Method and device for operating a direct control type virtual power plant energy storage station taking into account peak loads

Abstract

The present application relates to a kind of direct control type virtual power plant energy storage station operating method and device considering peak load, the method includes with energy storage charge-discharge model as foundation construction DVPP report model, solving DVPP report model, obtain the power report curve of virtual power plant and report to dispatch center and carry out audit;Obtain the dispatching instruction of each direct control type virtual power plant;According to dispatching instruction generation operation boundary condition, with minimum response deviation as new objective function, and construct DVPP decomposition model;To the aggregation optimization decomposition of DVPP decomposition model, generate the dispatching control instruction of each energy storage station.The present application is under the premise of fully considering user side peak load management, obtains the power report curve of virtual power plant and report to dispatch center and carry out audit, constructs DVPP decomposition model, finally decomposes DVPP decomposition model and obtains the dispatching control instruction of each energy storage station, so as to prevent the additional electricity bill loss caused by peak load sudden increase to user, improve user benefit.

Description

Technical Field

[0001] This invention belongs to the field of power technology, specifically relating to an operation method and device for a direct-control virtual power plant energy storage station that takes into account peak load. Background Technology

[0002] To achieve the strategic goals of "carbon neutrality" and "carbon peaking," and to promote the reform of the power system's energy structure from "source following load" to "coordinated interaction between source, grid, load, and storage," my country needs to increase the proportion of non-fossil energy power generation to 39% of total power generation by 2025. With the rapid growth of installed capacity of clean energy equipment, represented by wind and solar power, in my country's power system, a large amount of uncontrollable, highly random, and intermittent energy with strong anti-peak-shaving characteristics is significantly reducing the rotational inertia resources of the power system, while also bringing new challenges to power quality, grid security, and grid dispatch. Virtual power plants, through pricing and incentive mechanisms, effectively integrate controllable loads on the user side, distributed energy resources, and user-side energy storage, offering unique advantages in improving renewable energy absorption rates, optimizing energy resource allocation, and increasing energy efficiency.

[0003] In the current research field of virtual power plants, there are still gaps in research on market-based capacity competition bidding strategies for directly controlled virtual power plants and optimized decomposition schemes for dispatch center control commands that consider the characteristics of user-side energy storage. Furthermore, most current research on the optimized dispatch of user-side energy storage devices often neglects peak loads on the user side. This leads to a situation where, while maximizing peak-valley price arbitrage profits, energy storage devices are prone to exceeding user demand limits due to sudden surges in peak loads, resulting in additional basic electricity cost losses for users. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to overcome the shortcomings of the prior art and provide a direct-control type virtual power plant energy storage station operation method and device that takes into account peak load, so as to solve the problem in the prior art that the user demand exceeds the limit due to the sudden increase of peak load during a period, thereby causing additional basic electricity cost loss to the user.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a method for operating a direct-controllable virtual power plant energy storage station that takes into account peak load, comprising:

[0006] A DVPP reporting model is constructed based on the energy storage charge and discharge model;

[0007] Based on the objective of maximizing revenue, the DVPP reporting model is solved to obtain the power reporting curve of the virtual power plant and reported to the dispatch center for review.

[0008] Receive dispatch instructions from the dispatch center for each directly controlled virtual power plant based on the power reporting curve;

[0009] Based on the scheduling instructions, generate the running boundary conditions, take the minimum response deviation as the new objective function, determine the priority of the objective function and the new objective function, and construct the DVPP decomposition model;

[0010] The DVPP decomposition model is aggregated and optimized to generate scheduling and control instructions for each energy storage station.

[0011] Furthermore, the energy storage charging and discharging model is as follows:

[0012]

[0013]

[0014] in, and D represents the charging and discharging power of the i-th energy storage unit at time t, respectively. i,t With P i,t Let E be the user load and net load of the i-th energy storage at time t. i,t Let η be the battery charge at time t. ch and η dis These represent the charging and discharging efficiencies of the energy storage device, respectively; Δt represents the charging and discharging duration.

[0015] Furthermore, the DVPP reporting model includes a multi-objective optimization function and corresponding constraints formed by maximizing the revenue of energy storage aggregators and the electricity revenue of users.

[0016] The multi-objective optimization function is,

[0017] max C(x)=(C A (x), C1(x), C2(x), C3(x),...) T

[0018]

[0019]

[0020]

[0021]

[0022] Among them, C A C represents the total revenue of the aggregator. i For the demand response revenue of the i-th energy storage user, C i,j For the peak load management revenue of the i-th user, Revenue from charging command calls on platform during time period t. For the platform discharge command call revenue during time period t, C represents the revenue sharing ratio between the energy storage system i and the platform. i,o λ is the original basic electricity cost for the i-th energy storage unit. Xmax R is the maximum demand unit price, α is the over-limit penalty factor, and R is the maximum demand unit price. i diff and R l diff，max R represents the excess load and maximum excess load of the i-th energy storage user, respectively. i Xmax The maximum demand load set for the i-th energy storage user in the current month; Represents an n-dimensional real number space;

[0023] The constraints include:

[0024] Energy storage power constraints

[0025]

[0026]

[0027] SOC state constraints

[0028]

[0029] Load constraints,

[0030]

[0031]

[0032] Energy storage power unit cycle count limit constraint,

[0033]

[0034] Among them, P i min P i max These represent the minimum and maximum power, respectively; a SOC of 0% indicates that the battery is at zero charge, and a SOC of 100% indicates that the battery is fully charged. During operation, the SOC state of the energy storage system battery must not exceed its upper and lower limits. Indicates the minimum state of charge (SOC) of the battery in the energy storage system. Indicates the upper limit of the SOC state of the battery in the energy storage system; This represents the maximum electrical energy storage capacity of the i-th energy storage device; Let P be the excess load of the i-th energy storage at time t; i X，maxThis represents the maximum demand load set by the i-th energy storage user in the current month.

[0035] Furthermore, the step of solving the DVPP reporting model with the objective of maximizing revenue includes:

[0036] Characterizes the relationship between multiple objective functions;

[0037] Dimensionless processing is performed on different objective functions respectively;

[0038] By setting multi-objective weights, the multi-objective model can be transformed into a single objective, and the DVPP reporting model can be solved based on the single objective.

[0039] Furthermore, the dispatch instructions generated by the dispatch center for each directly controlled virtual power plant based on the power reporting curve include:

[0040] Receive power reporting curves from various directly controlled virtual power plants;

[0041] The power reporting curve is optimized for scheduling and safety verification by the regional power grid, and day-ahead scheduling instructions for each directly controlled virtual power plant are generated. The day-ahead scheduling instructions include the day-ahead load curve, the scheduling instruction curve, and the response time period information.

[0042] Furthermore, the step of generating operational boundary conditions based on the scheduling instructions, using the minimum response deviation as the new objective function, determining the priority of the objective function and the new objective function, and constructing the DVPP decomposition model includes:

[0043] The monthly maximum load generated according to the scheduling instruction is used as the load upper limit constraint;

[0044] The objective function in the DVPP reporting model is used as the priority objective, and the minimization of response deviation is used as the follow-up objective to determine the decomposition objective function;

[0045] The DVPP decomposition model is constructed based on the load ceiling constraint and the decomposition objective function.

[0046] Furthermore, the objective function can be decomposed as follows:

[0047]

[0048] in, The total charging and discharging commands during the demand response period t after market clearing;

[0049] The operating boundary conditions include load limit constraints.

[0050] P i,n ≤P i,max

[0051] Among them, Pi,max P represents the maximum load during the reporting phase for the i-th user. i,n Let be the peak load of the i-th user.

[0052] Furthermore, the gurobi solver is invoked to solve the DVPP reporting model;

[0053] The DVPP decomposition model is solved by calling the gurobi solver.

[0054] This application provides an optimized operation device for a direct-controllable virtual power plant energy storage station that takes into account peak loads, including:

[0055] The first construction module is used to build a DVPP reporting model based on the energy storage charging and discharging model;

[0056] The solution module is used to solve the DVPP reporting model with the goal of maximizing revenue, obtain the power reporting curve of the virtual power plant, and report it to the dispatch center for review.

[0057] The generation module is used to receive dispatch instructions from the dispatch center for each directly controlled virtual power plant generated based on the power reporting curve;

[0058] The second construction module is used to generate running boundary conditions according to the scheduling instructions, take the minimum response deviation as the new objective function, determine the priority of the objective function and the new objective function, and construct the DVPP decomposition model.

[0059] The decomposition module is used to perform aggregation optimization decomposition on the DVPP decomposition model and generate scheduling control instructions for each energy storage station.

[0060] The beneficial effects that can be achieved by adopting the above technical solution in this invention include:

[0061] This invention provides an operation method and apparatus for a directly controlled virtual power plant energy storage station that takes into account peak load. This application focuses on a directly controlled virtual power plant aggregated from user-side energy storage. Under the premise of fully considering user-side peak load management, the power reporting curve of the virtual power plant is obtained and reported to the dispatch center for review. Then, based on the control task instructions issued by the dispatch center, operating boundary conditions are generated. Next, using minimum response deviation as a new objective function, the priority of the objective function and the new objective function is determined, and a DVPP decomposition model is constructed. Finally, the DVPP decomposition model is decomposed to obtain the dispatch control instructions for each energy storage station. This can prevent additional electricity cost losses to users due to sudden increases in peak load and improve user revenue. Attached Figure Description

[0062] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0063] Figure 1 This is a schematic diagram illustrating the steps of the direct-control virtual power plant energy storage station operation method of the present invention, which takes into account peak load.

[0064] Figure 2 This is a flowchart illustrating the operation method of the direct-control virtual power plant energy storage station that takes into account peak load according to the present invention.

[0065] Figure 3 This is a schematic diagram of the structure of the optimized operation device for a direct-control virtual power plant energy storage station that takes into account peak loads, as described in this invention. Detailed Implementation

[0066] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0067] To facilitate the integration of wind power into the market, energy storage aggregators bring together the energy storage systems of different users within a region to participate in the market-based operation and service of the electricity market. They can generate additional revenue through demand response market transactions and capacity trading. This application primarily focuses on energy storage aggregators as the market players, and studies the optimized operation model of energy storage stations from two dimensions: information reporting and instruction decomposition in directly-controlled virtual power plants (DVPPs).

[0068] The following describes, with reference to the accompanying drawings, a specific method and apparatus for operating a direct-controllable virtual power plant energy storage station that takes into account peak load, as provided in the embodiments of this application.

[0069] like Figure 1 As shown in the embodiments of this application, the operation method of a direct-controllable virtual power plant energy storage station considering peak load includes:

[0070] S101, a DVPP reporting model is constructed based on the energy storage charging and discharging model;

[0071] It is understandable that the energy storage charging and discharging model is the basis for constructing the information reporting and instruction decomposition model of the direct-controlled virtual power plant. Therefore, this application constructs the DVPP reporting model based on the energy storage charging and discharging model.

[0072] In some embodiments, the energy storage charge-discharge model is as follows:

[0073]

[0074]

[0075] in, and D represents the charging and discharging power of the i-th energy storage unit at time t, respectively. i,t With P i,t Let E be the user load and net load of the i-th energy storage at time t. i,t Let η be the battery charge at time t. ch and η dis These represent the charging and discharging efficiencies of the energy storage device, respectively; Δt represents the charging and discharging duration.

[0076] In constructing the DVPP reporting model, this application uses the predicted electricity price of the previous day (month, or multiple typical days) as a basis for user reporting, and comprehensively considers the revenue of energy storage aggregators and the electricity revenue of users to form a multi-objective optimization problem (MOP), where T is the response period. Therefore, this application uses the multi-objective optimization function and corresponding constraints formed by maximizing the revenue of energy storage aggregators and the electricity revenue of users;

[0077] The multi-objective optimization function is,

[0078] max C(x)=(C A (x), C1(x), C2(x), C3(x),...) T

[0079]

[0080]

[0081]

[0082]

[0083] Among them, C A C represents the total revenue of the aggregator. i For the demand response revenue of the i-th energy storage user, C i,j For the peak load management revenue of the i-th user, Revenue from charging command calls on platform during time period t. For the platform discharge command call revenue during time period t, C represents the revenue sharing ratio between the energy storage system i and the platform. i,o λ is the original basic electricity cost for the i-th energy storage unit. Xmax R is the maximum demand unit price, α is the over-limit penalty factor, and R is the maximum demand unit price. i diff and R i diff，max P represents the excess load and maximum excess load of the i-th energy storage user, respectively. i Xmax The maximum demand load set for the i-th energy storage user in the current month; Represents an n-dimensional real number space;

[0084] The constraints include:

[0085] Energy storage power constraints

[0086]

[0087]

[0088] SOC state constraints

[0089]

[0090] Load constraints,

[0091]

[0092]

[0093] Energy storage power unit cycle count limit constraint,

[0094]

[0095] Among them, P i max P i max These represent the minimum and maximum power, respectively; a SOC of 0% indicates that the battery is at zero charge, and a SOC of 100% indicates that the battery is fully charged. During operation, the SOC state of the energy storage system battery must not exceed its upper and lower limits. Indicates the minimum state of charge (SOC) of the battery in the energy storage system. Indicates the upper limit of the SOC state of the battery in the energy storage system; This represents the maximum electrical energy storage capacity of the i-th energy storage device; Let P be the excess load of the i-th energy storage at time t; i X，max This represents the maximum demand load set by the i-th energy storage user in the current month.

[0096] In some embodiments, solving the DVPP reporting model based on maximizing revenue includes:

[0097] Characterizes the relationship between multiple objective functions;

[0098] Dimensionless processing is performed on different objective functions respectively;

[0099] By setting multi-objective weights, the multi-objective model can be transformed into a single objective, and the DVPP reporting model can be solved based on the single objective.

[0100] S102, Solve the DVPP reporting model based on the objective of maximizing revenue, obtain the power reporting curve of the virtual power plant, and report it to the dispatch center for review;

[0101] Specifically, after forming the DVPP reporting model of the direct-controlled virtual power plant based on the input of monthly load, electricity price and other forecast data, the DVPP reporting model needs to be solved. First, the relationship between objective functions is characterized, and different objective functions are dimensionless. After dimensionless processing, the multi-objective problem is transformed into a single-objective problem by designing multi-objective weights.

[0102] Secondly, based on the basic information parameters of the energy storage station, and taking into full account the energy storage power constraints, SOC state constraints, user load constraints, and cycle number limits of the energy storage power unit, the boundary constraints for solving the reporting model are formed.

[0103] Finally, the gurobi solver is called to solve the established model, thereby generating the reported power curve of the directly controlled virtual power plant, which is then uploaded to the dispatch center for review and verification.

[0104] Specifically, such as Figure 2 As shown, in the reporting stage of the directly controlled virtual power plant, the energy storage aggregator needs to fully consider the characteristics of the distributed energy storage system (DESS) it aggregates and the revenue sharing with the agent users. Based on the overall goal of maximizing revenue, it forms the reporting curve of the virtual power plant and uploads it to the dispatch center for review and adjustment.

[0105] S103, Receive dispatch instructions from the dispatch center for each directly controlled virtual power plant generated based on the power reporting curve;

[0106] In some embodiments, the dispatch instructions generated by the dispatch center based on the power reporting curve for each directly controlled virtual power plant include:

[0107] Receive power reporting curves from various directly controlled virtual power plants;

[0108] The power reporting curve is optimized for scheduling and safety verification by the regional power grid, and day-ahead scheduling instructions for each directly controlled virtual power plant are generated. The day-ahead scheduling instructions include the day-ahead load curve, the scheduling instruction curve, and the response time period information.

[0109] S104, Generate running boundary conditions according to the scheduling instructions, take the minimum response deviation as the new objective function, determine the priority of the objective function and the new objective function, and construct the DVPP decomposition model;

[0110] In some embodiments, the step of generating runtime boundary conditions according to the scheduling instructions, using minimum response deviation as the new objective function, determining the priority of the objective function and the new objective function, and constructing a DVPP decomposition model includes:

[0111] The monthly maximum load generated according to the scheduling instruction is used as the load upper limit constraint;

[0112] The objective function in the DVPP reporting model is used as the priority objective, and the minimization of response deviation is used as the follow-up objective to determine the decomposition objective function;

[0113] The DVPP decomposition model is constructed based on the load ceiling constraint and the decomposition objective function.

[0114] It is understandable that this application performs rolling clearing on a daily basis. In addition to the change in time scale, in order to maximize demand response, the DVPP decomposition model incorporates minimizing response deviation into the objective function of the reporting model. Based on the dispatch control scheme issued by the dispatch center as input conditions, a DVPP decomposition model for a directly controlled virtual power plant is formed.

[0115] Therefore, the objective function is decomposed as follows:

[0116]

[0117] in, The total charging and discharging commands during the demand response period t after market clearing;

[0118] The operational boundary conditions include load ceiling constraints. It should be noted that, in order to fully utilize the optimization results of the DVPP reporting model to achieve peak load management, the DVPP decomposition model uses the monthly maximum load generated by the DVPP reporting model as the load ceiling constraint.

[0119] P i,n ≤P i,max (14)

[0120] Among them, P i,max P represents the maximum load during the reporting phase for the i-th user. i,n Let be the peak load of the i-th user.

[0121] S105, perform aggregation optimization decomposition on the DVPP decomposition model to generate scheduling control instructions for each energy storage station.

[0122] Based on the day-ahead dispatch control scheme issued by the dispatch center, including the day-ahead load curve, dispatch instruction curve, and response time information, a dispatch decomposition model of the direct-control virtual power plant is formed. By setting different priorities for different objective functions, the benefit objective function in the reported model is taken as the priority objective, and minimizing the response deviation is taken as the follow-up objective, so as to ensure the degree of demand response while maximizing economic benefits.

[0123] Finally, after the model is built, the gurobi solver is called to solve the established model, forming the day-ahead scheduling and control strategy for each distributed energy storage resource under the direct-control virtual power plant, and then distributing it to each energy storage station.

[0124] This application focuses on directly controlled virtual power plants formed by user-side energy storage aggregation. Under the premise of fully considering user-side peak load management, it forms a multi-objective optimization function to maximize the revenue of energy storage aggregators and the electricity revenue of users. Based on the cluster characteristics of the aggregated distributed energy storage devices and the revenue sharing ratio of the signed contracts, it formulates a market-competitive capacity allocation application strategy in combination with market rules and market conditions. At the same time, based on the control task instructions issued by the dispatch center, and combined with the life status, capacity status, efficiency status of the distributed energy storage devices, external price status, contract revenue sharing and other conditions, it formulates and issues control instructions for each energy storage site.

[0125] like Figure 3 As shown, this application provides an optimized operation device for a direct-controllable virtual power plant energy storage station that takes into account peak loads, including:

[0126] The first construction module 201 is used to construct a DVPP reporting model based on the energy storage charging and discharging model;

[0127] Solver module 202 is used to solve the DVPP reporting model based on the objective of maximizing revenue, obtain the power reporting curve of the virtual power plant, and report it to the dispatch center for review.

[0128] The generation module 203 is used to receive the dispatch instructions of each directly controlled virtual power plant generated by the dispatch center based on the power reporting curve;

[0129] The second construction module 204 is used to generate running boundary conditions according to the scheduling instructions, take the minimum response deviation as the new objective function, determine the priority of the objective function and the new objective function, and construct the DVPP decomposition model.

[0130] The decomposition module 205 is used to perform aggregation optimization decomposition on the DVPP decomposition model to generate scheduling control instructions for each energy storage station.

[0131] The working principle of the optimized operation device for direct-controllable virtual power plant energy storage stations considering peak load provided in this application is as follows: the first construction module 201 constructs a DVPP reporting model based on the energy storage charging and discharging model; the solution module 202 solves the DVPP reporting model with the goal of maximizing revenue, obtains the power reporting curve of the virtual power plant, and reports it to the dispatch center for review; the generation module 203 receives the dispatch instructions for each direct-controllable virtual power plant generated by the dispatch center based on the power reporting curve; the second construction module 204 generates operating boundary conditions according to the dispatch instructions, takes the minimum response deviation as the new objective function, determines the priority of the objective function and the new objective function, and constructs a DVPP decomposition model; the decomposition module 205 performs aggregation optimization decomposition on the DVPP decomposition model to generate dispatch control instructions for each energy storage station.

[0132] In summary, this invention provides an operation method and apparatus for a direct-controllable virtual power plant energy storage station that takes into account peak load. The method includes constructing a DVPP reporting model based on an energy storage charging and discharging model; solving the DVPP reporting model with the objective of maximizing revenue; obtaining the power reporting curve of the virtual power plant and reporting it to the dispatch center for review; receiving dispatch instructions from the dispatch center for each direct-controllable virtual power plant generated based on the power reporting curve; generating operating boundary conditions according to the dispatch instructions; using minimum response deviation as a new objective function; determining the priority of the objective function and the new objective function and constructing a DVPP decomposition model; performing aggregation optimization decomposition on the DVPP decomposition model; and generating dispatch control instructions for each energy storage station.

[0133] It is understood that the method embodiments provided above correspond to the device embodiments described above, and the specific details can be referred to each other, which will not be repeated here.

[0134] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0135] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0136] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction methods implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0137] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0138] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for operating a direct-controllable virtual power plant energy storage station that takes into account peak load, characterized in that, include: A DVPP reporting model is constructed based on the energy storage charge and discharge model; Based on the objective of maximizing revenue, the DVPP reporting model is solved to obtain the power reporting curve of the virtual power plant and reported to the dispatch center for review. Receive dispatch instructions from the dispatch center for each directly controlled virtual power plant based on the power reporting curve; Based on the scheduling instructions, generate the running boundary conditions, take the minimum response deviation as the new objective function, determine the priority of the objective function and the new objective function, and construct the DVPP decomposition model; The DVPP decomposition model is aggregated and optimized to generate scheduling and control instructions for each energy storage station.

2. The method according to claim 1, characterized in that, The energy storage charging and discharging model is as follows: in, and The first i Energy storage t The power of constant charging and discharging. and The first i Energy storage t User load and net load at any given time for t Battery level at all times and These are the charging and discharging efficiencies of the energy storage device, respectively. Indicates the duration of charging and discharging.

3. The method according to claim 2, characterized in that, The DVPP reporting model includes a multi-objective optimization function and corresponding constraints formed by maximizing the revenue of energy storage aggregators and the electricity revenue of users. The multi-objective optimization function is, in, For the total revenue of the aggregator, For the first i Demand response benefits for individual energy storage users For the first i Peak load management benefits for individual users For time period Revenue from platform charging command invocation For time period Platform discharge command call revenue, For energy storage systems The revenue sharing ratio with the platform, The original basic electricity cost for the i-th energy storage unit is... The unit price for maximum demand. As a penalty factor for exceeding the limit, and The first i The excess load and maximum excess load of each energy storage user. For the first i The maximum demand load set by each energy storage user for the current month; Represents an n-dimensional real number space; The constraints include: Energy storage power constraints SOC state constraints Load constraints, Energy storage power unit cycle count limit constraint, in, , These represent the minimum and maximum power, respectively; a SOC of 0% indicates zero charge, and a SOC of 100% indicates a fully charged battery. During operation, the SOC state of the energy storage system battery must not exceed its upper and lower limits. Indicates the minimum state of charge (SOC) of the battery in the energy storage system. Indicates the upper limit of the SOC state of the battery in the energy storage system; E i max Indicates the first i The maximum electrical energy storage capacity of each energy storage unit; For the first i Energy storage t The amount of overload at any given moment; Represented as the first i The maximum demand load set by each energy storage user for the current month.

4. The method according to claim 3, characterized in that, The process of solving the DVPP reporting model with the objective of maximizing revenue includes: Characterizes the relationship between multiple objective functions; Dimensionless processing is performed on different objective functions respectively; By setting multi-objective weights, the multi-objective model can be transformed into a single objective, and the DVPP reporting model can be solved based on the single objective.

5. The method according to claim 1, characterized in that, The dispatch instructions generated by the dispatch center based on the power reporting curve for each directly controlled virtual power plant include: Receive power reporting curves from various directly controlled virtual power plants; The power reporting curve is optimized for scheduling and safety verification by the regional power grid, and day-ahead scheduling instructions for each directly controlled virtual power plant are generated. The day-ahead scheduling instructions include the day-ahead load curve, the scheduling instruction curve, and the response time period information.

6. The method according to claim 1, characterized in that, The step of generating operational boundary conditions according to the scheduling instructions, using the minimum response deviation as the new objective function, determining the priority of the objective function and the new objective function, and constructing the DVPP decomposition model includes: The monthly maximum load generated according to the scheduling instruction is used as the load upper limit constraint; The objective function in the DVPP reporting model is used as the priority objective, and the minimization of response deviation is used as the follow-up objective to determine the decomposition objective function; The DVPP decomposition model is constructed based on the load ceiling constraint and the decomposition objective function.

7. The method according to claim 6, characterized in that, Therefore, the objective function is decomposed as follows: in, The total charging and discharging commands during the demand response period t after market clearing; The operating boundary conditions include load limit constraints. in, This represents the maximum load during the reporting phase for the i-th user. Let be the peak load of the i-th user.

8. The method according to claim 1, characterized in that, The DVPP reporting model was solved using the gurobi solver. The DVPP decomposition model is solved by calling the gurobi solver.

9. A direct-control type virtual power plant energy storage station optimized operation device considering peak load, characterized in that, include: The first construction module is used to build a DVPP reporting model based on the energy storage charging and discharging model; The solution module is used to solve the DVPP reporting model with the goal of maximizing revenue, obtain the power reporting curve of the virtual power plant, and report it to the dispatch center for review. The generation module is used to receive dispatch instructions from the dispatch center for each directly controlled virtual power plant generated based on the power reporting curve; The second construction module is used to generate running boundary conditions according to the scheduling instructions, take the minimum response deviation as the new objective function, determine the priority of the objective function and the new objective function, and construct the DVPP decomposition model. The decomposition module is used to perform aggregation optimization decomposition on the DVPP decomposition model and generate scheduling control instructions for each energy storage station.

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