Optimized regulation and control method for participation of building cluster air conditioner cooling station system in demand side response

Abstract

The invention relates to an optimal regulation and control method for participation of a building cluster air conditioner cooling station system in demand side response, and is suitable for the field of building cluster demand side response regulation and control. The method comprises the following steps: S1, on the basis of historical operation data, determining reference energy consumption of each building air conditioner cooling station system in a building cluster at each moment in a future preset time period; s2, establishing a demand side response capacity optimization model of the building cluster air conditioner cooling station system, and solving to obtain the optimal response capacity of each building; s3, on the basis of the real-time adjusting signal of the power grid, each building calculates the required energy of the air conditioner cold station system according to the reference energy consumption and the response capacity of the building; and S4, equipment operation parameters of the air conditioner cooling station systems of all the buildings are adjusted in real time based on the required energy of all the buildings. According to the invention, demand side response management and online control of the building cluster air-conditioning cooling station system are realized by adopting a day-ahead planning-real-time adjustment hierarchical framework.

Description

Technical Field

[0001] The present invention relates to an optimized regulation method for a building cluster air-conditioning chilled water station system to participate in demand-side response, which is applicable to the field of building cluster demand-side response regulation. Background Art

[0002] Through research on existing studies in the field, including the optimized control method for demand-side response of central air-conditioning cold sources (Chinese invention patent, publication number: CN115235046B), an air-conditioning optimized control strategy method for demand-side response (Chinese invention patent, publication number: CN117212977A), a control process for a central air-conditioning system to participate in power system demand-side response (Chinese invention patent, publication number: CN117212977A), etc., the above methods have all achieved demand-side response regulation for the air-conditioning chilled water station systems of individual buildings.

[0003] Some literature has carried out research on the participation of building cluster air-conditioning loads in demand-side response scheduling. For example, a regulation method and system for user-side air-conditioning load response requirements (Chinese invention patent, publication number: CN118729485A), a method for regulating user-side resources based on demand response (Chinese invention patent, publication number: CN117077919A), a method, device and storage medium for collaborative control of cluster air-conditioning (Chinese invention patent, publication number: CN117870079A), a method and system for demand-side energy management of building clusters (Chinese invention patent, publication number: CN112465212A), etc.

[0004] However, most of the above methods are only applicable to the scenario of individual air-conditioning systems in residential building clusters, and there is still a lack of research on demand-side response regulation in the scenario of central air-conditioning chilled water stations in public building clusters. On the other hand, existing research only considers the demand-side response regulation of building clusters at the day-ahead planning level, and further research is needed on how to perform real-time operation control of building cluster air-conditioning chilled water stations according to the demand-side response requirements in the day-ahead planning stage. Summary of the Invention

[0005] The technical problem to be solved by the present invention is: in view of the above problems, to provide an optimized regulation method for a building cluster air-conditioning chilled water station system to participate in demand-side response.

[0006] The technical solution adopted by the present invention is: an optimized regulation method for a building cluster air-conditioning chilled water station system to participate in demand-side response, including: S1. Based on historical operation data, determine the benchmark energy consumption of each building's air-conditioning chilled water station system in the building cluster at each moment within a future preset time period; S2. Establish an optimization model for the demand-side response capacity of the building cluster air-conditioning chilled water station system, and solve to obtain the optimal response capacity of each building; The demand-side response capacity optimization model includes: The overall optimization goal of the building cluster, including the electricity purchase cost of the building cluster from the power grid, the revenue from providing capacity regulation services, and the thermal comfort of building users; The indoor temperature balance equation of the building, which describes the change of indoor temperature with cooling capacity and building heat gain; The demand-side response capacity constraint of the building air-conditioning chilled water system, where the upper limit of the response capacity is the minimum of the deviation between the actual energy consumption and the upper limit of the operating energy consumption, the deviation between the operating energy consumption and the lower limit, and the deviation from the reference energy consumption of the air-conditioning chilled water system; S3. Based on the real-time regulation signal of the power grid, each building calculates the required energy consumption of the air-conditioning chilled water system according to its own reference energy consumption and response capacity; S4. Based on the required energy consumption of each building, the equipment operation parameters of the air-conditioning chilled water system of each building are adjusted in real time.

[0007] The demand-side response capacity optimization model includes: ; ; ; ; ; Among them, is the total optimization duration, and are respectively the total energy consumption and the total response capacity of the building cluster at time is the electricity purchase price of the power grid, is the revenue per unit capacity for providing capacity regulation services, is the number of buildings in the building cluster, and are respectively the energy consumption and response capacity of building at time is an indication factor for whether capacity regulation service revenue can be obtained, is the minimum threshold for obtaining capacity regulation service revenue, is the thermal comfort weight coefficient of building , is the indoor temperature value of building at time is the reference value of the indoor temperature of building at time and are respectively the heat capacity and thermal resistance of building , For a chiller , the coefficient of performance and are respectively the outdoor temperature of the building and the predicted value of heat input at a certain moment, is the optimization interval, is the function to take the minimum value, and are respectively the upper limit and lower limit of the energy consumption of the air-conditioning chilled water station in the building is the reference energy consumption value of building n at time t.

[0008] For the real-time regulation signal based on the power grid, each building calculates the required energy of the air-conditioning chilled water station system according to its own reference energy consumption and response capacity, including: ; Among them, is the required energy of the air-conditioning chilled water station system under the power grid regulation signal, is the regulation signal of the power grid, and its range is [-1, 1], and are respectively the reference energy consumption and response capacity values during the corresponding period.

[0009] For the required energy of each building, the equipment operation parameters of the air-conditioning chilled water station system of each building are adjusted in real time, including: Adopt the equipment operation parameter optimization model to determine the equipment operation parameters of the air-conditioning chilled water station system of each building; The equipment operation parameter optimization model includes: The optimization goal of the chilled water station during the real-time adjustment stage includes minimizing the deviation between the actual energy consumption and the required energy consumption, and the deviation between the actual indoor temperature and the set indoor temperature; The calculation formula of the actual energy consumption of the chilled water station; The calculation formula of the indoor temperature of the building, which is determined by the energy conservation relationship between the water side and the air side.

[0010] The equipment operation parameter optimization model includes: ; ; ; Among them, is the actual operation energy consumption of the chilled water station, is the weight coefficient between the operation energy consumption and the thermal comfort, is the actual value of the indoor temperature to be optimized, is the set value of the indoor temperature, , and are respectively the operating energy consumptions of the th chiller, chilled water pump, and air handling unit. , and are respectively the numbers of chillers, chilled water pumps, and air handling units. and are respectively the constant pressure specific heat capacities on the water side and air side. and are respectively the flow rates on the water side and air side. is the chilled water outlet temperature. and are respectively the chilled water return temperature value and the air handling unit supply air temperature value at the current moment.

[0011] An optimized control device for a building cluster air-conditioning cold station system to participate in demand-side response, comprising: A reference energy consumption prediction module, configured to determine the reference energy consumption of each building air-conditioning cold station system in the building cluster at each moment within a preset future time period based on historical operation data; A response capacity solving module, configured to establish an optimization model for the demand-side response capacity of the building cluster air-conditioning cold station system and solve to obtain the optimal response capacity of each building; The demand-side response capacity optimization model includes: The overall optimization objective of the building cluster, including the building cluster's electricity purchase cost from the grid, the revenue from providing capacity regulation services, and the thermal comfort of building users; The building indoor temperature balance equation, which describes the change of indoor temperature with the cooling capacity and building heat gain; The demand-side response capacity constraint of the building air-conditioning cold station system, where the upper limit of the response capacity is the minimum of the deviation between the actual energy consumption of the air-conditioning cold station system and the upper limit of the operating energy consumption, the deviation between the lower limit of the operating energy consumption, and the deviation of the reference energy consumption; A demand energy consumption calculation module, configured to calculate the demand energy consumption of the air-conditioning cold station system based on the real-time regulation signal of the power grid, and each building calculates according to its own reference energy consumption and response capacity; An operating parameter adjustment module, configured to perform real-time adjustment of the equipment operating parameters of each building air-conditioning cold station system based on the demand energy consumption of each building.

[0012] A storage medium, on which a computer program executable by a processor is stored, and when the computer program is executed, the steps of the optimized control method for the building cluster air-conditioning cold station system to participate in demand-side response are implemented.

[0013] An optimization control device for a building cluster air-conditioning chilled water station system to participate in demand-side response, which has a memory and a processor. A computer program capable of being executed by the processor is stored on the memory. When the computer program is executed, the steps of the optimization control method for the building cluster air-conditioning chilled water station system to participate in demand-side response are realized.

[0014] The beneficial effects of the present invention are as follows: The present invention adopts a hierarchical framework of day-ahead planning - real-time adjustment to realize the demand-side response management and online control of the building cluster air-conditioning chilled water station system. In the upper-layer day-ahead planning stage, the building cluster determines the optimal response capacity by solving the demand-side response capacity optimization model to maximize the revenue of the building cluster participating in demand-side response services. In the lower-layer real-time adjustment stage, each building performs online adjustment of the operating parameters of the air-conditioning chilled water station equipment based on the response capacity in the day-ahead planning stage and the real-time power grid adjustment signal, realizing the real-time optimal operation of the air-conditioning chilled water station system. Description of the Drawings

[0015] Figure 1 It is a schematic diagram of the building cluster air-conditioning chilled water station system in the embodiment.

[0016] Figure 2 It is a flowchart of the optimization control method for the building cluster air-conditioning chilled water station system in the embodiment.

[0017] Figure 3 It is a comparison of the total operating costs of each building participating in the demand-side response in the embodiment.

[0018] Figure 4 It is a comparison of the optimal response capacity results of each building in the embodiment.

[0019] Figure 5 It is the real-time power following deviation and indoor temperature deviation of each building in the embodiment. Detailed Embodiments

[0020] The present invention will be further explained and illustrated below in conjunction with the drawings and embodiments. It should be noted that the following embodiments are intended to facilitate the understanding of the present invention and do not limit it in any way.

[0021] Embodiment 1: The building cluster air-conditioning chilled water station system of this embodiment is as Figure 1 shown. The building cluster includes six public buildings. Each public building is cooled by an air-conditioning chilled water station. Each air-conditioning chilled water station consists of a chiller, a chilled water pump, and an air handling unit.

[0022] As Figure 2 shown, this embodiment is an optimization control method for a building cluster air-conditioning chilled water station system to participate in demand-side response, specifically including the following steps: S1. In the daily planning stage, based on historical operation data, predict the hourly baseline energy consumption of the air-conditioning chillers system of each building in the building cluster for the next day. Make a prediction.

[0023] In this embodiment, an artificial neural network is used to train the historical operation data, and the model parameters are set as shown in Table 1 below: Table 1

[0024] Among them, the baseline energy consumption of the building ( , ,..., ) can be directly obtained by collecting the energy consumption data of the system operating normally under the non-demand response scenario, and the outdoor temperature ( ) and outdoor relative humidity ( ) data can be collected by outdoor temperature and humidity sensors.

[0025] S2. Establish an optimization model for the demand response capacity of the air-conditioning chillers system in the building cluster, and solve to obtain the optimal response capacity of each building.

[0026] In this embodiment, the optimization model for the demand response capacity includes: (Equation 1); (Equation 2); (Equation 3); (Equation 4); (Equation 5); Among them, is the total optimization duration, and are respectively the total energy consumption and total response capacity of the building cluster at time is the grid power purchase price, is the income per unit capacity for providing capacity regulation services, is the number of buildings in the building cluster, and are respectively the energy consumption and response capacity of building at time is an indication factor for whether capacity regulation service income can be obtained, is the minimum threshold for obtaining capacity regulation service income, is the thermal comfort weight coefficient of building , is the energy consumption of building The indoor temperature value, is the reference value of the building indoor temperature at time and are respectively the heat capacity and heat resistance of the building ; is the performance coefficient of the chiller ; and are respectively the outdoor temperature and the predicted value of heat input of the building at time in this embodiment, which are obtained by least - squares fitting of historical data using a polynomial function, is the optimization interval, is the function to take the minimum value, and are respectively the upper and lower limits of the energy consumption of the building air - conditioning cold station ;

[0027] The above formula (1) describes the overall optimization goal of the building cluster, that is, to maximize the total revenue of the building cluster by providing capacity - regulation services, including reducing the electricity purchase cost of the building cluster from the power grid, increasing the revenue from providing capacity - regulation services, and minimizing the deviation of the reference temperature to ensure the thermal comfort of building users; formula (2) represents the indoor temperature balance equation of the building based on the heat - capacity - heat - resistance model, which describes the change of indoor temperature with factors such as cooling capacity and building heat gain; formula (3) represents the demand - side response capacity constraint of the building air - conditioning cold - station system, and the upper limit of the response capacity is the minimum value of the deviation between the actual energy consumption and the rated energy consumption of the air - conditioning cold - station system, the minimum operating energy - consumption deviation, and the reference energy - consumption deviation; formula (4) describes the calculation formula of the total energy consumption and total response capacity of the building cluster; formula (5) describes the calculation formula of the revenue indicator factor of the capacity - regulation service.

[0028] In this embodiment, the basic information of each building is shown in Table 2 below; Table 2

[0029] S3. In the real - time regulation stage, based on the real - time regulation signal of the power grid, each building calculates the required energy consumption of the air - conditioning cold - station system according to its own reference energy consumption and response capacity.

[0030] ; Among them, is the required energy consumption of the building air - conditioning cold - station system under the power - grid regulation signal, is the regulation signal of the power grid, and its range is [-1, 1], which is given by the superior power grid in each real - time regulation period, and They are the benchmark energy consumption and the response capacity value within the corresponding time periods, respectively.

[0031] S4. Based on the energy consumption requirements of each building, use the equipment operation parameter optimization model to adjust the equipment operation parameters of the air-conditioning chilled water system of each building in real time.

[0032] In this embodiment, the equipment operation parameter optimization model includes: (Equation VI); (Equation VII); (Equation VIII); Wherein, is the actual operation energy consumption of the air-conditioning chilled water station, is the weight coefficient between the operation energy consumption and the thermal comfort, is the actual value of the indoor temperature to be optimized, is the set value of the indoor temperature, , and are the operation energy consumptions of the th chiller, chilled water pump and air handling unit, respectively, , and are the numbers of the chiller, chilled water pump and air handling unit, respectively, and are the constant pressure specific heat capacities on the water side and the air side, respectively, and are the flow rates on the water side and the air side, respectively, is the chilled water outlet temperature, and are the chilled water return temperature value and the air handling unit supply air temperature value at the current moment, respectively.

[0033] Equation VI represents the optimization objective of the air-conditioning chilled water station in the real-time adjustment stage, including minimizing the deviation between the actual energy consumption and the required energy consumption and the deviation between the actual indoor temperature and the set indoor temperature; Equation VII describes the calculation formula of the actual energy consumption of the air-conditioning chilled water station; Equation VIII describes the calculation formula of the indoor temperature of the building, which is determined by the energy conservation relationship between the water side and the air side.

[0034] In some specific embodiments, the main control parameters considered include the chilled water outlet temperature , the chilled water pump frequency and the air handling unit supply fan frequency , then the equipment operation parameter optimization model includes: ; ; ; Among them, is the actual operating energy consumption of the air-conditioning chilled water station, including three parts: the chiller, the chilled water pump, and the air handling unit. is the set value of the indoor temperature, which is uniformly taken as 26 °C in this embodiment. , and are the rated energy consumptions of the chiller, the chilled water pump, and the air handling unit respectively. is the undetermined coefficient of the chiller. is the rated frequency of the pump and the fan, which is taken as 50 Hz in this embodiment. and are the specific heat capacities at constant pressure on the water side and the air side respectively. and are the rated flow rates of the chilled water pump and the air handling unit respectively. and are the return water temperature value of the chilled water and the supply air temperature value of the air handling unit at the current adjustment moment respectively.

[0035] To verify the optimized regulation performance of this embodiment, a traditional independent regulation method for individual buildings is set for verification. This method does not consider the scenario where building clusters participate in demand response together, and only conducts independent operation regulation for each individual building. The demand response period is 8:00 - 22:00 ( take 14), the time interval in the day-ahead planning stage is 1 h, and the time interval in the real-time adjustment stage is 5 min, that is, the AGC frequency modulation signal of the power grid changes every five minutes, and the minimum threshold for obtaining the capacity regulation service revenue is 500 kW.

[0036] The total operating costs of each building in the building cluster participating in demand response under different methods are as Figure 3 shown. Since each building is independently regulated under the traditional independent regulation method and cannot reach the minimum threshold for demand response market access, each building cannot obtain revenue by providing demand response services, and the daily operating total cost is 10,527.5 yuan. In contrast, as Figure 4 shown, this embodiment optimizes the response capacity of each building through hierarchical optimized regulation, and can reach the market access threshold in most periods to obtain revenue by providing demand response services. The daily operating total cost is 8,731.1 yuan, a decrease of 17.1%.

[0037] The hourly average power following deviation and indoor temperature deviation results of each building in the real-time adjustment stage under this embodiment are as Figure 5As shown. The results show that the method of the present invention can enable each building to stably follow the energy regulation requirements of the power grid in real time, and its maximum average following deviation is less than 3%. At the same time, when each building participates in demand-side response, the maximum deviation between the actual indoor temperature and the reference temperature is less than 4.0 K, and the indoor temperature can be maintained within 28.0 °C, so as to realize the real-time optimal operation of the air-conditioning chilled water system while ensuring the thermal comfort of end-users.

[0038] Embodiment 2: This embodiment is an optimized control device for an air-conditioning chilled water system of a building cluster to participate in demand-side response, including: A reference energy consumption prediction module, which is used to determine the reference energy consumption of the air-conditioning chilled water system of each building in the building cluster at each moment within a preset future time period based on historical operation data; A response capacity solving module, which is used to establish an optimization model for the demand-side response capacity of the air-conditioning chilled water system of the building cluster and solve the optimal response capacity of each building; A demand energy consumption calculation module, which is used to calculate the demand energy consumption of the air-conditioning chilled water system based on the real-time regulation signal of the power grid, and each building calculates according to its own reference energy consumption and response capacity; An operation parameter adjustment module, which is used to adjust the equipment operation parameters of the air-conditioning chilled water system of each building in real time based on the demand energy consumption of each building.

[0039] In this embodiment, the optimization model for the demand-side response capacity includes: The overall optimization objective of the building cluster, including the power purchase cost of the building cluster from the power grid, the income from providing capacity regulation services, and the thermal comfort of building users; The building indoor temperature balance equation, which describes the change of indoor temperature with the cooling capacity and building heat gain; The demand-side response capacity constraint of the air-conditioning chilled water system of the building, and the upper limit of the response capacity is the minimum value of the deviation between the actual energy consumption of the air-conditioning chilled water system and the upper limit of the operation energy consumption, the deviation between the lower limit of the operation energy consumption, and the deviation of the reference energy consumption; Embodiment 3: This embodiment is a storage medium, on which a computer program executable by a processor is stored, and when the computer program is executed, the steps of the optimized control method for the air-conditioning chilled water system of the building cluster to participate in demand-side response described in Embodiment 1 are realized.

[0040] Embodiment 4: This embodiment is an optimized control device for an air-conditioning chilled water system of a building cluster to participate in demand-side response, which has a memory and a processor. A computer program executable by the processor is stored on the memory, and when the computer program is executed, the steps of the optimized control method for the air-conditioning chilled water system of the building cluster to participate in demand-side response described in Embodiment 1 are realized.

[0041] In addition, although the present invention has been described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the above-described functions and / or features may be integrated in a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It should also be understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present invention. Rather, given the attributes, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the modules will be understood within the ordinary skills of an engineer. Thus, those skilled in the art can implement the present invention as set forth in the claims without undue experimentation. It should also be understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0042] If the above functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the above methods of the various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0043] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0044] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable media can even be paper or other suitable media on which the above programs can be printed, because the above programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, deciphering, or otherwise processing as appropriate, and then storing them in a computer memory.

[0045] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0046] In the above description of this specification, the descriptions referring to the terms "one embodiment / example", "another embodiment / example", or "certain embodiments / examples", etc., mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0047] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the claims and their equivalents.

[0048] The above has specifically described the preferred embodiments of the present invention, but the present invention is not limited to the above embodiments. Those skilled in the art can also make various equivalent deformations or substitutions without departing from the spirit of the present invention, and these equivalent deformations or substitutions are all included in the scope defined by the claims of this application.

Claims

1. An optimization control method for building cluster air conditioning and cooling plant systems participating in demand-side response, characterized in that: include: S1. Based on historical operation data, determine the baseline energy consumption of the air conditioning and cooling station systems of each building in the building cluster at each time in the future preset time period; S2. Establish a demand-side response capacity optimization model for the building cluster air conditioning and cooling station system to obtain the optimal response capacity of each building; The demand side response capacity optimization model includes: The overall optimization goal of the building cluster includes the cost of purchasing electricity from the building cluster grid, the revenue from providing capacity regulation services, and the thermal comfort of building users; The building indoor temperature balance equation describes the change of indoor temperature with cooling supply and building heat gain; The demand-side response capacity constraint of the building air conditioning and cooling station system, the upper limit of the response capacity is the minimum value of the deviation between the actual energy consumption of the air conditioning and cooling station system and the upper limit of the operating energy consumption, the lower limit of the operating energy consumption and the benchmark energy consumption deviation; S3. Based on the real-time regulation signal of the power grid, each building calculates the required energy consumption of the air conditioning and cooling system according to its own benchmark energy consumption and response capacity; S4. Based on the energy demand of each building, the equipment operating parameters of the air conditioning and cooling station system of each building are adjusted in real time.

2. The optimization control method for building cluster air conditioning and cooling plant systems participating in demand-side response according to claim 1 is characterized in that: The demand side response capacity optimization model includes: ； ； ； ； ； in, is the total optimization time, and They are Total energy consumption and total response capacity of the building cluster at all times, The price of electricity purchased from the grid, The revenue per unit of capacity for providing capacity regulation services is: is the number of buildings in the building cluster, and They are Moment Architecture energy consumption and response capacity, is an indicator factor for whether the capacity regulation service benefits can be obtained. To obtain the minimum threshold of capacity regulation service benefits, For Architecture The thermal comfort weight coefficient is for Moment Architecture The indoor temperature value, for Moment Architecture The reference value of indoor temperature, and Building The thermal capacitance and thermal resistance, For chillers The performance coefficient, and They are Moment Architecture The predicted values ​​of outdoor temperature and heat input, To find the optimal interval, To obtain the minimum function, and Building The upper and lower limits of energy consumption for air conditioning cooling stations, is the benchmark energy consumption value of building n at time t.

3. The optimization control method for building cluster air conditioning and cooling plant systems participating in demand-side response according to claim 1 is characterized in that: Based on the real-time regulation signal of the power grid, each building calculates the required energy consumption of the air conditioning and cooling station system according to its own benchmark energy consumption and response capacity, including: ； in, It is the energy demand of the building air conditioning cooling system under the grid regulation signal. is the regulation signal of the power grid, and its range is [-1, 1], and are the baseline energy consumption and response capacity values ​​in the corresponding time period respectively.

4. The optimization control method for building cluster air conditioning and cooling plant systems participating in demand-side response according to claim 1 is characterized in that: The real-time adjustment of the equipment operating parameters of the air conditioning and cooling station systems of each building based on the energy demand of each building includes: Adopt the equipment operation parameter optimization model to determine the equipment operation parameters of each building's air conditioning and cooling station system; The device operation parameter optimization model includes: The optimization goal of the air conditioning cooling station in the real-time adjustment stage includes minimizing the deviation between the actual energy consumption and the required energy consumption, and the deviation between the actual indoor temperature and the indoor set temperature. The calculation formula for the actual energy used by the air conditioning cooling station; The calculation formula for the indoor temperature of a building is determined by the energy conservation relationship between the water side and the air side.

5. The optimization control method for building cluster air conditioning and cooling plant systems participating in demand-side response according to claim 4 is characterized in that: The device operation parameter optimization model includes: ； ； ； in, is the actual operating energy consumption of the air conditioning cooling station, is the weight coefficient between operating energy consumption and thermal comfort, is the actual value of the indoor temperature to be optimized, is the set value of the indoor temperature, , and Respectively The operating energy consumption of each chiller, chilled water pump and air handling unit is , and are the number of chillers, chilled water pumps and air handling units, respectively. and are the constant pressure specific heat capacities of the water side and the air side, and are the flow rates on the water side and the wind side, respectively. is the chilled water outlet temperature, and They are respectively the chilled water return temperature value and the air supply temperature value of the air handling unit at the current moment.

6. An optimization control device for a building cluster air conditioning and cooling station system participating in demand-side response, characterized in that: include: The benchmark energy consumption prediction module is used to determine the benchmark energy consumption of the air conditioning and cooling plant systems of each building in the building cluster at each moment in the future preset time period based on historical operation data; The response capacity solution module is used to establish a demand-side response capacity optimization model for the building cluster air conditioning and cooling plant system, and solve the optimal response capacity of each building; The demand side response capacity optimization model includes: The overall optimization goal of the building cluster includes the cost of purchasing electricity from the building cluster grid, the revenue from providing capacity regulation services, and the thermal comfort of building users; The building indoor temperature balance equation describes the change of indoor temperature with cooling supply and building heat gain; The demand-side response capacity constraint of the building air conditioning and cooling station system, the upper limit of the response capacity is the minimum value of the deviation between the actual energy consumption of the air conditioning and cooling station system and the upper limit of the operating energy consumption, the lower limit of the operating energy consumption and the benchmark energy consumption deviation; The energy demand calculation module is used to calculate the energy demand of the air conditioning and cooling system based on the real-time regulation signal of the power grid. Each building calculates the energy demand of the air conditioning and cooling system according to its own benchmark energy consumption and response capacity. The operating parameter adjustment module is used to adjust the equipment operating parameters of the air conditioning and cooling system of each building in real time based on the energy demand of each building.

7. A storage medium having stored thereon a computer program executable by a processor, characterized in that: When the computer program is executed, the steps of the method for optimizing and controlling the building cluster air conditioning and cooling station system participating in demand-side response as described in any one of claims 1 to 5 are implemented.

8. An optimization control device for a building cluster air conditioning and cooling station system participating in demand-side response, comprising a memory and a processor, wherein a computer program executable by the processor is stored in the memory, characterized in that: When the computer program is executed, the steps of the method for optimizing and controlling the building cluster air conditioning and cooling station system participating in demand-side response as described in any one of claims 1 to 5 are implemented.

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