Optimal control method for building cluster air conditioning and cooling station systems participating in demand-side response

Abstract

The present invention relates to an optimization and control method for a building cluster air-conditioning and cooling station system participating in demand-side response, which is applicable to the field of demand-side response control of building clusters. The method comprises: S1, based on historical operating data, determining the baseline energy consumption of the air-conditioning and cooling station system of each building in the building cluster at each moment in a preset time period in the future; S2, establishing a demand-side response capacity optimization model for the building cluster air-conditioning and cooling station system, and solving to obtain the optimal response capacity of each building; S3, based on the real-time adjustment signal of the power grid, each building calculates the required energy consumption of the air-conditioning and cooling station system according to its own baseline energy consumption and response capacity; S4, based on the required energy consumption of each building, adjusting the equipment operating parameters of the air-conditioning and cooling station system of each building in real time. The present invention adopts a hierarchical framework of day-ahead planning and real-time adjustment to realize demand-side response management and online control of the building cluster air-conditioning and cooling station system.

Description

Technical Field

[0001] The present invention relates to an optimization control method for building cluster air conditioning and cooling plant systems participating in demand-side response, and is applicable to the field of building cluster demand-side response control. Background Art

[0002] By investigating existing research in the field, including an optimization control method for demand-side response of central air-conditioning cooling sources (Chinese invention patent, publication number: CN115235046B), an air-conditioning optimization control strategy method for demand-side response (Chinese invention patent, publication number: CN117212977A), and a control process for central air-conditioning systems participating in demand-side response of power systems (Chinese invention patent, publication number: CN114640103A), the above methods all achieve demand-side response regulation for air-conditioning cooling station systems in single buildings.

[0003] Some literature has conducted research on the participation of building cluster air-conditioning load in demand-side response scheduling, such as a method and system for regulating user-side air-conditioning load in response to demand (Chinese invention patent, publication number: CN118729485A), a user-side resource regulation method based on demand response (Chinese invention patent, publication number: CN117077919A), a cluster air-conditioning collaborative control method, device and storage medium (Chinese invention patent, publication number: CN117870079A), and a building cluster demand-side energy management method and system (Chinese invention patent, publication number: CN112465212A).

[0004] However, most of these methods are only applicable to single air conditioning systems in residential building clusters, and research on demand-side response control for central air conditioning and cooling plants in public building clusters remains lacking. Furthermore, existing research only considers demand-side response control for building clusters at the day-ahead planning level. Further research is needed to determine how to implement real-time operational control of building cluster air conditioning and cooling plants based on demand-side response requirements during the day-ahead planning phase. Summary of the Invention

[0005] The technical problem to be solved by the present invention is: in response to the above-mentioned problems, a method for optimizing and controlling the building cluster air-conditioning and cooling station system to participate in demand-side response is provided.

[0006] The technical solution adopted by the present invention is: a method for optimizing and controlling a building cluster air conditioning and cooling station system participating in demand-side response, comprising:

[0007] S1. Based on historical operating data, determine the baseline energy consumption of the air conditioning and cooling system of each building in the building cluster at each time in the future preset time period;

[0008] S2. Establish a demand-side response capacity optimization model for the building cluster air conditioning and cooling plant system to obtain the optimal response capacity of each building;

[0009] The demand-side response capacity optimization model includes:

[0010] The overall optimization goal of the building cluster includes the cost of purchasing electricity from the grid, the revenue from providing capacity regulation services, and the thermal comfort of building users;

[0011] The building indoor temperature balance equation describes the changes in indoor temperature with cooling supply and building heat gain;

[0012] The demand-side response capacity constraint of the building air conditioning and cooling station system is defined as follows: 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 baseline energy consumption deviation;

[0013] S3. Based on the real-time regulation signal from the power grid, each building calculates the required energy consumption of the air conditioning and cooling system according to its own baseline energy consumption and response capacity;

[0014] 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.

[0015] The demand-side response capacity optimization model includes:

[0016] ;

[0017] ;

[0018] ;

[0019] ;

[0020] ;

[0021] in, is the total optimization time, and They are Total energy consumption and total response capacity of the building cluster at any moment, 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 value of capacity regulation service benefits, For buildings 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.

[0022] Based on the real-time regulation signal from the power grid, each building calculates the required energy consumption of the air conditioning and cooling system according to its own baseline energy consumption and response capacity, including:

[0023] ;

[0024] in, 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.

[0025] The real-time adjustment of the equipment operating parameters of the air conditioning and cooling plant systems of each building based on the energy demand of each building includes:

[0026] Adopting the equipment operation parameter optimization model to determine the equipment operation parameters of each building's air conditioning and cooling station system;

[0027] The device operation parameter optimization model includes:

[0028] 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, as well as the deviation between the actual indoor temperature and the indoor set temperature.

[0029] Calculation formula for actual energy consumption of air conditioning cooling station;

[0030] 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.

[0031] The device operation parameter optimization model includes:

[0032] ;

[0033] ;

[0034] ;

[0035] 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, 、 and are the number of chillers, chilled water pumps and air handling units, 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 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.

[0036] An optimization and control device for a building cluster air conditioning and cooling station system participating in demand-side response, comprising:

[0037] The baseline energy consumption prediction module is used to determine the baseline energy consumption of the air conditioning and cooling system of each building in the building cluster at each moment in the future preset time period based on historical operating data;

[0038] 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;

[0039] The demand-side response capacity optimization model includes:

[0040] The overall optimization goal of the building cluster includes the cost of purchasing electricity from the grid, the revenue from providing capacity regulation services, and the thermal comfort of building users;

[0041] The building indoor temperature balance equation describes the changes in indoor temperature with cooling supply and building heat gain;

[0042] The demand-side response capacity constraint of the building air conditioning and cooling station system is defined as follows: 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 baseline energy consumption deviation;

[0043] 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 baseline energy consumption and response capacity.

[0044] 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.

[0045] A storage medium stores a computer program executable by a processor, wherein 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 are implemented.

[0046] A device for optimizing and controlling a building cluster air-conditioning and cooling station system participating in demand-side response comprises a memory and a processor. The memory stores a computer program executable by the processor. When the computer program is executed, the steps of a method for optimizing and controlling the building cluster air-conditioning and cooling station system participating in demand-side response are implemented.

[0047] The present invention achieves the following beneficial effects: It utilizes a hierarchical framework of day-ahead planning and real-time adjustment to implement demand-side response management and online control of building cluster air conditioning and cooling plant systems. In the upper-level day-ahead planning phase, the building cluster determines the optimal response capacity by solving a demand-side response capacity optimization model to maximize the benefits of participating in demand-side response services. In the lower-level real-time adjustment phase, each building adjusts the operating parameters of its air conditioning and cooling plant equipment online based on the response capacity determined in the day-ahead planning phase and real-time grid regulation signals, achieving real-time optimized operation of the air conditioning and cooling plant system. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 Schematic diagram of a building cluster air conditioning and cooling station system in an embodiment.

[0049] Figure 2 Flowchart of the method for optimizing and controlling the building cluster air conditioning and cooling plant system in the embodiment.

[0050] Figure 3 Comparison of the total operating costs of each building participating in demand-side response in the embodiment.

[0051] Figure 4 4 is a comparison of the optimal response capacity results of each building in the embodiment.

[0052] Figure 5 are the real-time power following deviation and indoor temperature deviation of each building in the embodiment. DETAILED DESCRIPTION

[0053] The present invention will be further explained and illustrated below in conjunction with the accompanying drawings and embodiments. It should be noted that the embodiments described below are intended to facilitate understanding of the present invention and do not have any limiting effect on it.

[0054] Example 1: The building cluster air conditioning cooling station system of this embodiment is as follows Figure 1 As shown, the building cluster includes six public buildings, each of which is cooled by an air-conditioning cooling station. Each air-conditioning cooling station consists of a chiller, a chilled water pump and an air handling unit.

[0055] like Figure 2 As shown, this embodiment is a method for optimizing and controlling a building cluster air conditioning and cooling plant system participating in demand-side response, specifically comprising the following steps:

[0056] S1. In the day-ahead planning stage, based on historical operating data, the hourly benchmark energy consumption of the air conditioning and cooling system of each building in the building cluster for the next day is calculated. Make predictions.

[0057] This embodiment uses an artificial neural network to train historical operation data, and the model parameters are set as shown in Table 1 below:

[0058] Table 1

[0059]

[0060] Among them, the building's benchmark energy consumption ( , ,..., ) can be directly obtained by collecting the energy consumption data of the normal operation of the system in the non-demand side response scenario, and the outdoor temperature ( ) and outdoor relative humidity ( ) data can be collected through outdoor temperature and humidity sensors.

[0061] 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.

[0062] The demand-side response capacity optimization model in this embodiment includes:

[0063] (Formula 1)

[0064] (Formula 2)

[0065] (Formula 3)

[0066] (Formula 4)

[0067] (Formula 5)

[0068] in, is the total optimization time, and They are Total energy consumption and total response capacity of the building cluster at any moment, 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 value of capacity regulation service benefits, For buildings 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 outdoor temperature and heat input prediction values are obtained by least square fitting the historical data using a polynomial function in this embodiment. 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.

[0069] Equation 1 above describes the overall optimization goal of the building cluster, which is to maximize the total benefits of the building cluster by providing capacity regulation services, including reducing the grid electricity purchase cost of the building cluster, increasing the benefits of providing capacity regulation services, and minimizing the baseline temperature deviation to ensure the thermal comfort of building users. Equation 2 represents the building indoor temperature balance equation based on the thermal capacitance-thermal resistance model, which describes the changes in indoor temperature with factors such as cooling capacity and building heat gain. Equation 3 represents 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 rated energy consumption, the minimum operating energy consumption deviation, and the baseline energy consumption deviation. Equation 4 describes the calculation formula for the total energy consumption and total response capacity of the building cluster. Equation 5 describes the calculation formula for the capacity regulation service benefit indicator factor.

[0070] In this embodiment, the basic information of each building is shown in Table 2 below;

[0071] Table 2

[0072]

[0073] S3. In the real-time adjustment stage, based on the real-time adjustment 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.

[0074] ;

[0075] in, The energy demand of the building air conditioning cooling system under the grid regulation signal, is the regulation signal of the power grid, which ranges from [-1, 1] and is given by the upper power grid in each real-time regulation period. and are the baseline energy consumption and response capacity values in the corresponding time period respectively.

[0076] S4. Based on the energy demand of each building, the equipment operation parameter optimization model is used to adjust the equipment operation parameters of the air conditioning and cooling station system of each building in real time.

[0077] In this embodiment, the device operating parameter optimization model includes:

[0078] (Formula 6)

[0079] (Formula 7)

[0080] (Form 8)

[0081] 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, 、 and are the number of chillers, chilled water pumps and air handling units, 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 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.

[0082] Equation 6 represents the optimization objective for the air conditioning cooling station during the real-time adjustment phase, which consists of minimizing the deviation between actual energy usage and required energy usage, as well as the deviation between the actual indoor temperature and the set indoor temperature. Equation 7 describes the formula for calculating the actual energy usage of the air conditioning cooling station. Equation 8 describes the formula for calculating the indoor temperature of the building, which is determined by the energy conservation relationship between the water side and the air side.

[0083] In some specific embodiments, the main control parameters to be considered include the chilled water outlet temperature , chilled water pump frequency and air handling unit blower frequency , then the equipment operation parameter optimization model includes:

[0084] ;

[0085] ;

[0086] ;

[0087] in, The actual operating energy consumption of the air conditioning cooling station includes three parts: chiller, chilled water pump and air handling unit. is the indoor temperature setting value, which is uniformly set to 26°C in this embodiment. 、 and are the rated energy consumption of chiller, chilled water pump and air handling unit respectively, is the undetermined coefficient of the chiller, is the rated frequency of the water pump and fan, which is 50 Hz in this embodiment. and are the constant pressure specific heat capacities of the water side and the air side, and are the rated flow rates of the chilled water pump and air handling unit respectively, and They are respectively the chilled water return temperature value and the air supply temperature value of the air handling unit at the current adjustment moment.

[0088] To verify the optimized control performance of this embodiment, a traditional independent control method for a single building is set for verification. This method does not consider the scenario where a cluster of buildings participates in the demand-side response, and only performs independent operation control on each single building. The demand-side response period is 8:00-22:00 ( Take 14), the time interval of the day-ahead planning stage The minimum threshold for obtaining capacity regulation service benefits is 1 hour, and the time interval of the real-time regulation phase is 5 minutes, that is, the grid AGC frequency regulation signal changes every five minutes. is 500 kW.

[0089] The total operating cost of each building in the building cluster participating in demand-side response under different methods is as follows: Figure 3 As shown in the figure. Since each building is regulated independently under the traditional independent regulation method, it is impossible to reach the minimum threshold for demand-side response market access. Therefore, each building cannot obtain benefits by providing demand-side response services, and the total daily operating cost is 10,527.5 yuan. In contrast, Figure 4 As shown, this embodiment optimizes the response capacity of each building through hierarchical optimization and control, and can reach the market access threshold in most time periods, thereby obtaining revenue by providing demand-side response services. The total daily operating cost is 8,731.1 yuan, a reduction of 17.1%.

[0090] In this embodiment, the hourly average power following deviation and indoor temperature deviation of each building in the real-time adjustment stage are as follows: Figure 5 Results show that the proposed method enables each building to stably and in real time follow the grid's energy regulation needs, with a maximum average tracking deviation of less than 3%. Furthermore, when participating in demand-side response, the maximum deviation between the actual indoor temperature and the reference temperature in each building is less than 4.0 K, maintaining the indoor temperature within 28.0°C. This enables real-time optimized operation of the air conditioning and cooling station system while ensuring thermal comfort for end users.

[0091] Example 2: This example is an optimization and control device for a building cluster air conditioning and cooling plant system participating in demand-side response, including:

[0092] The baseline energy consumption prediction module is used to determine the baseline energy consumption of the air conditioning and cooling system of each building in the building cluster at each moment in the future preset time period based on historical operating data;

[0093] 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;

[0094] 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 based on its own baseline energy consumption and response capacity.

[0095] 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.

[0096] The demand-side response capacity optimization model in this embodiment includes:

[0097] The overall optimization goal of the building cluster includes the cost of purchasing electricity from the grid, the revenue from providing capacity regulation services, and the thermal comfort of building users;

[0098] The building indoor temperature balance equation describes the change of indoor temperature with cooling supply and building heat gain;

[0099] The demand-side response capacity constraint of the building air conditioning and cooling station system is defined as follows: 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 baseline energy consumption deviation;

[0100] Example 3: This example is a storage medium on which a computer program that can be executed by a processor is stored. When the computer program is executed, the steps of the optimization and control method for the building cluster air-conditioning and cooling station system participating in demand-side response described in Example 1 are implemented.

[0101] Example 4: This example is an optimization and control device for a building cluster air-conditioning and cooling station system participating in demand-side response, comprising a memory and a processor. The memory stores a computer program that can be executed by the processor. When the computer program is executed, the steps of the optimization and control method for a building cluster air-conditioning and cooling station system participating in demand-side response described in Example 1 are implemented.

[0102] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the above-mentioned functions and / or features can be integrated into a single physical device and / or software module, or one or more functions and / or features can be implemented in separate physical devices or software modules. It is also understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present invention. More specifically, given the properties, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the module will be understood within the routine skills of an engineer. Therefore, a person skilled in the art can implement the present invention set forth in the claims using ordinary skills without undue experimentation. It is also 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.

[0103] If the above functions are implemented as software functional 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, or the portion that contributes to the prior art, or a portion of the 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 can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the above methods in various embodiments of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0104] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For 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 conjunction with, an instruction execution system, apparatus, or device.

[0105] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable media on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other media, and then editing, interpreting, or processing it in another suitable manner as necessary, and then storing it in a computer memory.

[0106] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0107] In the above description of this specification, reference to the terms "one embodiment / example," "another embodiment / example," or "certain embodiments / examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0108] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

[0109] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A method for optimizing and controlling building cluster air conditioning and cooling plant systems participating in demand-side response, characterized in that: include: S1. Based on historical operating data, determine the baseline energy consumption of the air conditioning and cooling system 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 plant 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 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 is defined as follows: 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 baseline energy consumption deviation; S3. Based on the real-time regulation signal from the power grid, each building calculates the required energy consumption of the air conditioning and cooling system according to its own baseline energy consumption and response capacity; S4. Based on the energy demand of each building, the equipment operating parameters of the air conditioning and cooling system of each building are adjusted in real time; 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 any moment, 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 value of capacity regulation service benefits, For buildings 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.

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: Based on the real-time regulation signal from the power grid, each building calculates the required energy consumption of the air conditioning and cooling system according to its own baseline energy consumption and response capacity, including: ； in, 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.

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: The real-time adjustment of the equipment operating parameters of the air conditioning and cooling plant systems of each building based on the energy demand of each building includes: Adopting 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, as well as the deviation between the actual indoor temperature and the indoor set temperature. Calculation formula for actual energy consumption of 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.

4. The optimization control method for building cluster air conditioning and cooling plant systems participating in demand-side response according to claim 3 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, 、 and are the number of chillers, chilled water pumps and air handling units, 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 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.

5. An optimization and control device for a building cluster air conditioning and cooling station system participating in demand-side response, characterized in that: include: The baseline energy consumption prediction module is used to determine the baseline energy consumption of the air conditioning and cooling system of each building in the building cluster at each moment in the future preset time period based on historical operating 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 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 is defined as follows: 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 baseline 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 based on its own baseline 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; 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 any moment, 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 value of capacity regulation service benefits, For buildings 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.

6. 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 4 are implemented.

7. An optimization and control device for a building cluster air conditioning and cooling plant system participating in demand-side response, comprising a memory and a processor, wherein the memory stores a computer program executable by the 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 4 are implemented.

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