Independent energy storage power station time-sharing composite operation method and system based on target optimization

Abstract

The invention provides a time-phased composite operation method and system for an independent energy storage power station based on target optimization, and the method comprises the steps: constructing a time-phased multi-transaction variety regulation and control model of the independent energy storage power station through the income analysis, energy storage charging and discharging power analysis and risk analysis of the independent energy storage power station in peak regulation service, frequency modulation service and peak-valley arbitrage; and a target function of target optimization and constraint conditions thereof are introduced to obtain a comprehensive regulation and control strategy of the energy storage power station in different time periods and different transaction varieties, so that the elastic profit maximization of the energy storage power station as an independent individual in a power transaction market is realized. Through income analysis, energy storage charging and discharging power analysis and risk analysis of the independent energy storage power station in peak regulation service, frequency modulation service and peak-valley arbitrage, a target regulation and control model of the independent energy storage power station is constructed, and a comprehensive regulation and control strategy of the energy storage power station in different time periods is obtained. Therefore, the elastic revenue maximization of the energy storage power station as an independent individual in the electricity transaction market is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of independent energy storage power station operation mode analysis, and in particular to a time-division composite operation method and system for an independent energy storage power station based on target optimization. Background Art

[0002] Independent energy storage power stations have efficient and flexible grid regulation capabilities and the ability to participate in transactions between different types of markets to obtain greater profits. They are an important part of new energy storage participating in the operation of the power market. At present, the participation of domestic independent energy storage power stations (IESPS) in power market transactions is in the initial stage of commercialization. The operation strategy is not perfect, the transaction mechanism is single, and the profit model is not clear, which limits their profit from participating in market transactions. This has brought great resistance to energy storage as an independent individual to participate in the transformation of the entire power system. Therefore, in the power trading market environment, how to enhance the competitive advantage of IESPS and the benefits of participating in market transactions is closely related to the formulated energy storage operation and regulation strategy.

[0003] At present, the research focus on energy storage operation and control strategies is mostly on joint optimization with other objects. Its types are divided into three types: energy storage on the power generation side coordinated with renewable energy generation joint optimization and control, energy storage on the grid side to assist the stable operation of the grid, and energy storage on the user side with user loads and distributed power sources. Among them, on the power generation side, coordinated with renewable energy generation joint optimization and control refers to the joint control of energy storage and renewable energy to achieve overall optimization of power generation, storage and scheduling, but its integration and operation are complex, the initial investment cost is high, and the space is limited, which is not conducive to popularization in different regions; on the grid side, assisting the stable operation strategy of the grid refers to regulating energy storage to participate in the peak and frequency regulation, power leveling and short-term support of electricity to achieve joint overall economic regulation. However, its high dependence on the grid structure, poor flexibility and single trading varieties lead to low overall returns; on the user side, joint optimization and control with user loads and distributed power sources refers to the use of user-side energy storage technology to coordinate the management of user loads and distributed power sources to achieve optimal scheduling of the overall energy system. However, it has limited transaction stages that can be participated in, inconsistent technical standards, and does not consider the possible income risks when market price fluctuations are not considered. Summary of the invention

[0004] In order to solve the problems of imperfect operation and control strategies and unclear profit models of independent energy storage power stations in the power trading market, the present invention provides a method and system for composite operation of independent energy storage power stations in different time periods based on target optimization, which is suitable for the operation and control of different trading products in different time periods jointly regulated by IESPS in the power spot market and auxiliary market. Through the profit analysis, energy storage charging and discharging power analysis, and risk analysis of independent energy storage power stations in peak-shaving services, frequency regulation services, and peak-valley arbitrage, a target control model of independent energy storage power stations is constructed, and a comprehensive control strategy for energy storage power stations in different time periods is obtained, thereby realizing the maximization of the elastic benefits of energy storage power stations as independent individuals in the power trading market.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A time-divided composite operation method for an independent energy storage power station based on target optimization, the method constructs a time-divided multi-transaction product control model for an independent energy storage power station through revenue analysis, energy storage charging and discharging power analysis, and risk analysis in peak-shaving services, frequency regulation services, and peak-valley arbitrage, introduces an objective function of target optimization and its constraints, and derives a comprehensive control strategy for different transaction products of the energy storage power station in different time periods, thereby maximizing the elastic benefits of the energy storage power station as an independent individual in the power trading market;

[0007] The specific steps include:

[0008] S1. Analyze the market benefits of independent energy storage power stations and establish a multi-transaction regulation model for independent energy storage power stations to participate in the power market; specifically include: establishing the benefits of participating in the power grid frequency regulation service Model, benefits of participating in power grid peak load regulation services Model, peak-to-valley arbitrage benefits Model, and derive the charging power P of the independent energy storage power station c,t , discharge power P dc,t Model;

[0009] S2. Introduce the conditional value at risk indicator CVaR to conduct risk assessment of transaction price fluctuations; calculate the return risk cost under transaction price fluctuations

[0010] S3. Establish an objective function and its constraints, the objective function comprehensively considers the benefits of participating in the power grid frequency regulation service Revenue from participating in power grid peak load regulation services Peak-to-valley arbitrage benefits At the same time, the operating costs of independent energy storage power stations are also considered. The cost of shifting deviations from participating in the day-ahead to real-time market The return risk cost under the transaction price fluctuation obtained in step S2 Maximize the benefits of independent energy storage power stations participating in the market with multiple trading products in different time periods;

[0011] S4. Taking the optimal profit of independent energy storage power stations participating in multi-time trading products as the goal, the objective function is solved by evolution to obtain the upward frequency modulation power of independent energy storage power stations. Downward FM power Peak load P t pl , Electricity Sales Electricity Purchase The optimal control plan is generated and executed in real time.

[0012] Furthermore, in step S1, the market revenue analysis of the independent energy storage power station includes: participating in the peak load regulation and frequency regulation services of the auxiliary market and participating in the peak-valley arbitrage services of the electricity spot market; establishing an independent energy storage power station participating in the electricity market time-divided multi-transaction product regulation model specifically includes the following:

[0013] (1) Participation in grid frequency regulation services: Independent energy storage power stations provide frequency regulation capacity and frequency regulation mileage auxiliary services based on market frequency regulation needs and their own regulation performance to maintain the system frequency of the power system within an appropriate range and ensure the stable operation of the power system. The profit model for participating in grid frequency regulation services is as follows:

[0014]

[0015] In the formula, Profits from independent energy storage power stations participating in frequency regulation services; They are frequency regulation capacity price and frequency regulation mileage price respectively; They are the upward frequency regulation power and downward frequency regulation power reported by the independent energy storage power station; To provide actual power frequency modulation coefficient;

[0016] (2) Participation in grid peak load regulation services: Independent energy storage power stations charge in the valley part of the load curve and discharge in the peak part according to the market peak load regulation demand. However, considering that the peak load discharge of independent energy storage power stations can be directly connected to the grid for arbitrage when participating in peak-valley arbitrage, the income only includes the peak load compensation during valley charging. The income model of participating in grid peak load regulation services is as follows:

[0017]

[0018] In the formula, The revenue from independent energy storage power stations participating in peak load regulation services; P is the peak load compensation price; t pl The peak-shaving electricity reported for IESPS;

[0019] (3) Peak-valley arbitrage: Independent energy storage power stations purchase electricity at low prices and then sell them at high prices based on the fluctuations in electricity prices in the energy market, earning the time-sharing price difference in the middle; this income is determined by the price difference between the peak and valley periods and the day-ahead-real-time period. The profit model of peak-valley arbitrage for independent energy storage power stations is as follows:

[0020]

[0021] In the formula, Profits from peak-valley arbitrage for independent energy storage power stations; The price difference of electricity for buying low and buying high; They are the electricity sold and electricity purchased reported by the independent energy storage power station;

[0022] (4) Construct a charging and discharging power model control model for independent energy storage power stations participating in multi-variety transactions in the electricity market, as follows:

[0023]

[0024]

[0025] Where P c,t , P dc,t They are the charging and discharging power of the independent energy storage power station. They are 0-1 state variables for independent energy storage power stations participating in frequency regulation auxiliary services, peak regulation auxiliary services and peak-valley arbitrage.

[0026] Furthermore, in step S2, the risk assessment based on transaction price fluctuations specifically includes the following contents:

[0027] According to the profit model of independent energy storage power stations, the fluctuation of market transaction prices can directly affect the profits of power stations participating in multi-variety transactions in the electricity market. If the actual transaction price does not reach the expected price, the original regulation plan cannot achieve the optimal profit; conversely, the original regulation plan misses the optimal discharge interval, thus failing to maximize profits.

[0028] Taking into account the impact of price fluctuations, the conditional risk value indicator CVaR is now introduced. CVaR is the expected value of loss at a given confidence level. It can reasonably quantify the risk cost of the independent energy storage power station participating in market transactions under different price fluctuation scenarios. The specific expression formula is as follows:

[0029]

[0030] In the formula, is the risk cost of return under transaction price fluctuations; T is the total number of market trading periods in which independent energy storage power stations participate; λ and α are risk attitude coefficients and confidence levels, respectively; η and Z tare the risk value of market price fluctuations and the portion of the profit of the independent energy storage power station that exceeds the risk value during the t-th trading period;

[0031] As a conditional risk metric, CVaR provides the worst-case loss that may occur at different confidence levels; this helps operators to more fully understand the profit risk of independent energy storage power stations under market price fluctuations, and to better make risk management and decisions.

[0032] Furthermore, in step S3, an optimization control strategy for independent energy storage power stations to participate in multi-variety transactions in the market under the target optimization algorithm is designed; the target function of the target optimization is specifically as follows:

[0033] Comprehensive consideration of the benefits of participating in power grid frequency regulation services Revenue from participating in power grid peak load regulation services Peak-to-valley arbitrage benefits At the same time, the operating costs of independent energy storage power stations are also considered. The cost of shifting deviations from participating in the day-ahead to real-time market The return risk cost under the transaction price fluctuation obtained in step S2 In order to maximize the benefits of independent energy storage power stations participating in the market with multiple trading products in different time periods, the objective function calculation is shown in formula (7) to formula (9):

[0034]

[0035]

[0036]

[0037] In the formula, are the operating cost of an independent energy storage power station and the deviation transfer cost of participating in the day-ahead-real-time market; B av is the unit operating cost of energy storage; c , dc are the charging and discharging efficiencies of the independent energy storage power station respectively; μ is the allowable deviation ratio of power purchase in the day-ahead and real-time stages; To provide the actual power frequency regulation coefficient, the superscripts da and rt represent the parameters or variables of the day-ahead and real-time stages respectively, the subscript t represents the tth transaction, and T represents the total number of market trading periods in which the independent energy storage power station participates; They are 0-1 state variables for independent energy storage power stations participating in frequency regulation auxiliary services, peak regulation auxiliary services and peak-valley arbitrage.

[0038] Furthermore, in step S3, in order to prevent the energy storage in the independent energy storage power station from being overcharged or over-discharged and to ensure that the declared power is within the range of the energy storage regulation characteristics, it is necessary to constrain the energy storage's own regulation characteristics and the declared power when participating in multi-product transactions. The constraint conditions include:

[0039] Energy storage regulation characteristics constraints

[0040]

[0041]

[0042]

[0043] In the formula, They are respectively the energy storage charging and discharging power limits; SOC max , SOC min They are the upper and lower limits of the energy storage state of charge; SOC T SOC0 and SOC1 are the end and start states of charge of the energy storage power station respectively; E N is the rated capacity of the energy storage station; Δc is the self-discharge rate of the energy storage station, SOC t is the charge state of the energy storage power station at time t.

[0044] Furthermore, in step S3, in order to prevent the energy storage in the independent energy storage power station from being overcharged or over-discharged and to ensure that the declared power is within the range of the energy storage regulation characteristics, it is necessary to constrain the energy storage's own regulation characteristics and the declared power when participating in multi-product transactions. The constraint conditions include:

[0045] Constraints on Energy Storage Participating in Multiple Trading Products

[0046]

[0047]

[0048]

[0049]

[0050]

[0051] In the formula, They are respectively the energy storage charging and discharging power limits.

[0052] Furthermore, in step S4, a non-dominated genetic algorithm NSGA-III with an elite strategy is used to evolve and solve the objective function, which specifically includes the following:

[0053] Step 1: Population initialization, where each individual is a multidimensional vector, representing the upward frequency regulation power, downward frequency regulation power, peak load, power sales, power purchase, and current time and regional electricity sales price reported by the independent energy storage power station, and the evolutionary generation is set to 1;

[0054] Step 2: Determine whether the first generation subpopulation has completed the non-dominated sorting, selection, crossover, and mutation operations. If not, return to the previous step and repeat the operation. If the above operations are completed, the evolutionary generation is increased by 1 and the process goes to step 3.

[0055] Step 3: Merge the parent population with the offspring population generated in step 2;

[0056] Step 4: Determine whether the new parent population meets the requirements. If not, perform fast non-dominated sorting, crowding and elite strategy operations, and return to step 3; if the new parent population meets the requirements, go to step 5;

[0057] Step 5: Perform selection, crossover, and mutation operations on the new parent population;

[0058] Step 6: Determine whether the algorithm has run to the maximum number of iterations. If not, return to step 3; if the number of iterations reaches the maximum, the algorithm ends.

[0059] The upward frequency regulation power of the final independent energy storage power station Downward FM power Peak load P t pl , Electricity Sales Electricity Purchase The optimal control plan is generated and executed in real time.

[0060] A system for implementing the time-division composite operation method of an independent energy storage power station based on target optimization, the system comprising:

[0061] (1) Independent energy storage power station market revenue analysis module, which analyzes the market revenue of independent energy storage power stations and establishes a multi-transaction regulation model for independent energy storage power stations to participate in the power market; specifically, it includes: establishing the revenue of participating in the power grid frequency regulation service Model, benefits of participating in power grid peak load regulation services Model, peak-to-valley arbitrage benefits Models are used to derive charging and discharging power models of independent energy storage power stations;

[0062] (2) a risk assessment module, which performs risk assessment on transaction price fluctuations and calculates the return risk cost under transaction price fluctuations;

[0063] (3) An objective function establishment module, which establishes an objective function and its constraints, and the objective function comprehensively considers the benefits of participating in the power grid frequency regulation service Revenue from participating in power grid peak load regulation services Peak-to-valley arbitrage benefits At the same time, the operating costs of independent energy storage power stations are also considered. The cost of shifting the deviation from participating in the day-ahead to real-time market The return risk cost under the transaction price fluctuation obtained in step S2 Maximize the benefits of independent energy storage power stations participating in the market with multiple trading products in different time periods;

[0064] (4) An objective function solving module, which solves the objective function by evolution and obtains the upward frequency modulation power of the independent energy storage power station Downward FM power Peak load P t pl , Electricity Sales Electricity Purchase The optimal control plan is generated and executed in real time.

[0065] The present invention also provides a computer, comprising a processor and a memory connected thereto;

[0066] Wherein, the processor is configured to execute the time-division composite operation method of an independent energy storage power station based on target optimization;

[0067] The memory is used to store executable instructions of the processor.

[0068] The present invention also provides a computer storable medium on which a computer program is stored. The computer program is executed by a processor to implement the time-divided composite operation method of an independent energy storage power station based on target optimization.

[0069] Compared with the prior art, the present invention has the following beneficial effects:

[0070] 1. The present invention provides a method and system for composite operation of independent energy storage power stations in different time periods based on target optimization, which is suitable for the operation and regulation of different trading products in different time periods of IESPS in the joint regulation of the electricity spot market and the auxiliary market. Through the profit analysis, energy storage charging and discharging power analysis, and risk analysis of independent energy storage power stations in peak-shaving services, frequency regulation services, and peak-valley arbitrage, a target regulation model for independent energy storage power stations is constructed, and a comprehensive regulation strategy for energy storage power stations in different time periods is obtained, thereby maximizing the elastic benefits of energy storage power stations as independent individuals in the electricity trading market.

[0071] 2. The present invention clarifies the development mode and basic control requirements of IESPS in participating in various trading products in combination with its profit model in the market, and establishes the IESPS control model after comprehensive analysis; based on the control model, the conditional risk value is used to express the profit risk cost caused by market price fluctuations when IESPS participates in the market day-ahead and real-time transactions, and combined with the electricity consumption and electricity prices in different time periods, establishes the day-ahead and real-time optimization control strategy for IESPS participating in multi-product transactions in the market under the target optimization algorithm; it is feasible and economical. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 It is a schematic diagram of the overall control strategy of the present invention.

[0073] Figure 2 It is a schematic diagram of the time-division control strategy of the present invention.

[0074] Figure 3 It is a flow chart of the proposed NSGA-III optimization algorithm provided by the present invention.

[0075] Figure 4 This is a schematic diagram of IESPS participating only in frequency regulation ancillary services.

[0076] Figure 5 This is a schematic diagram of IESPS only participating in peak load ancillary services.

[0077] Figure 6 This is a schematic diagram of IESPS only participating in peak-to-valley arbitrage.

[0078] Figure 7 This is a schematic diagram of IESPS’s participation in multiple trading products.

[0079] Figure 8 It is the regulation income of the power station under different risk attitudes. DETAILED DESCRIPTION

[0080] The specific implementation of the present invention will be further described below in conjunction with the accompanying drawings:

[0081] Embodiment 1

[0082] This embodiment provides a time-divided composite operation method for an independent energy storage power station based on target optimization. The method is to construct a time-divided multi-transaction product control model for an independent energy storage power station through revenue analysis, energy storage charging and discharging power analysis, and risk analysis of the independent energy storage power station in peak-shaving service, frequency regulation service, and peak-valley arbitrage, introduce the objective function of target optimization and its constraints, and derive a comprehensive control strategy for different transaction products of the energy storage power station in different time periods, thereby maximizing the elastic revenue of the energy storage power station as an independent individual in the power trading market.

[0083] like Figure 1The specific implementation process is as follows:

[0084] (1) Establishment of a multi-trading regulation model for IESPS in the electricity market

[0085] S1-1. Market revenue analysis of independent energy storage

[0086] In the context of the power market, the current independent energy storage power station profit model can be determined from different types of power markets, participating in joint transactions in the power spot market and the power auxiliary market, which brings new opportunities for the provincial IESPS profit model. The main sources of income include participating in the peak load and frequency regulation services of the auxiliary market and participating in the peak-valley arbitrage services of the power spot market.

[0087] (1) Participate in grid frequency regulation services. Independent energy storage power stations provide frequency regulation capacity and frequency regulation mileage auxiliary services based on market frequency regulation needs and their own regulation performance. Maintain the system frequency of the power system within an appropriate range to ensure stable operation of the power system. The specific revenue regulation model is as follows:

[0088]

[0089] In the formula, Profits from independent energy storage power stations participating in frequency regulation services; They are frequency regulation capacity price and frequency regulation mileage price respectively; They are the upward frequency modulation power and downward frequency modulation power reported by IESPS respectively; Provides actual power frequency modulation coefficient.

[0090] (2) Participate in the peak load regulation service of the power grid. Independent energy storage power stations charge in the valley part of the load curve and discharge in the peak part according to the market peak load regulation demand. However, considering that the peak discharge can be directly connected to the grid for arbitrage when IESPS participates in peak-valley arbitrage, the income only includes the peak load compensation during valley charging. The specific income regulation model is as follows:

[0091]

[0092] In the formula, The revenue from independent energy storage power stations participating in peak load regulation services; is the peak load compensation price; P t pl This is the peak-shaving electricity declared by IESPS.

[0093] (3) Peak-valley arbitrage. Independent energy storage power stations purchase electricity at low prices and then sell them at high prices based on the fluctuations in electricity prices in the electricity market, earning the time-sharing price difference. This income is determined by the price difference between the peak and valley periods and the day-ahead-real-time stage. The specific income regulation model is as follows:

[0094]

[0095] In the formula, Profits from peak-valley arbitrage for independent energy storage power stations; The price difference of electricity for buying low and buying high; They are respectively the electricity sold and electricity purchased as reported by IESPS.

[0096] S1-2. Charging and discharging power model of independent energy storage power station

[0097] Construct a control model for independent energy storage power stations to participate in multi-variety transactions in the electricity market, as follows:

[0098]

[0099]

[0100] Where P c,t , P dc,t They are the charging and discharging power of the independent energy storage power station. They are 0-1 state variables for independent energy storage power stations participating in frequency regulation auxiliary services, peak regulation auxiliary services and peak-valley arbitrage.

[0101] The value of is set as follows:

[0102] According to the characteristics of system power load and new energy consumption, the daily power consumption is divided into four periods: peak, peak, normal and valley. The profit model of IESPS in the power market environment is analyzed based on the distribution of peak, peak, normal and valley time to maximize the benefits of energy storage power stations. Figure 2 shown.

[0103] Setting up IESPS can participate in the purchase and sale of electricity in the electric energy market, peak load ancillary service transactions and frequency regulation ancillary service transactions in the power ancillary service market. Its profit model is as follows:

[0104] 1) Participate in the frequency regulation auxiliary service period. IESPS provides frequency regulation capacity service or frequency regulation mileage service based on the market frequency regulation demand and its own regulation performance. The income is calculated according to the two-part compensation mechanism. is 1, and the rest are 0.

[0105] 2) Participate in peak load shaving auxiliary service period. IESPS charges during low load valleys and discharges during peak load valleys according to the market peak load shaving demand. Considering that when IESPS participates in peak-valley arbitrage, peak discharge can be directly connected to the grid for arbitrage. This income only includes peak load shaving compensation for valley charging. At this time, is 1, and the rest are 0.

[0106] 3) Peak-valley price arbitrage period. IESPS purchases electricity at low prices and sells electricity at high prices according to the price fluctuations in the energy market. The income is determined by the price difference between the peak-valley period and the day-ahead-real-time stage. is 1, and the rest are 0.

[0107] S1-3. Risk assessment based on transaction price fluctuations

[0108] According to the profit model of independent energy storage power stations, the fluctuation of market transaction prices will directly affect the income of power stations participating in multi-variety transactions in the electricity market. If the actual transaction price does not reach the expected price, the original regulation plan may not be able to achieve the optimal income; conversely, the original regulation plan may miss the optimal discharge interval, thus failing to maximize income.

[0109] Taking into account the impact of price fluctuations, the conditional value at risk (CVaR) indicator is now introduced. CVaR is the expected value of loss at a given confidence level. It can reasonably quantify the risk-benefit cost of IESPS participating in market transactions under different price fluctuation scenarios. The specific expression formula is as follows:

[0110]

[0111] In the formula, is the risk cost of return under transaction price fluctuations; T is the total number of market trading periods in which IESPS participates; λ and α are risk attitude coefficient and confidence level respectively; η, Z t They are the risk value of market price fluctuations and the part of the profit of IESPS that exceeds the risk value in the tth trading period.

[0112] As a conditional risk metric, CVaR provides the worst-case loss that may occur at different confidence levels. This helps operators to more fully understand the return risk of IESPS under market price fluctuations and make better risk management and decisions.

[0113] (2) IESPS time-based operation mode control strategy

[0114] S2-1. Optimization objectives of analysis and control models

[0115] Considering the operating cost of IESPS, the deviation transfer cost of participating in the day-ahead-real-time market, and the risk of regulatory income caused by market price fluctuations, the benefits of IESPS participating in multiple trading products in the market are maximized. The optimization target calculation is shown in formulas (7) to (9):

[0116]

[0117]

[0118]

[0119] In the formula, are the operating cost of an independent energy storage power station and the deviation transfer cost of participating in the day-ahead-real-time market; B av is the unit operating cost of energy storage; c , dc are the charging and discharging efficiencies of IESPS respectively; μ is the allowable deviation ratio of electricity purchase in the day-ahead and real-time stages; the superscripts da and rt represent the parameters or variables in the day-ahead and real-time stages respectively.

[0120] S2. Constraints

[0121] In order to prevent overcharging and over-discharging of energy storage in IESPS and ensure that its reported power is within the range of energy storage regulation characteristics, it is necessary to restrict the regulation characteristics of the energy storage itself and the reported power when participating in multi-variety transactions.

[0122] (1) Constraints on energy storage regulation characteristics

[0123]

[0124]

[0125]

[0126] In the formula, They are respectively the energy storage charging and discharging power limits; SOC max , SOC min They are the upper and lower limits of the energy storage state of charge; SOC T SOC0 and SOC1 are the end and start states of charge of the energy storage power station respectively; E N is the rated capacity of the energy storage power station; Δc is the self-discharge rate of the energy storage power station.

[0127] (2) Constraints on Energy Storage Participating in Multiple Trading Products

[0128]

[0129]

[0130]

[0131]

[0132]

[0133] S2-3. Optimizing control strategies

[0134] Establish an optimal control strategy for IESPS to participate in multiple trading products in the market under multiple time periods. Combined with the market's day-ahead price and return risk, with the goal of optimizing the return of IESPS participating in multiple trading products, use the non-dominated sorting genetic algorithm to optimize the IESPS's upward frequency modulation power, downward frequency modulation power, peak load, power sales, and power purchase, and obtain the optimal control plan and execute it in real time.

[0135] In this embodiment, a non-dominated sorting genetic algorithm, namely, NSGA-III algorithm, is used to solve the objective function to obtain the optimal control plan for upward frequency regulation power, downward frequency regulation power, peak power, power sales, and power purchase.

[0136] The process of NSGA-III algorithm is as follows Figure 3 The specific steps are as follows:

[0137] Step 1: Population initialization, where each individual is a six-dimensional vector, representing the upward frequency regulation power, downward frequency regulation power, peak-shaving power, power sales, power purchase, and current time and regional power sales price reported by IESPS, and the evolutionary generation is set to 1;

[0138] Step 2: Determine whether the first generation subpopulation has completed the non-dominated sorting, selection, crossover, and mutation operations. If not, return to the previous step and repeat the operation. If the above operations are completed, the evolutionary generation is increased by 1 and the process goes to step 3.

[0139] Step 3: Merge the parent population with the offspring population generated in step 2;

[0140] Step 4: Determine whether the new parent population meets the requirements. If not, perform fast non-dominated sorting, crowding and elite strategy operations, and return to step (3); if the new parent population meets the requirements, go to step 5;

[0141] Step 5: Perform selection, crossover, and mutation operations on the new parent population;

[0142] Step 6: Determine whether the algorithm has run to the maximum number of iterations. If not, return to step 3; if the number of running iterations reaches the maximum value, the algorithm ends.

[0143] Finally, the upward frequency modulation power of the independent energy storage power station is obtained Downward FM power Peak load P t pl , Electricity Sales Electricity Purchase The optimal control plan is generated and executed in real time. [Specific embodiment]

[0145] According to the changes in electricity prices in different time periods, the costs, profits and risks of various operating modes are calculated, and the operating mode with maximum profit is selected as the only operating mode in the current period. The corresponding auxiliary service parameters or peak-valley electricity prices and electricity volume differences are calculated to obtain the operating strategy under the condition of multiple trading products in different time periods.

[0146] The present invention designs a multi-time period and different trading varieties operation and control strategy suitable for IESPS in the electricity spot market and auxiliary market joint regulation to solve the problems of imperfect operation and control strategy and unclear profit model of independent energy storage power stations in the electricity trading market. In order to verify the proposed operation and control strategy, we selected an independent energy storage power station with energy storage parameters of 100MW / 200MWh for simulation example.

[0147] First, in combination with the new version of the “Two Detailed Rules” policy of Liaoning Province, the parameter values ​​of the relevant constraints are set. The specific adjustment parameters of the energy storage power station are shown in Table 1.

[0148] Table 1 IESPS adjustment parameter settings

[0149]

[0150] Secondly, the confidence level α=0.95 is set in the profit risk analysis, and the relevant profit analysis parameters are set according to the latest electricity market price released by Liaoning Province. The specific parameter settings are shown in Table 2.

[0151] Table 2 Market electricity price parameter settings

[0152]

[0153] Then, select IESPS risk attitude as λ da =λ rt =0.5 to analyze the benefits of the optimal control strategy of the power plant. The simulation scenarios set include: participating in a single transaction product; participating in multiple transactions. By comparing the market benefits under different scenarios, determine the control strategy that maximizes the benefits. The specific analysis is as follows:

[0154] (1) Scenario 1: Participating in a single trading product

[0155] When IESPS only participates in frequency regulation service transactions, power station operators need to regulate energy storage to meet the frequency regulation capacity of their own capacity in the day-ahead market during the corresponding period, and at the same time regulate the charging and discharging power of the energy storage power station to meet the required frequency regulation mileage, so as to ensure the stable and safe operation of the power grid. However, considering the power loss and self-discharge behavior of the power station during the charging and discharging process, the IESPS gradually loses power during the frequency regulation process. Therefore, if the power station cannot provide upward frequency regulation services at a certain moment, it needs to purchase electricity in the power market to meet the capacity constraints of the power station after the frequency regulation service ends. Specific charging and discharging strategies are as follows: Figure 4 shown.

[0156] When IESPS only participates in peak-shaving service transactions, compared to frequency regulation services, which require consideration of frequency regulation capacity and mileage, peak-shaving only requires operators to consider peak-shaving capacity at the time of bidding and regulate the energy storage power station for charging. At the same time, it is sold in a timely manner when the electricity market price is high to ensure that the power station capacity meets the next peak-shaving demand. However, compared with only participating in frequency regulation services, the full-time utilization rate of peak-shaving services is lower, so the regulation income is lower. The charging and discharging strategies of peak-shaving auxiliary services are as follows: Figure 5 shown.

[0157] However, when IESPS only participates in peak-valley arbitrage transactions, IESPS operators need to purchase electricity at the low price of the day-ahead electricity spot market and sell it at the high price of the real-time market, thereby achieving energy storage peak-valley arbitrage. However, since the price of the day-ahead-real-time market has always fluctuated, IESPS does not conduct arbitrage based on the electricity price of a single stage. The specific arbitrage methods are as follows:

[0158] Under the market's allowable deviation ratio constraint, energy storage power stations give priority to purchasing electricity when the real-time electricity price is low and the day-ahead-real-time price difference is positive; they give priority to selling electricity when the real-time price is high and the day-ahead-real-time price difference is negative. At this time, the independent energy storage power station needs to bear part of the charging costs, and under the constraints of the regulation characteristics, the amount of electricity purchased and sold by IESPS participating in peak-valley arbitrage is limited, that is, the arbitrage income is limited. Specific power stations only participate in peak-valley arbitrage operation strategies such as Figure 6 shown.

[0159] (2) Scenario 2: Participating in multiple trading products

[0160] According to the operation control strategy of the present invention, the operation of IESPS in the day-ahead-real-time market is optimized and controlled in multiple time periods. In this scenario, the energy storage power station can control the charging and discharging power of the energy storage in IESPS according to the control requirements of each transaction product and the control characteristics of the energy storage itself. The specific multi-transaction product control strategy diagram is shown in Figure 7 shown.

[0161] At the same time, the benefit value of the regulation benefits of scenario 1 and scenario 2 is calculated. The results of the regulation benefits of IESPS under different scenarios are shown in Table 3. Combining the above results, we can get:

[0162] Compared with participating in a single transaction, the regulation income of IESPS is significantly improved. Compared with scenario 1, the energy storage power station can obtain income in scenario 2 for a longer period of time. The energy storage is regulated to charge at the peak demand time to provide peak-shaving auxiliary services, charge and discharge at the right time of electricity price and electricity price difference to conduct price arbitrage, and participate in frequency regulation auxiliary transactions at other times, which significantly improves the utilization rate of energy storage.

[0163] Table 3 Analysis of regulatory benefits under different scenarios

[0164]

[0165] In addition, in order to determine a reasonable risk attitude and help power plants avoid the risk of market electricity price fluctuations, the parameters are adjusted within the range of 0.4 to 1.5, and the IESPS profit risk under different risk attitudes is analyzed. Figure 8 As shown in Figure 1, the IESPS regulation income decreases with the increase of risk attitude. This is because the IESPS's willingness to deal with the risk of market price fluctuations decreases after the risk attitude increases, causing it to reduce its own market trading activities to reduce the possible income risks, resulting in a decrease in regulation income. Figure 8 It can be concluded that when the risk cost parameter λ in the operation strategy is 0.5, it can ensure that IESPS has a higher regulation income and a higher risk resistance.

[0166] Embodiment 2

[0167] This embodiment provides a system for implementing the above-mentioned method for time-divided composite operation of an independent energy storage power station based on target optimization, and the system includes:

[0168] (1) Independent energy storage power station market revenue analysis module, which analyzes the market revenue of independent energy storage power stations and establishes a multi-transaction regulation model for independent energy storage power stations to participate in the power market; specifically, it includes: establishing the revenue of participating in the power grid frequency regulation service Model, benefits of participating in power grid peak load regulation services Model, peak-to-valley arbitrage benefits Models are used to derive charging and discharging power models of independent energy storage power stations;

[0169] (2) a risk assessment module, which performs risk assessment on transaction price fluctuations and calculates the return risk cost under transaction price fluctuations;

[0170] (3) An objective function establishment module, which establishes an objective function and its constraints, and the objective function comprehensively considers the benefits of participating in the power grid frequency regulation service Revenue from participating in power grid peak load regulation services Peak-to-valley arbitrage benefits At the same time, the operating costs of independent energy storage power stations are also considered. The cost of shifting the deviation from participating in the day-ahead to real-time market The return risk cost under the transaction price fluctuation obtained in step S2 Maximize the benefits of independent energy storage power stations participating in the market with multiple trading products in different time periods;

[0171] (4) An objective function solving module, which solves the objective function by evolution and obtains the upward frequency modulation power of the independent energy storage power station Downward FM power Peak load P t pl , Electricity Sales Electricity Purchase The optimal control plan is generated and executed in real time.

[0172] Embodiment 3

[0173] This embodiment provides a computer, including a processor and a memory connected thereto;

[0174] Wherein, the processor is configured to execute the time-sharing composite operation method of an independent energy storage power station based on target optimization; and the memory is used to store executable instructions of the processor.

[0175] Embodiment 4

[0176] This embodiment provides a computer storable medium on which a computer program is stored. The computer program is executed by a processor to implement the time-divided composite operation method of an independent energy storage power station based on target optimization.

[0177] In summary, the actual effects of the present invention are as follows:

[0178] 1. Maximize profits:

[0179] Adjusting energy storage to obtain benefits under the real-time electricity price environment. With the improvement of the peak-valley electricity price policy, through the establishment of a dynamic adjustment mechanism for peak-valley electricity prices, the peak-valley time periods are dynamically matched, the peak-valley price difference and floating range are expanded, thereby guiding users to use electricity in a staggered manner and encouraging market participants to sign electricity trading contracts that include peak, valley, and even-split time periods. Based on this policy scenario expectation, independent energy storage power stations can formulate the optimal charging and discharging strategy for energy storage power stations according to the dynamic changes of peak-valley electricity prices under the real-time electricity price environment, and obtain the best benefits by optimizing energy storage charging and discharging.

[0180] 2. Improved risk resistance:

[0181] The process of comprehensively analyzing and evaluating the financial risks faced by energy storage power stations by establishing a conditional risk value model aims to identify potential risks in advance and take appropriate management measures to reduce the adverse effects of risks. By predicting possible risks and issuing warning signals in a timely manner, independent energy storage power stations are reminded to take appropriate countermeasures; by assessing risks, formulating and implementing appropriate risk management measures, the losses caused by potential risks are reduced; and the information on risks and returns required for energy storage power station decision-making is provided, which helps to improve the accuracy and effectiveness of decision-making.

[0182] 3. Parameters can be adjusted according to special regional policies, with strong adaptability:

[0183] As a newly proposed hybrid time-sharing energy storage power station operation mode, this study can be adjusted according to different policies in different regions, and realize multi-condition regional combination while keeping the original model structure unchanged, thereby improving the overall operation capability. The model optimization effect is significant and adaptable, which effectively fits the current pace of power market reform and is flexibly applicable to multiple development areas of energy storage power stations.

[0184] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0185] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0186] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0187] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0188] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0189] The above embodiments are implemented on the premise of the technical solution of the present invention, and detailed implementation methods and specific operation processes are given, but the protection scope of the present invention is not limited to the above embodiments. All other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

Claims

1. A time-divided composite operation method for an independent energy storage power station based on target optimization, characterized in that: The method constructs a time-divided multi-transaction product control model for independent energy storage power stations by analyzing the benefits of independent energy storage power stations in peak-shaving services, frequency regulation services, and peak-valley arbitrage, energy storage charging and discharging power analysis, and risk analysis, introduces the objective function of target optimization and its constraints, and obtains a comprehensive control strategy for energy storage power stations with different transaction products in different time periods, thereby maximizing the elastic benefits of energy storage power stations as independent individuals in the power trading market; The specific steps include: S1. Analyze the market benefits of independent energy storage power stations and establish a multi-transaction regulation model for independent energy storage power stations to participate in the power market; specifically include: establishing the benefits of participating in the power grid frequency regulation service Model, benefits of participating in power grid peak load regulation services Model, peak-to-valley arbitrage benefits Model, and derive the charging power P of the independent energy storage power station c,t , discharge power P dc,t Model; S2. Introduce the conditional value at risk indicator CVaR to conduct risk assessment of transaction price fluctuations; calculate the return risk cost under transaction price fluctuations S3. Establish an objective function and its constraints, the objective function comprehensively considers the benefits of participating in the power grid frequency regulation service Profits from participating in power grid peak load regulation services Peak-to-valley arbitrage benefits At the same time, the operating cost of independent energy storage power stations is also considered The cost of shifting the deviation from participating in the day-ahead to real-time market The return risk cost under the transaction price fluctuation obtained in step S2 Maximize the benefits of independent energy storage power stations participating in the market with multiple trading products in different time periods; S4. Taking the optimal profit of independent energy storage power stations participating in multi-time trading products as the goal, the objective function is solved by evolution to obtain the upward frequency modulation power of independent energy storage power stations. Downward FM power Peak load P t pl , Electricity Sales Electricity Purchase The optimal control plan is generated and executed in real time.

2. The method for composite operation of an independent energy storage power station based on target optimization according to claim 1 is characterized in that: In step S1, the market revenue analysis of the independent energy storage power station includes: participating in the peak load regulation and frequency regulation services of the auxiliary market and participating in the peak-valley arbitrage services of the power spot market; establishing an independent energy storage power station participating in the power market multi-transaction product regulation model in different time periods specifically includes the following: (1) Participation in grid frequency regulation services: Independent energy storage power stations provide frequency regulation capacity and frequency regulation mileage auxiliary services based on market frequency regulation needs and their own regulation performance to maintain the system frequency of the power system within an appropriate range and ensure the stable operation of the power system. The profit model for participating in grid frequency regulation services is as follows: In the formula, Profits from independent energy storage power stations participating in frequency regulation services; They are frequency regulation capacity price and frequency regulation mileage price respectively; They are the upward frequency regulation power and downward frequency regulation power reported by the independent energy storage power station; To provide actual power frequency modulation coefficient; (2) Participation in grid peak load regulation services: Independent energy storage power stations charge in the valley part of the load curve and discharge in the peak part according to the market peak load regulation demand. However, considering that the peak load discharge of independent energy storage power stations can be directly connected to the grid for arbitrage when participating in peak-valley arbitrage, the income only includes the peak load compensation during valley charging. The income model of participating in grid peak load regulation services is as follows: In the formula, The revenue from independent energy storage power stations participating in peak load regulation services; is the peak load compensation price; P t pl The peak-shaving electricity reported for IESPS; (3) Peak-valley arbitrage: Independent energy storage power stations purchase electricity at low prices and then sell them at high prices based on the fluctuations in electricity prices in the energy market, earning the time-sharing price difference in the middle; this income is determined by the price difference between the peak and valley periods and the day-ahead-real-time period. The profit model of peak-valley arbitrage for independent energy storage power stations is as follows: In the formula, is the income of peak-valley arbitrage of independent energy storage power station; t pa The price difference of electricity for buying low and buying high; They are the electricity sold and electricity purchased reported by the independent energy storage power station; (4) Construct a charging and discharging power model control model for independent energy storage power stations participating in multi-variety transactions in the electricity market, as follows: Where P c,t , P dc,t They are the charging and discharging power of the independent energy storage power station. They are 0-1 state variables for independent energy storage power stations participating in frequency regulation auxiliary services, peak regulation auxiliary services and peak-valley arbitrage.

3. The time-divided composite operation method of an independent energy storage power station based on target optimization according to claim 1 is characterized in that: In step S2, the risk assessment based on transaction price fluctuations specifically includes the following: According to the profit model of independent energy storage power stations, the fluctuation of market transaction prices can directly affect the profits of power stations participating in multi-variety transactions in the electricity market. If the actual transaction price does not reach the expected price, the original regulation plan cannot achieve the optimal profit; conversely, the original regulation plan misses the optimal discharge interval, thus failing to maximize profits. Taking into account the impact of price fluctuations, the conditional risk value indicator CVaR is now introduced. CVaR is the expected value of loss at a given confidence level. It can reasonably quantify the risk cost of the independent energy storage power station participating in market transactions under different price fluctuation scenarios. The specific expression formula is as follows: In the formula, It is the risk cost of return under transaction price fluctuation; T is the total number of market trading periods in which independent energy storage power stations participate; λ and α are risk attitude coefficient and confidence level respectively; η, Z t are the risk value of market price fluctuations and the portion of the profit of the independent energy storage power station that exceeds the risk value during the t-th trading period; As a conditional risk metric, CVaR provides the worst-case loss that may occur at different confidence levels; this helps operators to more fully understand the profit risk of independent energy storage power stations under market price fluctuations, and to better make risk management and decisions.

4. The method for composite operation of an independent energy storage power station based on target optimization according to claim 1 is characterized in that: In step S3, an optimization control strategy for independent energy storage power stations to participate in multi-variety transactions in the market is designed under the target optimization algorithm; the target optimization objective function is as follows: Comprehensive consideration of the benefits of participating in power grid frequency regulation services Profits from participating in power grid peak load regulation services Peak-to-valley arbitrage benefits At the same time, the operating costs of independent energy storage power stations are also considered. The cost of shifting the deviation from participating in the day-ahead to real-time market The return risk cost under the transaction price fluctuation obtained in step S2 In order to maximize the benefits of independent energy storage power stations participating in the market with multiple trading products in different time periods, the objective function calculation is shown in formula (7) to formula (9): In the formula, are the operating cost of an independent energy storage power station and the deviation transfer cost of participating in the day-ahead-real-time market; B av is the unit operating cost of energy storage; c , dc are the charging and discharging efficiencies of the independent energy storage power station respectively; μ is the allowable deviation ratio of power purchase in the day-ahead and real-time stages; To provide the actual power frequency regulation coefficient, the superscripts da and rt represent the parameters or variables of the day-ahead and real-time stages respectively, the subscript t represents the tth transaction, and T represents the total number of market trading periods in which the independent energy storage power station participates; They are 0-1 state variables for independent energy storage power stations participating in frequency regulation auxiliary services, peak regulation auxiliary services and peak-valley arbitrage.

5. The method for composite operation of an independent energy storage power station based on target optimization according to claim 4 is characterized in that: In step S3, in order to prevent the energy storage in the independent energy storage power station from being overcharged or over-discharged and to ensure that the declared power is within the range of the energy storage regulation characteristics, it is necessary to constrain the energy storage's own regulation characteristics and the declared power when participating in multi-product transactions. The constraint conditions include: Energy storage regulation characteristics constraints In the formula, They are respectively the energy storage charging and discharging power limits; SOC max , SOC min They are the upper and lower limits of the energy storage state of charge; SOC T SOC0 and SOC1 are the end and start states of charge of the energy storage power station respectively; E N is the rated capacity of the energy storage station; Δc is the self-discharge rate of the energy storage station, SOC t is the charge state of the energy storage power station at time t.

6. The method for composite operation of an independent energy storage power station based on target optimization according to claim 4 is characterized in that: In step S3, in order to prevent the energy storage in the independent energy storage power station from being overcharged or over-discharged and to ensure that the declared power is within the range of the energy storage regulation characteristics, it is necessary to constrain the energy storage's own regulation characteristics and the declared power when participating in multi-product transactions. The constraint conditions include: Constraints on Energy Storage Participating in Multiple Trading Products In the formula, They are respectively the energy storage charging and discharging power limits.

7. The method for composite operation of an independent energy storage power station based on target optimization according to claim 1 is characterized in that: In step S4, the non-dominated genetic algorithm NSGA-III with an elite strategy is used to evolve and solve the objective function, which specifically includes the following: Step 1: Population initialization, where each individual is a multidimensional vector, representing the upward frequency regulation power, downward frequency regulation power, peak load, power sales, power purchase, and current time and regional electricity sales price reported by the independent energy storage power station, and the evolutionary generation is set to 1; Step 2: Determine whether the first generation subpopulation has completed the non-dominated sorting, selection, crossover, and mutation operations. If not, return to the previous step and repeat the operation. If the above operations are completed, the evolutionary generation is increased by 1 and the process goes to step 3. Step 3: Merge the parent population with the offspring population generated in step 2; Step 4: Determine whether the new parent population meets the requirements. If not, perform fast non-dominated sorting, crowding and elite strategy operations, and return to step 3; if the new parent population meets the requirements, go to step 5; Step 5: Perform selection, crossover, and mutation operations on the new parent population; Step 6: Determine whether the algorithm has run to the maximum number of iterations. If not, return to step 3; if the number of iterations reaches the maximum, the algorithm ends. Finally, the upward frequency modulation power of the independent energy storage power station is obtained Downward FM power Peak load P t pl , Electricity Sales Electricity Purchase The optimal control plan is generated and executed in real time.

8. A system for implementing the time-division composite operation method of an independent energy storage power station based on target optimization as described in claim 1, characterized in that: The system comprises: (1) Independent energy storage power station market revenue analysis module, which analyzes the market revenue of independent energy storage power stations and establishes a multi-transaction regulation model for independent energy storage power stations to participate in the power market; specifically, it includes: establishing the revenue of participating in the power grid frequency regulation service Model, benefits of participating in power grid peak load regulation services Model, peak-to-valley arbitrage benefits Models are used to derive charging and discharging power models of independent energy storage power stations. (2) a risk assessment module, which performs risk assessment on transaction price fluctuations and calculates the return risk cost under transaction price fluctuations; (3) An objective function establishment module, which establishes an objective function and its constraints, and the objective function comprehensively considers the benefits of participating in the power grid frequency regulation service Profits from participating in power grid peak load regulation services Peak-to-valley arbitrage benefits At the same time, the operating costs of independent energy storage power stations are also considered. The cost of shifting the deviation from participating in the day-ahead to real-time market The return risk cost under the transaction price fluctuation obtained in step S2 Maximize the benefits of independent energy storage power stations participating in the market with multiple trading products in different time periods; (4) An objective function solving module, which solves the objective function by evolution and obtains the upward frequency modulation power of the independent energy storage power station Downward FM power Peak load P t pl , Electricity Sales Electricity Purchase The optimal control plan is generated and executed in real time.

9. A computer, characterized in that: including a processor and a memory connected thereto; Wherein, the processor is configured to execute a time-division composite operation method of an independent energy storage power station based on target optimization as described in any one of claims 1 to 7; The memory is used to store executable instructions of the processor.

10. A computer storable medium, characterized in that: A computer program is stored thereon, and the computer program is executed by a processor to implement a time-sharing composite operation method of an independent energy storage power station based on target optimization as described in any one of claims 1 to 7.

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