Building cluster demand response coordinated multi-objective optimization scheduling method and system

Abstract

The invention discloses a building cluster demand response coordinated multi-objective optimization scheduling method and system, and the method comprises the steps: predicting the energy demand and supply information of a building cluster, and building an energy transaction model of a building unit and the cluster based on renewable energy supply and battery storage electric energy; the building cluster is integrated into a single entity, the energy supply of the single entity is the total energy supply of all buildings, the energy demand is the total energy demand of all buildings, the battery capacity is the total battery capacity of all buildings, and the energy transaction model takes a cost function of the whole cluster as a target function; and optimizing the overall cost function of the building cluster through an iterative demand response algorithm, calculating to obtain an optimal operation parameter, controlling the operation of each building unit according to the optimal operation parameter of the cluster by using a nonlinear programming algorithm, and carrying out multi-target optimization scheduling on the building cluster. Flexible resources in the building cluster can be fully utilized, and efficient utilization and supply and demand balance of energy are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of intersection between smart grid and building energy efficiency management, and in particular to a method and system for coordinated multi-objective optimization scheduling of building cluster demand response. Background Art

[0002] With the acceleration of urbanization and the continuous growth of building energy consumption, building clusters have become one of the main areas of energy consumption. The traditional energy management model is difficult to cope with the increasingly complex energy supply and demand relationship and diversified user needs. At the same time, the development of smart grids provides building clusters with more flexible and efficient energy management and scheduling methods. The coordinated multi-objective optimization scheduling strategy considering the demand response of building clusters can make full use of the flexibility resources within the building cluster to achieve efficient use of energy and balance supply and demand. Summary of the invention

[0003] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a building cluster demand response coordination multi-objective optimization scheduling method and system to solve the problems mentioned in the background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a method for coordinated multi-objective optimization scheduling of a building cluster demand response, including: predicting energy demand and supply information of a building cluster, establishing an energy trading model between building units and clusters based on renewable energy supply and battery storage of electrical energy;

[0008] The building cluster is integrated into a single entity, the energy supply of the single entity is the total energy supply of all buildings, the energy demand is the total energy demand of all buildings, the battery capacity is the total battery capacity of all buildings, and the energy trading model uses the cost function of the entire cluster as the objective function;

[0009] The overall cost function of the building cluster is optimized through an iterative demand response algorithm, the optimal operating parameters are calculated, and the operation of each building unit is controlled according to the optimal operating parameters of the cluster using a nonlinear programming algorithm to perform multi-objective optimization scheduling on the building cluster.

[0010] As a preferred solution of the building cluster demand response coordinated multi-objective optimization scheduling method of the present invention, the predicted energy demand and supply information of the building cluster includes:

[0011] The next day's energy demand and supply of the building cluster are expressed as:

[0012]

[0013]

[0014] in, is the energy demand of the jth building at the i-th hour, is the energy supply of the jth building at the i-th hour, and N is the number of buildings in the cluster.

[0015] As a preferred solution of the building cluster demand response coordinated multi-objective optimization scheduling method described in the present invention, the energy trading model takes the cost function of the entire cluster as the objective function, including: converting the multi-objective optimization problem of multiple individuals into the optimization problem of the entire cluster, and taking the operation cost of the entire cluster as the objective function to express it as:

[0016]

[0017]

[0018] in, is the cost function of the entire building cluster in period i, C co,k is the total cost function of the entire building cluster on that day, M is the quadratic coefficient matrix, Q is the linear coefficient matrix, a, b, c are the coefficients of the cost model, is the energy transaction value of the entire building cluster in period i, is the energy transaction value of the entire building cluster, and T is the matrix transpose.

[0019] As a preferred solution of the multi-objective optimization scheduling method for building cluster demand response coordination described in the present invention, wherein: the cost function of the entire building cluster is optimized by an iterative demand response algorithm, and the optimal operating parameters are calculated to include:

[0020] In each time period t∈{1,2,...,24}, when the charging rate of the battery in the building cluster is less than the remaining storage capacity of the battery, and the discharging rate is less than the total energy stored in the battery, the operation of the battery in the building cluster satisfies the constraints expressed as:

[0021]

[0022] in, is the energy stored in the battery at the initial state, CP cois the battery capacity of the entire building cluster, I 24 Represents a 24-dimensional column vector with all elements being 1. The size of the matrix A is 24*24, all elements on the diagonal and lower triangle are 1, and the rest are 0. is the charging rate of the batteries in the building cluster.

[0023] As a preferred solution of the multi-objective optimization scheduling method for building cluster demand response coordination described in the present invention, it also includes: the control variables to be constrained in the iteration process The rate of change is expressed as:

[0024]

[0025] Wherein, d is the preset limit value;

[0026] The convergence condition is expressed as:

[0027] |C co,k -C co,k-1 |≤ε

[0028] Among them, ε is the user-defined threshold;

[0029] The optimal solution of battery charging rate and energy trading of the entire building cluster is obtained by iterative calculation: and

[0030]

[0031] As a preferred solution of the building cluster demand response coordinated multi-objective optimization scheduling method described in the present invention, wherein: using a nonlinear programming algorithm to control the operation of each building unit according to the optimal operation parameters of the cluster, the multi-objective optimization scheduling of the building cluster includes: controlling the battery charging rate of a single building unit with one cycle per hour and energy trading

[0032] At the i-th hour, the input to the nonlinear programming algorithm is the energy demand of a single building unit Energy supply Battery charging rate and energy trading and the optimal battery charging rate for the building cluster as a whole Energy Trading

[0033] As a preferred solution of the building cluster demand response coordinated multi-objective optimization scheduling method described in the present invention, it also includes: for a cluster consisting of N buildings, there are 2N unknown parameters to be solved, there are N+2 equality constraints and 2N inequality constraints; the output of the nonlinear programming algorithm is each building unit in each hour and It is expressed as:

[0034]

[0035]

[0036]

[0037] in, is the battery charging rate of the jth building unit at the mth hour.

[0038] In a second aspect, the present invention provides a building cluster demand response coordination multi-objective optimization scheduling system, comprising:

[0039] A model building module is used to predict the energy demand and supply information of the building cluster, and to establish an energy trading model between the building unit and the cluster based on renewable energy supply and battery storage. The energy trading model uses the cost function of each building unit as the objective function;

[0040] An integration module, used for integrating the building cluster into a single entity, wherein the energy supply of the single entity is the total energy supply of all buildings, the energy demand is the total energy demand of all buildings, and the battery capacity is the total battery capacity of all buildings;

[0041] The control module is used to optimize the overall cost function of the building cluster through an iterative demand response algorithm, calculate the optimal operating parameters, use a nonlinear programming algorithm to control the operation of each building unit according to the optimal operating parameters of the cluster, and perform multi-objective optimization scheduling on the building cluster.

[0042] In a third aspect, the present invention provides a computing device, comprising:

[0043] Memory and processor;

[0044] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the building cluster demand response coordination multi-objective optimization scheduling method are implemented.

[0045] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the building cluster demand response coordinated multi-objective optimization scheduling method.

[0046] Compared with the prior art, the present invention has the following beneficial effects: the present invention considers the prediction of energy demand and supply of building clusters and the battery charging rate to model energy trading. For dynamic pricing, the cost of the entire building cluster is considered as the objective function, and the complex multi-objective optimization problem is converted into a single-objective optimization. A collaborative demand response control strategy for the building cluster is proposed. The building cluster is regarded as a whole, and the iterative demand response control algorithm is only applied to the building cluster as a whole to respond to the dynamic pricing of cluster-level performance improvement and determine the best performance in response to dynamic pricing. On the basis of determining the optimal performance parameters of the cluster as a whole, it is considered as a constraint condition that each building unit needs to meet, and a nonlinear programming method is used to coordinate the operation of each building to achieve collaborative scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0048] Figure 1 A collaborative demand response control flow chart of building cluster response dynamic pricing of a building cluster demand response coordinated multi-objective optimization scheduling method and system according to an embodiment of the present invention;

[0049] Figure 2 A schematic diagram of a building unit energy model of a building cluster demand response coordinated multi-objective optimization scheduling method and system according to an embodiment of the present invention;

[0050] Figure 3 A control principle diagram of non-cooperative demand response control of a building cluster demand response coordinated multi-objective optimization scheduling method and system according to an embodiment of the present invention;

[0051] Figure 4 A control principle diagram of a coordinated demand response control method and system for building cluster demand response coordination multi-objective optimization scheduling according to an embodiment of the present invention;

[0052] Figure 5 A result diagram of energy flow under non-cooperative demand response control of a building cluster demand response coordinated multi-objective optimization scheduling method and system according to an embodiment of the present invention;

[0053] Figure 6 This is a result diagram of energy flow under collaborative demand response control of a building cluster demand response coordinated multi-objective optimization scheduling method and system described in an embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0055] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0056] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0057] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0058] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0059] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0060] Example 1

[0061] Reference Figure 1-2, is an embodiment of the present invention, which provides a building cluster demand response coordination multi-objective optimization scheduling method, including:

[0062] It should be noted that the collaborative demand response control strategy in the embodiment of the present application enables the buildings in the cluster to interact with each other, and the buildings cooperate with each other to achieve cluster-level performance improvement, and each iteration only needs to calculate the cost of the entire cluster.

[0063] S1: Predict the energy demand and supply information of the building cluster, and establish an energy trading model between building units and clusters based on renewable energy supply and battery storage;

[0064] In the embodiment of the present application, predicting the energy demand and supply information of the building cluster includes:

[0065] The next day's energy demand and supply of the building cluster are expressed as:

[0066]

[0067]

[0068] in, is the energy demand of the jth building at the i-th hour, is the energy supply of the jth building at the i-th hour, and N is the number of buildings in the cluster.

[0069] It should be noted that before iterative optimization, the initial value of the battery charging rate of each building is Then the initial value of energy trading for each building is:

[0070]

[0071] in, is the energy demand of building j at time point i, is the initial value of the battery charging rate of building j at time point i.

[0072] The initial value of the cluster's battery charging rate is:

[0073]

[0074] The initial value of energy transaction of the cluster is:

[0075]

[0076] S2: The building cluster is integrated into a single entity. The energy supply of the single entity is the total energy supply of all buildings, the energy demand is the total energy demand of all buildings, the battery capacity is the total battery capacity of all buildings, and the energy trading model uses the cost function of the entire cluster as the objective function;

[0077] It should be noted that in the building cluster collaborative demand response control stage, the operating cost of each building is considered as the objective function, and the operating cost of building j is expressed as:

[0078]

[0079] Among them, a, b, and c are the coefficients of the cost model.

[0080] when When the building purchases electricity from the grid, When the building is powered on, it sells electricity to the grid; the pricing model effectively reduces the peak energy demand on the grid by charging more for greater demand.

[0081] It should also be noted that since renewable energy exports will put a lot of pressure on the power grid, the price of electricity sold by the building to the grid is set lower than the purchase price. The existing non-cooperative demand response control considers the optimization of each individual separately, and each building will perform appropriate control algorithms after receiving price information to optimize its operation. Under this control strategy, there is no interaction between buildings, so it is difficult for buildings to cooperate with each other to achieve cluster-level performance improvements, and each iteration requires the control algorithm for each individual building, which has a high computational cost.

[0082] It should be noted that the integration of building clusters is optimized through an iterative convergent demand response algorithm. Ideally, the performance of the building cluster as a whole is considered to be the optimal performance achieved at the cluster level; its output is the optimal operating parameters of the building cluster as a whole, namely, battery charging rate and energy exchange.

[0083] In the embodiment of the present application, the energy trading model uses the cost function of the entire cluster as the objective function, including: converting the multi-objective optimization problem of multiple individuals into the optimization problem of the entire cluster, and using the operation cost of the entire cluster as the objective function to express it as:

[0084]

[0085]

[0086] in, is the cost function of the entire building cluster in period i, C co,k is the total cost function of the entire building cluster on that day, M is the quadratic coefficient matrix, Q is the linear coefficient matrix, a, b, c are the coefficients of the cost model, is the energy transaction value of the entire building cluster in period i, is the energy transaction value of the entire building cluster, and T is the matrix transpose.

[0087] Specifically, the quadratic coefficient matrix is ​​expressed as:

[0088] M=aΓ co,k

[0089] Among them, Γ co,k is the energy trading state variable for the building cluster;

[0090] The linear coefficient matrix is ​​expressed as:

[0091] Q = diag(bΓ co,k +cI-cΓ co,k )

[0092]

[0093]

[0094] in, is the energy transaction state variable of the building cluster in time period i, which is used to indicate whether the building cluster is in the power purchasing state or the power selling state.

[0095] It should be noted that the cost function of the cluster as a whole is not equal to the simple superposition of individual cost functions, that is:

[0096]

[0097] The iterative convergence demand response algorithm is used to optimize the overall operation of the building cluster. In the kth iteration, the battery operation of the building cluster for the next 24 hours is Optimize demand response control algorithm.

[0098] S3: Optimize the overall cost function of the building cluster through an iterative demand response algorithm, calculate the optimal operating parameters, use a nonlinear programming algorithm to control the operation of each building unit according to the optimal operating parameters of the cluster, and perform multi-objective optimization scheduling on the building cluster;

[0099] In the embodiment of the present application, the cost function of the entire building cluster is optimized by iterative demand response algorithm, and the optimal operating parameters are calculated to include:

[0100] In each time period t∈{1,2,...,24}, when the charging rate of the battery in the building cluster is less than the remaining storage capacity of the battery, and the discharging rate is less than the total energy stored in the battery, the operation of the battery in the building cluster satisfies the constraints expressed as:

[0101]

[0102] in, is the energy stored in the battery at the initial state, CP co is the battery capacity of the entire building cluster, I24 Represents a 24-dimensional column vector with all elements being 1. The size of the matrix A is 24*24, all elements on the diagonal and lower triangle are 1, and the rest are 0. is the charging rate of the batteries in the building cluster.

[0103] In the embodiment of the present application, it also includes: the control variables need to be constrained during the iteration process The rate of change is expressed as:

[0104]

[0105] Wherein, d is the preset limit value;

[0106] It should be noted that if the control variables are not constrained The iteration may fall into a local loop if the rate of change is too large.

[0107] The convergence condition is expressed as:

[0108] |C co,k -C co,k-1 |≤ε

[0109] Among them, ε is the user-defined threshold;

[0110] The optimal solution of battery charging rate and energy trading of the entire building cluster is obtained by iterative calculation: and

[0111]

[0112] It should be noted that in the embodiment of the present application, the user-defined threshold is selected according to the convergence situation, and usually takes a smaller value, about 0.001.

[0113] In the embodiment of the present application, the operation of each building unit is controlled according to the optimal operation parameters of the cluster by using a nonlinear programming algorithm, and the multi-objective optimization scheduling of the building cluster includes: controlling the battery charging rate of a single building unit in a cycle of one hour and energy trading

[0114] At the i-th hour, the input to the nonlinear programming algorithm is the energy demand of a single building unit Energy supply Battery charging rate and energy trading and the optimal battery charging rate for the building cluster as a whole Energy Trading

[0115] In the embodiment of the present application, it also includes: for a cluster consisting of N buildings, there are 2N unknown parameters to be solved, N+2 equality constraints and 2N inequality constraints; the output of the nonlinear programming algorithm is the number of units per hour per building unit. and It is expressed as:

[0116]

[0117]

[0118]

[0119] in, is the battery charging rate of the jth building unit at the mth hour.

[0120] The above is a schematic scheme of a building cluster demand response coordination multi-objective optimization scheduling method of this embodiment. It should be noted that the technical scheme of this building cluster demand response coordination multi-objective optimization scheduling system and the technical scheme of the above-mentioned building cluster demand response coordination multi-objective optimization scheduling method belong to the same concept. The details of the technical scheme of the building cluster demand response coordination multi-objective optimization scheduling system not described in detail in this embodiment can be found in the description of the technical scheme of the above-mentioned building cluster demand response coordination multi-objective optimization scheduling method.

[0121] In this embodiment, a building cluster demand response coordination multi-objective optimization scheduling system includes:

[0122] Model building module, used to predict the energy demand and supply information of building clusters, and establish an energy trading model between building units and clusters based on renewable energy supply and battery storage. The energy trading model uses the cost function of each building unit as the objective function;

[0123] An integration module, used to integrate the building cluster into a single entity, the energy supply of the single entity is the total energy supply of all buildings, the energy demand is the total energy demand of all buildings, and the battery capacity is the total battery capacity of all buildings;

[0124] The control module is used to optimize the overall cost function of the building cluster through an iterative demand response algorithm, calculate the optimal operating parameters, use a nonlinear programming algorithm to control the operation of each building unit according to the optimal operating parameters of the cluster, and perform multi-objective optimization scheduling of the building cluster.

[0125] This embodiment further provides a computing device, which is applicable to the case of a building cluster demand response coordination multi-objective optimization scheduling method, including:

[0126] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the multi-objective optimization scheduling method for demand response coordination of building clusters as proposed in the above embodiment.

[0127] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for realizing the coordinated multi-objective optimization scheduling of building cluster demand response as proposed in the above embodiment is implemented.

[0128] The storage medium proposed in this embodiment and the method for realizing building cluster demand response coordination multi-objective optimization scheduling proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0129] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0130] Example 2

[0131] Reference Figure 3 to Figure 6 , is an embodiment of the present invention. This embodiment is different from the first embodiment in that this embodiment verifies the beneficial effects of the present invention through comparative experiments.

[0132] Reference Figure 3 and Figure 4 In this embodiment, in the non-cooperative demand response control, one power grid is connected to multiple local controllers, each local controller interacts with the power grid through current and information flow, and the local controllers are connected to their respective buildings. Under the non-cooperative demand response control, each building unit directly trades with the power grid. In the cooperative demand response control, only one central controller is connected to the power grid, and interacts through current and information flow. The central controller then transmits the signal to each building. The demand response of all buildings is uniformly managed and coordinated by a central organization, which can reduce the complexity of calculation.

[0133] Reference Figure 5 and Figure 6 This embodiment also takes the energy transaction of two building units in a certain time period as an example to demonstrate the cost calculation under two scenarios; under non-cooperative demand response control, the total cost is 174.3225 yuan. Among them, the power grid consumed 203kW·h of energy and spent 245.1225 yuan. Building 1 reduced electricity consumption by 49kW·h, saving -70.8 yuan (probably due to cost reduction caused by energy-saving measures). Building 2 reduced electricity consumption by 530kW·h, saving -70.8 yuan. The price model is a=0.0025a=0.0025, b=0.7b=0.7, c=0.2c=0.2.

[0134] Under collaborative demand response control, the total cost is -13.2 yuan. Among them, the power grid provides 66kW·h of energy, which costs -13.2 yuan. Building 1 reduces electricity consumption by 49kW·h, saving -70.8 yuan. Building 2 reduces electricity consumption by 530kW·h, saving -70.8 yuan. The building cluster saves a total of 288kW·h of electricity. Compared with traditional non-cooperative demand response control, the collaborative demand response control algorithm proposed in the present invention reduces the control cost and improves the performance of the cluster.

[0135] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A building cluster demand response coordination multi-objective optimization scheduling method, characterized in that: include: Predict the energy demand and supply information of building clusters, and establish energy trading models between building units and clusters based on renewable energy supply and battery storage; The building cluster is integrated into a single entity, the energy supply of the single entity is the total energy supply of all buildings, the energy demand is the total energy demand of all buildings, the battery capacity is the total battery capacity of all buildings, and the energy trading model uses the cost function of the entire cluster as the objective function; The overall cost function of the building cluster is optimized through an iterative demand response algorithm, the optimal operating parameters are calculated, and the operation of each building unit is controlled according to the optimal operating parameters of the cluster using a nonlinear programming algorithm to perform multi-objective optimization scheduling on the building cluster.

2. The building cluster demand response coordination multi-objective optimization scheduling method according to claim 1, characterized in that: The energy demand and supply information of the predicted building cluster includes: The next day's energy demand and supply of the building cluster are expressed as: in, is the energy demand of the jth building at the i-th hour, is the energy supply of the jth building at the i-th hour, and N is the number of buildings in the cluster.

3. The building cluster demand response coordination multi-objective optimization scheduling method according to claim 2, characterized in that: The energy trading model takes the cost function of the entire cluster as the objective function, including: converting the multi-objective optimization problem of multiple individuals into the optimization problem of the entire cluster, and taking the operation cost of the entire cluster as the objective function to express it as: in, is the cost function of the entire building cluster in period i, C co,k is the total cost function of the entire building cluster on that day, M is the quadratic coefficient matrix, Q is the linear coefficient matrix, a, b, c are the coefficients of the cost model, is the energy transaction value of the entire building cluster in period i, is the energy transaction value of the entire building cluster, and T is the matrix transpose.

4. The building cluster demand response coordination multi-objective optimization scheduling method according to claim 3, characterized in that: The cost function of the entire building cluster is optimized by iterative demand response algorithm, and the optimal operating parameters are calculated including: In each time period t∈{1,2,...,24}, when the charging rate of the battery in the building cluster is less than the remaining storage capacity of the battery, and the discharging rate is less than the total energy stored in the battery, the operation of the battery in the building cluster satisfies the constraints expressed as: in, is the energy stored in the battery at the initial state, CP co is the battery capacity of the entire building cluster, I 24 Represents a 24-dimensional column vector with all elements being 1. The size of the matrix A is 24*24, all elements on the diagonal and lower triangle are 1, and the rest are 0. is the charging rate of the batteries in the building cluster.

5. The building cluster demand response coordination multi-objective optimization scheduling method according to claim 4, characterized in that: Also includes: The control variables need to be constrained during the iteration process The rate of change is expressed as: Wherein, d is the preset limit value; The convergence condition is expressed as: |C co,k -C co,k-1 |≤ε Among them, ε is the user-defined threshold; The optimal solution of battery charging rate and energy trading of the entire building cluster is obtained by iterative calculation: and 6. The building cluster demand response coordination multi-objective optimization scheduling method according to claim 5, characterized in that: Using nonlinear programming algorithm to control the operation of each building unit according to the optimal operation parameters of the cluster, multi-objective optimization scheduling of the building cluster includes: controlling the battery charging rate of a single building unit in a cycle of one hour and energy trading At the i-th hour, the input to the nonlinear programming algorithm is the energy demand of a single building unit Energy supply Battery charging rate and energy trading and the optimal battery charging rate for the building cluster as a whole Energy Trading 7. The building cluster demand response coordination multi-objective optimization scheduling method according to claim 6, characterized in that: Also includes: For a cluster of N buildings, there are 2N unknown parameters to be solved, N+2 equality constraints and 2N inequality constraints; the output of the nonlinear programming algorithm is the number of units per hour. and It is expressed as: in, is the battery charging rate of the jth building unit at the mth hour.

8. A building cluster demand response coordination multi-objective optimization scheduling system, characterized in that: include: A model building module is used to predict the energy demand and supply information of the building cluster, and to establish an energy trading model between the building unit and the cluster based on renewable energy supply and battery storage. The energy trading model uses the cost function of each building unit as the objective function; An integration module, used for integrating the building cluster into a single entity, wherein the energy supply of the single entity is the total energy supply of all buildings, the energy demand is the total energy demand of all buildings, and the battery capacity is the total battery capacity of all buildings; The control module is used to optimize the overall cost function of the building cluster through an iterative demand response algorithm, calculate the optimal operating parameters, use a nonlinear programming algorithm to control the operation of each building unit according to the optimal operating parameters of the cluster, and perform multi-objective optimization scheduling on the building cluster.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the building cluster demand response coordinated multi-objective optimization scheduling method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the building cluster demand response coordinated multi-objective optimization scheduling method as described in any one of claims 1 to 7.

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