by the set of data points of the load-optimal running efficiency is the running envelope curve of the optimal efficiency of the water chilling unit. From the above definition, it can be known that the upper boundary line of the chiller operation performance map image is the chiller operation performance envelope. The upper boundary line is the set of the running state points with the best running efficiency, and the running state points are closer to the running performance envelope diagram of the water chilling unit.
In the running performance diagram of the water chilling unit, each data point not only describes the total load and the running efficiency born by the water chilling unit, but also contains the current running state of the water chilling unit, including the start-stop condition, the load distribution condition and the like of each unit.
By establishing a water chiller system model and optimizing the system model by adopting a global optimization algorithm, the optimal running efficiency of the water chiller can be obtained, so that the optimal control strategy of the water chiller is determined. When the chiller model is accurate and the optimization algorithm is reasonable, the optimal control strategy of the chiller can be determined theoretically based on the strategy of the empirical model.
However, the magnitude of the cooling load of the water chiller obtained according to the actual operation data fluctuates up and down in the process of time variation, rather than a smooth variation curve. Since the operational data has been data pre-processed, outlier data points have been culled, and thus data anomalies are not responsible for such fluctuations. Fluctuations in the cold load over time are caused by errors in the measurement uncertainty of the sensor. In the field test, the measured values of all variables of the water chilling unit are acquired by the sensors, and because errors are unavoidable, a certain deviation exists between the data value and the true value of the variables acquired on the site.
Because the sensors have measurement uncertainty with different sizes, errors exist in the measured values of the chilled water flow rate, the chilled water inlet temperature, the chilled water outlet temperature and the like of the water chilling unit, and then errors exist in the value of the cooling load. If the value of the cooling load is not accurate enough, the generated performance envelope graph is caused to generate deviation, so that the running condition of the water chilling unit is influenced and unnecessary energy consumption is generated. Although errors are unavoidable, in the control strategy, the effect of errors should be reduced as much as possible in order to guarantee the control effect, and therefore an event-driven based strategy is proposed to reduce the effect of errors generated by uncertainty in sensor measurements on the measured value of the cold load.
Disclosure of Invention
In order to avoid the problems of the prior art, the invention aims to provide an optimal control method of a chilled water system based on an event-driven mechanism.
In order to achieve the above purpose, the present invention provides the following technical solutions: the chilled water system optimal control method based on the event-driven mechanism comprises the following steps:
s1: collecting historical operation data of a water chilling unit system, and carrying out data detection and processing on the historical data; screening effective operation data according to equipment performance parameters provided by manufacturers, and fitting the effective operation data to a data model of each equipment in a water chilling unit system, wherein the data model of the water chilling unit comprises a water chilling unit mathematical model, a water pump mathematical model and a cooling tower mathematical model;
s2: dividing a water chilling unit system into two subsystems, including a freezing side subsystem and a cooling side subsystem, and constructing mathematical models of the freezing side subsystem and the cooling side subsystem based on the operating condition characteristics of the system;
S3: classifying working conditions according to the actual operation working conditions of the refrigerating unit system, and determining event types;
S4: and (3) formulating a corresponding execution strategy according to the event type, and triggering the corresponding execution strategy to optimally control the chilled water system based on the event type.
The invention is further provided with: the mathematical model of the cold water main machine in step S1 is as follows,
Pchiller,i＝f(Qchiller,i,Tcw,out,i,Tcond,in,i)
The mathematical model of a single cold water host is simplified into:
Wherein Q chiller,i is the current cooling capacity of the ith host; t cw,out,i is the freezing water outlet temperature of the ith host; cooling water inlet temperature of a T cond,in,i ith host; a 0-a9 is the host model fitting coefficient.
The mathematical model of a plurality of cold water hosts is simplified into:
Wherein n is the number of cold water main machine start-up machines, and P chiller,i is the power of the ith cold water main machine.
The invention is further provided with: the water pump mathematical model in the step S1 comprises a cooling water pump mathematical model and a freezing water pump mathematical model;
Single water pump power-frequency model:
Wherein, P design,i is the rated power of the ith water pump, f i is the rated frequency of the ith water pump, f design,i is the rated frequency of the ith water pump, and c 0-c3 is the fitting coefficient of the water pump model.
The invention is further provided with: the freezing side subsystem model and the cooling side subsystem model in the step S2 are used for establishing a coupling relation between the freezing side subsystem and the cooling side subsystem by taking the water outlet temperature of the cooling tower as an intermediate variable;
the function of the cooling tower outlet water temperature is:
Wherein T cond,out is the water inlet temperature of the cooling tower; m cw is cooling tower water flow; f cw is the cooling tower fan operating frequency; t out is the current outdoor temperature; t w-t is the current outdoor humidity; a1-a9 are cooling tower model fitting coefficients.
The invention is further provided with: the freeze side subsystem model is as follows,
The freeze side subsystem mathematical model is as follows,
According to the power-frequency model of the single water pump, the fitting function of the power-frequency of the single chilled water pump is as follows:
Wherein, P design,i is the rated power of the ith chilled water pump. f i is the i-th chilled water pump frequency. And f design,i is the rated frequency of the i-th chilled water pump. And c 0-c3 is a model fitting coefficient of the chilled water pump.
Simplifying the mathematical model of a plurality of chilled water pumps into:
The total power of the refrigerating side subsystem is as follows:
wherein, P chiller,i is the power of the ith chilled water host in the refrigeration subsystem, P chiller pump,i is the power of the ith chilled water pump, and n is the number of the chilled water hosts and the chilled water pumps which are started.
The invention is further provided with: the cooling side subsystem model is used for constructing a water outlet temperature function of the cooling tower as an intermediate variable, and a power fitting function of the cooling tower is as follows:
Wherein f cw,i is the running frequency of the ith cooling tower fan, and a 1-a3 is the model coefficient.
The mathematical model of a plurality of cooling towers is simplified into:
Wherein P tower,i is the power of the ith cooling tower, and m is the number of cooling towers started;
According to the power-frequency model of the single water pump, the fitting function of the power-frequency of the single cooling water pump is as follows:
Wherein, P design,i is the rated power of the ith cooling water pump. And f i is the frequency of the i-th cooling water pump. And f design,i is the rated frequency of the ith cooling water pump. And c 0-c3 is a model fitting coefficient of the cooling water pump.
Simplifying the mathematical model of a plurality of cooling water pumps into:
the total power of the cooling side subsystem is as follows:
Wherein P tower,i is the power of the ith cooling tower, P cooling pump,i is the power of the ith cooling water pump, m is the number of cooling tower opening stages, and n is the number of cooling water pump opening stages.
The invention is further provided with: the event types described in step S3 include,
(1) The outdoor temperature and humidity change; while the end load demand does not change;
(2) The outdoor temperature and humidity are not changed; while the end load demand changes;
(3) The outdoor temperature and humidity and the end load requirements are changed.
The invention is further provided with: and calculating the cooling side power variation delta P2 according to the cooling side subsystem model and the cooling side subsystem model under the three event types in the step S3.
Wherein,
ΔP1＝ΔPchiller,all+ΔPchiller pump,all；
ΔP2＝ΔPtower,all+ΔPcooling pump,all。
The invention is further provided with: step S4 is specifically to determine outdoor temperature and end load requirements in real time after optimally controlling the current system according to the data fitting model, as shown in fig. 2:
S41: the outdoor temperature and humidity change; while the end load demand does not change; at the moment, the temperature change of the cooling water is directly caused, and the change of the cooling water temperature can cause the power change of the freezing side, so that the power change delta P1 of the freezing side can be calculated according to the subsystem model of the freezing side; calculating a cooling side power variation delta P2 according to the cooling side subsystem model; comparing the delta P1 with the delta P2, if delta P1 is larger than delta P2, locally optimizing the cooling side, solving the minimum value of the fitting function of the subsystem of the cooling side, otherwise locally optimizing the cooling side, and solving the minimum value of the fitting function of the subsystem of the cooling side;
S42: the outdoor temperature and humidity are not changed; the end load demand is changed, and the refrigerating side power change quantity delta P1 can be calculated according to the refrigerating side subsystem model; calculating a cooling side power variation delta P2 according to the cooling side subsystem model; comparing the delta P1 with the delta P2, if delta P1 is larger than delta P2, locally optimizing the cooling side, solving the minimum value of the fitting function of the subsystem of the cooling side, otherwise locally optimizing the cooling side, and solving the minimum value of the fitting function of the subsystem of the cooling side;
S43: if the outdoor temperature and humidity and the end load requirements are changed, performing global optimization of the system to ensure that P total =Min (P1+P2); when the system reaches the relative equilibrium state, the step S41 or the step S42 is performed to determine the local optimization strategy according to the current system variation trend.
In summary, the technical scheme of the invention has the following beneficial effects:
1. The invention provides an optimal control method of a chilled water system based on an event-driven mechanism, which can reduce the system to sink into a local system power minimum value due to the deviation of a sensor based on an event-triggered driving strategy; meanwhile, the system can be quickly optimized to an optimal running state.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a general flow chart of the method of the present invention.
Fig. 2 is a flowchart of triggering a corresponding execution policy based on the event type in step S4 of the present invention.
Detailed Description
In order to make the technical solution of the present application better understood by those skilled in the art, the technical solution of the present application will be clearly and completely described in the following with reference to the accompanying drawings, and based on the embodiments of the present application, other similar embodiments obtained by those skilled in the art without making creative efforts should fall within the protection scope of the present application.
The invention will be further described with reference to the drawings and preferred embodiments.
Examples:
referring to fig. 1, in a preferred embodiment of the present invention, the chilled water system optimization control method based on an event-driven mechanism comprises the following steps:
S1: collecting historical operation data of a water chilling unit system, and carrying out data detection and processing on the historical data; screening effective operation data according to equipment performance parameters provided by manufacturers to fit data models of all equipment in a water chilling unit system, wherein the data fitting models of the water chilling unit comprise a water chilling unit mathematical model, a water pump mathematical model and a cooling tower mathematical model;
the mathematical model of the cold water main machine is as follows,
Pchiller,i＝f(Qchiller,i,Tcw,out,i,Tcond,in,i)
The mathematical model of a single cold water host is simplified into:
Wherein Q chiller,i is the current cooling capacity of the ith host; t cw,out,i is the freezing water outlet temperature of the ith host; cooling water inlet temperature of a T cond,in,i ith host; a 0-a9 is the host model fitting coefficient.
The mathematical model of a plurality of cold water hosts is simplified into:
Wherein n is the number of cold water main machine start-up machines, and P chiller,i is the power of the ith cold water main machine.
In the step S1, the water pump comprises a cooling water pump and a freezing water pump, and the power-frequency mathematical model of the single water pump is as follows:
Wherein, P design,i is the rated power of the ith water pump. f i is the i-th water pump frequency. And f design,i is the rated frequency of the ith water pump. And c 0-c3 is a fitting coefficient of the water pump model.
S2: dividing a water chilling unit system into two subsystems, including a freezing side subsystem and a cooling side subsystem, and constructing a freezing side subsystem and a cooling side subsystem model based on the system operation condition characteristics;
The mathematical model of the refrigeration side subsystem is as follows:
according to the power-frequency model of the single water pump, the fitting function of the power-frequency of the single chilled water pump is as follows:
Wherein, P design,i is the rated power of the ith chilled water pump. f i is the i-th chilled water pump frequency. And f design,i is the rated frequency of the i-th chilled water pump. And c 0-c3 is a model fitting coefficient of the chilled water pump.
Simplifying the mathematical model of a plurality of chilled water pumps into:
Wherein P chiller pump,i is the power of the ith chilled water pump, and n is the number of started chilled water pumps.
The total power of the refrigerating side subsystem is as follows:
wherein, P chiller,i is the power of the ith chilled water host in the refrigeration subsystem, P chiller pump,i is the power of the ith chilled water pump, and n is the number of the chilled water hosts and the chilled water pumps which are started.
The cooling side subsystem model and the cooling side subsystem model are used for establishing a coupling relation between the cooling side subsystem and the cooling side subsystem by taking the water outlet temperature of the cooling tower as an intermediate variable;
the function of the cooling tower outlet water temperature is:
Wherein T cond,out is the water inlet temperature of the cooling tower; m cw is cooling tower water flow; f cw is the cooling tower fan operating frequency; t out is the current outdoor temperature; t w-t is the current outdoor humidity; a 1-a9 is the cooling tower model fitting coefficient.
The power fitting function of the single cooling tower is as follows:
Wherein f cw,i is the running frequency of the ith cooling tower fan, and a 1-a3 is the fitting coefficient of the cooling tower model;
the mathematical model of a plurality of cooling towers is simplified into:
Wherein P tower,i is the power of the ith cooling tower, and m is the number of cooling towers started;
According to the power-frequency model of the single water pump, the fitting function of the power-frequency of the single cooling water pump is as follows:
Wherein, P design,i is the rated power of the ith cooling water pump. And f i is the frequency of the i-th cooling water pump. And f design,i is the rated frequency of the ith cooling water pump. And c 0-c3 is a model fitting coefficient of the cooling water pump.
Simplifying the mathematical model of a plurality of cooling water pumps into:
Wherein P cooling pump,i is the power of the ith cooling water pump, and n is the number of the cooling water pumps started;
the total power of the cooling side subsystem is as follows:
Wherein P tower,i is the power of the ith cooling tower, P cooling pump,i is the power of the ith cooling water pump, m is the number of cooling tower opening stages, and n is the number of cooling water pump opening stages.
Under the condition of certain terminal cold energy requirement, the operating state point for obtaining the optimal system operating efficiency is as follows: p total =min (p1+p2); the working condition corresponding to the minimum value of P total is ensured to be the optimal state of the current system operation efficiency; while we optimize the targets to be tuned by the strategy.
In order to ensure that the chilled water system always operates in an optimal operation state, the system controllable variable parameters enable the system to operate in the optimal operation state, and the system controllable variable parameters are related to the terminal cold energy requirement and the outdoor temperature and humidity. Based on the factors, an event-triggered driving strategy is provided for optimally controlling the system, so that the system can quickly reach the optimal running state of the next working condition due to the change of uncontrollable factors.
The performance characteristics of the water chilling unit are that when other conditions are unchanged, the evaporation temperature of the water chilling unit is increased at the same time when the temperature of chilled water is increased, the refrigeration Coefficient (COP) of the water chilling unit is increased, namely the electric quantity consumed by unit refrigeration capacity is reduced, and the temperature of the chilled water is reduced otherwise;
The influence of the temperature change of the cooling water on the performance of the water chilling unit is opposite to that of the chilled water, and the lower the cooling water temperature is, the higher the refrigeration coefficient is; when the flow of the chilled water and the flow of the cooling water are increased, the heat exchange performance of the evaporator and the condenser is improved, the refrigeration coefficient of the water chilling unit is improved, and meanwhile, the energy consumption of the water pump is increased. In summary, the presence of an optimal amount of chilled water and cooling water minimizes the sum of the water chiller and water pump energy consumption.
Both the cold and the outside temperature and humidity changes can cause the power of the two subsystems to change. The power variation fluctuation of a specific subsystem is as follows:
(1) The power change value of the refrigeration side subsystem is:
(2) The amount of change in cooling side subsystem power is:
The change amount according to the outlet water temperature of the cooling tower is as follows:
The corresponding required power change amounts are:
s3: classifying the working conditions according to the actual operation working conditions of the refrigerating unit system, determining event types, including:
(1) The outdoor temperature and humidity change; while the end load demand does not change;
(2) The outdoor temperature and humidity are not changed; while the end load demand changes;
(3) The outdoor temperature and humidity and the end load requirements are changed.
And calculating the cooling side power variation delta P2 according to the cooling side subsystem model and the cooling side subsystem model under the three event types in the step S3.
Wherein,
ΔP1＝ΔPchiller,all+ΔPchiller pump,all；
ΔP2＝ΔPtower,all+ΔPcooling pump,all。
S4: and (3) formulating a corresponding execution strategy according to the event type, and triggering the corresponding execution strategy to optimally control the chilled water system based on the event type.
After the optimal control is carried out on the current system according to the data fitting model, real-time judgment is carried out on the outdoor temperature and the end load demand:
S41: the outdoor temperature and humidity change; while the end load demand does not change; at the moment, the temperature change of the cooling water is directly caused, and the change of the cooling water temperature can cause the power change of the freezing side, so that the power change delta P1 of the freezing side can be calculated according to the subsystem model of the freezing side; calculating a cooling side power variation delta P2 according to the cooling side subsystem model; comparing the delta P1 with the delta P2, if delta P1 is larger than delta P2, locally optimizing the cooling side, solving the minimum value of the fitting function of the subsystem of the cooling side, otherwise locally optimizing the cooling side, and solving the minimum value of the fitting function of the subsystem of the cooling side;
S42: the outdoor temperature and humidity are not changed; the end load demand is changed, and the refrigerating side power change quantity delta P1 can be calculated according to the refrigerating side subsystem model; calculating a cooling side power variation delta P2 according to the cooling side subsystem model; comparing the delta P1 with the delta P2, if delta P1 is larger than delta P2, locally optimizing the cooling side, solving the minimum value of the fitting function of the subsystem of the cooling side, otherwise locally optimizing the cooling side, and solving the minimum value of the fitting function of the subsystem of the cooling side;
S43: if the outdoor temperature and humidity and the end load requirements are changed, performing global optimization of the system to ensure that P total =Min (P1+P2); when the system reaches the relative equilibrium state, the step S41 or the step S42 is performed to determine the local optimization strategy according to the current system variation trend.
The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above examples, and all technical solutions belonging to the concept of the present invention belong to the protection scope of the present invention. It should be noted that modifications and adaptations to the present invention may occur to one skilled in the art without departing from the principles of the present invention and are intended to be within the scope of the present invention.
Claims (8)
1. The chilled water system optimal control method based on the event-driven mechanism is characterized by comprising the following steps of:
S1: collecting historical operation data of a water chilling unit system, and carrying out data detection and processing on the historical data; fitting a data fitting model of each device in a water chilling unit system, wherein the data fitting model of the water chilling unit comprises a water chilling unit mathematical model, a water pump mathematical model and a cooling tower mathematical model;
S2: dividing a water chilling unit system into two subsystems, including a freezing side subsystem and a cooling side subsystem, and constructing mathematical models of the freezing side subsystem and the cooling side subsystem based on the operating condition characteristics of the system; the water outlet temperature of the cooling tower is used as an intermediate variable, and a coupling relation between the refrigerating side subsystem and the cooling side subsystem is established;
The mathematical model of the refrigerating side subsystem is as follows:
Wherein P chiller,i is the power of the ith chilled water host in the refrigeration side subsystem, P chiller pump,i is the power of the ith chilled water pump, and n is the number of the chilled water hosts and the chilled water pumps; p chiller,all represents a mathematical model of a plurality of cold water hosts, and P chiller pump,all represents a mathematical model of a plurality of chilled water pumps; p1 is the total power of the refrigerating side subsystem;
the cooling side subsystem mathematical model is:
Wherein P tower,i is the power of the ith cooling tower, P cooling pump,i is the power of the ith cooling water pump, m is the number of cooling tower starting units, and n is the number of cooling water pump starting units; p tower,all is a mathematical model of a plurality of cooling towers, P cooling pump,all is a mathematical model of a plurality of cooling water pumps, and P2 is the total power of a cooling side subsystem;
S3: classifying working conditions according to the actual operation working conditions of the refrigerating unit system, and determining event types;
the type of event may include a type of event,
(1) The outdoor temperature and humidity change; while the end load demand does not change;
(2) The outdoor temperature and humidity are not changed; while the end load demand changes;
(3) The outdoor temperature and humidity and the terminal load demand are changed;
Calculating the refrigerating side power variation delta P1 and the cooling side power variation delta P2 under three event types in the step S3 according to the refrigerating side subsystem model and the cooling side subsystem mathematical model;
S4: setting a corresponding execution strategy according to the event type, and triggering the corresponding execution strategy to optimally control the chilled water system based on the event type;
And comparing the delta P1 with the delta P2 according to the event type, if delta P1 is larger than delta P2, locally optimizing the cooling side, solving the minimum value of the fitting function of the cooling side subsystem, otherwise locally optimizing the freezing side, and solving the minimum value of the fitting function of the freezing side subsystem.
2. The method for optimizing control of a chilled water system based on an event driven mechanism according to claim 1, wherein the mathematical model of the chilled water host in step S1 is as follows,
Pchiller,i＝f(Qchiller,i,Tcw,out,i,Tcond,in,i)
The mathematical model of a single cold water host is simplified into:
Wherein Q chiller,i is the current cooling capacity of the ith host; t cw,out,i is the freezing water outlet temperature of the ith host; t cond,in,i is the cooling water inlet temperature of the ith host; a 0-a9 is a host model fitting coefficient;
the mathematical model of a plurality of cold water hosts is simplified into:
Wherein n is the number of cold water main machine start-up machines, and P chiller,i is the power of the ith cold water main machine.
3. The chilled water system optimization control method based on an event driven mechanism according to claim 2, wherein the water pump mathematical model in step S1 includes a cooling water pump mathematical model and a chilled water pump mathematical model;
Single water pump power-frequency model:
Wherein, P design,i is the rated power of the ith water pump, f i is the rated frequency of the ith water pump, f design,i is the rated frequency of the ith water pump, and c 0-c3 is the fitting coefficient of the water pump model.
4. The chilled water system optimization control method based on an event driven mechanism according to claim 3, wherein the function of the cooling tower outlet water temperature in step S2 is:
Tcond,in＝a1+a2*Tcond,out+a3*mcw+a4*fcw+a5*Tout+a6*Tw-t
Wherein T cond,out is the water inlet temperature of the cooling tower; m cw is cooling tower water flow; f cw is the cooling tower fan operating frequency; t out is the current outdoor temperature; t w-t is the current outdoor humidity; a 1-a9 is the cooling tower model fitting coefficient.
5. The method for optimizing control of a chilled water system based on an event driven mechanism of claim 4, wherein the mathematical model of the chilled-side subsystem is as follows,
According to the power-frequency model of the single water pump, the fitting function of the power-frequency of the single chilled water pump is as follows:
wherein P design,i is the rated power of the ith chilled water pump, f i is the frequency of the ith chilled water pump, f design,i is the rated frequency of the ith chilled water pump, and c 0-c3 is the fitting coefficient of the chilled water pump model;
simplifying the mathematical model of a plurality of chilled water pumps into:
solving the total power P1 of the refrigerating side subsystem.
6. The chilled water system optimization control method based on an event driven mechanism according to claim 5, wherein the mathematical model of the cooling side subsystem constructs a water outlet temperature function of a cooling tower as an intermediate variable, and a single cooling tower power fitting function is:
Wherein f cw,i is the running frequency of the ith cooling tower fan, and a 1-a3 is the fitting coefficient of the cooling tower model;
the mathematical model of a plurality of cooling towers is simplified into:
Wherein P tower,i is the power of the ith cooling tower, and m is the number of cooling towers started;
According to the power-frequency model of the single water pump, the fitting function of the power-frequency of the single cooling water pump is as follows:
Wherein P design,i is the rated power of the ith cooling water pump, f i is the frequency of the ith cooling water pump, f design,i is the rated frequency of the ith cooling water pump, and c 0-c3 is the fitting coefficient of the cooling water pump model;
Simplifying the mathematical model of a plurality of cooling water pumps into:
the total power P2 of the cooling side subsystem is solved.
7. The chilled water system optimization control method based on an event-driven mechanism according to claim 6, wherein the chilled-side power variation Δp1 is:
ΔP1＝ΔPchiller,all+ΔPchiller pump,all；
the cooling-side power variation Δp2 is:
ΔP2＝ΔPtower,all+ΔPcooling pump,all。
8. The method for optimizing control of chilled water system based on event driven mechanism as claimed in claim 7, wherein step S4 is specifically to determine outdoor temperature and end load requirements in real time after optimally controlling the current system according to a data fitting model:
S41: the outdoor temperature and humidity change; while the end load demand does not change; calculating a refrigerating side power variation delta P1 according to the refrigerating side subsystem model; calculating a cooling side power variation delta P2 according to the cooling side subsystem model; comparing the delta P1 with the delta P2, if delta P1 is larger than delta P2, locally optimizing the cooling side, solving the minimum value of the fitting function of the subsystem of the cooling side, otherwise locally optimizing the cooling side, and solving the minimum value of the fitting function of the subsystem of the cooling side;
S42: the outdoor temperature and humidity are not changed; the end load demand is changed, and the refrigerating side power change quantity delta P1 can be calculated according to the refrigerating side subsystem model; calculating a cooling side power variation delta P2 according to the cooling side subsystem model; comparing the delta P1 with the delta P2, if delta P1 is larger than delta P2, locally optimizing the cooling side, solving the minimum value of the fitting function of the subsystem of the cooling side, otherwise locally optimizing the cooling side, and solving the minimum value of the fitting function of the subsystem of the cooling side;
s43: if the outdoor temperature and humidity and the end load requirements are changed, performing global system optimization to ensure that P total =Min (P1+P2); when the system reaches the relative equilibrium state, the step S41 or the step S42 is performed to determine the local optimization strategy according to the current system variation trend.
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