Optimized energy storage charging and discharging method and device for virtual power plant

Abstract

The invention relates to an optimized energy storage charging and discharging method and device for a virtual power plant. The optimized energy storage charging and discharging method for the virtual power plant comprises the following steps: constructing a declaration response model and a bid winning response model participating in a power grid response invitation based on invitation information of the power grid response invitation, a response price of a response time period and energy storage equipment information of energy storage equipment participating in power grid response; wherein the declaration response model comprises a first objective function which aims at revenue maximization in a declaration stage of the power grid response invitation and a first constraint condition of the declaration stage; the bid winning response model comprises a second objective function which aims at revenue maximization when the power grid response invitation is executed after the bid winning stage of the power grid response invitation and a second constraint condition after the bid winning stage; and based on the declaration response model and the bid winning response model, performing invitation response on a power grid response invitation issued by the power grid, and performing charging and discharging strategy distribution of each energy storage device participating in the power grid response.

Description

Technical Field

[0001] The present disclosure relates to the technical field of integrated energy systems, and in particular to an optimized energy storage charging and discharging method for a virtual power plant, an optimized energy storage charging and discharging device for a virtual power plant, a storage medium and a server. Background Art

[0002] A virtual power plant is a power coordination management system that uses advanced information and communication technologies and software systems to achieve the aggregation and coordinated optimization of distributed resources such as distributed power sources, energy storage systems, controllable loads, and electric vehicles, so as to participate in the power market and power grid operation as a special power plant. The interactive response technology of virtual power plants can effectively reduce the demand for electricity during peak hours, improve the operational flexibility of the power grid, and significantly reduce equipment investment and improve resource utilization efficiency. With the gradual liberalization of the national medium- and long-term electricity market, spot market, green power trading market, and ancillary service market, virtual power plants have gradually participated in the power market as a market entity. At present, the business of virtual power plants is sporadic and the frequency of transactions is uncertain. Therefore, every time a virtual power plant participates in the demand response process, how to adjust the power consumption behavior of the user-side flexible load with energy storage as the main body to obtain the maximum response benefit has become a key factor in improving the overall benefits of virtual power plants. Summary of the invention

[0003] In view of this, the embodiments of the present disclosure hope to provide a method for optimizing energy storage charging and discharging of a virtual power plant, an optimized energy storage charging and discharging device, a storage medium and a server for a virtual power plant.

[0004] The technical solution of the present disclosure is achieved as follows:

[0005] In a first aspect, the present disclosure provides a method for optimizing energy storage charging and discharging of a virtual power plant.

[0006] The method for optimizing energy storage charging and discharging of a virtual power plant provided in an embodiment of the present disclosure includes:

[0007] Acquire invitation information of a power grid response invitation issued by the power grid; wherein the invitation information at least includes a response date, a response time period, a response granularity, and a response mode;

[0008] Obtain the response price for the response time period issued by the power grid and the energy storage device information of the energy storage devices participating in the power grid response;

[0009] Based on the invitation information of the grid response invitation, the response price of the response time period and the energy storage equipment information of the energy storage equipment participating in the grid response, a declaration response model and a winning bid response model for participating in the grid response invitation are constructed; wherein the declaration response model includes a first objective function with the goal of maximizing benefits in the declaration stage of the grid response invitation and a first constraint condition in the declaration stage; the winning bid response model includes a second objective function with the goal of maximizing benefits when executing the grid response invitation after the winning bid stage of the grid response invitation and a second constraint condition after the winning bid stage; the first objective function is the same as the second objective function; the first constraint condition is different from the second constraint condition;

[0010] Based on the application response model and the bid winning response model, an invitation response is made to the grid response invitation issued by the grid, and a charging and discharging strategy is allocated to each energy storage device participating in the grid response.

[0011] In some embodiments, the first objective function with the goal of maximizing revenue during the declaration phase of the power grid response invitation includes:

[0012] Among them, Obj is the maximum benefit during the declaration phase of the grid response invitation;

[0013] represents the total revenue of peak-valley arbitrage; ρ(i) is the time-of-use electricity price of the i-th response section;

[0014] represents the total response subsidy; where,

[0015] ξ represents the charging and discharging efficiency of the energy storage device; c is the energy storage charging efficiency, ξ d is the energy storage device discharge efficiency; η represents the demand response electricity price subsidy coefficient;

[0016] w i represents the charging and discharging capacity of the i-th decision-making unit, w i <0 means charging, w i >0 means discharge, w i =0 means neither discharging nor charging; s i Represents the charging and discharging time of the i-th decision unit, s i The value is 0 or 1, s i =0 means no charging or discharging, s i =1 means uniform charging and discharging in the entire decision-making unit for T minutes;

[0017] b(i) is the baseline load at time i; the baseline load is the power output of the energy storage device before participating in the grid response invitation; v irepresents the response price of the ith response interval; t js ,t je They represent the start and end time of the j-th response interval respectively.

[0018] In some embodiments, the step of responding to the grid response invitation issued by the grid based on the application response model and the winning bid response model, and allocating charging and discharging strategies for each energy storage device participating in the grid response, includes:

[0019] Obtain the declared response volume, estimated revenue and historical charging and discharging strategies of energy storage devices participating in grid response;

[0020] Import relevant parameters into the declared response model, construct a mixed integer nonlinear programming, and use the solver to solve the optimal charging and discharging strategy θ for the mth energy storage device response day. m for:

[0021] θ m =[w1s1,w2s2,...,w i s i ,...,w N s N ];

[0022] The response power of the mth device in the jth response interval φ mj =Re s mj ;

[0023] The maximum response power of the mth device in each response interval on the response day [φ m1 ,φ m2 ,φ m3 ,...,φ mK ], the total response power of the jth response interval The response strategy of each device in this response is Θ=[θ1,θ2,...,θ m ,...,θ M ], the total response power of each response interval that can be reported for this response

[0024] Total_Re s=[Total_Re s1,Total_Re s2,...Total_Re s j ...,Total_Re s K ], the mth device execution strategy Θ m Total revenue p m =Obj and the total benefit of this response

[0025] In some embodiments, the step of responding to the grid response invitation issued by the grid based on the application response model and the winning bid response model, and allocating charging and discharging strategies for each energy storage device participating in the grid response, includes:

[0026] Submit the declared response quantity and response price of the grid response invitation to the grid, and wait for the grid to clear the demand response quantity and response price;

[0027] Get the total winning bid response quantity and response clearing price; where the winning bid response quantity is expressed as [χ1, χ2, ..., χ K ],χ i represents the winning quantity of the ith response interval, in kilowatt-hours (KWh), and the response clearing price is [v′1,v′2,...,v′ K ];

[0028] Splitting the actual required response quantity of each device in equal proportion according to the winning bid quantity of the i-th response interval;

[0029] The actual winning response quantity φ′ of the mth energy storage device in the jth response interval mj for:

[0030] Will Re s j <=x j χ j Replaced by the actual scalar φ′ mj ,Combined with the first constraint condition, a bid winning response algorithm model is constructed;

[0031] Obtain the winning response amount, estimated revenue and charging and discharging strategies of each energy storage device in the declaration stage of the energy storage devices participating in the grid response; import the relevant parameters into the winning response model, construct a mixed integer nonlinear programming, and use the solver to solve it, and obtain the daily charging and discharging strategy Θ′ of each energy storage device response:

[0032] Θ′=[θ′1,θ′2,...,θ′ m ,...,θ′ M ]; where θ′ m Indicates that the mth energy storage device actually implements the charging and discharging strategy in response to the day.

[0033] In a second aspect, the present disclosure provides an optimized energy storage charging and discharging device for a virtual power plant, comprising:

[0034] A first information acquisition module is used to acquire the invitation information of the power grid response invitation issued by the power grid; wherein the invitation information at least includes a response date, a response time period, a response granularity, and a response mode;

[0035] A second information acquisition module is used to obtain the response price of the response time period issued by the power grid and the energy storage device information of the energy storage devices participating in the power grid response;

[0036] A model building module, for building a declaration response model and a winning bid response model for participating in a grid response invitation based on the invitation information of the grid response invitation, the response price of the response time period and the energy storage device information of the energy storage device participating in the grid response; wherein the declaration response model includes a first objective function with the goal of maximizing benefits in the declaration stage of the grid response invitation and a first constraint condition in the declaration stage; the winning bid response model includes a second objective function with the goal of maximizing benefits when executing the grid response invitation after the winning bid stage of the grid response invitation and a second constraint condition after the winning bid stage; the first objective function is the same as the second objective function; the first constraint condition is different from the second constraint condition;

[0037] The charging and discharging strategy allocation module is used to respond to the grid response invitation issued by the grid based on the application response model and the bid winning response model, and allocate charging and discharging strategies to each energy storage device participating in the grid response.

[0038] In some embodiments, the first objective function with the goal of maximizing revenue during the declaration phase of the power grid response invitation includes:

[0039] Among them, Obj is the maximum benefit during the declaration phase of the grid response invitation;

[0040] represents the total revenue of peak-valley arbitrage; ρ(i) is the time-of-use electricity price of the i-th response section;

[0041] represents the total response subsidy; where,

[0042] ξ represents the charging and discharging efficiency of the energy storage device; c is the energy storage charging efficiency, ξ d is the energy storage device discharge efficiency; η represents the demand response electricity price subsidy coefficient;

[0043] w i represents the charging and discharging capacity of the i-th decision-making unit, w i <0 means charging, w i >0 means discharge, w i =0 means neither discharging nor charging; s i Represents the charging and discharging time of the i-th decision unit, s i The value is 0 or 1, s i =0 means no charging or discharging, s i=1 means uniform charging and discharging in the entire decision-making unit for T minutes;

[0044] b(i) is the baseline load at time i; the baseline load is the power output of the energy storage device before participating in the grid response invitation; v i represents the response price of the ith response interval; t js ,t je They represent the start and end time of the j-th response interval respectively.

[0045] In a third aspect, the present disclosure provides a computer-readable storage medium having stored thereon a program for optimizing energy storage charging and discharging of a virtual power plant. When the program for optimizing energy storage charging and discharging of a virtual power plant is executed by a processor, the method for optimizing energy storage charging and discharging of a virtual power plant described in the first aspect above is implemented.

[0046] In a fourth aspect, the present disclosure provides a server comprising a memory, a processor, and an optimized energy storage charging and discharging program for a virtual power plant stored in the memory and executable on the processor, wherein when the processor executes the optimized energy storage charging and discharging program for the virtual power plant, the optimized energy storage charging and discharging method for the virtual power plant described in the first aspect above is implemented.

[0047] According to the method for optimizing energy storage charging and discharging of a virtual power plant according to an embodiment of the present disclosure, the method comprises: obtaining invitation information of a grid response invitation issued by a grid; wherein the invitation information comprises at least a response date, a response time period, a response granularity, and a response mode; obtaining a response price of the response time period issued by the grid and energy storage equipment information of energy storage equipment participating in the grid response; constructing a declaration response model and a winning response model for participating in the grid response invitation based on the invitation information of the grid response invitation, the response price of the response time period, and the energy storage equipment information of energy storage equipment participating in the grid response; wherein the declaration response model comprises a first objective function with the goal of maximizing revenue in the declaration stage of the grid response invitation and a first constraint condition in the declaration stage; the winning response model comprises a second objective function with the goal of maximizing revenue when executing the grid response invitation after the winning stage of the grid response invitation and a second constraint condition after the winning stage; the first objective function is the same as the second objective function; the first constraint condition is different from the second constraint condition; based on the declaration response model and the winning response model, an invitation response is made to the grid response invitation issued by the grid, and a charging and discharging strategy allocation is made to each energy storage equipment participating in the grid response. This application establishes a mixed integer nonlinear model through the processes of response invitation, response capability assessment, response declaration, response bidding and response execution, with economic optimization as the guide in both the declaration and bidding stages. It also adapts the declaration response model and the bidding response model respectively by combining different constraints, so that the declaration and bidding stages are coordinated and optimized, so as to achieve the purpose of optimizing the benefits of the virtual power plant participating in the entire process of user-side demand response.

[0048] Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will be obvious from the following description or learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is a flow chart of a method for optimizing energy storage charging and discharging of a virtual power plant according to an exemplary embodiment;

[0050] Figure 2 is a schematic diagram of an optimized energy storage charging and discharging process of a virtual power plant according to an exemplary embodiment;

[0051] Figure 3 It is a schematic diagram of the structure of an optimized energy storage charging and discharging device of a virtual power plant according to an exemplary embodiment. DETAILED DESCRIPTION

[0052] Embodiments of the present disclosure are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0053] Energy storage peak-valley arbitrage projects mainly use energy storage systems to charge during the low electricity price stage, and store energy for load use during the peak electricity price stage. Since electricity load fluctuates due to factors such as season, output, and holidays, the installed capacity of energy storage in energy storage peak-valley arbitrage projects is calculated based on the daily electricity curve of the load (called daily load curve). Because electricity load fluctuates, the selection and determination method of the daily load curve will directly affect the installed capacity of energy storage.

[0054] In view of the above situation, the present disclosure provides a method for optimizing energy storage charging and discharging of a virtual power plant. Figure 1 FIG. 1 is a flow chart of a method for optimizing energy storage charging and discharging of a virtual power plant according to an exemplary embodiment. Figure 1 As shown, the optimized energy storage charging and discharging method of the virtual power plant includes:

[0055] Step 10: Obtain the invitation information of the power grid response invitation issued by the power grid; wherein the invitation information at least includes a response date, a response time period, a response granularity, and a response mode;

[0056] Step 11: Obtain the response price of the response time period issued by the power grid and the energy storage device information of the energy storage devices participating in the power grid response;

[0057] Step 12: Based on the invitation information of the grid response invitation, the response price of the response time period, and the energy storage equipment information of the energy storage equipment participating in the grid response, a declaration response model and a winning bid response model for participating in the grid response invitation are constructed; wherein the declaration response model includes a first objective function with the goal of maximizing benefits in the declaration stage of the grid response invitation and a first constraint condition in the declaration stage; the winning bid response model includes a second objective function with the goal of maximizing benefits when executing the grid response invitation after the winning bid stage of the grid response invitation and a second constraint condition after the winning bid stage; the first objective function is the same as the second objective function; the first constraint condition is different from the second constraint condition;

[0058] Step 13: Based on the application response model and the bid winning response model, respond to the grid response invitation issued by the grid, and allocate charging and discharging strategies for each energy storage device participating in the grid response.

[0059] In this exemplary embodiment, a power grid response invitation is received. The invitation information includes the response date, response time period, response granularity and response mode; wherein the response mode is generally "peak shaving" and "valley filling". Considering that the invitation information may contain K response intervals, it is assumed that the winning interval is expressed as [[t 1s ,t 1e ],[t 2s ,t 2e ],...,[t Ks ,t Ke ]], t is ,t ie They represent the start and end time of the ith response interval, respectively. The unit of time t is minute (min), where 1≤i≤K. The theoretical load response rate of the ith response interval is [r1,r2,...,r K] The time-of-use electricity price at time t is obtained according to the power market to which the invitation response belongs, which is ρ(t); T represents the response granularity, which is generally 15T, 30T, 60T or 120T. Considering the technical reusability, after the decision-making scheme with a granularity of 15T is implemented, other decision-making schemes with coarser granularity can be obtained based on the scheme by summing the electricity. For example, the response interval [10.00, 10.15, 10.30, 10.45] obtained by the 15T granularity decision is [100, 150, 200, 100], that is, the energy storage discharge response is (100 / 0.25)KW, (150 / 0.25)KW, (200 / 0.25)KW, and (100 / 0.25)KW in the time periods 10.00-10.15, 10.15-10.30, 10.30-10.45, and 10.45-11.00, respectively. Then the response plan corresponding to the 30T granularity is [100+150, 200+100], and so on. The response plan corresponding to the 60T granularity is [100+150+200+100]. Among them, this application defaults to the implementation of the 15T declaration and winning bid response algorithm strategy, and other granularities are obtained by merging the average based on the 15T strategy. The historical load average information of the 5 days before the response day is taken as the baseline load of the response day, and the baseline load at time t is b(t).

[0060] In this exemplary embodiment, a response price is specified. The response price is represented as [v1, v2, ..., v K ],v i Indicates the response price of the i-th response interval. Specifies the equipment that can participate in this response. For virtual power plants, energy storage equipment can participate in different business scenarios in different time periods. Assuming that there are M energy storage devices, the equipment index is represented by [1,2,3,...,m,...M], and m represents the number of the m-th energy storage device participating in the demand response. The attribute information (such as rated power, rated capacity, charging and discharging efficiency, and charging and discharging depth, etc.) and baseline information corresponding to the device can be associated through the number index. Then, the invitation information, price information, and information about the equipment to be responded to are integrated. The invitation information of the grid response invitation, the response price of the response time period, and the energy storage equipment information of the energy storage equipment participating in the grid response are used as input parameters of the declaration algorithm model to construct a declaration response algorithm model. With T as the response granularity, the corresponding number of strategy units is N=24*60 / T, and the "declaration-winning bid" two-stage collaborative optimization algorithm model is established with the economic optimization of the charging and discharging behavior of a single energy storage device as the goal.

[0061] In this exemplary embodiment, by analyzing the differences in characteristics between the declaration and bid winning stages, the two stages are integrated into a collaborative optimization algorithm model. The model internally associates the stage with the corresponding constraints for modeling, thereby adapting to the integrated decision-making needs of the entire chain from declaration to bid winning when the virtual power plant participates in demand response. The declaration and bid winning stages are collaboratively optimized to ultimately achieve the goal of maximizing the economic benefits of the virtual power plant.

[0062] According to the method for optimizing energy storage charging and discharging of a virtual power plant according to an embodiment of the present disclosure, the method comprises: obtaining invitation information of a grid response invitation issued by a grid; wherein the invitation information comprises at least a response date, a response time period, a response granularity, and a response mode; obtaining a response price of the response time period issued by the grid and energy storage equipment information of energy storage equipment participating in the grid response; constructing a declaration response model and a winning response model for participating in the grid response invitation based on the invitation information of the grid response invitation, the response price of the response time period, and the energy storage equipment information of energy storage equipment participating in the grid response; wherein the declaration response model comprises a first objective function with the goal of maximizing revenue in the declaration stage of the grid response invitation and a first constraint condition in the declaration stage; the winning response model comprises a second objective function with the goal of maximizing revenue when executing the grid response invitation after the winning stage of the grid response invitation and a second constraint condition after the winning stage; the first objective function is the same as the second objective function; the first constraint condition is different from the second constraint condition; based on the declaration response model and the winning response model, an invitation response is made to the grid response invitation issued by the grid, and a charging and discharging strategy allocation is made to each energy storage equipment participating in the grid response. This application establishes a mixed integer nonlinear model through the processes of response invitation, response capability assessment, response declaration, response bidding and response execution, with economic optimization as the guide in both the declaration and bidding stages. It also adapts the declaration response model and the bidding response model respectively by combining different constraints, so that the declaration and bidding stages are coordinated and optimized, so as to achieve the purpose of optimizing the benefits of the virtual power plant participating in the entire process of user-side demand response.

[0063] In some embodiments, the first objective function with the goal of maximizing revenue during the declaration phase of the power grid response invitation includes:

[0064] Among them, Obj is the maximum benefit during the declaration phase of the grid response invitation;

[0065] represents the total revenue of peak-valley arbitrage; ρ(i) is the time-of-use electricity price of the i-th response section;

[0066] represents the total response subsidy; where,

[0067] ξ represents the charging and discharging efficiency of the energy storage device; cis the energy storage charging efficiency, ξ d is the energy storage device discharge efficiency; η represents the demand response electricity price subsidy coefficient;

[0068] w i represents the charging and discharging capacity of the i-th decision-making unit, w i <0 means charging, w i >0 means discharge, w i =0 means neither discharging nor charging; s i Represents the charging and discharging time of the i-th decision unit, s i The value is 0 or 1, s i =0 means no charging or discharging, s i =1 means uniform charging and discharging in the entire decision-making unit for T minutes;

[0069] b(i) is the baseline load at time i; the baseline load is the power output of the energy storage device before participating in the grid response invitation; v i represents the response price of the ith response interval; t js ,t je They represent the start and end time of the j-th response interval respectively.

[0070] In this exemplary embodiment, the first objective function with the goal of maximizing revenue during the declaration phase of the power grid response invitation includes:

[0071] Among them, the benefit of the first objective function is divided into two parts, one part is represents the total profit of peak-to-valley arbitrage; the other part is It represents the total response subsidy, that is, the total response subsidy given by the power grid to the virtual power plant according to the power output of the virtual power plant. The above two parts of income are added together as the income in the declaration stage of the power grid response invitation to maximize the income.

[0072] In some embodiments, the first constraint condition in the application stage includes:

[0073] The energy storage state of the i-th decision unit soc i In normal range:

[0074] dod max *rated_capacity≤soc i ≤cod max *rated_capacity, where dod max is the maximum depth coefficient of energy storage device discharge; cod max is the maximum depth factor of energy storage device charging; rated_capacity is the rated capacity of energy storage device; where,

[0075] In a strategy cycle, the initial state of the energy storage device is the same as the final state;

[0076] soc0=soc N ,in,

[0077] ξ c is the energy storage charging efficiency, ξ d is the energy storage device discharge efficiency; soc0 is the energy storage device initial state, soc i is the state of the energy storage device in the i-th decision unit, 1≤i≤N.

[0078] In this exemplary embodiment, the first constraint condition in the declaration phase mainly includes two. The first constraint condition is the energy storage state soc of the i-th decision unit. i In the normal range, the energy storage state soc of the i-th decision unit i It is neither too large nor too small. The second is that within a strategy cycle, the initial state and the final state of the energy storage of the i-th decision unit are the same. That is, if the initial state of the energy storage of the i-th decision unit is SOC 10%, then the final state SOC of the energy storage of the i-th decision unit is also 10%, so that the power of the energy storage of the i-th decision unit is fully utilized to maximize the power utilization efficiency.

[0079] In some embodiments, the second constraint condition after the bid winning stage includes:

[0080] The energy storage state of the i-th decision unit soc i In normal range:

[0081] dod max *rated_capacity≤soc i ≤cod max *rated_capacity, where dod max is the maximum depth coefficient of energy storage device discharge; cod max is the maximum depth factor of energy storage device charging; rated_capacity is the rated capacity of energy storage device; where,

[0082] In a policy cycle, the initial state of the energy storage device is the same as the final state:

[0083] soc0=soc N ,in, ξ c is the energy storage charging efficiency, ξ d is the energy storage discharge efficiency; soc0 is the initial state of the energy storage, soci is the energy storage state in the i-th decision unit, 1≤i≤N;

[0084] The response quantity of the jth response interval is subject to the constraints of the actual winning bid response quantity:

[0085]

[0086] Re s j <=x j Among them, Re s j is the response power of the jth response interval, χ j is the response power of the jth response interval after winning the bid; t js ,t je They represent the start and end time of the j-th response interval respectively.

[0087] In this exemplary embodiment, the second constraint condition after the bid winning stage is based on the first constraint condition in the declaration stage, and adds that the response power of the j-th response interval is subject to the constraint of the actual bid winning response amount.

[0088] The response quantity of the jth response interval is subject to the constraints of the actual winning bid response quantity: The response quantity Re s of the jth response interval j , so that Re s j <=x j , so that the response power of the jth response interval will not be too high and exceed the actual winning bid response amount.

[0089] In some embodiments, the step of responding to the grid response invitation issued by the grid based on the application response model and the winning bid response model, and allocating charging and discharging strategies for each energy storage device participating in the grid response, includes:

[0090] Obtain the declared response volume, estimated revenue and historical charging and discharging strategies of energy storage devices participating in grid response;

[0091] Import relevant parameters into the declared response model, construct a mixed integer nonlinear programming, and use the solver to solve the optimal charging and discharging strategy θ for the mth energy storage device response day. m for:

[0092] θ m =[w1s1,w2s2,...,w i s i ,...,w N s N ];

[0093] The response power of the mth device in the jth response interval φ mj =Re s mj ;

[0094] The maximum response power of the mth device in each response interval on the response day [φm1 ,φ m2 ,φ m3 ,...,φ mK ], the total response power of the jth response interval The response strategy of each device in this response is Θ=[θ1,θ2,...,θ m ,...,θ M ], the total response power of each response interval that can be reported for this response

[0095] Total_Re s=[Total_Re s1,Total_Re s2,...Total_Re s j ...,Total_Re s K ], the mth device execution strategy Θ m Total revenue p m =Obj and the total benefit of this response

[0096] In this exemplary embodiment, relevant parameters are imported into the declaration response model, a mixed integer nonlinear programming is constructed, and a solver is used to solve the problem to obtain the response power and corresponding benefits of each declaration stage. The relevant parameters include at least one of the energy storage rated power, energy storage charging and discharging efficiency, energy storage charging and discharging maximum depth, time-of-use electricity price and other information of the energy storage equipment.

[0097] In some embodiments, the step of responding to the grid response invitation issued by the grid based on the application response model and the winning bid response model, and allocating charging and discharging strategies for each energy storage device participating in the grid response, includes:

[0098] Submit the declared response quantity and response price of the grid response invitation to the grid, and wait for the grid to clear the demand response quantity and response price;

[0099] Get the total winning bid response quantity and response clearing price; where the winning bid response quantity is expressed as [χ1, χ2, ..., χ K ],χ i represents the winning quantity of the ith response interval, in kilowatt-hours (KWh), and the response clearing price is [v′1,v′2,...,v′ K ];

[0100] Splitting the actual required response quantity of each device in equal proportion according to the winning bid quantity of the i-th response interval;

[0101] The actual winning response quantity φ′ of the mth energy storage device in the jth response interval mj for:

[0102] Will Re sj <=x j χ j Replaced by the actual scalar φ′ mj ,Combined with the first constraint condition, a bid winning response algorithm model is constructed;

[0103] Obtain the winning response amount, estimated revenue and charging and discharging strategies of each energy storage device in the declaration stage of the energy storage devices participating in the grid response; import the relevant parameters into the winning response model, construct a mixed integer nonlinear programming, and use the solver to solve it, and obtain the daily charging and discharging strategy Θ′ of each energy storage device response:

[0104] Θ′=[θ′1,θ′2,...,θ′ m ,...,θ′ M ]; where θ′ m Indicates that the mth energy storage device actually implements the charging and discharging strategy in response to the day.

[0105] In this exemplary embodiment, after the power grid clears the demand response quantity and response price, the actual required response quantity of each device can be divided proportionally according to the winning bid quantity of the i-th response interval, and the winning bid response quantity, estimated profit and charging and discharging strategy of each energy storage device in the declaration stage of the energy storage device participating in the grid response can be obtained; the relevant parameters are imported into the winning bid response model, a mixed integer nonlinear programming is constructed, and the solver is used to solve it to obtain the charging and discharging strategy actually implemented on the m-th energy storage device response day.

[0106] In this application, each device responds to the energy storage charging and discharging plan to be executed on the day. Generally speaking, the virtual power plant participating in demand response needs to have the ability to remotely control each energy storage device and collect IoT data. When the time reaches 0:00:00 on the response day, the device is instructed to charge and discharge according to the strategy Θ′ until 0:00:00 the next day of the response day to end this round of demand response.

[0107] Figure 2 FIG. 1 is a schematic diagram showing an optimized energy storage charging and discharging process of a virtual power plant according to an exemplary embodiment. Figure 2 As shown in the figure, the optimized energy storage charging and discharging process of the virtual power plant includes:

[0108] Step 20, respond to the invitation;

[0109] Step 21, specify the response price;

[0110] Step 22: specify the devices that can participate in this response;

[0111] Step 23, integrating the invitation information, price information and information of the device to be responded to;

[0112] Step 24: Declare a response algorithm;

[0113] Step 25: Each device declares the response volume, estimated revenue, and charging and discharging strategy of each device;

[0114] Step 26: Submit the declared response quantity and response price to the power grid;

[0115] Step 27, obtaining the total winning bid response amount and response clearing price;

[0116] Step 28: Split the actual required response quantity of each device in proportion to the winning bid quantity;

[0117] Step 29, winning bid response algorithm;

[0118] Step 30, each device responds to the energy storage charging and discharging plan to be executed on the day;

[0119] Step 31, end.

[0120] The present disclosure provides an optimized energy storage charging and discharging device for a virtual power plant. Figure 3 FIG. 1 is a schematic diagram of a structure of an optimized energy storage charging and discharging device of a virtual power plant according to an exemplary embodiment. Figure 3 As shown in the figure, the optimized energy storage charging and discharging device of the virtual power plant includes:

[0121] The first information acquisition module 30 is used to acquire the invitation information of the power grid response invitation issued by the power grid; wherein the invitation information at least includes a response date, a response time period, a response granularity, and a response mode;

[0122] The second information acquisition module 31 is used to obtain the response price of the response time period issued by the power grid and the energy storage device information of the energy storage devices participating in the power grid response;

[0123] A model building module 32 is used to build a declaration response model and a winning bid response model for participating in the grid response invitation based on the invitation information of the grid response invitation, the response price of the response time period and the energy storage device information of the energy storage device participating in the grid response; wherein the declaration response model includes a first objective function with the goal of maximizing benefits in the declaration stage of the grid response invitation and a first constraint condition in the declaration stage; the winning bid response model includes a second objective function with the goal of maximizing benefits when executing the grid response invitation after the winning bid stage of the grid response invitation and a second constraint condition after the winning bid stage; the first objective function is the same as the second objective function; the first constraint condition is different from the second constraint condition;

[0124] The charging and discharging strategy allocation module 33 is used to respond to the grid response invitation issued by the grid based on the application response model and the bid winning response model, and allocate charging and discharging strategies to each energy storage device participating in the grid response.

[0125] In this exemplary embodiment, a power grid response invitation is received. The invitation information includes the response date, response time period, response granularity and response mode; wherein the response mode is generally "peak shaving" and "valley filling". Considering that the invitation information may contain K response intervals, it is assumed that the winning interval is expressed as [[t 1s ,t 1e ],[t 2s ,t 2e ],...,[t Ks ,t Ke ]], t is ,t ie They represent the start and end time of the ith response interval, respectively. The unit of time t is minute (min), where 1≤i≤K. The theoretical load response rate of the ith response interval is [r1,r2,...,r K ] The time-of-use electricity price at time t is obtained according to the power market to which the invitation response belongs, which is ρ(t); T represents the response granularity, which is generally 15T, 30T, 60T or 120T. Considering the technical reusability, after the decision-making scheme with a granularity of 15T is implemented, other decision-making schemes with coarser granularity can be obtained based on the scheme by summing the electricity. For example, the response interval [10.00, 10.15, 10.30, 10.45] obtained by the 15T granularity decision is [100, 150, 200, 100], that is, the energy storage discharge response is (100 / 0.25)KW, (150 / 0.25)KW, (200 / 0.25)KW, and (100 / 0.25)KW in the time periods 10.00-10.15, 10.15-10.30, 10.30-10.45, and 10.45-11.00, respectively. Then the response plan corresponding to the 30T granularity is [100+150, 200+100], and so on. The response plan corresponding to the 60T granularity is [100+150+200+100]. Among them, this application defaults to the implementation of the 15T declaration and winning bid response algorithm strategy, and other granularities are obtained by merging the average based on the 15T strategy. The historical load average information of the 5 days before the response day is taken as the baseline load of the response day, and the baseline load at time t is b(t).

[0126] In this exemplary embodiment, a response price is specified. The response price is expressed as [v1, v2, ..., v K ],v iIndicates the response price of the i-th response interval. Specifies the equipment that can participate in this response. For virtual power plants, energy storage equipment can participate in different business scenarios in different time periods. Assuming that there are M energy storage devices, the equipment index is represented by [1,2,3,...,m,...M], and m represents the number of the m-th energy storage device participating in the demand response. The attribute information (such as rated power, rated capacity, charging and discharging efficiency, and charging and discharging depth, etc.) and baseline information corresponding to the device can be associated through the number index. Then, the invitation information, price information, and information about the equipment to be responded to are integrated. The invitation information of the grid response invitation, the response price of the response time period, and the energy storage equipment information of the energy storage equipment participating in the grid response are used as input parameters of the declaration algorithm model to construct a declaration response algorithm model. With T as the response granularity, the corresponding number of strategy units is N=24*60 / T, and the "declaration-winning bid" two-stage collaborative optimization algorithm model is established with the economic optimization of the charging and discharging behavior of a single energy storage device as the goal.

[0127] In this exemplary embodiment, by analyzing the differences in characteristics between the declaration and bid winning stages, the two stages are integrated into a collaborative optimization algorithm model. The model internally associates the stage with the corresponding constraints for modeling, thereby adapting to the integrated decision-making needs of the entire chain from declaration to bid winning when the virtual power plant participates in demand response. The declaration and bid winning stages are collaboratively optimized to ultimately achieve the goal of maximizing the economic benefits of the virtual power plant.

[0128] According to the optimized energy storage charging and discharging device of the virtual power plant of the embodiment of the present disclosure, it is used to obtain the invitation information of the grid response invitation issued by the power grid; wherein the invitation information at least includes the response date, response time period, response granularity, and response mode; obtain the response price of the response time period issued by the power grid and the energy storage equipment information of the energy storage equipment participating in the grid response; based on the invitation information of the grid response invitation, the response price of the response time period and the energy storage equipment information of the energy storage equipment participating in the grid response, construct a declaration response model and a winning response model for participating in the grid response invitation; wherein the declaration response model includes a first objective function with the goal of maximizing benefits in the declaration stage of the grid response invitation and a first constraint condition in the declaration stage; the winning response model includes a second objective function with the goal of maximizing benefits when executing the grid response invitation after the winning stage of the grid response invitation and a second constraint condition after the winning stage; the first objective function is the same as the second objective function; the first constraint condition is different from the second constraint condition; based on the declaration response model and the winning response model, an invitation response is made to the grid response invitation issued by the power grid, and the charging and discharging strategy allocation of each energy storage equipment participating in the grid response is performed. This application establishes a mixed integer nonlinear model through the processes of response invitation, response capability assessment, response declaration, response bidding and response execution, with economic optimization as the guide in both the declaration and bidding stages. It also adapts the declaration response model and the bidding response model respectively by combining different constraints, so that the declaration and bidding stages are coordinated and optimized, so as to achieve the purpose of optimizing the benefits of the virtual power plant participating in the entire process of user-side demand response.

[0129] In some embodiments, the first objective function with the goal of maximizing revenue during the declaration phase of the power grid response invitation includes:

[0130] Among them, Obj is the maximum benefit during the declaration phase of the grid response invitation;

[0131] represents the total revenue of peak-valley arbitrage; ρ(i) is the time-of-use electricity price of the i-th response section;

[0132] represents the total response subsidy; where,

[0133] ξ represents the charging and discharging efficiency of the energy storage device; c is the energy storage charging efficiency, ξ d is the energy storage device discharge efficiency; η represents the demand response electricity price subsidy coefficient;

[0134] w i represents the charging and discharging capacity of the i-th decision-making unit, w i <0 means charging, w i >0 means discharge, w i=0 means neither discharging nor charging; s i Represents the charging and discharging time of the i-th decision unit, s i The value is 0 or 1, s i =0 means no charging or discharging, s i =1 means uniform charging and discharging in the entire decision-making unit for T minutes;

[0135] b(i) is the baseline load at time i; the baseline load is the power output of the energy storage device before participating in the grid response invitation; v i represents the response price of the ith response interval; t js ,t je They represent the start and end time of the j-th response interval respectively.

[0136] In this exemplary embodiment, the first objective function with the goal of maximizing revenue during the declaration phase of the power grid response invitation includes:

[0137] Among them, the benefit of the first objective function is divided into two parts, one part is represents the total profit of peak-to-valley arbitrage; the other part is It represents the total response subsidy, that is, the total response subsidy given by the power grid to the virtual power plant according to the power output of the virtual power plant. The above two parts of income are added together as the income in the declaration stage of the power grid response invitation to maximize the income.

[0138] In some embodiments, the first constraint condition in the application stage includes:

[0139] The energy storage state soci of the i-th decision unit is in the normal range:

[0140] dod max *rated_capacity≤soc i ≤cod max *rated_capacity, where dod max is the maximum depth coefficient of energy storage device discharge; cod max is the maximum depth factor of energy storage device charging; rated_capacity is the rated capacity of energy storage device; where,

[0141] In a strategy cycle, the initial state of the energy storage device is the same as the final state;

[0142] soc0=soc N ,in,

[0143] ξ c is the energy storage charging efficiency, ξ dis the energy storage device discharge efficiency; soc0 is the energy storage device initial state, soc i is the state of the energy storage device in the i-th decision unit, 1≤i≤N.

[0144] In this exemplary embodiment, the first constraint condition in the declaration phase mainly includes two. The first constraint condition is the energy storage state soc of the i-th decision unit. i In the normal range, the energy storage state soc of the i-th decision unit i It is neither too large nor too small. The second is that within a strategy cycle, the initial state and the final state of the energy storage of the i-th decision unit are the same. That is, if the initial state of the energy storage of the i-th decision unit is SOC 10%, then the final state SOC of the energy storage of the i-th decision unit is also 10%, so that the power of the energy storage of the i-th decision unit is fully utilized to maximize the power utilization efficiency.

[0145] In some embodiments, the second constraint condition after the bid winning stage includes:

[0146] The energy storage state of the i-th decision unit soc i In normal range:

[0147] dod max *rated_capacity≤soc i ≤cod max *rated_capacity, where dod max is the maximum depth coefficient of energy storage device discharge; cod max is the maximum depth factor of energy storage device charging; rated_capacity is the rated capacity of energy storage device; where,

[0148] In a policy cycle, the initial state of the energy storage device is the same as the final state:

[0149] soc0=soc N ,in, ξ c is the energy storage charging efficiency, ξ d is the energy storage device discharge efficiency; soc0 is the energy storage device initial state, soc i is the state of the energy storage device in the i-th decision unit, 1≤i≤N;

[0150] The response quantity of the jth response interval is subject to the constraints of the actual winning bid response quantity:

[0151]

[0152] Re s j <=x j Among them, Re sj is the response power of the jth response interval, χ j is the response power of the jth response interval after winning the bid; t js ,t je They represent the start and end time of the j-th response interval respectively.

[0153] In this exemplary embodiment, the second constraint condition after the bid winning stage is based on the first constraint condition in the declaration stage, and adds that the response power of the j-th response interval is subject to the constraint of the actual bid winning response amount.

[0154] The response quantity of the jth response interval is subject to the constraints of the actual winning bid response quantity: The response quantity Re s of the jth response interval j , so that Re s j <=x j , so that the response power of the jth response interval will not be too high and exceed the actual winning bid response amount.

[0155] In some embodiments, the charge and discharge strategy allocation module 33 is used to

[0156] Obtain the declared response volume, estimated revenue and historical charging and discharging strategies of energy storage devices participating in grid response;

[0157] Import relevant parameters into the declared response model, construct a mixed integer nonlinear programming, and use the solver to solve the optimal charging and discharging strategy θ for the mth energy storage device response day. m for:

[0158] θ m =[w1s1,w2s2,...,w i s i ,...,w N s N ];

[0159] The response power of the mth device in the jth response interval φ mj =Re s mj ;

[0160] The maximum response power of the mth device in each response interval on the response day [φ m1 ,φ m2 ,φ m3 ,...,φ mK ], the total response power of the jth response interval The response strategy of each device in this response is Θ=[θ1,θ2,...,θ m ,...,θ M ], the total response power of each response interval that can be reported for this response

[0161] Total_Re s=[Total_Re s1,Total_Re s2,...Total_Re s j ...,Total_Re s K ], the mth device execution strategy Θ m Total revenue p m =Obj and the total benefit of this response

[0162] In this exemplary embodiment, relevant parameters are imported into the declaration response model, a mixed integer nonlinear programming is constructed, and a solver is used to solve the model to obtain each response power and corresponding revenue in the declaration stage.

[0163] In some embodiments, the charge and discharge strategy allocation module 33 is used to

[0164] Submit the declared response quantity and response price of the grid response invitation to the grid, and wait for the grid to clear the demand response quantity and response price;

[0165] Get the total winning bid response quantity and response clearing price; where the winning bid response quantity is expressed as [χ1, χ2, ..., χ K ],χ i represents the winning quantity of the ith response interval, in kilowatt-hours (KWh), and the response clearing price is [v′1,v′2,...,v′ K ];

[0166] Splitting the actual required response quantity of each device in equal proportion according to the winning bid quantity of the i-th response interval;

[0167] The actual winning response quantity φ′ of the mth energy storage device in the jth response interval mj for:

[0168] Will Re s j <=x j χ j Replaced by the actual scalar φ′ mj ,Combined with the first constraint condition, a bid winning response algorithm model is constructed;

[0169] Obtain the winning response amount, estimated revenue and charging and discharging strategies of each energy storage device in the declaration stage of the energy storage devices participating in the grid response; import the relevant parameters into the winning response model, construct a mixed integer nonlinear programming, and use the solver to solve it, and obtain the daily charging and discharging strategy Θ′ of each energy storage device response:

[0170] Θ′=[θ′1,θ′2,...,θ′ m ,...,θ′ M ]; where θ′m Indicates that the mth energy storage device actually implements the charging and discharging strategy in response to the day.

[0171] In this exemplary embodiment, after the power grid clears the demand response quantity and response price, the actual required response quantity of each device can be divided proportionally according to the winning bid quantity of the i-th response interval, and the winning bid response quantity, estimated profit and charging and discharging strategy of each energy storage device in the declaration stage of the energy storage device participating in the grid response can be obtained; the relevant parameters are imported into the winning bid response model, a mixed integer nonlinear programming is constructed, and the solver is used to solve it to obtain the charging and discharging strategy actually implemented on the m-th energy storage device response day.

[0172] In this application, each device responds to the energy storage charging and discharging plan to be executed on the day. Generally speaking, the virtual power plant participating in demand response needs to have the ability to remotely control each energy storage device and collect IoT data. When the time reaches 0:00:00 on the response day, the device is instructed to charge and discharge according to the strategy Θ′ until 0:00:00 the next day of the response day to end this round of demand response.

[0173] The present disclosure provides a computer-readable storage medium on which a program for optimizing energy storage charging and discharging of a virtual power plant is stored. When the program for optimizing energy storage charging and discharging of a virtual power plant is executed by a processor, the method for optimizing energy storage charging and discharging of a virtual power plant described in the above embodiments is implemented.

[0174] The present disclosure provides a server, including a memory, a processor, and an optimized energy storage charging and discharging program for a virtual power plant stored in the memory and executable on the processor. When the processor executes the optimized energy storage charging and discharging program for the virtual power plant, the optimized energy storage charging and discharging method for the virtual power plant described in the above-mentioned embodiments is implemented.

[0175] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or otherwise processing in a suitable manner if necessary, and then stored in a computer memory.

[0176] It should be understood that the various parts of the present disclosure can be implemented in hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0177] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some 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 disclosure. 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 one or more embodiments or examples in a suitable manner.

[0178] In the description of the present disclosure, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present disclosure and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present disclosure.

[0179] In addition, the terms "first", "second", etc. used in the embodiments of the present disclosure are only used for descriptive purposes and should not be understood as indicating or implying relative importance, or implicitly indicating the number of technical features indicated in the embodiments. Therefore, the features defined by the terms "first", "second", etc. in the embodiments of the present disclosure may explicitly or implicitly indicate that at least one of the features is included in the embodiment. In the description of the present disclosure, the word "multiple" means at least two or two or more, such as two, three, four, etc., unless otherwise clearly and specifically defined in the embodiments.

[0180] In the present disclosure, unless otherwise clearly specified or limited in the embodiments, the terms "installed", "connected", "connected" and "fixed" etc. appearing in the embodiments should be understood in a broad sense. For example, the connection can be a fixed connection, a detachable connection, or an integrated connection. It can be understood that it can also be a mechanical connection, an electrical connection, etc.; of course, it can also be a direct connection, or an indirect connection through an intermediate medium, or it can be the internal connection of two elements, or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present disclosure can be understood according to the specific implementation situation.

[0181] In the present disclosure, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, a first feature being "above", "above" or "above" a second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. A first feature being "below", "below" or "below" a second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0182] Although the embodiments of the present disclosure have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present disclosure. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present disclosure.

Claims

1. A method for optimizing energy storage charging and discharging of a virtual power plant, characterized in that: include: Acquire invitation information of a power grid response invitation issued by the power grid; wherein the invitation information at least includes a response date, a response time period, a response granularity, and a response mode; Obtain the response price for the response time period issued by the power grid and the energy storage device information of the energy storage devices participating in the power grid response; Based on the invitation information of the grid response invitation, the response price of the response time period and the energy storage equipment information of the energy storage equipment participating in the grid response, a declaration response model and a winning bid response model for participating in the grid response invitation are constructed; wherein the declaration response model includes a first objective function with the goal of maximizing benefits in the declaration stage of the grid response invitation and a first constraint condition in the declaration stage; the winning bid response model includes a second objective function with the goal of maximizing benefits when executing the grid response invitation after the winning bid stage of the grid response invitation and a second constraint condition after the winning bid stage; the first objective function is the same as the second objective function; the first constraint condition is different from the second constraint condition; Based on the application response model and the bid winning response model, an invitation response is made to the grid response invitation issued by the grid, and a charging and discharging strategy is allocated to each energy storage device participating in the grid response.

2. The method for optimizing energy storage charging and discharging of a virtual power plant according to claim 1, characterized in that: The first objective function with the goal of maximizing revenue during the declaration phase of the power grid response invitation includes: Among them, Obj is the maximum benefit during the declaration phase of the grid response invitation; represents the total revenue of peak-valley arbitrage; ρ(i) is the time-of-use electricity price of the i-th response section; represents the total response subsidy; where, ξ represents the charging and discharging efficiency of the energy storage device; c is the energy storage charging efficiency, ξ d is the energy storage device discharge efficiency; η represents the demand response electricity price subsidy coefficient; w i represents the charging and discharging capacity of the i-th decision-making unit, w i <0 means charging, w i >0 means discharge, w i =0 means neither discharging nor charging; s i Represents the charging and discharging time of the i-th decision unit, s i The value is 0 or 1, s i =0 means no charging or discharging, s i =1 means uniform charging and discharging in the entire decision-making unit for T minutes; b(i) is the baseline load at time i; the baseline load is the power output of the energy storage device before participating in the grid response invitation; v i represents the response price of the ith response interval; t js ,t je They represent the start and end time of the j-th response interval respectively.

3. The method for optimizing energy storage charging and discharging of a virtual power plant according to claim 1, characterized in that: The first constraint condition of the application stage includes: The energy storage state of the i-th decision unit soc i In normal range: dod max *rated_capacity≤soc i ≤cod max *rated_capacity, where dod max is the maximum depth coefficient of energy storage device discharge; cod max is the maximum depth coefficient of energy storage device charging; rated_capacity is the rated capacity of energy storage device; where, In a strategy cycle, the initial state of the energy storage device is the same as the final state; soc0=soc N ,in, ξ c is the energy storage charging efficiency, ξ d is the energy storage device discharge efficiency; soc0 is the energy storage device initial state, soc i is the state of the energy storage device in the i-th decision unit, 1≤i≤N.

4. The method for optimizing energy storage charging and discharging of a virtual power plant according to claim 2, characterized in that: The second constraint condition after the bid winning stage includes: The energy storage state of the i-th decision unit soc i In normal range: dod max *rated_capacity≤soc i ≤cod max *rated_capacity, where dod max is the maximum depth coefficient of energy storage device discharge; cod max is the maximum depth factor of energy storage device charging; rated_capacity is the rated capacity of energy storage device; where, In a policy cycle, the initial state of the energy storage device is the same as the final state: soc0=soc N ,in, ξ c is the energy storage charging efficiency, ξ d is the energy storage device discharge efficiency; soc0 is the energy storage device initial state, soc i is the state of the energy storage device in the i-th decision unit, 1≤i≤N; The response quantity of the jth response interval is subject to the constraints of the actual winning bid response quantity: Res j <=x j Among them, Res j is the response power of the jth response interval, χ j is the response power of the jth response interval after winning the bid; t js ,t je They represent the start and end time of the j-th response interval respectively.

5. The method for optimizing energy storage charging and discharging of a virtual power plant according to claim 4, characterized in that: The step of responding to the grid response invitation issued by the grid based on the application response model and the bid winning response model, and allocating charging and discharging strategies for each energy storage device participating in the grid response, includes: Obtain the declared response volume, estimated revenue and historical charging and discharging strategies of energy storage devices participating in grid response; Import relevant parameters into the declared response model, construct a mixed integer nonlinear programming, and use the solver to solve the optimal charging and discharging strategy θ for the mth energy storage device response day. m for: θ m =[w1s1,w2s2,...,w i s i ,...,In N s N ]; The response power of the mth device in the jth response interval φ mj =Res mj ; The maximum response power of the mth device in each response interval on the response day [φ m1 ,φ m2 ,φ m3 ,...,φ mK ], the total response power of the jth response interval The response strategy of each device in this response is Θ=[θ1,θ2,...,θ m ,...,θ M ], the total response power of each response interval that can be reported for this response Total_Res=[Total_Res1,Total_Res2,...Total_Res j ...,Total_Res K ], the mth device execution strategy Θ m Total revenue p m =Obj and the total benefit of this response 6. The method for optimizing energy storage charging and discharging of a virtual power plant according to claim 5, characterized in that: The step of responding to the grid response invitation issued by the grid based on the application response model and the bid winning response model, and allocating charging and discharging strategies for each energy storage device participating in the grid response, includes: Submit the declared response quantity and response price of the grid response invitation to the grid, and wait for the grid to clear the demand response quantity and response price; Get the total winning bid response quantity and response clearing price; where the winning bid response quantity is expressed as [χ1, χ2, ..., χ K ],χ i represents the winning quantity of the ith response interval, in kilowatt-hours (KWh), and the response clearing price is [v1′,v′2,...,v′ K ]; Splitting the actual required response quantity of each device in equal proportion according to the winning bid quantity of the i-th response interval; The actual winning response quantity φ′ of the mth energy storage device in the jth response interval mj for: Will Res j <=x j χ j Replaced by the actual scalar φ′ mj ,Combined with the first constraint condition, a bid winning response algorithm model is constructed; Obtain the winning response amount, estimated revenue and charging and discharging strategies of each energy storage device in the declaration stage of the energy storage devices participating in the grid response; import the relevant parameters into the winning response model, construct a mixed integer nonlinear programming, and use the solver to solve it, and obtain the daily charging and discharging strategy Θ′ of each energy storage device response: Θ′=[θ′1,θ′2,...,θ′ m ,...,θ′ M ]; where θ′ m Indicates that the mth energy storage device actually implements the charging and discharging strategy in response to the day.

7. An optimized energy storage charging and discharging device for a virtual power plant, characterized in that: include: A first information acquisition module is used to acquire the invitation information of the power grid response invitation issued by the power grid; wherein the invitation information at least includes a response date, a response time period, a response granularity, and a response mode; A second information acquisition module is used to obtain the response price of the response time period issued by the power grid and the energy storage device information of the energy storage devices participating in the power grid response; A model building module, for building a declaration response model and a winning bid response model for participating in a grid response invitation based on the invitation information of the grid response invitation, the response price of the response time period and the energy storage device information of the energy storage device participating in the grid response; wherein the declaration response model includes a first objective function with the goal of maximizing benefits in the declaration stage of the grid response invitation and a first constraint condition in the declaration stage; the winning bid response model includes a second objective function with the goal of maximizing benefits when executing the grid response invitation after the winning bid stage of the grid response invitation and a second constraint condition after the winning bid stage; the first objective function is the same as the second objective function; the first constraint condition is different from the second constraint condition; The charging and discharging strategy allocation module is used to respond to the grid response invitation issued by the grid based on the application response model and the bid winning response model, and allocate charging and discharging strategies to each energy storage device participating in the grid response.

8. The optimized energy storage charging and discharging device of a virtual power plant according to claim 7, characterized in that: The first objective function with the goal of maximizing revenue during the declaration phase of the power grid response invitation includes: Among them, Obj is the maximum benefit during the declaration phase of the grid response invitation; represents the total revenue of peak-valley arbitrage; ρ(i) is the time-of-use electricity price of the i-th response section; represents the total response subsidy; where, ξ represents the charging and discharging efficiency of the energy storage device; c is the energy storage charging efficiency, ξ d is the energy storage device discharge efficiency; η represents the demand response electricity price subsidy coefficient; w i represents the charging and discharging capacity of the i-th decision-making unit, w i <0 means charging, w i >0 means discharge, w i =0 means neither discharging nor charging; s i Represents the charging and discharging time of the i-th decision unit, s i The value is 0 or 1, s i =0 means no charging or discharging, s i =1 means uniform charging and discharging in the entire decision-making unit for T minutes; b(i) is the baseline load at time i; the baseline load is the power output of the energy storage device before participating in the grid response invitation; v i represents the response price of the ith response interval; t js ,t je They represent the start and end time of the j-th response interval respectively.

9. A computer-readable storage medium, characterized in that: An optimized energy storage charging and discharging program for a virtual power plant is stored thereon. When the optimized energy storage charging and discharging program for a virtual power plant is executed by a processor, the optimized energy storage charging and discharging method for a virtual power plant described in any one of claims 1-6 is implemented.

10. A server, characterized in that: The method comprises a memory, a processor and an optimized energy storage charging and discharging program for a virtual power plant which is stored in the memory and can be run on the processor. When the processor executes the optimized energy storage charging and discharging program for the virtual power plant, the optimized energy storage charging and discharging method for the virtual power plant described in any one of claims 1 to 6 is implemented.

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