Economic set configuration system and method for multi-side energy storage

Abstract

The invention relates to a multi-side energy storage economy set configuration system and method. The method comprises the steps that S1, economy evaluation indexes for constructing multi-side energy storage are set; s2, under the economic evaluation indexes, obtaining quantitative indexes according to actual conditions; s3, obtaining a standardized fuzzy decision matrix; s4, obtaining a fuzzy optimal comparison vector; s5, obtaining a fuzzy worst comparison vector; s6, obtaining a weighted standardized fuzzy decision matrix; s7, calculating geometric distances between the multi-type energy storage system and the fuzzy positive and negative ideal solutions according to the obtained positive and negative ideal solutions; and S8, calculating the pasting progress of the multi-side energy storage, and sorting the economy of the multi-side energy storage according to the pasting progress. The method has the advantages that energy storage of multiple sides (the power generation side, the power grid side and the user side) is concentrated for comprehensive configuration, economy serves as a core judgment standard, and benefit maximization of energy storage configuration is achieved.

Description

Technical Field

[0001] The present invention relates to the field of clean energy storage technology, and in particular to an economical integrated configuration system and method for multi-side energy storage. Background Art

[0002] In recent years, with the large-scale access to new energy sources such as wind power and photovoltaics, the energy and electricity needs of economic and social development have been met, thus promoting the healthy and sustainable development of human society. The rapid development of renewable energy power generation has solved the resource and environmental problems faced by human development to a certain extent, but the output of renewable energy power generation is random and volatile. Large-scale grid connection will affect the safe and stable operation of the power system and increase the difficulty and economic cost of system scheduling.

[0003] The significance of energy storage development lies in that it can encourage potential investors to invest in the market, reduce the cost of energy storage technology, thereby realizing large-scale application of energy storage on multiple sides, improving the operating efficiency of energy storage systems, and promoting the coordinated development of energy storage and clean energy, improving the absorption capacity of clean energy power generation, and ultimately improving the utilization efficiency of clean energy resources. Multi-side energy storage and the optimal utilization of clean energy are the core of energy storage development. The benefit distribution of multi-side energy storage stakeholders is the guarantee of energy storage development and determines whether the energy storage industry can achieve sustainable development.

[0004] At present, there are many invention patents involving the optimization configuration method of single-side energy storage power stations. The method can effectively improve the economy and technology of the energy storage system configuration process and solve the problem of volatility of renewable energy. However, the methods in the prior art all have a single energy storage configuration goal and do not efficiently utilize the advantages of multi-side energy storage. Summary of the invention

[0005] In view of the above problems, the purpose of the present invention is to provide an economical collective configuration system and method for multi-sided energy storage, which is used to collectively configure from single-sided energy storage to multi-sided energy storage based on economic considerations, to achieve more efficient and accurate energy storage optimization configuration under single-sided demand scenarios, so as to overcome the shortcomings of the above-mentioned prior art.

[0006] To achieve the above object, the present invention adopts the following technical solution:

[0007] An economical collective configuration method for multi-side energy storage comprises the following steps:

[0008] Step S1: Setting economic evaluation indicators for constructing multi-side energy storage, wherein the economic evaluation indicators include energy storage construction cost, energy storage operation and maintenance cost, and energy storage income;

[0009] Step S2: Under the economic evaluation index, quantitative indicators are obtained according to actual conditions, wherein the quantitative indicators include selecting the best indicator and selecting the worst indicator, wherein the actual conditions are a comprehensive evaluation of energy storage construction costs, energy storage operation and maintenance costs, and energy storage benefits, wherein selecting the best indicator and selecting the worst indicator refer to the conditions that decision makers expect to achieve and the bottom line that they can accept;

[0010] Step S3: construct a fuzzy decision matrix, obtain an initial fuzzy decision matrix according to quantitative indicators, standardize the initial fuzzy decision matrix, and obtain a standardized fuzzy decision matrix;

[0011] Step S4: Perform fuzzy preference comparison between the optimal index and the economic evaluation index in pairs to obtain a fuzzy optimal comparison vector;

[0012] Step S5: Perform fuzzy preference comparison between the economic evaluation index and the worst index in pairs to obtain a fuzzy worst comparison vector;

[0013] Step S6: multiplying the standardized fuzzy decision matrix by the indicator fuzzy weight vector to obtain a weighted standardized fuzzy decision matrix, wherein the indicator fuzzy weight vector refers to the numerical value of the decision maker's rating;

[0014] Step S7: Calculate the fuzzy positive and negative ideal solutions using the weighted standardized fuzzy decision matrix, and calculate the geometric distances between the multi-type energy storage systems and the fuzzy positive and negative ideal solutions based on the obtained positive and negative ideal solutions;

[0015] Step S8: Calculate the progress of multi-side energy storage and rank the economic efficiency of multi-side energy storage according to the progress. The progress refers to the degree of economic closeness between the actual solution and the ideal solution. The closer the economic closeness, the better the economic efficiency.

[0016] As a preferred embodiment of the present invention, the economic evaluation index of the energy storage construction cost in step S1 includes:

[0017] The energy storage construction cost is expressed as:

[0018] C1=k p P es +k q S es

[0019] Where C1 is the initial investment cost of energy storage; P es is the rated power of energy storage; S es is the rated capacity of energy storage; k p is the cost coefficient related to energy storage power; k q is the cost coefficient related to energy storage capacity;

[0020] Energy storage operation and maintenance cost, expressed as:

[0021]

[0022] Where C2 is the initial investment cost of energy storage; k F The unit price for operating and maintaining the energy storage system; k V is the discharge unit price of the energy storage system; is the annual cumulative discharge of the energy storage system;

[0023] Energy storage benefits, expressed as:

[0024]

[0025] In the formula, is the peak load of the energy storage system on the dth day; p pr To compensate for the peak load; D pr is the number of days the energy storage system operates for peak load regulation in a year; X fm M is the number of trading cycles in the monthly FM market; fm (x) is the frequency regulation mileage provided by the energy storage system in the xth trading cycle; fm (x) is the frequency regulation mileage compensation of the energy storage system in the xth trading cycle; Kap fm (x) is the average value of the comprehensive frequency regulation performance of the energy storage system in the xth trading cycle;

[0026] The expression of the average value of the comprehensive frequency regulation performance of the xth trading cycle in the energy storage income in step S1 is:

[0027] Kap fm (x)=0.25×(2kap1(x)+kap2(x)+kap3(x))

[0028] In the formula, kap1 is the regulation rate indicator; kap2 is the regulation accuracy indicator; kap3 is the response time indicator.

[0029] As a preferred embodiment of the present invention, step S2 comprises the following steps:

[0030] Step S21: constructing an evaluation index system;

[0031] Step S22: Determine the best index and the worst index;

[0032] Step S3 includes the following steps:

[0033] Step S31: construct an initial fuzzy decision matrix according to the following expression:

[0034]

[0035] In the formula, is the fuzzy preference degree of indicator i relative to indicator k;

[0036] Step S32: normalize the matrix. The maximum normalized expression is:

[0037]

[0038] The minimum normalized expression is:

[0039]

[0040] Step S33: Get the standardized fuzzy decision matrix, expressed as:

[0041]

[0042] In the formula, is the fuzzy preference degree of indicator i relative to indicator k after matrix standardization;

[0043] Step S4 includes the following steps:

[0044] Step S41: Determine the optimal index based on the actual real-time energy storage conditions at each side and the experience of the decision maker;

[0045] Step S42: Compare the optimal index and each evaluation index in pairs to obtain the comparison result

[0046] Step S43: Constructing the fuzzy optimal comparison vector

[0047]

[0048] In the formula, is the comparison result of the optimal index relative to index k.

[0049] As a preferred embodiment of the present invention, step S5 includes the following steps:

[0050] Step S51: Determine the worst indicator based on the actual real-time energy storage conditions at each side and the experience of the decision maker;

[0051] Step S52: Compare the worst indicator with each evaluation indicator pairwise to obtain the comparison result.

[0052] Step S53: Constructing the fuzzy optimal comparison vector

[0053]

[0054] In the formula, is the comparison result of index i relative to the worst index;

[0055] The expression of the weighted normalized fuzzy decision matrix in step S6 is:

[0056]

[0057] In the formula, c ik is the fuzzy preference degree of indicator i relative to indicator k after weighted standardization; is the comprehensive fuzzy weight of the evaluation index; It is the fuzzy preference degree of index i relative to index k after matrix standardization.

[0058] As a preferred embodiment of the present invention, step S7 includes the following steps:

[0059] Step S71: Calculate the fuzzy positive ideal solution according to the following expression:

[0060]

[0061] In the formula, C + is the fuzzy positive ideal solution; is the fuzzy positive preference degree of index i relative to index k after weighted standardization; J1 is a very large index set; J2 is a very small index set;

[0062] Step S72: Calculate the fuzzy negative ideal solution according to the following expression:

[0063]

[0064] In the formula, C - is the fuzzy positive ideal solution; is the fuzzy negative preference degree of index i relative to index k after weighted standardization; J1 is a very large index set; J2 is a very small index set;

[0065] Step S73: Calculate the geometric distance between the multi-side energy storage system and the fuzzy positive and negative ideal solutions according to the following solution expression:

[0066]

[0067] In the formula, The geometric distance between the actual and ideal blur situation; is the actual blur vector; is the ideal fuzzy solution vector.

[0068] As a preferred embodiment of the present invention, the expression of the progress of the multi-side energy storage in step S8 is:

[0069]

[0070] In the formula, CC i Post progress for energy storage; is the distance between fuzzy positive ideal solutions; is the distance between the fuzzy negative ideal solutions.

[0071] The second object of the present invention is to provide an economical integrated configuration system for multi-side energy storage, including an evaluation module, a solution module and a decision module;

[0072] The evaluation module is used to establish a multi-side energy storage joint configuration model;

[0073] The solution module is used to solve the multi-side energy storage joint configuration model;

[0074] The decision module uses the solution result of the multi-side energy storage joint configuration model for the capacity and configuration of the multi-side energy storage power station.

[0075] As a preferred embodiment of the present invention, the joint configuration model includes an evaluation optimization model and a decision optimization model. The evaluation optimization model aims to minimize the cost and maximize the benefit of the energy storage power station with evaluation indicators, and the decision optimization model aims to minimize the cost and maximize the benefit of the energy storage power station with weighted standardized indicators.

[0076] The third object of the present invention is to provide an electronic device, comprising: a memory, a processor and a computer program, wherein the computer program is stored in the memory and is configured to be executed by the processor to implement an economical integrated configuration system for multi-side energy storage.

[0077] A fourth object of the present invention is to provide a computer-readable storage medium, in which a computer program is stored, and the computer program is executed by a processor to implement an economical integrated configuration system for multi-side energy storage.

[0078] The advantages and positive effects of the present invention are: by concentrating energy storage on multiple sides (power generation side, grid measurement, user side) for comprehensive configuration, taking economy as the core evaluation criterion, the benefits of energy storage configuration are maximized. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0080] Example 1

[0081] See also Figure 1 This embodiment provides an economical set configuration method for multi-side energy storage, including the following steps:

[0082] Step S1: Setting economic evaluation indicators for constructing multi-side energy storage, wherein the economic evaluation indicators include energy storage construction cost, energy storage operation and maintenance cost, and energy storage income;

[0083] The economic evaluation indicators of energy storage construction cost include:

[0084] The energy storage construction cost is expressed as:

[0085] C1=k p P es +k q S es

[0086] Where C1 is the initial investment cost of energy storage; P es is the rated power of energy storage; S es is the rated capacity of energy storage; k p is the cost coefficient related to energy storage power; k q is the cost coefficient related to energy storage capacity;

[0087] Energy storage operation and maintenance cost, expressed as:

[0088]

[0089] Where C2 is the initial investment cost of energy storage; k F The unit price for operating and maintaining the energy storage system; k V is the discharge unit price of the energy storage system; is the annual cumulative discharge of the energy storage system;

[0090] Energy storage benefits, expressed as:

[0091]

[0092] In the formula, is the peak load of the energy storage system on the dth day; p pr To compensate for the peak load; D pr is the number of days the energy storage system operates for peak load regulation in a year; X fm M is the number of trading cycles in the monthly FM market; fm (x) is the frequency regulation mileage provided by the energy storage system in the xth trading cycle; fm (x) is the frequency regulation mileage compensation of the energy storage system in the xth trading cycle; Kap fm (x) is the average value of the comprehensive frequency regulation performance of the energy storage system in the xth trading cycle;

[0093] The expression of the average value of the comprehensive frequency regulation performance of the xth trading cycle in the energy storage income in step S1 is:

[0094] Kap fm (x)=0.25×(2kap1(x)+kap2(x)+kap3(x))

[0095] In the formula, kap1 is the regulation rate indicator; kap2 is the regulation accuracy indicator; kap3 is the response time indicator.

[0096] Step S2: Under the economic evaluation index, quantitative indicators are obtained according to actual conditions, wherein the quantitative indicators include selecting the best indicator and selecting the worst indicator, wherein the actual conditions are a comprehensive evaluation of energy storage construction costs, energy storage operation and maintenance costs, and energy storage benefits, wherein selecting the best indicator and selecting the worst indicator refer to the conditions that decision makers expect to achieve and the bottom line that they can accept;

[0097] Step S21: constructing an evaluation index system;

[0098] Step S22: Determine the best index and the worst index.

[0099] Step S3: construct a fuzzy decision matrix, obtain an initial fuzzy decision matrix according to quantitative indicators, standardize the initial fuzzy decision matrix, and obtain a standardized fuzzy decision matrix;

[0100] Step S31: construct an initial fuzzy decision matrix according to the following expression:

[0101]

[0102] In the formula, is the fuzzy preference degree of indicator i relative to indicator k;

[0103] Step S32: normalize the matrix. The maximum normalized expression is:

[0104]

[0105] The minimum normalized expression is:

[0106]

[0107] Step S33: Get the standardized fuzzy decision matrix, expressed as:

[0108]

[0109] In the formula, It is the fuzzy preference degree of index i relative to index k after matrix standardization.

[0110] Step S4: Perform fuzzy preference comparison between the optimal index and the economic evaluation index in pairs to obtain a fuzzy optimal comparison vector;

[0111] Step S41: Determine the optimal index based on the actual real-time energy storage conditions at each side and the experience of the decision maker;

[0112] Step S42: Compare the optimal index and each evaluation index in pairs to obtain the comparison result

[0113] Step S43: Constructing the fuzzy optimal comparison vector

[0114]

[0115] In the formula, is the comparison result of the optimal index relative to index k.

[0116] Step S5: Perform fuzzy preference comparison between the economic evaluation index and the worst index in pairs to obtain a fuzzy worst comparison vector;

[0117] Step S51: Determine the worst indicator based on the actual real-time energy storage situation on each side and the experience of the decision maker, wherein the experience of the decision maker refers to the acceptable cost level and the bottom line of the expected acceptable benefits, and the actual real-time energy storage situation on each side refers to the real-time data of energy storage construction cost, energy storage operation and maintenance cost and energy storage benefits;

[0118] Step S52: Compare the worst indicator with each evaluation indicator pairwise to obtain the comparison result.

[0119] Step S53: Constructing the fuzzy optimal comparison vector

[0120]

[0121] In the formula, is the comparison result of index i relative to the worst index.

[0122] Step S6: multiplying the standardized fuzzy decision matrix by the indicator fuzzy weight vector to obtain a weighted standardized fuzzy decision matrix, wherein the indicator fuzzy weight vector refers to the numerical value of the decision maker's rating;

[0123] The expression of the weighted normalized fuzzy decision matrix in step S6 is:

[0124]

[0125] In the formula, c ik is the fuzzy preference degree of indicator i relative to indicator k after weighted standardization; is the comprehensive fuzzy weight of the evaluation index; It is the fuzzy preference degree of index i relative to index k after matrix standardization.

[0126] Step S7: Calculate the fuzzy positive and negative ideal solutions using the weighted standardized fuzzy decision matrix, and calculate the geometric distances between the multi-type energy storage systems and the fuzzy positive and negative ideal solutions based on the obtained positive and negative ideal solutions;

[0127] Step S71: Calculate the fuzzy positive ideal solution according to the following expression:

[0128]

[0129] In the formula, C + is the fuzzy positive ideal solution; is the fuzzy positive preference degree of index i relative to index k after weighted standardization; J1 is a very large index set; J2 is a very small index set;

[0130] Step S72: Calculate the fuzzy negative ideal solution according to the following expression:

[0131]

[0132] In the formula, C - is the fuzzy positive ideal solution; is the fuzzy negative preference degree of index i relative to index k after weighted standardization; J1 is a very large index set; J2 is a very small index set;

[0133] Step S73: Calculate the geometric distance between the multi-side energy storage system and the fuzzy positive and negative ideal solutions according to the following solution expression:

[0134]

[0135] In the formula, The geometric distance between the actual and ideal blur situation; is the actual blur vector; is the ideal fuzzy solution vector.

[0136] Step S8: Calculate the progress of multi-side energy storage and rank the economic efficiency of multi-side energy storage according to the progress. The progress refers to the degree of economic closeness between the actual solution and the ideal solution. The closer the economic closeness, the better the economic efficiency.

[0137] The expression of the progress of the multi-side energy storage is:

[0138]

[0139] In the formula, CC i Post progress for energy storage; is the distance between fuzzy positive ideal solutions; is the distance between the fuzzy negative ideal solutions.

[0140] Example 2

[0141] This embodiment provides an economical set configuration system for multi-side energy storage, including an evaluation module, a solution module and a decision module;

[0142] The evaluation module is used to establish a multi-side energy storage joint configuration model;

[0143] The solution module is used to solve the multi-side energy storage joint configuration model;

[0144] The decision module uses the solution result of the multi-side energy storage joint configuration model for the capacity and configuration of the multi-side energy storage power station.

[0145] The joint configuration model includes an evaluation optimization model and a decision optimization model. The evaluation optimization model aims to minimize the cost and maximize the benefit of the energy storage power station with evaluation indicators, and the decision optimization model aims to minimize the cost and maximize the benefit of the energy storage power station with weighted standardized indicators.

[0146] Example 3

[0147] This embodiment provides an electronic device, including: a memory, a processor and a computer program, wherein the computer program is stored in the memory and is configured to be executed by the processor to implement an economical integrated configuration system for multi-side energy storage as described in Example 2.

[0148] Example 4

[0149] This embodiment provides a computer-readable storage medium, in which a computer program is stored. The computer program is executed by a processor to implement an economical integrated configuration system for multi-side energy storage as described in Embodiment 2.

[0150] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An economical collective configuration method for multi-side energy storage, characterized in that: The following steps are involved: Step S1: Setting economic evaluation indicators for constructing multi-side energy storage, wherein the economic evaluation indicators include energy storage construction cost, energy storage operation and maintenance cost, and energy storage income; Step S2: Under the economic evaluation index, quantitative indicators are obtained according to actual conditions, wherein the quantitative indicators include selecting the best indicator and selecting the worst indicator, wherein the actual conditions are a comprehensive evaluation of energy storage construction costs, energy storage operation and maintenance costs, and energy storage benefits, wherein selecting the best indicator and selecting the worst indicator refer to the conditions that decision makers expect to achieve and the bottom line that they can accept; Step S3: construct a fuzzy decision matrix, obtain an initial fuzzy decision matrix according to quantitative indicators, standardize the initial fuzzy decision matrix, and obtain a standardized fuzzy decision matrix; Step S4: Perform fuzzy preference comparison between the optimal index and the economic evaluation index in pairs to obtain a fuzzy optimal comparison vector; Step S5: Perform fuzzy preference comparison between the economic evaluation index and the worst index in pairs to obtain a fuzzy worst comparison vector; Step S6: multiplying the standardized fuzzy decision matrix by the indicator fuzzy weight vector to obtain a weighted standardized fuzzy decision matrix, wherein the indicator fuzzy weight vector refers to the numerical value of the decision maker's rating; Step S7: Calculate the fuzzy positive and negative ideal solutions using the weighted standardized fuzzy decision matrix, and calculate the geometric distances between the multi-type energy storage systems and the fuzzy positive and negative ideal solutions based on the obtained positive and negative ideal solutions; Step S8: Calculate the progress of multi-side energy storage and rank the economic efficiency of the multi-side energy storage according to the progress.

2. The economical collective configuration method of multi-side energy storage according to claim 1 is characterized in that: The economic evaluation indicators of the energy storage construction cost in step S1 include: The energy storage construction cost is expressed as: C1=k p P es +k q S es Where C1 is the initial investment cost of energy storage; P es is the rated power of energy storage; S es is the rated capacity of energy storage; k p is the cost coefficient related to energy storage power; k q is the cost coefficient related to energy storage capacity; Energy storage operation and maintenance cost, expressed as: Where C2 is the initial investment cost of energy storage; k F The unit price for operating and maintaining the energy storage system; k V is the discharge unit price of the energy storage system; is the annual cumulative discharge of the energy storage system; Energy storage benefits, expressed as: In the formula, is the peak load of the energy storage system on the dth day; p pr To compensate for the peak load; D pr is the number of days the energy storage system operates for peak load regulation in a year; X fm M is the number of trading cycles in the monthly FM market; fm (x) is the frequency regulation mileage provided by the energy storage system in the xth trading cycle; fm (x) is the frequency regulation mileage compensation of the energy storage system in the xth trading cycle; Kap fm (x) is the average value of the comprehensive frequency regulation performance of the energy storage system in the xth trading cycle; The expression of the average value of the comprehensive frequency regulation performance of the xth trading cycle in the energy storage income in step S1 is: Kap fm (x)=0.25×(2kap1(x)+kap2(x)+kap3(x)) In the formula, kap1 is the regulation rate indicator; kap2 is the regulation accuracy indicator; kap3 is the response time indicator.

3. The economical collective configuration method of multi-side energy storage according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: constructing an evaluation index system; Step S22: Determine the best index and the worst index; Step S3 includes the following steps: Step S31: construct an initial fuzzy decision matrix according to the following expression: In the formula, is the fuzzy preference degree of indicator i relative to indicator k; Step S32: normalize the matrix. The maximum normalized expression is: The minimum normalized expression is: Step S33: Get the standardized fuzzy decision matrix, expressed as: In the formula, is the fuzzy preference degree of indicator i relative to indicator k after matrix standardization; Step S4 includes the following steps: Step S41: Determine the optimal index based on the actual real-time energy storage conditions at each side and the experience of the decision maker; Step S42: Compare the optimal index and each evaluation index in pairs to obtain the comparison result Step S43: Constructing the fuzzy optimal comparison vector In the formula, is the comparison result of the optimal index relative to index k.

4. The economical collective configuration method of multi-side energy storage according to claim 1 is characterized in that: Step S5 includes the following steps: Step S51: Determine the worst indicator based on the actual real-time energy storage conditions at each side and the experience of the decision maker; Step S52: Compare the worst indicator with each evaluation indicator pairwise to obtain the comparison result. Step S53: Constructing the fuzzy optimal comparison vector In the formula, is the comparison result of index i relative to the worst index; The expression of the weighted normalized fuzzy decision matrix in step S6 is: In the formula, c ik is the fuzzy preference degree of indicator i relative to indicator k after weighted standardization; is the comprehensive fuzzy weight of the evaluation index; It is the fuzzy preference degree of index i relative to index k after matrix standardization.

5. The economical collective configuration method of multi-side energy storage according to claim 1, characterized in that: Step S7 includes the following steps: Step S71: Calculate the fuzzy positive ideal solution according to the following expression: In the formula, C + is the fuzzy positive ideal solution; is the fuzzy positive preference degree of index i relative to index k after weighted standardization; J1 is a very large index set; J2 is a very small index set; Step S72: Calculate the fuzzy negative ideal solution according to the following expression: In the formula, C - is the fuzzy positive ideal solution; is the fuzzy negative preference degree of index i relative to index k after weighted standardization; J1 is a very large index set; J2 is a very small index set; Step S73: Calculate the geometric distance between the multi-side energy storage system and the fuzzy positive and negative ideal solutions according to the following solution expression: In the formula, The geometric distance between the actual and ideal blur situation; is the actual blur vector; is the ideal fuzzy solution vector.

6. The economical collective configuration method of multi-side energy storage according to claim 1, characterized in that: The expression of the progress of multi-side energy storage in step S8 is: In the formula, CC i Post progress for energy storage; is the distance between fuzzy positive ideal solutions; is the distance between the fuzzy negative ideal solutions.

7. An economical integrated configuration system for multi-side energy storage according to any one of claims 1 to 6, characterized in that: It includes evaluation module, solution module and decision module; The evaluation module is used to establish a multi-side energy storage joint configuration model; The solution module is used to solve the multi-side energy storage joint configuration model; The decision module uses the solution result of the multi-side energy storage joint configuration model for the capacity and configuration of the multi-side energy storage power station.

8. The economical integrated configuration system of multi-side energy storage according to claim 7, characterized in that: The joint configuration model includes an evaluation optimization model and a decision optimization model. The evaluation optimization model aims to minimize the cost and maximize the benefit of the energy storage power station with evaluation indicators, and the decision optimization model aims to minimize the cost and maximize the benefit of the energy storage power station with weighted standardized indicators.

9. An electronic device, comprising: A memory, a processor and a computer program, characterized in that: the computer program is stored in the memory and is configured to be executed by the processor to implement an economical integrated configuration system for multi-side energy storage as described in claim 7 or 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement an economical integrated configuration system for multi-side energy storage as described in claim 7 or 8.

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