Wind and light storage network synchronous configuration method and system considering flexible power supply and economy

Abstract

The invention relates to the technical field of new energy power grids, and discloses a wind and light storage network synchronous configuration method and system considering flexible power supply and economy. Comprising the following steps: acquiring data such as wind resources, light resources, land resources, economy, environmental protection, social acceptance and the like through a data collection module; performing cleaning and deep analysis on the data by using a resource analysis module, and evaluating the resource potential of an alternative site; constructing an optimization model, comprehensively considering flexible power supply capability, economy, technicality and environment targets, and adopting a multi-target optimization algorithm to obtain an optimal configuration scheme; and calculating an optimization result through a model solving module. According to the method and the system, the concept of flexible power supply capability is introduced, the synergistic effect between different energy technologies is fully considered, the performance of a site selection scheme in the aspects of total cost, risk degree, greenhouse gas emission, fossil energy use, social acceptance, employment opportunities and the like is comprehensively evaluated, and the optimal configuration of the wind and light storage network system is realized.

Description

Technical Field

[0001] The present invention relates to the field of new energy power grid technology, and in particular to a method and system for synchronously configuring a wind, solar, and energy storage network that takes into account flexible power supply and economy. Background Art

[0002] With the continued growth of global energy demand and increasingly severe environmental challenges, the development and utilization of renewable energy has become a key focus of energy strategies worldwide. Wind and solar energy, two of the most promising renewable energy sources, have been widely adopted due to their clean and sustainable nature. However, relying solely on wind or solar power generation presents numerous limitations, such as significant susceptibility to natural conditions, high power generation volatility, and difficulty ensuring stable power supply. To overcome these shortcomings, wind, solar, and energy storage systems (i.e., a combination of wind, solar, and energy storage systems) are gaining attention as a multi-energy integrated solution.

[0003] By integrating wind, solar, and energy storage technologies, wind, solar, and energy storage networks can balance the temporal and spatial disparities between power generation and consumption, improving energy efficiency and power supply reliability. Furthermore, energy storage systems can provide auxiliary services during periods of significant grid load fluctuations, enhancing the grid's regulation and resilience. Furthermore, wind, solar, and energy storage networks help optimize distributed energy management, reduce reliance on centralized power systems, and promote diversified energy development.

[0004] Despite the significant advantages of wind, solar and energy storage networks, their large-scale promotion still faces many technical challenges that need to be solved:

[0005] 1. Insufficient site selection optimization: Existing methods mostly focus on optimizing a single energy source or a few objectives, ignoring the comprehensive considerations of economic, technical, environmental and social factors, making it difficult to achieve comprehensive optimization of site selection plans.

[0006] 2. Insufficient quantification of synergistic effects: The temporal and spatial complementarity of wind and solar energy can effectively improve energy utilization and reduce energy storage requirements. However, existing research lacks quantitative analysis of multi-energy synergistic effects, which limits the potential for site optimization.

[0007] 3. Lack of dynamic adjustment capabilities: Most existing models are static optimizations, which are difficult to adapt to dynamic changes in resource conditions, technical parameters, and policies and regulations, resulting in insufficient adaptability and sustainability of site selection plans.

[0008] 4. High data processing complexity: Wind, solar and energy storage network site selection involves multi-objective, multi-constraint and multi-energy integrated optimization, requires processing large amounts of data, and has high computational complexity. Existing methods still have shortcomings in efficiency and accuracy.

[0009] 5. Incomplete consideration of multiple constraints: The construction of wind, solar and storage networks must meet multiple constraints such as land use, environmental protection, laws and regulations. The existing methods have limited comprehensive optimization capabilities under multiple constraints. Summary of the Invention

[0010] This invention addresses the shortcomings of existing wind, solar, and energy storage network site selection methods in terms of multi-objective optimization, quantification of synergistic effects, dynamic adjustment capabilities, data processing, and comprehensive consideration of multiple constraints. It proposes a method and system for synchronously configuring wind, solar, and energy storage networks that takes into account flexible power supply capacity and economic efficiency. This method constructs a multi-objective optimization model that comprehensively considers economic, technological, environmental, and social objectives, quantitatively analyzes the synergistic effects of multiple energy sources, comprehensively incorporates multiple constraints, and utilizes an improved grey correlation model and multi-objective planning algorithm to achieve the optimal site configuration for wind, solar, and energy storage networks. This method addresses many key issues in existing technologies and offers significant theoretical innovation and broad application prospects.

[0011] In order to achieve the above object, the present invention adopts the following technical solutions:

[0012] A method for synchronously configuring a wind, solar, and storage grid, taking into account flexible power supply capability and economy, comprises the following steps:

[0013] Step 1: Construct objective function: Construct a multi-objective function including economic objectives, technical objectives, environmental objectives and social objectives to comprehensively evaluate various indicators;

[0014] Step 2: Design constraints: Design corresponding constraints including resource constraints, association constraints, mandatory constraints, project mutual exclusion constraints, energy supply and demand balance constraints, energy storage charge and discharge cycle constraints, land use constraints, grid access capacity constraints, and investment return rate constraints to ensure the rational use of each resource and the rationality of the site selection;

[0015] Step 3: Model solution: Use the improved grey correlation model to screen relevant alternative addresses, determine the weight of each indicator based on grey correlation analysis, build a multiple selection target planning model and introduce the synergistic effect of the multi-energy complementary system, and use the optimization algorithm to solve the model; comprehensively consider the economic, technical, environmental and social multi-objective constraints, and finally rank the alternative locations and select the optimal wind, solar and storage site selection plan.

[0016] Furthermore, in the multi-objective function, the objective function of the economic goal is expressed as follows:

[0017]

[0018] Where Z arepresents the economic objective function, i.e., the minimization objective of the total cost of the power station; p represents the total number of alternative energy types; q represents the total number of alternative addresses; i, j∈A, i and j represent two different alternative energy types, belonging to the alternative energy set A; k∈B, k represents the alternative address number, belonging to the alternative address set B; K represents the set of implemented energy types; a i represents the cost of implementing the i-th energy source alone; a ij represents the cost reduction due to synergy effect when the i-th and j-th energy sources are implemented simultaneously; a iK The cost reduction due to synergy effect when implementing the i-th energy source and the already implemented K-th energy source at the same time; x i , x j , x k Is a binary variable, indicating whether to choose the i-th, j-th and k-th energy sources: x i =1 means selecting the i-th energy source, x i =0 means not selecting the i-th energy source; y k Is a binary variable, indicating whether to select the kth alternative address: y k =1 means select the kth address, y k =0 means not selecting the kth address; represents the total cost of implementing each energy source individually among all alternative energy sources and alternative locations; It represents the total cost reduction due to the simultaneous implementation of two energy sources among all alternative energy sources and alternative addresses; It represents the total cost reduction due to the synergy between the simultaneous implementation of a certain energy source and the implemented energy sources among all alternative energy sources; i ,a ij ,a iK ≥0, all cost-related parameters are non-negative;

[0019] Among the multi-objective functions, the objective function of the technical goal is expressed as follows:

[0020]

[0021] Where Z b represents the technical objective function, i.e. the goal of minimizing technical risk; b i represents the technical risk of implementing the i-th energy source alone; b ij represents the technical risk reduced due to synergy effect when the i-th and j-th energy sources are implemented simultaneously; b iK represents the technical risk reduced due to synergy when the i-th energy source is implemented simultaneously with the already implemented K-th energy source; y j Represents a binary variable, indicating whether to select the jth alternative address: y j =1 means select the jth address, y j=0 means not selecting the jth address; Represents the total technical risk of implementing each energy source individually among all alternative energy sources and alternative locations; It represents the total technical risk reduction due to the simultaneous implementation of two energy sources among all alternative energy sources and alternative locations; It represents the total technical risk reduced by the synergy between the simultaneous implementation of a certain energy source and the implemented energy sources among all alternative energy sources; b i ,b ij ,b iK ≥0, all technical risk-related parameters are non-negative;

[0022] Among the multi-objective functions, the objective function of the environmental objective is expressed as follows:

[0023]

[0024] Where Z c represents the environmental objective function, i.e., the minimization of fossil energy use and greenhouse gas emissions; C i represents the greenhouse gas emissions generated when the i-th energy source is used alone; D i represents the amount of fossil energy consumed when the i-th energy source is implemented alone; C ij represents the amount of greenhouse gas emissions reduced due to synergistic effects when the i-th and j-th energy sources are implemented simultaneously; D ij C represents the amount of fossil energy used that is reduced due to the synergistic effect when the i-th and j-th energy sources are implemented simultaneously; iK D represents the amount of greenhouse gas emissions reduced due to the synergistic effect when the i-th energy source is implemented simultaneously with the already implemented K-th energy source; iK represents the reduction in fossil energy use due to synergistic effects when the i-th energy source is implemented simultaneously with the already implemented K-th energy source; represents the total greenhouse gas emissions of each energy source implemented individually across all energy alternatives and locations; It represents the total greenhouse gas emissions reduction due to the synergy between the simultaneous implementation of a certain energy source and the implemented energy sources among all alternative energy sources; It represents the total greenhouse gas emissions reduction due to the synergy between the simultaneous implementation of a certain energy source and the implemented energy sources among all alternative energy sources; It represents the total fossil energy use of each energy source implemented individually among all alternative energy sources and alternative locations; It represents the total fossil energy reduction due to the simultaneous implementation of two energy sources among all alternative energy sources and alternative locations; It represents the total amount of fossil energy used that is reduced due to the synergy between the simultaneous implementation of a certain energy source and the implemented energy source among all alternative energy sources; C i ,Di ,C ij ,D ij ,C iK ,D iK ≥0, all environment-related parameters are non-negative;

[0025] Among the multi-objective functions, the objective function of social objectives is expressed as follows:

[0026]

[0027] Where Z d represents the social objective function, i.e., maximizing the social acceptance and employment opportunities created after the construction of wind, solar and energy storage grids; E i is the social acceptance when the i-th energy source is implemented alone; F i The number of jobs created when implementing the i-th energy source alone; E ij F represents the social acceptance that is enhanced due to the synergistic effect when the i-th and j-th energy sources are implemented simultaneously; ij E represents the number of jobs reduced due to synergy effects when the i-th and j-th energy sources are implemented simultaneously; iK F represents the social acceptance that is enhanced due to the synergistic effect when the i-th energy source is implemented simultaneously with the already implemented K-th energy source; iK represents the number of jobs reduced due to synergy effects when the i-th energy source is implemented simultaneously with the already implemented K-th energy source; represents the total social acceptability of implementing each energy source individually among all alternative energy sources and alternative locations; It represents the total social acceptance that is improved by implementing two energy sources simultaneously among all alternative energy sources and alternative addresses; It represents the total social acceptance of all alternative energy sources that is enhanced by the synergy between the simultaneous implementation of a certain energy source and the implemented energy sources; represents the total number of jobs that could be created by implementing each energy source individually across all energy alternatives and locations; represents the total number of jobs lost due to the simultaneous implementation of both energy sources across all alternative energy sources and alternative locations; It represents the total number of jobs that are reduced due to the synergy between the simultaneous implementation of a certain energy source and the implemented energy sources among all alternative energy sources; E i ,F i ,E ij ,F ij ,E iK ,F iK ≥0, all social-related parameters are non-negative.

[0028] Furthermore, the resource constraint is the land use constraint, and the land use constraint is in the form of:

[0029]

[0030] Where p represents the total number of alternative energy types; q represents the total number of alternative addresses; i, j∈A, i and j represent two different alternative energy types, belonging to the alternative energy set A; k∈B, k represents the number of the alternative address, belonging to the alternative address set B; K represents the set of implemented energy types; represents the land use required when implementing the i-th energy source alone; represents the amount of land use reduced due to synergistic effects when the i-th and j-th energy sources are implemented simultaneously; represents the amount of land use reduced due to synergistic effects when the i-th energy source is implemented simultaneously with the already implemented K-th energy source; x i , x j Is a binary variable, indicating whether to choose the i-th and j-th energy sources: x i =1 means selecting the i-th energy source, x i =0 means not selecting the i-th energy source; y k Is a binary variable, indicating whether to select the kth alternative address: y k =1 means select the kth address, y k =0 means not selecting the kth address; Represent the total land use required to implement each energy source individually across all energy alternatives and address alternatives; represents the total land use reduction due to the simultaneous implementation of two energy sources among all alternative energy sources and alternative locations; It represents the total land use reduction due to the synergy between the simultaneous implementation of a certain energy source and the implemented energy sources among all alternative energy sources; l is the total available land, which represents the maximum developable land area within the site selection area; All land-related parameters are non-negative; a l ≥0, the total amount of available land is positive;

[0031] Association constraints are system constraints that ensure that related items can be selected. Association constraints include two types of constraints:

[0032] The first form of association constraint is: j∈Q M , suitable for weak correlation scenarios;

[0033] The second form of association constraint is: j∈Q A , suitable for strong correlation scenarios;

[0034] Where M j represents the energy set associated with the j-th energy; Q MIt represents the energy subset that needs to satisfy the association constraint, which is a subset of the set {1,2,...,P}; Represents the energy set M associated with the j-th energy j The sum of all selected energy quantities; A s represents the energy set associated with the j-th energy; |A s | represents the set A s The number of elements in Q, that is, the total number of energy types associated with the jth energy; A It represents the energy subset that needs to satisfy the association constraint, which is a subset of the set {1,2,...,P}; Represents the energy set A associated with the j-th energy s , the sum of all selected energy quantities;

[0035] Mandatory constraints are constraints of implemented projects. The mandatory constraints are expressed as: j =1,j∈Q A ;

[0036] The project mutual exclusion constraint is to select at most one project. The form of the project mutual exclusion constraint is expressed as:

[0037] Where G represents a set of mutually exclusive items; It represents the sum of all selected items in the mutually exclusive item set G;

[0038] The energy supply and demand balance constraint is that the total power supply meets the regional power demand. The energy supply and demand balance constraint is expressed as:

[0039]

[0040] Where A' represents the set of alternative addresses or energy projects; P wind,i,t is the wind power output of the ith candidate address at time t; P PV,i,t is the photovoltaic power generation output of the i-th candidate address at time t; η discharge is the discharge efficiency of the energy storage system, and its value range is η discharge ∈[0,1]; and They represent the charging and discharging behavior of the energy storage system within time t, represents the energy absorbed by the energy storage system at the i-th candidate address from the grid or other energy sources within time t, represents the energy released by the energy storage system at the i-th candidate address to the grid or other loads within time t; D t is the total load of regional power demand at time t; T is the time set, which represents the time range of the entire analysis;

[0041] Energy storage charge and discharge cycle constraints are used to avoid simultaneous charging and discharging during the same period and to limit the charge and discharge power. The energy storage charge and discharge cycle constraints are expressed as:

[0042]

[0043] Where z i,t Represents a binary variable used to control the charge and discharge state of the energy storage system, z i,t =1 means the energy storage system is in the discharge state at time t, z i,t =0 means that the energy storage system is in the charging state at time t; M is the maximum constant; P charge,max is the maximum charging power of the energy storage system; P discharge,max is the maximum discharge power of the energy storage system;

[0044] Land use constraints are the maximum developable area limits for each address. Land use constraints are expressed as follows:

[0045]

[0046] Where S wind,i The area per unit installed capacity required to build wind power facilities at the i-th alternative location; x wind,i is a binary variable, indicating whether to build wind power facilities at the i-th alternative address, x wind,i =1 means building wind power facilities at this address, x wind,i =0 means no wind power facilities will be built at this address; S PV,i The area per unit installed capacity required to build a photovoltaic facility at the i-th candidate address; x PV,i is a binary variable, indicating whether to build a photovoltaic facility at the i-th alternative address, x PV,i =1 means building photovoltaic facilities at this address, x PV,i =0 means no photovoltaic facilities will be built at this address; S storage,i The area required to build the energy storage system for the i-th candidate address; x storage,i is a binary variable, indicating whether to build energy storage facilities at the i-th alternative address, x storage,i =1 means building energy storage facilities at this address, x storage,i =0 means no energy storage facilities will be built at this address; is the maximum developable land area of the ith alternative address;

[0047] The total grid-connected power of wind power and photovoltaic facilities is limited by the grid access capacity constraint to ensure that it does not exceed the maximum allowable access power of the regional power grid. The grid access capacity constraint is expressed as:

[0048]

[0049] Where, represents the rated power of the wind power facility at the i-th candidate address; represents the rated power of the photovoltaic facility at the i-th candidate address; Indicates the maximum allowable access power of the regional power grid;

[0050] By using the return on investment constraint, we can ensure that the internal rate of return is not lower than the minimum requirement. The return on investment constraint is expressed as:

[0051]

[0052] In the formula, NetCashFlow t is the net cash flow in the tth year. If it is positive, it means there is net income in that year. If it is negative, it means there is net expenditure in that year. t is the time variable, which means the tth year of the project. T is the life cycle of the project. IRR min is the minimum required internal rate of return; (1+IRR min ) t is the discount factor used to discount the net cash flow in year t to the present value.

[0053] Furthermore, the expression of the multiple choice goal programming model is:

[0054]

[0055] Where r t is the positive deviation weight of the t-th target in the objective function, which is calculated by the grey relational degree; is the positive deviation variable of the i-th target; is the negative deviation variable of the i-th target; is the i-th target value and the upper limit h t,max Positive deviation variables; is the i-th target value and the lower limit h t,min Negatively biased variables; is a linear function of the i-th target candidate position; h t is the expected level of the t-th target; h t,max is the upper limit of the t-th target; h t,min is the lower limit of the t-th target; n is the total number of targets; is a decision variable vector, indicating the selection of alternative addresses, where each y t Indicates whether to select a certain alternative address or energy source;

[0056] A represents the set of candidate project numbers, and B represents the set of implemented wind, solar, and energy storage network project numbers. Let A = {1, 2, ..., p} and B = {1, 2, ..., q}. Based on this, x and y are defined as:

[0057]

[0058] Where x i is a binary variable, indicating whether energy i is selected; y i is a binary variable indicating whether energy source i is selected; p is the total number of energy types; and q is the number of alternative locations for wind, solar, and energy storage networks.

[0059] A multiple choice target programming model is provided. The multiple choice target programming model is used in the above-mentioned wind, solar, and energy storage network synchronization configuration method taking into account flexible power supply capacity and economy. The solution algorithm of the multiple choice target programming model includes the following steps:

[0060] First, determine the indicators and candidate addresses to be considered, and use the improved grey correlation model to determine whether they are correlated; if they are correlated, proceed to the next step; if not, select another model;

[0061] Secondly, for the models that have correlation, the correlation between indicators and criteria is analyzed based on the grey correlation method, which makes the importance ranking among the numerous goals more prominent, and thus makes the site selection decision through the multiple choice target planning model;

[0062] Then, considering the synergistic effect of multi-energy complementary systems, the multiple resource constraints and multi-objective constraints of wind, solar, and energy storage grid site selection decisions are described in detail;

[0063] Finally, the weights of the alternative addresses are combined to make a multi-objective planning model that considers the deviations of each objective to make a site selection decision.

[0064] Furthermore, when the improved grey relational model is used to determine whether it has relevance, the grey relational formula is as follows:

[0065]

[0066] Where, ξ i (k) is the grey correlation degree of the kth candidate address on the i-th indicator, which indicates the closeness between the candidate address and the ideal value on the i-th indicator. The value range is [0,1]. The closer the value is to 1, the higher the correlation. ζ is the resolution coefficient, which is used to adjust the resolution ability of the grey correlation degree. k is the candidate address number. i is the evaluation indicator number.

[0067] Δ i (k) is the difference of the kth candidate address on the i-th indicator; the calculation formula is:

[0068]

[0069] Where x i (k) is the actual value of the k-th candidate address on the i-th indicator, is the ideal value of the i-th indicator;

[0070] Δ min is the minimum difference of the i-th indicator among all the alternative addresses; the calculation formula is:

[0071]

[0072] Δ max is the maximum difference of the i-th indicator among all the alternative addresses; the calculation formula is:

[0073]

[0074] If i (k) If it exceeds the preset threshold, it is considered to be relevant and proceeds to the next step; otherwise, it is necessary to select another model or re-screen the alternative addresses.

[0075] Furthermore, the correlation between the indicators and the criteria is analyzed, including the following steps:

[0076] First, each evaluation index is standardized to eliminate the dimension effect;

[0077] Then, for each candidate address, calculate its comprehensive correlation on all indicators;

[0078] The calculation formula is:

[0079] Where Γ(k) is the comprehensive correlation degree of the kth candidate address; ξ i (k) is the grey relational degree of the kth candidate address on the i-th indicator; n is the total number of evaluation indicators, is the averaging factor; k is the candidate address number; i is the evaluation index number;

[0080] Finally, the grey correlation results are used to determine the weight of each indicator through the hierarchical analysis method or the entropy weight method to clarify the importance ranking of the goals.

[0081] Furthermore, a multiple choice objective programming model is established based on multiple objective functions and constraints;

[0082] The basic form of the model is:

[0083]

[0084] In the formula, Minimize means that the optimization goal is to minimize; m is the total number of goals; w i is the weight of the i-th target; f i (x) is the i-th objective function; x is the decision variable vector; d i is the expected value of the i-th target; (f i (x)-d i ) 2 is the square of the deviation between the actual value and the expected value of the i-th target; g j (x) is the jth constraint; g j (x)≤0 is the form of the constraint; p is the total number of constraints.

[0085] Furthermore, in the synergistic effect, the synergistic effect function is:

[0086]

[0087] Where, E(x) is the synergistic effect function of the multi-energy complementary system; α i is the synergy coefficient of the i-th target; f i (x) is the i-th objective function; n is the total number of objectives;

[0088] The objective function is adjusted to:

[0089]

[0090] Where, is the original objective function; w i is the weight of the i-th target; d i is the expected value of the i-th target; m is the total number of targets; x is the decision variable vector; -λE(x) is the synergistic effect term; λ is the weight factor.

[0091] A wind, solar, and storage network synchronization configuration system taking into account flexible power supply capability and economy is used in the above-mentioned wind, solar, and storage network synchronization configuration method taking into account flexible power supply capability and economy, including:

[0092] Data collection module: responsible for obtaining raw data from multiple channels, including wind and light resource data, site land resource data, as well as economic, environmental, and social acceptance data;

[0093] Resource Analysis Module: This module receives the data collected by the Data Collection Module, cleans and deeply analyzes the data, and evaluates the resource potential of candidate sites;

[0094] Optimization module: Based on the analysis results, the optimization module combines other relevant data such as economic efficiency, environmental protection and social acceptance to build an optimization model and run the algorithm to obtain the optimal configuration plan;

[0095] Model solving module: Based on the optimization model established by the optimization module, it uses mathematical tools to solve the optimization model and obtain a specific optimization solution;

[0096] Result display module: Based on the results obtained by the model solving module, it visualizes the optimization results to facilitate decision makers' understanding;

[0097] Feedback module: After decision makers view the optimization results through the result display module, they can provide feedback through the user interface; the feedback module receives user feedback and analyzes it into specific improvement requirements.

[0098] The beneficial effects of the present invention are:

[0099] 1. Multi-dimensional Comprehensive Optimization: The proposed method for synchronous wind, solar, and energy storage grid configuration balances economic, flexibility, technical, environmental, and social considerations. By establishing a site selection model that incorporates evaluation criteria such as total power plant cost, risk, greenhouse gas emissions, fossil energy use, social acceptance, and employment opportunities, decision-making is more comprehensive and scientific.

[0100] 2. Modeling tailored to practical needs: This method constructs a multi-objective planning model based on a variety of practical conditions, including resource constraints, association constraints, mandatory constraints, and project mutual exclusion constraints. This modeling approach better meets practical needs and ensures that the final site selection plan is highly operational and practical.

[0101] 3. Balancing flexibility and economic efficiency: Wind, solar, and energy storage networks can dynamically adjust power supply capacity based on grid demand while minimizing investment and operating costs. This approach not only improves the system's economic efficiency but also enhances its operational flexibility and reliability, adapting to the actual needs of renewable energy development.

[0102] 4. Environmental protection and sustainability improvement: The method deeply considers the impact of wind, solar and energy storage networks on the environment, such as greenhouse gas emissions, as well as social acceptance, reducing damage to the ecology, improving public recognition of the project, and further enhancing the sustainability of the project.

[0103] 5. Application of the grey correlation method: The grey correlation method is used to analyze the correlation between various indicators and criteria, clarify the importance ranking of the goals, significantly improve the efficiency and accuracy of site selection decisions, and provide a scientific basis for complex multi-objective problems.

[0104] In summary, the present invention provides a comprehensive and practical method and system for synchronous configuration of wind, solar and energy storage networks, which effectively improves economic benefits, operational efficiency and flexibility, reduces environmental impact, and takes into account social acceptance and employment opportunities. It has important guiding significance for the site selection and layout planning of wind, solar and energy storage networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0105] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction to the drawings required for use in the embodiments will be given below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0106] Figure 1 This is a flow chart of the wind-solar-storage-grid synchronization configuration method of the present invention that takes into account flexible power supply and economy;

[0107] Figure 2 This is a flow chart of the wind, solar, energy storage and grid synchronization configuration method of the present invention, which takes into account the flexible power supply capacity and economy of the multiple selection target planning model. DETAILED DESCRIPTION

[0108] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0109] This embodiment addresses the shortcomings of existing wind, solar, and energy storage network site selection methods in terms of multi-objective optimization, quantification of synergistic effects, dynamic adjustment capabilities, data processing, and comprehensive consideration of multiple constraints. By proposing a method for synchronously configuring wind, solar, and energy storage networks that takes into account flexible power supply capacity and economic efficiency, this method constructs a multi-objective optimization model that comprehensively considers economic, technological, environmental, and social objectives, quantitatively analyzes the synergistic effects of multiple energy sources, comprehensively incorporates multiple constraints, and utilizes an improved grey correlation model and multi-objective planning algorithm to achieve the optimal site configuration for wind, solar, and energy storage networks, resolving many key issues in existing technologies.

[0110] Specifically, such as Figure 1 As shown, the wind-solar-storage-grid synchronization configuration method taking into account flexible power supply capability and economy includes the following steps:

[0111] Step 1: Construct objective function: Construct a multi-objective function including economic goals, technical goals, environmental goals and social goals to comprehensively evaluate various indicators.

[0112] Economic Objectives: Based on the above analysis, the economic objectives of wind, solar, and energy storage networks can be measured by total cost. The total cost of energy infrastructure typically includes investment, operation and maintenance, production, and R&D. Because two or more energy sources have synergies, implementing multiple energy sources can bring greater economic benefits than implementing any one energy source alone.

[0113] Therefore, assuming that only the synergy between the two energy sources is considered and the economic goal is to minimize the total cost of the power plant, the specific expression is as follows:

[0114]

[0115] Where Z a represents the economic objective function, i.e., the minimization objective of the total cost of the power station; p represents the total number of alternative energy types; q represents the total number of alternative addresses; i, j∈A, i and j represent two different alternative energy types, belonging to the alternative energy set A; k∈B, k represents the alternative address number, belonging to the alternative address set B; K represents the set of implemented energy types; a i represents the cost of implementing the i-th energy source alone; a ij represents the cost reduction due to synergy effect when the i-th and j-th energy sources are implemented simultaneously; a iK The cost reduction due to synergy effect when implementing the i-th energy source and the already implemented K-th energy source at the same time; x i , x j , x k Is a binary variable, indicating whether to choose the i-th, j-th and k-th energy sources: x i =1 means selecting the i-th energy source, x i =0 means not selecting the i-th energy source; y k Is a binary variable, indicating whether to select the kth alternative address: y k =1 means select the kth address, y k =0 means not selecting the kth address; represents the total cost of implementing each energy source individually among all alternative energy sources and alternative locations; It represents the total cost reduction due to the simultaneous implementation of two energy sources among all alternative energy sources and alternative addresses; It represents the total cost reduction due to the synergy between the simultaneous implementation of a certain energy source and the implemented energy sources among all alternative energy sources; i ,a ij ,a iK ≥0, all cost-related parameters are non-negative.

[0116] Technical objectives: Risks such as technical efficiency, technical maturity, technical reliability and resource availability are the main factors affecting project progress.

[0117] Therefore, from the perspective of risk, the objective function of the technical goal can be expressed as:

[0118]

[0119] Where Z b represents the technical objective function, i.e. the goal of minimizing technical risk; b i represents the technical risk of implementing the i-th energy source alone; b ij represents the technical risk reduced due to synergy effect when the i-th and j-th energy sources are implemented simultaneously; b iK represents the technical risk reduced due to synergy when the i-th energy source is implemented simultaneously with the already implemented K-th energy source; y j Represents a binary variable, indicating whether to select the jth alternative address: y j =1 means select the jth address, y j =0 means not selecting the jth address; Represents the total technical risk of implementing each energy source individually among all alternative energy sources and alternative locations; It represents the total technical risk reduction due to the simultaneous implementation of two energy sources among all alternative energy sources and alternative locations; It represents the total technical risk reduced by the synergy between the simultaneous implementation of a certain energy source and the implemented energy sources among all alternative energy sources; b i ,b ij ,b iK ≥0, all technical risk-related parameters are non-negative.

[0120] Environmental goals: Environmental goals should minimize fossil energy use and greenhouse gas emissions. Since implementing multiple energy sources can reduce fossil energy use and greenhouse gas emissions, the objective function can be expressed as:

[0121]

[0122] Where Z c represents the environmental objective function, i.e., the minimization of fossil energy use and greenhouse gas emissions; C i represents the greenhouse gas emissions generated when the i-th energy source is used alone; D i represents the amount of fossil energy consumed when the i-th energy source is implemented alone; C ij represents the amount of greenhouse gas emissions reduced due to synergistic effects when the i-th and j-th energy sources are implemented simultaneously; D ij C represents the amount of fossil energy used that is reduced due to the synergistic effect when the i-th and j-th energy sources are implemented simultaneously; iK D represents the amount of greenhouse gas emissions reduced due to the synergistic effect when the i-th energy source is implemented simultaneously with the already implemented K-th energy source;iK represents the reduction in fossil energy use due to synergistic effects when the i-th energy source is implemented simultaneously with the already implemented K-th energy source; represents the total greenhouse gas emissions of each energy source implemented individually across all energy alternatives and locations; It represents the total greenhouse gas emissions reduction due to the synergy between the simultaneous implementation of a certain energy source and the implemented energy sources among all alternative energy sources; It represents the total greenhouse gas emissions reduction due to the synergy between the simultaneous implementation of a certain energy source and the implemented energy sources among all alternative energy sources; It represents the total fossil energy use of each energy source implemented individually among all alternative energy sources and alternative locations; It represents the total fossil energy reduction due to the simultaneous implementation of two energy sources among all alternative energy sources and alternative locations; It represents the total amount of fossil energy used that is reduced due to the synergy between the simultaneous implementation of a certain energy source and the implemented energy source among all alternative energy sources; C i ,D i ,C ij ,D ij ,C iK ,D iK ≥0, all environment-related parameters are non-negative.

[0123] Social objectives: Because the site selection of wind, solar, and energy storage systems is closely related to the production and daily lives of the public, social objectives are primarily measured in terms of employment opportunities created and social acceptance after the construction of the wind, solar, and energy storage systems. Employment opportunities refer to the creation of new jobs after the system is built, but the synergistic effects of multiple energy sources will also reduce some existing jobs, thereby reducing employment opportunities. Social acceptance refers to the public's acceptance of energy use after the construction of the wind, solar, and energy storage systems. Factors that influence public acceptance include noise, cost, land and water resource use, and greenhouse gas emissions. Generally speaking, the combined use of multiple energy sources is more popular with the public than the use of any single energy source alone.

[0124] The specific form of its objective function can be expressed as:

[0125]

[0126] Where Z d represents the social objective function, i.e., maximizing the social acceptance and employment opportunities created after the construction of wind, solar and energy storage grids; E i is the social acceptance when the i-th energy source is implemented alone; F i The number of jobs created when implementing the i-th energy source alone; E ijF represents the social acceptance that is enhanced due to the synergistic effect when the i-th and j-th energy sources are implemented simultaneously; ij E represents the number of jobs reduced due to synergy effects when the i-th and j-th energy sources are implemented simultaneously; iK F represents the social acceptance that is enhanced due to the synergistic effect when the i-th energy source is implemented simultaneously with the already implemented K-th energy source; iK represents the number of jobs reduced due to synergy effects when the i-th energy source is implemented simultaneously with the already implemented K-th energy source; represents the total social acceptability of implementing each energy source individually among all alternative energy sources and alternative locations; It represents the total social acceptance that is improved by implementing two energy sources simultaneously among all alternative energy sources and alternative addresses; It represents the total social acceptance of all alternative energy sources that is enhanced by the synergy between the simultaneous implementation of a certain energy source and the implemented energy sources; represents the total number of jobs that could be created by implementing each energy source individually across all energy alternatives and locations; represents the total number of jobs lost due to the simultaneous implementation of both energy sources across all alternative energy sources and alternative locations; It represents the total number of jobs that are reduced due to the synergy between the simultaneous implementation of a certain energy source and the implemented energy sources among all alternative energy sources; E i ,F i ,E ij ,F ij ,E iK ,F iK ≥0, all social-related parameters are non-negative.

[0127] Step 2: Design constraints: Design corresponding constraints including resource constraints, association constraints, mandatory constraints, project mutual exclusion constraints, energy supply and demand balance constraints, energy storage charging and discharging cycle constraints, land use constraints, grid access capacity constraints, and investment return rate constraints to ensure the rational use of each resource and the rationality of the site selection.

[0128] Resource constraints: Resource constraints primarily refer to the land usage requirements of different energy sources. Hybrid energy systems, which combine multiple energy sources, can share some infrastructure, such as power grids and infrastructure, thereby saving land. Therefore, resource constraints need to be considered when selecting sites for wind, solar, and energy storage systems.

[0129] Land resource constraints are in the form of:

[0130]

[0131] Where p represents the total number of alternative energy types; q represents the total number of alternative addresses; i, j∈A, i and j represent two different alternative energy types, belonging to the alternative energy set A; k∈B, k represents the number of the alternative address, belonging to the alternative address set B; K represents the set of implemented energy types; represents the land use required when implementing the i-th energy source alone; represents the amount of land use reduced due to synergistic effects when the i-th and j-th energy sources are implemented simultaneously; represents the amount of land use reduced due to the synergistic effect when the i-th energy source is implemented simultaneously with the already implemented K-th energy source; x i , x j Is a binary variable, indicating whether to choose the i-th and j-th energy sources: x i =1 means selecting the i-th energy source, x i =0 means not selecting the i-th energy source; y k Is a binary variable, indicating whether to select the kth alternative address: y k =1 means select the kth address, y k =0 means not selecting the kth address; Represent the total land use required to implement each energy source individually across all energy alternatives and address alternatives; represents the total land use reduction due to the simultaneous implementation of two energy sources among all alternative energy sources and alternative locations; It represents the total land use reduction due to the synergy between the simultaneous implementation of a certain energy source and the implemented energy sources among all alternative energy sources; l is the total available land, which represents the maximum developable land area within the site selection area; All land-related parameters are non-negative; a l ≥0, the total available land is positive.

[0132] Association constraint: An association constraint is a system constraint that ensures that related items can be selected.

[0133] The first form of association constraint is: j∈Q M , suitable for weak correlation scenarios;

[0134] The second form of association constraint is: j∈Q A , suitable for strong correlation scenarios;

[0135] Where M j represents the energy set associated with the j-th energy; Q M It represents the energy subset that needs to satisfy the association constraint, which is a subset of the set {1,2,...,P}; Represents the energy set M associated with the j-th energy j The sum of all selected energy quantities; A s represents the energy set associated with the j-th energy; |A s | represents the set A s The number of elements in Q, that is, the total number of energy types associated with the jth energy; A It represents the energy subset that needs to satisfy the association constraint, which is a subset of the set {1,2,...,P}; Represents the energy set A associated with the j-th energy s The sum of all selected energy quantities.

[0136] Mandatory constraints: In the process of developing and utilizing energy resources, laws, corporate policies, or environmental conditions stipulate the projects to be implemented, so projects that have been implemented can also be classified into this category.

[0137] The mandatory constraint is expressed as: j =1,j∈Q A .

[0138] Project mutual exclusion constraint: A project mutual exclusion constraint is to select at most one project. The form of the project mutual exclusion constraint is:

[0139] Where G represents a set of mutually exclusive items; Represents the sum of all selected items in the mutually exclusive item set G.

[0140] Energy supply and demand balance constraints: The total power supply of the wind, solar and storage combined system must meet the regional power demand and take into account the energy storage charging and discharging efficiency.

[0141] The energy supply and demand balance constraint is expressed as:

[0142]

[0143] Where A' represents the set of alternative addresses or energy projects; P wind,i,t is the wind power output of the ith candidate address at time t; P PV,i,t is the photovoltaic power generation output of the i-th candidate address at time t; η discharge is the discharge efficiency of the energy storage system, and its value range is η discharge ∈[0,1]; and They represent the charging and discharging behavior of the energy storage system within time t, represents the energy absorbed by the energy storage system at the i-th candidate address from the grid or other energy sources within time t, represents the energy released by the energy storage system at the i-th candidate address to the grid or other loads within time t; D t is the total load of regional power demand at time t; T is the time set, which represents the time range of the entire analysis.

[0144] Energy storage charge and discharge cycle constraints: Energy storage charge and discharge cycle constraints are used to avoid simultaneous charging and discharging during the same period and limit the charge and discharge power.

[0145] The energy storage charge and discharge cycle constraint is expressed as:

[0146]

[0147] Where z i,t Represents a binary variable used to control the charge and discharge state of the energy storage system, z i,t =1 means the energy storage system is in the discharge state at time t, z i,t =0 means that the energy storage system is in the charging state at time t; M is the maximum constant; P charge,max is the maximum charging power of the energy storage system; P discharge,max is the maximum discharge power of the energy storage system.

[0148] Land use constraints: The developable area of each alternative site limits the maximum installed capacity of wind, solar and storage equipment.

[0149] The land use constraint is expressed as:

[0150]

[0151] Where S wind,i The area per unit installed capacity required to build wind power facilities at the i-th alternative location; x wind,i is a binary variable, indicating whether to build wind power facilities at the i-th alternative address, x wind,i =1 means building wind power facilities at this address, x wind,i =0 means no wind power facilities will be built at this address; S PV,i The area per unit installed capacity required to build a photovoltaic facility at the i-th candidate address; x PV,i is a binary variable, indicating whether to build a photovoltaic facility at the i-th alternative address, x PV,i =1 means building photovoltaic facilities at this address, x PV,i =0 means no photovoltaic facilities will be built at this address; S storage,i The area required to build the energy storage system for the i-th candidate address; x storage,i is a binary variable, indicating whether to build energy storage facilities at the i-th alternative address, x storage,i =1 means building energy storage facilities at this address, x storage,i =0 means no energy storage facilities will be built at this address; is the maximum developable land area of the ith alternative address.

[0152] Grid access capacity constraints: Grid access capacity constraints are used to limit the total grid-connected power of wind power and photovoltaic facilities to ensure that it does not exceed the maximum allowable access power of the regional power grid.

[0153] The grid access capacity constraint is expressed as:

[0154]

[0155] Where, represents the rated power of the wind power facility at the i-th candidate address; represents the rated power of the photovoltaic facility at the i-th candidate address; Indicates the maximum allowable access power of the regional power grid.

[0156] IRR constraint: Ensure that the internal rate of return is not lower than the minimum required IRR through the IRR constraint min .

[0157] The form of the return on investment constraint is expressed as:

[0158]

[0159] In the formula, NetCashFlow t is the net cash flow in the tth year. If it is positive, it means there is net income in that year. If it is negative, it means there is net expenditure in that year. t is the time variable, which means the tth year of the project. T is the life cycle of the project. IRR min is the minimum required internal rate of return; (1+IRR min ) t is the discount factor used to discount the net cash flow in year t to the present value.

[0160] As the formula above demonstrates, the target expected value must be determined based on local conditions, including the level of economic development, resource availability, geographic location, and social customs and culture. Furthermore, it must also be aligned with objective constraints such as national policies, laws and regulations, and industry norms to achieve more effective implementation decisions.

[0161] Step 3: Model solution: Use the improved grey correlation model to screen relevant alternative addresses, determine the weight of each indicator based on grey correlation analysis, build a multiple selection target planning model and introduce the synergistic effect of the multi-energy complementary system, and use the optimization algorithm to solve the model; comprehensively consider the economic, technical, environmental and social multi-objective constraints, and finally rank the alternative locations and select the optimal wind, solar and storage site selection plan.

[0162] Among them, multiple choice goal planning is constructed, and the expected level standard of multiple choices is set for each goal to ensure the smooth progress of decision-making; the expression of the multiple choice goal planning model is:

[0163]

[0164] Where r t is the positive deviation weight of the t-th target in the objective function, which is calculated by the grey relational degree; is the positive deviation variable of the i-th target; is the negative deviation variable of the i-th target; is the i-th target value and the upper limit h t,max Positive deviation variables; is the i-th target value and the lower limit h t,min Negatively biased variables; is a linear function of the i-th target candidate position; h t is the expected level of the t-th target; h t,max is the upper limit of the t-th target; h t,min is the lower limit of the t-th target; n is the total number of targets; is a decision variable vector, indicating the selection of alternative addresses, where each y t Indicates whether an alternative address or energy source is selected.

[0165] A represents the set of candidate project numbers, and B represents the set of implemented wind, solar, and energy storage network project numbers. Let A = {1, 2, ..., p}, B = {1, 2, ..., q}, and define x and y as:

[0166]

[0167] Where x i is a binary variable, indicating whether energy i is selected; y i is a binary variable indicating whether energy source i is selected; p is the total number of energy types; and q is the number of alternative locations for wind, solar, and energy storage networks.

[0168] From the above analysis, we can see that multiple choice goal programming, as a linear form of goal programming, also achieves the optimal location of the system when achieving the expected goals.

[0169] Furthermore, based on the above steps, this embodiment proposes a multiple-choice objective programming model for the above-mentioned method for synchronous configuration of wind, solar, and energy storage networks that takes into account flexible power supply capability and economy. The solution algorithm of the multiple-choice objective programming model includes the following steps:

[0170] like Figure 2As shown, first, determine the indicators and alternative addresses to be considered, and use the improved grey correlation model to determine whether they are correlated. If they are correlated, proceed to the next step. If they are not correlated, other models need to be selected.

[0171] Secondly, for the model that already has correlation, the correlation between indicators and criteria is analyzed based on the grey correlation method, so that the importance ranking among many goals can be highlighted, and the site selection decision can be made through the multiple selection target planning model.

[0172] Then, considering the synergistic effect of multi-energy complementary systems, the multiple resource constraints and multi-objective constraints of wind, solar, storage and grid site selection decisions are described in detail.

[0173] Finally, the weights of the alternative addresses obtained above are combined to make a multi-objective planning model that considers the deviations of each objective to make a site selection decision.

[0174] The specific implementation process is as follows:

[0175] S1. Determine the indicators and alternative locations to be considered:

[0176] First, it's necessary to clearly define the indicators used to evaluate wind, solar, and energy storage network site selection. These indicators cover specific evaluation indicators within the four objective functions of economics, technology, environment, and society. For example, economic indicators include total investment cost and operation and maintenance costs; technical indicators cover energy conversion efficiency and system reliability; environmental indicators include greenhouse gas emissions and land use efficiency; and social indicators include social acceptance and job creation. At the same time, a list of candidate sites should be identified, each of which should have the potential and conditions for hosting wind, solar, and energy storage projects.

[0177] S402. Determine the correlation using the improved grey correlation model:

[0178] For each candidate address, the improved Grey Relational Analysis (GRA) model is used to evaluate its correlation with each evaluation index.

[0179] The grey relational formula is as follows:

[0180]

[0181] Where, ξ i (k) is the grey correlation degree of the kth candidate address on the i-th indicator, which indicates the closeness between the candidate address and the ideal value on the i-th indicator. The value range is [0,1]. The closer the value is to 1, the higher the correlation. ζ is the resolution coefficient, which is used to adjust the resolution ability of the grey correlation degree. It usually takes a value between 0 and 1. k is the candidate address number. i is the evaluation indicator number.

[0182] Δ i (k) is the difference of the kth candidate address on the i-th indicator; the calculation formula is:

[0183]

[0184] Where x i (k) is the actual value of the k-th candidate address on the i-th indicator, is the ideal value of the i-th indicator;

[0185] Δ min is the minimum difference of the i-th indicator among all the alternative addresses; the calculation formula is:

[0186]

[0187] Δ max is the maximum difference of the i-th indicator among all the alternative addresses; the calculation formula is:

[0188]

[0189] By calculating the grey correlation degree, we can determine the correlation degree of each candidate address in each indicator. If a candidate address has a high correlation degree in all indicators, ξ i (k) If it exceeds the preset threshold, it is considered to be relevant and proceeds to the next step; otherwise, it is necessary to select another model or re-screen the alternative addresses.

[0190] S3. Correlation analysis between indicators and criteria based on grey correlation:

[0191] For candidate addresses with correlation, further analysis of the correlation between various evaluation indicators is performed based on the grey correlation degree to determine the weight of each target. The specific steps are as follows:

[0192] 1. Standardization: Standardize each evaluation index to eliminate the influence of dimension.

[0193] 2. Calculate the comprehensive correlation: For each candidate address, calculate its comprehensive correlation across all indicators. The formula is:

[0194]

[0195] Where Γ(k) is the comprehensive correlation degree of the kth candidate address; ξ i (k) is the grey relational degree of the kth candidate address on the i-th indicator; n is the total number of evaluation indicators, is the averaging factor; k is the candidate address number; i is the evaluation index number.

[0196] 3. Determine the weight: Using the grey correlation results, determine the weight of each evaluation index through the Analytic Hierarchy Process (AHP) or Entropy Weight Method. i , so that the importance ranking of each goal can be made clear.

[0197] S4. Constructing a multiple choice goal programming model

[0198] Based on multi-objective functions and constraints, a multiple choice objective programming model is established.

[0199] The basic form of the model is:

[0200]

[0201] In the formula, Minimize means that the optimization goal is to minimize; m is the total number of goals; w i is the weight of the i-th target; f i (x) is the i-th objective function; x is the decision variable vector; d i is the expected value of the i-th target; (f i (x)-d i ) 2 is the square of the deviation between the actual value and the expected value of the i-th target; g j (x) is the jth constraint; g j (x)≤0 is the form of the constraint; p is the total number of constraints.

[0202] S5. Consider the synergistic effect of multi-energy complementary systems

[0203] In the multiple choice objective programming model, the synergistic effect of the multi-energy complementary system needs to be introduced, including:

[0204] 1. Synergistic effect function: The synergistic effect function is introduced to represent the improvement of system performance due to the synergistic effect between different energy sources. It can be defined as:

[0205]

[0206] Where, E(x) is the synergistic effect function of the multi-energy complementary system; α i is the synergy coefficient of the i-th target; f i (x) is the i-th objective function; n is the total number of objectives.

[0207] 2. Adjust the objective function: Based on the original objective function, taking into account the synergistic effect, the adjusted objective function is:

[0208]

[0209] Where, is the original objective function; w i is the weight of the i-th target; d i is the expected value of the i-th target; m is the total number of targets; x is the decision variable vector; -λE(x) is the synergistic effect term; λ is the weight factor.

[0210] S6. Comprehensive weights of alternative addresses for multi-objective planning optimization

[0211] Combining the weights of the alternative addresses on each evaluation index obtained in the above steps, a multi-objective programming model is used for optimization and solution to achieve comprehensive consideration of the deviations of each objective.

[0212] The specific steps are as follows:

[0213] 1. Construct a decision matrix: Organize the performance of each candidate address on each indicator into a decision matrix D = [d {ij} ], where d {ij} It represents the score of the i-th candidate address on the j-th indicator.

[0214] 2. Apply optimization algorithms: Select appropriate optimization algorithms, such as genetic algorithms, particle swarm optimization algorithms, simulated annealing algorithms, etc., to solve the multiple choice objective programming model to find the optimal location solution that meets all constraints and minimizes the comprehensive objective function.

[0215] 3. Sorting and screening: Based on the optimization results, the candidate locations are ranked and the location with the highest comprehensive score is selected as the final wind, solar and energy storage network site.

[0216] Through the above detailed solution algorithm process, combined with mathematical models and optimization methods, the optimal site selection decision of wind, solar and energy storage grid can be systematically achieved, ensuring the overall improvement of energy supply stability, economic benefits, environmental friendliness and social acceptance.

[0217] Furthermore, based on the above steps, this embodiment also proposes a wind, solar, and storage network synchronization configuration system that takes into account flexible power supply capability and economy for the above-mentioned wind, solar, and storage network synchronization configuration method that takes into account flexible power supply capability and economy. The wind, solar, and storage network synchronization configuration system that takes into account flexible power supply capability and economy includes the following modules:

[0218] Data Collection Module: This module's primary task is to collect wind and solar resource data, site land resource data, and other relevant data from various data sources, including economic, environmental, and social acceptance data. This process can include establishing a database, connecting to various data sources, such as the Meteorological Bureau and the Geological Survey, and configuring data capture algorithms to automatically collect data regularly. This can also include direct data collection or surveys; for example, social acceptance data can be obtained through questionnaires or interviews.

[0219] Resource Analysis Module: This module receives data collected by the Data Collection Module and processes and analyzes it. This process includes developing data processing rules to clean the raw data and remove noise. It then uses data analysis algorithms, such as machine learning, to conduct deep learning on the data and determine the wind and solar resource status of candidate sites. Finally, the analysis results are organized and output in the form of graphs or tables.

[0220] Optimization Module: This module is primarily responsible for optimizing the synchronous configuration of wind, solar, and energy storage systems based on the analysis results provided by the resource analysis module and collected data on economic, environmental, and social acceptance. This process involves establishing an optimization model and setting the objective function. Data is then input and the optimization algorithm is run to generate the optimal solution. Furthermore, the module requires an adaptive algorithm to adjust to real-time data changes.

[0221] Model Solver: This module applies algorithms to solve the optimization model. This involves selecting a mathematical model solver, such as Matlab, and writing the appropriate computational code to translate the optimization model into a computational problem and obtain the solution.

[0222] Results Presentation Module: The main task of the results presentation module is to visualize the solutions generated by the optimization model. This process may include graphical generation, which presents the solutions for each site in graphical form, such as map overlays or curve graphs; and comparison, which compares and summarizes the results from several sites to facilitate decision-making.

[0223] Feedback Module: This module's primary function is to receive user feedback on system performance and optimize the system based on that feedback. The feedback module's implementation involves building a user interface, receiving user feedback, parsing the feedback, and feeding it back to the relevant modules. If the feedback requires re-running a module, the system appropriately transmits the feedback to the relevant module, triggering its execution.

[0224] In order to verify the actual effect of the above-mentioned wind, solar, and energy storage network synchronization configuration method taking into account flexible power supply capability and economy, this embodiment carried out the following actual verification:

[0225] A province plans to select a suitable site for a wind, solar, and energy storage project from among five candidate sites: A, B, C, D, and E. The geographical location, resource conditions, and social environment of the five candidate sites (A, B, C, D, and E) vary. The steps for selecting a site for a wind, solar, and energy storage network using a synchronous wind, solar, and energy storage network configuration method, taking into account flexible power supply capacity and economic efficiency, are as follows:

[0226] 1. Determine the indicators and alternative locations to be considered;

[0227] Evaluation Indicator Categories: Economic Indicators E: Investment Cost E1, Operation and Maintenance Cost E2, Expected Return E3. Technical Indicators T: Wind Resource Availability T1, Solar Resource Availability T2, System Reliability T3. Environmental Indicators Env: Greenhouse Gas Emission Reduction Env1, Land Use Efficiency Env2. Social Indicators S: Social Acceptance S1, Job Creation S2.

[0228] The values of the indicators of the five alternative locations A, B, C, D, and E are shown in the following table:

[0229] index A B C D E E1 investment cost (million) 100 120 110 130 115 E2 operation and maintenance costs (millions / year) 10 12 11 13 11.5 E3 expected revenue (million / year) 20 18 19 17 19.5 T1 Wind energy resources (average wind speed, m / s) 7.5 6.0 7.0 5.5 7.2 <![CDATA[T2 Solar resource (average irradiance, kWh / m 2 )]]> 5.0 4.5 5.5 4.0 5.2 T3 System reliability (rating, 1-10) 8 7 8 6 8 <![CDATA[Env1 Emission Reduction (10,000 tons of CO2 / year)]]> 50 45 48 40 49 Env2 land use efficiency (MW / km2) 10 9 10.5 8 10 S1 Social acceptance (score, 1-10) 7 6 8 5 7.5 S2 employment opportunities (annual new employment numbers) 100 80 90 70 95

[0230] 2. Use the improved grey relational model to determine the correlation

[0231] Standardization: For positive indicators, such as expected returns and wind energy resources, the larger the value, the better. For negative indicators, such as investment costs and operation and maintenance costs, the smaller the value, the better.

[0232] The reverse indicator calculation formula is as follows:

[0233]

[0234] Where z i (k) is the normalized value of the kth candidate address on the i-th indicator; x i (k) is the original value of the kth candidate address on the i-th index; max(x i ) is the maximum value of the i-th indicator among all candidate addresses; min(x i ) The minimum value of the i-th indicator among all the alternative addresses; k is the alternative address number; i is the evaluation indicator number.

[0235] The standardized results are shown in the following table:

[0236]

[0237]

[0238] Calculate the grey correlation degree of each candidate location on each indicator:

[0239] Select the ideal solution, that is, the optimal value of each indicator, as the reference sequence. For each indicator, the standardized value of the ideal solution is 1. Grey relational formula:

[0240] in:

[0241] The calculation steps take the indicator investment cost E1 as an example:

[0242] First, calculate the difference:

[0243] Then, determine Δ min and Δ max :

[0244] Finally, calculate the grey relational degree:

[0245] Similar calculations are performed on all indicators to finally obtain the grey correlation degree of each candidate address on each indicator. The threshold is set to 0.5 to filter out locations that pass the correlation test. Alternative addresses with a correlation degree greater than or equal to 0.5 are considered to be relevant and proceed to the next step; otherwise, they are excluded. The correlation determination results are shown in the following table:

[0246]

[0247]

[0248] Calculate the average grey correlation degree of each candidate address on all indicators, only consider the indicators that pass the correlation, and set the comprehensive correlation threshold to 0.6. Assume that the comprehensive correlation is calculated as follows:

[0249] Place Comprehensive correlation A (1+1+1+1+1+1+1+1+0.666+1) / 10≈0.966 B (0.333+0+0+0+0+0.75+0+0+0+0) / 10＝0.108 C (0.5+0.5+0.75+0.666+1+1+0.8+0.95+1+0.916) / 10≈0.827 D (0.333+0+0+0+0+0.5+0+0+0+0) / 10＝0.087 E (0.636+0.666+1+0.933+0.8+1+0.96+1+0.833+0.958) / 10≈0.829

[0250] The results are: A, C, and E passed the correlation, while B and D failed the correlation.

[0251] 3. Analyze indicator weights based on grey correlation

[0252] Calculate the comprehensive correlation: The comprehensive correlation is used for preliminary sorting, and then the weight of each indicator needs to be determined.

[0253] Determine the indicator weights: Entropy weight method is used here to determine the weights;

[0254] First, standardize the decision matrix;

[0255] Then, calculate the information entropy of each indicator. The calculation formula is as follows:

[0256] in,

[0257] Where, e i is the information entropy of the ith indicator; k is the normalization constant; m is the number of alternative locations; p ij is the normalized probability of the jth candidate location on the i-th indicator; z ij is the normalized value of the jth candidate location on the i-th indicator;

[0258] Then, determine the redundancy: di = 1-ei; where di is the redundancy of the i-th indicator; ei is the information entropy of the i-th indicator;

[0259] Finally, calculate the weights: Where w i is the weight of the i-th indicator; the weights of each category of indicators are obtained by the entropy weight method as shown in the following table:

[0260] Indicator Category Weight E1 0.15 E2 0.10 E3 0.15 T1 0.10 T2 0.10 T3 0.10 Env1 0.10 Env2 0.10 S1 0.05 S2 0.05

[0261] The total weight is 1, and the specific weight value needs to be calculated in detail according to the entropy weight method.

[0262] 4. Construct and solve multiple choice goal programming models

[0263] Construct a multi-objective function and comprehensively consider the deviations of each objective. It takes the following form:

[0264]

[0265] Constraints: Resource constraints, land use restrictions; grid access capacity, meeting project requirements; IRR ≥ 10%. Select locations A, C, and E that pass the relevance test, and their standardized scores are as follows:

[0266]

[0267]

[0268] Calculate the objective function value:

[0269] pass Calculations yield: Location A, ZA ≈ 0.10555; Location C, ZC ≈ 0.0873; Location E, ZE ≈ 0.02932. Smaller objective function values indicate smaller deviations from the ideal solution; therefore, Location E is optimal, followed by C, and finally A.

[0270] 5. Select the optimal site plan

[0271] According to the objective function value, alternative location E has the smallest comprehensive deviation and is therefore the optimal location option; the alternative options are location C and location A.

[0272] Sensitivity analysis: Adjust the weights of various indicators to see if the site selection results change. For example, increasing the weight of environmental indicators may further strengthen the advantages of location E. Simulate indicator changes, assuming that a certain indicator, such as expected returns, changes and evaluate the impact on the site selection results.

[0273] Field research: Conduct an on-site inspection of Site E to verify the stability of its wind and solar energy resources, the convenience of grid access, and social acceptance. Dynamic adjustment: Regularly update evaluation indicators and data as policies or resource conditions change, and re-evaluate the adaptability of the site selection plan.

[0274] The above practical verification fully demonstrates the practical application of a method for synchronously configuring wind, solar, and energy storage grids, taking into account flexible power supply capabilities and economic efficiency. This method ensures the scientific and rationality of site selection through systematic indicator evaluation, grey correlation analysis, and multi-objective optimization. Using specific data and mathematical models, the optimal site E was ultimately selected, meeting multiple economic, technical, environmental, and social requirements.

Claims

1. A method for synchronously configuring wind, solar, and energy storage networks, taking into account flexible power supply capabilities and economic efficiency, is characterized by: The wind-solar-storage-grid synchronization configuration method includes the following steps: Step 1: Construct objective function: Construct a multi-objective function including economic objectives, technical objectives, environmental objectives and social objectives to comprehensively evaluate various indicators; Step 2: Design constraints: Design corresponding constraints including resource constraints, association constraints, mandatory constraints, project mutual exclusion constraints, energy supply and demand balance constraints, energy storage charge and discharge cycle constraints, land use constraints, grid access capacity constraints, and investment return rate constraints to ensure the rational use of each resource and the rationality of the site selection; Step 3: Model solution: Use the improved grey correlation model to screen relevant alternative addresses, determine the weight of each indicator based on grey correlation analysis, build a multiple selection target planning model and introduce the synergistic effect of the multi-energy complementary system, and use the optimization algorithm to solve the model; comprehensively consider the economic, technical, environmental and social multi-objective constraints, and finally rank the alternative locations and select the optimal wind, solar and storage site selection plan.

2. The method for synchronous configuration of wind, solar, and energy storage networks taking into account flexible power supply capability and economy according to claim 1 is characterized by: Among the multi-objective functions, the objective function of the economic goal is expressed as follows: Where Z a represents the economic objective function, i.e., the minimization objective of the total cost of the power station; p represents the total number of alternative energy types; q represents the total number of alternative addresses; i, j∈A, i and j represent two different alternative energy types, belonging to the alternative energy set A; k∈B, k represents the alternative address number, belonging to the alternative address set B; K represents the set of implemented energy types; a i represents the cost of implementing the i-th energy source alone; a ij represents the cost reduction due to synergy effect when the i-th and j-th energy sources are implemented simultaneously; a iK The cost reduction due to synergy effect when implementing the i-th energy source and the already implemented K-th energy source at the same time; x i , x j , x k Is a binary variable, indicating whether to choose the i-th, j-th and k-th energy sources: x i =1 means selecting the i-th energy source, x i =0 means not selecting the i-th energy source; y k Is a binary variable, indicating whether to select the kth alternative address: y k =1 means select the kth address, y k =0 means not selecting the kth address; represents the total cost of implementing each energy source individually among all alternative energy sources and alternative locations; It represents the total cost reduction due to the simultaneous implementation of two energy sources among all alternative energy sources and alternative addresses; It represents the total cost reduction due to the synergy between the simultaneous implementation of a certain energy source and the implemented energy sources among all alternative energy sources; i ,a ij ,a iK ≥0, all cost-related parameters are non-negative; Among the multi-objective functions, the objective function of the technical goal is expressed as follows: Where Z b represents the technical objective function, i.e. the goal of minimizing technical risk; b i represents the technical risk of implementing the i-th energy source alone; b ij represents the technical risk reduced due to synergy effect when the i-th and j-th energy sources are implemented simultaneously; b iK represents the technical risk reduced due to synergy when the i-th energy source is implemented simultaneously with the already implemented K-th energy source; y j Represents a binary variable, indicating whether to select the jth alternative address: y j =1 means select the jth address, y j =0 means not selecting the jth address; Represents the total technical risk of implementing each energy source individually among all alternative energy sources and alternative locations; It represents the total technical risk reduction due to the simultaneous implementation of two energy sources among all alternative energy sources and alternative locations; It represents the total technical risk reduced by the synergy between the simultaneous implementation of a certain energy source and the implemented energy sources among all alternative energy sources; b i ,b ij ,b iK ≥0, all technical risk-related parameters are non-negative; Among the multi-objective functions, the objective function of the environmental objective is expressed as follows: Where Z c represents the environmental objective function, i.e., the minimization of fossil energy use and greenhouse gas emissions; C i represents the greenhouse gas emissions generated when the i-th energy source is used alone; D i represents the amount of fossil energy consumed when the i-th energy source is implemented alone; C ij represents the amount of greenhouse gas emissions reduced due to synergistic effects when the i-th and j-th energy sources are implemented simultaneously; D ij C represents the amount of fossil energy used that is reduced due to the synergistic effect when the i-th and j-th energy sources are implemented simultaneously; iK D represents the amount of greenhouse gas emissions reduced due to the synergistic effect when the i-th energy source is implemented simultaneously with the already implemented K-th energy source; iK represents the reduction in fossil energy use due to synergistic effects when the i-th energy source is implemented simultaneously with the already implemented K-th energy source; represents the total greenhouse gas emissions of each energy source implemented individually across all energy alternatives and locations; It represents the total greenhouse gas emissions reduction due to the synergy between the simultaneous implementation of a certain energy source and the implemented energy sources among all alternative energy sources; It represents the total greenhouse gas emissions reduction due to the synergy between the simultaneous implementation of a certain energy source and the implemented energy sources among all alternative energy sources; It represents the total fossil energy use of each energy source implemented individually among all alternative energy sources and alternative locations; It represents the total fossil energy reduction due to the simultaneous implementation of two energy sources among all alternative energy sources and alternative locations; It represents the total amount of fossil energy used that is reduced due to the synergy between the simultaneous implementation of a certain energy source and the implemented energy source among all alternative energy sources; C i ,D i ,C ij ,D ij ,C iK ,D iK ≥0, all environment-related parameters are non-negative; Among the multi-objective functions, the objective function of social objectives is expressed as follows: Where Z d represents the social objective function, i.e., maximizing the social acceptance and employment opportunities created after the construction of wind, solar and energy storage grids; E i is the social acceptance when the i-th energy source is implemented alone; F i The number of jobs created when implementing the i-th energy source alone; E ij F represents the social acceptance that is enhanced due to the synergistic effect when the i-th and j-th energy sources are implemented simultaneously; ij E represents the number of jobs reduced due to synergy effects when the i-th and j-th energy sources are implemented simultaneously; iK F represents the social acceptance that is enhanced due to the synergistic effect when the i-th energy source is implemented simultaneously with the already implemented K-th energy source; iK represents the number of jobs reduced due to synergy effects when the i-th energy source is implemented simultaneously with the already implemented K-th energy source; represents the total social acceptability of implementing each energy source individually among all alternative energy sources and alternative locations; It represents the total social acceptance that is improved by implementing two energy sources simultaneously among all alternative energy sources and alternative addresses; It represents the total social acceptance of all alternative energy sources that is enhanced by the synergy between the simultaneous implementation of a certain energy source and the implemented energy sources; represents the total number of jobs that could be created by implementing each energy source individually across all energy alternatives and locations; represents the total number of jobs lost due to the simultaneous implementation of both energy sources across all alternative energy sources and alternative locations; It represents the total number of jobs that are reduced due to the synergy between the simultaneous implementation of a certain energy source and the implemented energy sources among all alternative energy sources; E i ,F i ,E ij ,F ij ,E iK ,F iK ≥0, all social-related parameters are non-negative.

3. The method for synchronous configuration of wind, solar, and energy storage networks taking into account flexible power supply capability and economy according to claim 1 is characterized by: The resource constraint is the land use constraint, and the land use constraint is in the form of: Where p represents the total number of alternative energy types; q represents the total number of alternative addresses; i, j∈A, i and j represent two different alternative energy types, belonging to the alternative energy set A; k∈B, k represents the number of the alternative address, belonging to the alternative address set B; K represents the set of implemented energy types; represents the land use required when implementing the i-th energy source alone; represents the amount of land use reduced due to synergistic effects when the i-th and j-th energy sources are implemented simultaneously; represents the amount of land use reduced due to the synergistic effect when the i-th energy source is implemented simultaneously with the already implemented K-th energy source; x i , x j , x k Is a binary variable, indicating whether to choose the i-th, j-th and k-th energy sources: x i =1 means selecting the i-th energy source, x i =0 means not selecting the i-th energy source; y k Is a binary variable, indicating whether to select the kth alternative address: y k =1 means select the kth address, y k =0 means not selecting the kth address; Represent the total land use required to implement each energy source individually across all energy alternatives and address alternatives; represents the total land use reduction due to the simultaneous implementation of two energy sources among all alternative energy sources and alternative locations; It represents the total land use reduction due to the synergy between the simultaneous implementation of a certain energy source and the implemented energy sources among all alternative energy sources; l is the total available land, which represents the maximum developable land area within the site selection area; All land-related parameters are non-negative; a l ≥0, the total amount of available land is positive; Association constraints are system constraints that ensure that related items can be selected. Association constraints include two types of constraints: The first form of association constraint is: Applicable to weak correlation scenarios; The second form of association constraint is: Applicable to scenarios with strong correlation; Where M j represents the energy set associated with the j-th energy; Q M It represents the energy subset that needs to satisfy the association constraint, which is a subset of the set {1,2,...,P}; Represents the energy set M associated with the j-th energy j The sum of all selected energy quantities; A s represents the energy set associated with the j-th energy; |A s | represents the set A s The number of elements in Q, that is, the total number of energy types associated with the jth energy; A It represents the energy subset that needs to satisfy the association constraint, which is a subset of the set {1,2,...,P}; Represents the energy set A associated with the j-th energy s , the sum of all selected energy quantities; Mandatory constraints are constraints of implemented projects. The mandatory constraints are expressed as: j =1,j∈Q A ; The project mutual exclusion constraint is to select at most one project. The form of the project mutual exclusion constraint is expressed as: Where G represents a set of mutually exclusive items; It represents the sum of all selected items in the mutually exclusive item set G; The energy supply and demand balance constraint is that the total power supply meets the regional power demand. The energy supply and demand balance constraint is expressed as: Where A' represents the set of alternative addresses or energy projects; P wind,i,t is the wind power output of the ith candidate address at time t; P PV,i,t is the photovoltaic power generation output of the i-th candidate address at time t; η discharge is the discharge efficiency of the energy storage system, and its value range is η discharge ∈[0,1]; and They represent the charging and discharging behavior of the energy storage system within time t, represents the energy absorbed by the energy storage system at the i-th candidate address from the grid or other energy sources within time t, represents the energy released by the energy storage system at the i-th candidate address to the grid or other loads within time t; D t is the total load of regional power demand at time t; T is the time set, which represents the time range of the entire analysis; Energy storage charge and discharge cycle constraints are used to avoid simultaneous charging and discharging during the same period and to limit the charge and discharge power. The energy storage charge and discharge cycle constraints are expressed as: Where z i,t Represents a binary variable used to control the charge and discharge state of the energy storage system, z i,t =1 means the energy storage system is in the discharge state at time t, z i,t =0 means that the energy storage system is in the charging state at time t; M is the maximum constant; P charge,max is the maximum charging power of the energy storage system; P discharge,max is the maximum discharge power of the energy storage system; Land use constraints are the maximum developable area limits for each address. Land use constraints are expressed as follows: Where S wind,i The area per unit installed capacity required to build wind power facilities at the i-th alternative location; x wind,i is a binary variable, indicating whether to build wind power facilities at the i-th alternative address, x wind,i =1 means building wind power facilities at this address, x wind,i =0 means no wind power facilities will be built at this address; S PV,i The area per unit installed capacity required to build a photovoltaic facility at the i-th candidate address; x PV,i is a binary variable, indicating whether to build a photovoltaic facility at the i-th alternative address, x PV,i =1 means building photovoltaic facilities at this address, x PV,i =0 means no photovoltaic facilities will be built at this address; S storage,i The area required to build the energy storage system for the i-th candidate address; x storage,i is a binary variable, indicating whether to build energy storage facilities at the i-th alternative address, x storage,i =1 means building energy storage facilities at this address, x storage,i =0 means no energy storage facilities will be built at this address; is the maximum developable land area of the ith alternative address; The total grid-connected power of wind power and photovoltaic facilities is limited by the grid access capacity constraint to ensure that it does not exceed the maximum allowable access power of the regional power grid. The grid access capacity constraint is expressed as: Where, represents the rated power of the wind power facility at the i-th candidate address; represents the rated power of the photovoltaic facility at the i-th candidate address; Indicates the maximum allowable access power of the regional power grid; By using the return on investment constraint, we can ensure that the internal rate of return is not lower than the minimum requirement. The return on investment constraint is expressed as: In the formula, NetCashFlow t is the net cash flow in the tth year. If it is positive, it means there is net income in that year. If it is negative, it means there is net expenditure in that year. t is the time variable, which means the tth year of the project. T is the life cycle of the project. IRR min is the minimum required internal rate of return; (1+IRR min ) t is the discount factor used to discount the net cash flow in year t to the present value.

4. The method for synchronous configuration of wind, solar, and energy storage networks taking into account flexible power supply capability and economy according to claim 1 is characterized by: The expression of the multiple choice goal programming model is: Where r t is the positive deviation weight of the t-th target in the objective function, which is calculated by the grey relational degree; is the positive deviation variable of the i-th target; is the negative deviation variable of the i-th target; is the i-th target value and the upper limit h t,max Positive deviation variables; is the i-th target value and the lower limit h t,min Negatively biased variables; is a linear function of the i-th target candidate position; h t is the expected level of the t-th target; h t,max is the upper limit of the t-th target; h t,min is the lower limit of the t-th target; n is the total number of targets; is a decision variable vector, indicating the selection of alternative addresses, where each y t Indicates whether to select a certain alternative address or energy source; A represents the set of candidate project numbers, and B represents the set of implemented wind, solar, and energy storage network project numbers. Let A = {1, 2, ..., p}, B = {1, 2, ..., q}, and define x and y as: Where x i is a binary variable, indicating whether energy i is selected; y i is a binary variable indicating whether energy source i is selected; p is the total number of energy types; and q is the number of alternative locations for wind, solar, and energy storage networks.

5. A multiple choice target programming model, which is used in the wind, solar, energy storage and grid synchronization configuration method taking into account flexible power supply capacity and economy as described in any one of claims 1-4, characterized in that: The process of solving the algorithm for the multiple choice goal programming model includes the following steps: First, determine the indicators and candidate addresses to be considered, and use the improved grey correlation model to determine whether they are correlated; if they are correlated, proceed to the next step; if not, select another model; Secondly, for the models that have correlation, the correlation between indicators and criteria is analyzed based on the grey correlation method, which makes the importance ranking among the numerous goals more prominent, and thus makes the site selection decision through the multiple choice target planning model; Then, considering the synergistic effect of multi-energy complementary systems, the multiple resource constraints and multi-objective constraints of wind, solar, and energy storage grid site selection decisions are described in detail; Finally, the weights of the alternative addresses are combined to make a multi-objective planning model that considers the deviations of each objective to make a site selection decision.

6. The multiple choice goal programming model according to claim 5, characterized in that: When the improved grey relational model is used to determine whether it has relevance, the grey relational formula is as follows: Where, ξ i (k) is the grey correlation degree of the kth candidate address on the i-th indicator, which indicates the closeness between the candidate address and the ideal value on the i-th indicator. The value range is [0,1]. The closer the value is to 1, the higher the correlation. ζ is the resolution coefficient, which is used to adjust the resolution ability of the grey relational degree; k is the candidate address number; i is the evaluation index number; Δ i (k) is the difference of the kth candidate address on the i-th indicator; the calculation formula is: Where x i (k) is the actual value of the k-th candidate address on the i-th indicator, is the ideal value of the i-th indicator; Δ min is the minimum difference of the i-th indicator among all the alternative addresses; the calculation formula is: Δ max is the maximum difference of the i-th indicator among all the alternative addresses; The calculation formula is: If i (k) If it exceeds the preset threshold, it is considered to be relevant and proceeds to the next step; otherwise, it is necessary to select another model or re-screen the alternative addresses.

7. The multiple choice goal programming model according to claim 5, characterized in that: The analysis of the correlation between indicators and criteria includes the following steps: First, each evaluation index is standardized to eliminate the dimension effect; Then, for each candidate address, calculate its comprehensive correlation on all indicators; Where Γ(k) is the comprehensive correlation degree of the kth candidate address; ξ i (k) is the grey relational degree of the kth candidate address on the i-th indicator; n is the total number of evaluation indicators, is the averaging factor; k is the candidate address number; i is the evaluation index number; Finally, the grey correlation results are used to determine the weight of each indicator through the hierarchical analysis method or the entropy weight method to clarify the importance ranking of the goals.

8. The multiple choice goal programming model according to claim 5, characterized in that: The multiple choice objective programming model is established based on multiple objective functions and constraints; The basic form of the model is: In the formula, Minimize means that the optimization goal is to minimize; m is the total number of goals; w i is the weight of the i-th target; f i (x) is the i-th objective function; x is the decision variable vector; d i is the expected value of the i-th target; (f i (x)-d i ) 2 is the square of the deviation between the actual value and the expected value of the i-th target; g j (x) is the jth constraint; g j (x)≤0 is the form of the constraint; p is the total number of constraints.

9. The multiple choice goal programming model according to claim 5, characterized in that: In the synergistic effect, the synergistic effect function is: Where, E(x) is the synergistic effect function of the multi-energy complementary system; α i is the synergy coefficient of the i-th target; f i (x) is the i-th objective function; n is the total number of objectives; The objective function is adjusted to: Where, is the original objective function; w i is the weight of the i-th target; d i is the expected value of the i-th target; m is the total number of targets; x is the decision variable vector; -λE(x) is the synergistic effect term; λ is the weight factor.

10. A wind, solar, and storage network synchronization configuration system taking into account flexible power supply capability and economy, wherein the wind, solar, and storage network synchronization configuration system taking into account flexible power supply capability and economy is used in the wind, solar, and storage network synchronization configuration method taking into account flexible power supply capability and economy as described in any one of claims 1-4, characterized in that: include: Data collection module: responsible for obtaining raw data from multiple channels, including wind and light resource data, site land resource data, as well as economic, environmental, and social acceptance data; Resource Analysis Module: This module receives the data collected by the Data Collection Module, cleans and deeply analyzes the data, and evaluates the resource potential of candidate sites; Optimization module: Based on the analysis results, the optimization module combines other relevant data such as economic efficiency, environmental protection and social acceptance to build an optimization model and run the algorithm to obtain the optimal configuration plan; Model solving module: Based on the optimization model established by the optimization module, it uses mathematical tools to solve the optimization model and obtain a specific optimization solution; Result display module: Based on the results obtained by the model solving module, it visualizes the optimization results to facilitate decision makers' understanding; Feedback module: After viewing the optimization results through the result display module, decision makers can provide feedback through the user interface; The feedback module receives user feedback and parses it into specific improvement requirements.

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