Industrial and commercial micro-grid comprehensive evaluation method, system and equipment and storage medium

Abstract

The invention relates to the technical field of micro-grid comprehensive evaluation, and provides an industrial and commercial micro-grid comprehensive evaluation method, system and device and a storage medium, and the method comprises the steps: obtaining a comprehensive evaluation index of each to-be-evaluated micro-grid scheme based on a micro-grid evaluation index system constructed based on technology, economic benefits and influences on a power grid; based on an alliance type cooperative game theory, carrying out fusion analysis on weight vectors obtained based on a subjective weighting method, an objective weighting method and a data weighting method to obtain a target weight vector; and according to the target weight vector, performing weighting processing on a normalized decision matrix constructed based on the comprehensive evaluation indexes of the to-be-evaluated micro-grid schemes to obtain a weighted normalized matrix, and then performing comprehensive sorting on the to-be-evaluated micro-grid schemes based on an ideal point approaching method to obtain a comprehensive evaluation result. According to the invention, based on reliable quantitative analysis of characteristics such as technical evaluation, economic effect and influence on the power grid, the comprehensiveness and accuracy of comprehensive evaluation of the micro-grid are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of microgrid comprehensive evaluation technology, and in particular to a method, system, equipment and storage medium for comprehensive evaluation of industrial and commercial microgrids. Background Technology

[0002] Commercial and industrial microgrids are small-scale energy systems designed for industrial or commercial users (such as factories or industrial parks). They integrate distributed energy resources, energy storage, loads, and intelligent control systems, and can operate independently or in parallel with the main grid. Commercial and industrial microgrids have developed rapidly and are expanding due to their flexibility and autonomy. However, the lack of unified understanding and technical standards regarding microgrids has led to problems in practical applications, such as arbitrary resource allocation and weak balancing capabilities. Therefore, a comprehensive evaluation of commercial and industrial microgrid solutions is crucial to ensuring their safe operation and maximizing their operational value.

[0003] Existing comprehensive evaluation methods for industrial and commercial microgrids typically only focus on the characteristics of the microgrids themselves, without considering the impact of their operational dynamics on the power grid. This makes it difficult to conduct a comprehensive and accurate assessment of industrial and commercial microgrids and fails to provide reliable analytical basis for optimizing their construction conditions, operational strategies, and dispatch control. Summary of the Invention

[0004] The purpose of this invention is to provide a comprehensive evaluation method for industrial and commercial microgrids. This method utilizes an evaluation index system for industrial and commercial microgrids, which comprehensively considers technical evaluation, economic benefits, and the impact of industrial and commercial microgrids on the power grid. Combined with an index weight acquisition mechanism based on cooperative game theory, it comprehensively evaluates industrial and commercial microgrids. This method can simultaneously achieve a reliable quantitative assessment of the characteristics of industrial and commercial microgrids themselves, as well as their impact on power grid operation and economic benefits. It effectively improves the comprehensiveness and accuracy of the comprehensive evaluation of industrial and commercial microgrids, thereby providing a reliable analytical and decision-making basis for optimizing the construction conditions, operation strategies, and dispatch control of industrial and commercial microgrids.

[0005] To achieve the above objectives, it is necessary to provide a comprehensive evaluation method, system, equipment, and storage medium for industrial and commercial microgrids to address the aforementioned technical problems.

[0006] In a first aspect, embodiments of the present invention provide a comprehensive evaluation method for industrial and commercial microgrids, the method comprising the following steps: Based on a pre-constructed evaluation index system for industrial and commercial microgrids, comprehensive evaluation indexes for each microgrid scheme to be evaluated are obtained. The evaluation index system for industrial and commercial microgrids includes technical evaluation indexes, economic benefit indexes, and grid impact indexes. Based on subjective weighting, objective weighting, and data weighting methods, the first weight vector, second weight vector, and third weight vector corresponding to the evaluation index system for industrial and commercial microgrids are obtained. Based on the theory of cooperative game theory, the first weight vector, second weight vector, and third weight vector are fused and analyzed to obtain the target weight vector. Based on the comprehensive evaluation indicators of each microgrid scheme to be evaluated, a normalized decision matrix is ​​constructed, and the normalized decision matrix is ​​weighted according to the target weight vector to obtain a weighted normalized matrix. Based on the weighted normalization matrix, the various microgrid schemes to be evaluated are comprehensively ranked using the approximation of ideal points method to obtain a comprehensive evaluation result.

[0007] Furthermore, the steps for constructing the evaluation index system for industrial and commercial microgrids include: Based on the operation data of renewable energy, load, and energy storage within the microgrid, the technical evaluation indicators are constructed. These indicators include the proportion of renewable energy installed capacity within the microgrid, the self-sufficiency rate of renewable energy within the microgrid, the power self-balance within the microgrid, the local consumption rate of renewable energy, the source-load matching degree within the microgrid, the ratio of energy storage power to maximum load within the microgrid, the ratio of energy storage power to renewable energy power within the microgrid, and the annual equivalent full charge and full discharge times of energy storage within the microgrid. Based on microgrid load cost per kilowatt-hour and microgrid investment return data, the aforementioned economic benefit indicators are constructed; the economic benefit indicators include comprehensive electricity cost, primary investment cost, and investment payback period. Based on the data on the impact of microgrids on the power grid, the power grid impact indicators are constructed. The power grid impact indicators include the peak-valley difference rate of the microgrid grid connection point's switching power, the average load rate of the microgrid grid-connected transformer, the peak value contribution rate of the power grid, the valley value contribution rate of the power grid, and the annual revenue of the power grid.

[0008] Furthermore, the renewable energy operation data within the microgrid includes renewable energy installed capacity, annual self-generated and self-consumed renewable energy electricity, annual renewable energy power generation, renewable energy grid-connected electricity, microgrid-disconnected electricity, annual renewable energy output curve, and annual maximum renewable energy power; the load operation data within the microgrid includes annual maximum load, annual load electricity consumption, and annual electricity load curve; the energy storage operation data within the microgrid includes maximum energy storage charging and discharging power, annual energy storage charging electricity consumption, and energy storage capacity. The step of constructing the technical evaluation index based on renewable energy operation data, load operation data, and energy storage operation data within the microgrid includes: Based on the installed capacity of renewable energy and the annual maximum load, the installed capacity ratio of renewable energy in the microgrid is obtained through installed capacity analysis. Based on the annual self-generated and self-consumed electricity of renewable energy and the annual load electricity consumption, the proportion of self-generated and self-consumed electricity is analyzed to obtain the self-sufficiency rate of renewable energy in the microgrid. Based on the renewable energy grid-connected electricity and the microgrid grid-disconnected electricity, the total grid-connected and grid-connected electricity of the microgrid is obtained. Based on the total grid-connected and grid-connected electricity of the microgrid and the annual load electricity consumption, the power balance capability is analyzed to obtain the power self-balance degree within the microgrid. Based on the annual self-generated and self-consumed electricity of the renewable energy and the annual power generation of the renewable energy, the grid connection and absorption capacity analysis is carried out to obtain the local absorption rate of the renewable energy; The source-load matching degree of the microgrid is obtained by superimposing and coupling analysis of the annual renewable energy output curve and the annual electricity load curve. The ratio of energy storage power to maximum load in the microgrid is obtained based on the maximum charging and discharging power of the energy storage and the maximum annual load. The ratio of energy storage power to renewable energy power in the microgrid is obtained based on the maximum charge and discharge power of the energy storage and the maximum annual power of the renewable energy. Based on the annual charging amount of the energy storage and the energy storage capacity, the annual equivalent number of full charge and full discharge cycles of the energy storage in the microgrid is obtained.

[0009] Furthermore, the microgrid load cost per kilowatt-hour data includes the annual value of basic investment, the annual value of operation and maintenance costs, the annual value of electricity charges, the annual value of demand charges, the annual value of grid connection revenue, and the annual load electricity consumption; the microgrid investment revenue data includes the microgrid basic investment, the microgrid operation and maintenance costs, and the microgrid annual revenue; The steps for constructing the economic benefit indicators based on microgrid load cost per kilowatt-hour data and microgrid investment return data include: The comprehensive electricity cost is obtained by analyzing the load cost per kilowatt-hour based on the annual value of the basic investment, the annual value of the operation and maintenance cost, the annual value of the electricity charge, the annual value of the demand charge, the annual value of the grid connection revenue, and the annual load electricity consumption. Based on the microgrid infrastructure investment and the microgrid operation and maintenance cost, a microgrid investment cost analysis is performed to obtain the primary investment cost. The investment payback period is obtained based on the initial investment cost and the annual revenue of the microgrid, using a preset investment cost model.

[0010] Furthermore, the data on the impact of the microgrid on the power grid includes the maximum net load at the microgrid grid connection point, the minimum net load at the microgrid grid connection point, the average power transmitted through the grid at the microgrid grid connection point, the transformer capacity, the user load at the peak of the power grid before the construction of the microgrid, the microgrid load at the peak of the power grid after the construction of the microgrid, the user load at the valley of the power grid before the construction of the microgrid, the microgrid load at the valley of the power grid after the construction of the microgrid, the annual user transmission and distribution fees, and the annual user demand fees. The step of constructing the power grid impact index based on the microgrid's impact data includes: Peak-valley load deviation analysis is performed based on the maximum net load and the minimum net load of the microgrid connection point to obtain the peak-valley difference rate of the switching power at the microgrid connection point. Load analysis is performed based on the average power transmitted through the grid at the microgrid connection point and the transformer capacity to obtain the average load rate of the microgrid grid-connected transformer; Peak load deviation analysis is performed based on the user load at the peak time of the power grid before the construction of the microgrid and the microgrid load at the peak time of the power grid after the construction of the microgrid to obtain the peak contribution rate of the power grid. Based on the user load at the grid's off-peak time before the microgrid construction and the microgrid load at the grid's off-peak time after the microgrid construction, an off-peak load deviation analysis is performed to obtain the grid's off-peak contribution rate; The annual revenue of the power grid is obtained by comprehensively analyzing the annual user transmission and distribution fees and the annual user demand fees.

[0011] Furthermore, the step of fusing and analyzing the first weight vector, the second weight vector, and the third weight vector based on alliance-based cooperative game theory to obtain the target weight vector includes: Based on the aforementioned cooperative game theory, the first weight vector, the second weight vector, and the third weight vector are combined, and the consistency correlation coefficients corresponding to the first weight vector, the second weight vector, and the third weight vector are obtained respectively based on a preset consistency correlation coefficient expression; the preset consistency correlation coefficient expression is: In the formula, L i The w represents the consistency correlation coefficient between the weight vector obtained by the i-th weighting method and the combined weight vector obtained by weighting methods other than the i-th weighting method, where i = 1, 2, 3; ij Let j represent the weight value of the j-th indicator in the weight vector obtained by the i-th weighting method, where j = 1, 2, ..., n; and n represents the number of indicators in the comprehensive evaluation index. This represents the average value of all index weights in the weight vector obtained by the i-th weighting method; w represents the average of all index weights in the combined weight vector obtained by weighting methods other than the i-th weighting method; -ij This represents the weight value of the j-th index in the combined weight vector obtained by weighting methods other than the i-th weighting method; Based on the consistency correlation coefficients corresponding to the first weight vector, the second weight vector, and the third weight vector, the first weight vector, the second weight vector, and the third weight vector are weighted and fused to obtain a fused weight vector; The fusion weight vector is normalized to obtain the target weight vector.

[0012] Furthermore, the step of comprehensively ranking the various microgrid schemes to be evaluated based on the weighted normalization matrix and the approximation of ideal points to obtain the comprehensive evaluation result includes: Based on preset classification rules, the indicators in the evaluation index system of industrial and commercial microgrids are divided into benefit-type indicators and cost-type indicators. The preset classification rules are to classify indicators that have a positive impact on the comprehensive evaluation results as benefit-type indicators and indicators that have a negative impact on the comprehensive evaluation results as cost-type indicators. Based on the weighted normalization matrix, a positive ideal solution is obtained with the objectives of maximizing the benefit-type index and minimizing the cost-type index; based on the weighted normalization matrix, a negative ideal solution is obtained with the objectives of minimizing the benefit-type index and maximizing the cost-type index; based on the positive ideal solution and the negative ideal solution, the positive ideal solution gap and negative ideal solution gap corresponding to each of the microgrid schemes to be evaluated are calculated respectively. Based on the positive ideal solution gap and negative ideal solution gap corresponding to each of the microgrid schemes to be evaluated, the corresponding positive ideal solution proximity is obtained; The comprehensive evaluation results are obtained by ranking the positive ideal solutions of each microgrid scheme to be evaluated based on their proximity.

[0013] Secondly, embodiments of the present invention provide a comprehensive evaluation system for industrial and commercial microgrids. The system includes: an evaluation index acquisition module, used to acquire comprehensive evaluation indicators for each microgrid scheme to be evaluated based on a pre-constructed evaluation index system for industrial and commercial microgrids; the evaluation index system for industrial and commercial microgrids includes technical evaluation indicators, economic benefit indicators, and indicators of impact on the power grid. The weight vector acquisition module is used to acquire the first weight vector, the second weight vector, and the third weight vector corresponding to the evaluation index system of the industrial and commercial microgrid based on subjective weighting method, objective weighting method, and data weighting method, and to perform fusion analysis on the first weight vector, the second weight vector, and the third weight vector based on the alliance cooperative game theory to obtain the target weight vector. The normalized matrix acquisition module is used to construct a normalized decision matrix based on the comprehensive evaluation index of each of the microgrid schemes to be evaluated, and to perform weighted processing on the normalized decision matrix according to the target weight vector to obtain a weighted normalized matrix. The evaluation result generation module is used to comprehensively rank each of the microgrid schemes to be evaluated based on the weighted normalization matrix and the approximation ideal point method to obtain a comprehensive evaluation result.

[0014] Thirdly, embodiments of the present invention also provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method.

[0015] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method.

[0016] This invention provides a comprehensive evaluation method, system, computer equipment, and storage medium for industrial and commercial microgrids. The method achieves comprehensive evaluation of each microgrid scheme based on a pre-constructed evaluation index system for industrial and commercial microgrids, including technical evaluation indicators, economic benefit indicators, and grid impact indicators. It obtains first, second, and third weight vectors corresponding to the evaluation index system based on subjective, objective, and data weighting methods. Then, it performs a fusion analysis of the first, second, and third weight vectors based on cooperative game theory to obtain a target weight vector. Next, it constructs a normalized decision matrix based on the comprehensive evaluation indicators of each microgrid scheme. Finally, it weights the normalized decision matrix according to the target weight vector to obtain a weighted normalized matrix. Finally, it ranks each microgrid scheme based on the approximation of ideal points using the weighted normalized matrix to obtain the comprehensive evaluation result. Compared with existing technologies, this comprehensive evaluation method for industrial and commercial microgrids can effectively evaluate the characteristics of industrial and commercial microgrids themselves, and reliably quantify the impact of microgrids on grid operation and economic benefits. It effectively improves the comprehensiveness and accuracy of the comprehensive evaluation of industrial and commercial microgrids, and provides a reliable basis for analysis and decision-making for optimizing the construction conditions, operation strategies, and dispatch control of industrial and commercial microgrids. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the comprehensive evaluation method for industrial and commercial microgrids in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of the integrated evaluation system for industrial and commercial microgrids in this embodiment of the invention; Figure 3This is an internal structural diagram of the computer device in an embodiment of the present invention; Figure label: The module includes: 1. Evaluation index acquisition module; 2. Weight vector acquisition module; 3. Normalized matrix acquisition module; and 4. Evaluation result generation module. Detailed Implementation

[0018] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. Obviously, the embodiments described below are only part of the embodiments of this invention and are used to illustrate the invention, but are not intended to limit the scope of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0019] In one embodiment, such as Figure 1 As shown, a comprehensive evaluation method for industrial and commercial microgrids is provided, including the following steps: S11. Based on the pre-constructed evaluation index system for industrial and commercial microgrids, obtain the comprehensive evaluation index for each microgrid scheme to be evaluated. The evaluation index system for industrial and commercial microgrids can be understood as setting multiple primary evaluation indicators from aspects such as technical evaluation, economic benefits, and impact on the power grid. That is, the evaluation index system for industrial and commercial microgrids includes three primary indicators: technical evaluation indicators, economic benefit indicators, and impact on the power grid indicators. Then, combined with the load characteristics of industrial and commercial microgrids and the actual construction situation of distributed power sources, each primary indicator such as technical evaluation, economic benefits, and impact on the power grid is further refined to obtain a comprehensive evaluation index system with multiple secondary indicators. Specifically, the construction steps of the evaluation index system for industrial and commercial microgrids include: Based on the operation data of renewable energy, load, and energy storage within the microgrid, the aforementioned technical evaluation indicators are constructed. These technical evaluation indicators can be understood as indicators that, while fully considering the basic requirements for the construction of industrial and commercial microgrids, break through the limitations of traditional evaluation systems that only consider static evaluation indicators by introducing dynamic time-series indicators such as the source-load matching degree within the microgrid and the annual equivalent full charge and discharge times of energy storage within the microgrid. They can also effectively characterize the coupling relationship between the fluctuation characteristics of renewable energy and the dynamic response of energy storage.

[0020] This embodiment preferably sets the technical evaluation indicators as follows: the proportion of renewable energy installed capacity in the microgrid, the self-sufficiency rate of renewable energy in the microgrid, the power self-balancing rate in the microgrid, the local consumption rate of renewable energy, the source-load matching degree in the microgrid, the ratio of energy storage power to maximum load in the microgrid, the ratio of energy storage power to renewable energy power in the microgrid, and the annual equivalent full charge and full discharge times of energy storage in the microgrid. Among them, the proportion of renewable energy installed capacity in the microgrid is used to characterize the proportion of renewable energy installed capacity; the self-sufficiency rate of renewable energy in the microgrid and the power self-balancing rate in the microgrid are also considered. The following parameters are used to characterize the self-balancing capacity of industrial and commercial microgrids: the local renewable energy absorption rate, the grid-connected absorption capacity of renewable energy in industrial and commercial microgrids, the source-load matching degree within the microgrid, and the ratio of energy storage power to maximum load, the ratio of energy storage power to renewable energy power, and the annual equivalent full charge and discharge times of energy storage within the microgrid, all based on the peak shaving, valley filling, and wind and solar fluctuation mitigation effects of energy storage in industrial and commercial microgrids, and are used to characterize energy storage utilization efficiency.

[0021] To reliably quantify the various secondary quantitative indicators corresponding to the technical evaluation indicators, and thus achieve accurate assessment of industrial and commercial microgrids from a technical perspective, this embodiment preferably constructs various technical evaluation indicators based on a comprehensive analysis of data on renewable energy operation, load operation, and energy storage operation within the microgrid. The renewable energy operation data used in the analysis includes renewable energy installed capacity, annual self-generated and self-consumed renewable energy, annual renewable energy power generation, renewable energy grid-connected electricity, microgrid off-grid electricity, annual renewable energy output curve, and annual maximum renewable energy power. The load operation data within the microgrid includes annual maximum load, annual load electricity consumption, and annual electricity load curve. The energy storage operation data within the microgrid includes maximum energy storage charging and discharging power, annual energy storage charging electricity, and energy storage capacity. Specifically, the steps for constructing the technical evaluation indicators based on the renewable energy operation data, load operation data, and energy storage operation data within the microgrid include: Based on the installed capacity of renewable energy and the annual maximum load, an analysis of the installed capacity of renewable energy within the microgrid is performed to obtain the proportion of renewable energy installed capacity. The installed capacity of renewable energy can be obtained from the technical parameters provided by renewable energy equipment manufacturers and is not specifically limited here. The annual maximum load can be understood as the maximum value in the 8760-hour (one-year) pure load characteristic curve (time-series data of load changes over time), and the corresponding pure load characteristic curve can be obtained from the user load side; specific methods for obtaining this curve can refer to relevant existing technologies. Correspondingly, the proportion of renewable energy installed capacity within the microgrid can be understood as the ratio of renewable energy installed capacity to the annual maximum load, facilitating the determination of whether the installed capacity of various distributed power sources within the industrial and commercial microgrid meets the relevant microgrid construction standards.

[0022] The self-consumption ratio of renewable energy within the microgrid is obtained by analyzing the proportion of self-consumption based on the annual self-generated and self-consumed electricity of renewable energy and the annual load electricity consumption. The annual load electricity consumption is the electricity consumption of the pure load for one year, which can be obtained from the users of the industrial and commercial microgrid. The annual self-generated and self-consumed electricity of renewable energy can be understood as the difference between the annual load electricity consumption and the electricity supplied to the grid by the microgrid (the electricity obtained from the grid in one year, which can be measured by the smart meters at the grid connection point). Correspondingly, the self-sufficiency rate of renewable energy within the microgrid can be understood as the proportion of self-generated and self-consumed electricity of renewable energy to the load electricity consumption in an industrial and commercial microgrid within one year.

[0023] Based on the renewable energy grid-connected electricity and the microgrid off-grid electricity, the total electricity connected to and from the microgrid is obtained. Then, based on the total electricity connected to and from the microgrid and the annual load electricity consumption, an electricity balance capability analysis is performed to obtain the electricity self-balance degree within the microgrid. The renewable energy grid-connected electricity can be understood as the electricity transmitted from the industrial and commercial microgrid to the grid within a year, which can be measured by smart meters at the grid connection point. Correspondingly, the electricity self-balance degree within the microgrid can be understood as the ratio of the total electricity connected to and from the microgrid to the annual load electricity consumption within a year for the industrial and commercial microgrid.

[0024] The local consumption rate of renewable energy is obtained by analyzing the grid connection and absorption capacity based on the annual self-generated and self-consumed electricity of renewable energy and the annual power generation of renewable energy. The annual power generation of renewable energy can be understood as the total annual power generation of renewable energy in industrial and commercial microgrids, which can be obtained from renewable energy equipment manufacturers. Correspondingly, the local consumption rate of renewable energy can be understood as the ratio of the annual self-generated and self-consumed electricity of renewable energy to the annual power generation of renewable energy.

[0025] The source-load matching degree of the microgrid is obtained by superimposing and coupling analysis of the annual renewable energy output curve and the annual electricity load curve. The annual renewable energy output curve can be understood as the output characteristic curve of renewable energy in an 8760-hour industrial and commercial microgrid, which can be obtained from renewable energy equipment manufacturers. The annual electricity load curve can be understood as the electricity load curve of an 8760-hour industrial and commercial microgrid, which can be obtained from industrial and commercial microgrid users. The corresponding source-load matching degree of the microgrid can be understood as an index of the degree of fitting between the electricity load curve and the output characteristic curve. In this embodiment, it is preferably obtained by superimposing and coupling analysis of the electricity load curve and the output characteristic curve. The specific steps for obtaining the source-load matching degree within a microgrid include: normalizing the annual renewable energy output curve and the annual electricity load curve to obtain normalized annual renewable energy output curve and normalized annual electricity load curve; superimposing the normalized annual renewable energy output curve and the normalized annual electricity load curve in the same coordinate system to obtain the corresponding superimposed coupling curve, i.e., each point on the superimposed coupling curve corresponds to the normalized value of renewable energy output and electricity load at a certain moment; then calculating the sum of the variances at each moment on the superimposed coupling curve, and based on the inverse relationship between the sum of variances and the source-load matching degree within the microgrid, calculating the source-load matching degree within the microgrid, expressed as: In the formula, Where ρ represents the matching degree of the source load within the microgrid; s represents the sum of variances, and the smaller the sum of variances, the higher the matching degree between the electricity load curve and the renewable energy output curve, and the larger the sum of variances, the lower the matching degree between the electricity load curve and the renewable energy output curve; ∈ represents a very small positive number, which can avoid the case of zero denominator and ensure that the calculation formula of the matching degree of the source load within the microgrid is always meaningful; x t and y t These represent the normalized renewable energy output and normalized electricity load values ​​corresponding to the t-th time point of the superimposed coupling curve, respectively. This represents the average of all normalized renewable energy output values ​​in the superimposed coupling curve. This represents the average value of all normalized electrical load values ​​on the superimposed coupling curve; T represents the total number of time points on the superimposed coupling curve, which is 8760.

[0026] The ratio of energy storage power to maximum load in the microgrid is obtained based on the maximum charge / discharge power of the energy storage and the maximum annual load. The maximum charge / discharge power of the energy storage can be understood as the maximum charge / discharge power value of the energy storage equipment in the industrial and commercial microgrid, which can be obtained based on the technical parameters provided by the energy storage equipment manufacturer. Correspondingly, the ratio of energy storage power to maximum load in the microgrid is the ratio of the maximum charge / discharge power of the energy storage to the maximum annual load.

[0027] The ratio of energy storage power to renewable energy power within the microgrid is obtained based on the maximum charge / discharge power of the energy storage and the maximum annual power of the renewable energy source. The maximum annual power of the renewable energy source can be understood as the maximum value in the 8760-hour renewable energy generation curve, which can be obtained from the renewable energy equipment manufacturer. The corresponding ratio of energy storage power to renewable energy power within the microgrid is the ratio of the maximum charge / discharge power of the energy storage to the maximum annual power of the renewable energy source.

[0028] Based on the annual charging amount of the energy storage and the energy storage capacity, the annual equivalent full charge and discharge cycles of the energy storage in the microgrid are obtained. The annual charging amount of the energy storage can be understood as the total charging amount of the energy storage devices in the industrial and commercial microgrid over 8760 hours, which can be obtained from the energy storage device manufacturer. The energy storage capacity can be understood as the configured capacity of the energy storage devices in the industrial and commercial microgrid, which can be obtained from the technical parameters provided by the energy storage device manufacturer. The corresponding annual equivalent full charge and discharge cycles of the energy storage in the microgrid are the ratio of the annual charging amount of the energy storage to the energy storage capacity.

[0029] Based on microgrid load cost per kilowatt-hour and microgrid investment return data, the aforementioned economic benefit index is constructed. This economic benefit index can be understood as considering that the electricity price for industrial and commercial microgrid users after building a microgrid will be lower than purchasing electricity directly from the grid, resulting in certain economic benefits. The introduction of a comprehensive electricity cost based on electricity market prices breaks through the limitations of traditional evaluation systems that only consider investment recovery. It achieves linkage with electricity market prices and effectively reflects the impact of different regions, different user load characteristics, and different microgrid operation strategies on economic efficiency (different regions have different electricity prices, and different operation strategies and load characteristics will affect the electricity consumption of industrial and commercial microgrid users at different times, thus affecting the final electricity cost).

[0030] In this embodiment, the economic benefit indicators preferably include secondary quantitative indicators such as comprehensive electricity cost, primary investment cost, and investment payback period. Among them, comprehensive electricity cost can be understood as an indicator constructed considering the characteristics of industrial and commercial microgrids being built independently by users with profitability as the main goal, used to characterize the cost per kilowatt-hour after the user builds the microgrid. Primary investment cost and investment payback period can be understood as indicators constructed considering that the reduction of comprehensive electricity cost is not the decisive factor for users to invest in microgrid construction. It is also necessary to comprehensively consider the electricity consumption and the recovery of power storage investment, and to evaluate the overall economic efficiency of the project before making a decision. These indicators are used to characterize the investment consumed in the construction project of the completed and put into operation microgrid.

[0031] To reliably quantify the various secondary quantitative indicators corresponding to the economic benefit indicators, and thus achieve accurate evaluation of industrial and commercial microgrids from an economic benefit perspective, this embodiment preferably constructs various economic benefit indicators based on a comprehensive analysis of microgrid load-levelized electricity cost data and microgrid investment return data. The microgrid load-levelized electricity cost data used in the analysis includes annual values ​​of basic investment, annual values ​​of operation and maintenance costs, annual values ​​of electricity charges, annual values ​​of demand charges, annual values ​​of grid connection revenue, and annual load electricity consumption. The microgrid investment return data includes microgrid basic investment, microgrid operation and maintenance costs, and microgrid annual revenue. Specifically, the steps for constructing the economic benefit indicators based on the microgrid load-levelized electricity cost data and microgrid investment return data include: The comprehensive electricity cost is obtained by analyzing the load-level cost based on the annual value of the basic investment, the annual value of operation and maintenance costs, the annual value of electricity charges, the annual value of demand charges, the annual value of grid connection revenue, and the annual load electricity consumption. The annual value of basic investment can be understood as the ratio of the sum of the costs of power source construction and replacement, energy storage construction and replacement, and energy management system construction to the operating period cost. The annual value of operation and maintenance costs can be understood as the sum of the annual values ​​of power source operation and maintenance costs and energy storage operation and maintenance costs. The annual value of electricity charges can be understood as the total grid connection cost of industrial and commercial microgrids, which is the sum of the products of the time-of-use electricity volume and the corresponding time-of-use electricity price. The annual value of demand charges can be understood as the cumulative annual load electricity cost, which is the sum of the products of the maximum monthly load and the corresponding demand electricity price. The annual value of grid connection revenue can be understood as the total annual grid connection cost of distributed power sources within the industrial and commercial microgrid, which is the product of the annual grid connection volume of distributed power sources within the industrial and commercial microgrid and the corresponding grid connection price. The process for obtaining the corresponding comprehensive electricity cost can be as follows: First, calculate the cumulative value of the annual value of basic investment, annual value of operation and maintenance costs, annual value of electricity consumption, and annual value of demand charges. Then, subtract the cumulative value from the annual value of grid connection revenue, and divide the difference with the annual load electricity consumption to obtain the comprehensive electricity cost. It should be noted that the costs of power source construction and replacement, energy storage construction and replacement, and energy management system construction are all obtained from the equipment manufacturers. During the operation period, they can be obtained from the industrial and commercial microgrid users. Furthermore, the electricity consumption price, demand price, and grid connection price can all be obtained from the monthly agency industrial and commercial electricity purchase price list published by the power grid company, which will not be detailed here.

[0032] The microgrid investment cost is analyzed based on the microgrid infrastructure investment and the microgrid operation and maintenance cost to obtain the primary investment cost; that is, the primary investment cost is the sum of the microgrid infrastructure investment and the microgrid operation and maintenance cost; similarly, the microgrid infrastructure investment and microgrid operation and maintenance cost can also be obtained from the relevant equipment manufacturers.

[0033] Based on the initial investment cost and the annual revenue of the microgrid, the payback period is obtained using a preset investment cost model. The annual revenue of the microgrid can be understood as the sum of the annual grid connection fee, the annual electricity savings fee, and the annual demand savings fee for industrial and commercial microgrids. The preset investment cost model can be understood as a dynamic payback model that considers the time value of money and the discount rate, or a static payback model that does not consider the time value of money. When the preset investment cost model is a dynamic payback model, the payback period can be calculated by discounting the annual revenue. When the preset investment cost model is a static payback model, the payback period can be obtained by dividing the initial investment cost by the annual revenue of the microgrid. No specific limitations are imposed here.

[0034] Based on the data on the impact of microgrids on the power grid, the aforementioned impact index on the power grid is constructed. The impact index on the power grid can be understood as an indicator that takes into account the bidirectional impact between industrial and commercial microgrids and the power grid, and is used to quantitatively evaluate the synergistic benefits between industrial and commercial microgrids and the power grid. This can break through the application limitations of traditional evaluation systems that only focus on the characteristics of microgrids themselves. This embodiment preferably sets secondary quantitative indicators for the impact on the power grid, including the peak-valley difference rate of the microgrid's grid-connected power exchange, the average load rate of the microgrid's grid-connected transformer, the peak contribution rate of the power grid, the valley contribution rate of the power grid, and the annual revenue of the power grid. The peak-valley difference rate of the microgrid's grid-connected power exchange is an indicator used to characterize the power interaction between the industrial and commercial microgrids and the power grid, considering the characteristic that large power flows in and out of industrial and commercial microgrids can degrade the operating characteristics of the distribution network. The average load rate of the microgrid's grid-connected transformer is an indicator used to characterize the power interaction between the industrial and commercial microgrids and the power grid, considering the characteristic that self-sufficiency in industrial and commercial microgrids can significantly reduce the utilization efficiency of power grid equipment. The peak contribution rate and valley contribution rate of the power grid are indicators used to characterize the contribution of industrial and commercial microgrids to peak shaving and valley filling, considering the role of energy storage in peak shaving and valley filling in industrial and commercial microgrids. The annual revenue of the power grid is an indicator used to characterize the economic impact of industrial and commercial users building microgrids on the power grid, considering the significant reduction in electricity purchased from the power grid after the construction of microgrids.

[0035] To reliably quantify the various secondary quantitative indicators corresponding to the microgrid impact indicators, and thus achieve accurate assessment of industrial and commercial microgrids from the perspective of their impact on the power grid, this embodiment preferably constructs various technical evaluation indicators based on the analysis of microgrid impact data on the power grid. The microgrid impact data used in the analysis includes the maximum net load at the microgrid grid connection point, the minimum net load at the microgrid grid connection point, the average power transmitted through the grid at the microgrid grid connection point, transformer capacity, user load at peak grid time before microgrid construction, microgrid load at peak grid time after microgrid construction, user load at valley grid time before microgrid construction, microgrid load at valley grid time after microgrid construction, annual user transmission and distribution fees, and annual user demand fees. Specifically, the step of constructing the power grid impact indicators based on the microgrid impact data includes: Peak-valley load deviation analysis is performed based on the maximum and minimum net loads at the microgrid grid connection point to obtain the peak-valley difference rate of the microgrid grid connection point's exchange power. The maximum and minimum net loads at the microgrid grid connection point are the maximum and minimum values ​​on the annual net load curve (8760-hour net load curve), respectively. The annual net load curve is a characteristic curve obtained by coupling the power output curve and the load curve, and can be measured by smart meters at the industrial and commercial microgrid grid connection point. In the net load, the downstream load of the industrial and commercial microgrid is positive, and the upstream load is negative. Correspondingly, the peak-valley difference rate of the microgrid grid connection point's exchange power can be understood as the ratio of the deviation between the maximum net load (peak load) and the minimum net load (valley load) at the microgrid grid connection point to the maximum net load at the microgrid grid connection point.

[0036] Load analysis is performed based on the average grid-connected power at the microgrid connection point and the transformer capacity to obtain the average load factor of the microgrid grid-connected transformer. The grid-connected power includes the grid-connected power and the power supplied to the grid from the industrial and commercial microgrid. Both grid-connected and power supplied to the grid are positive values. The grid-connected power is the quotient of the annual grid-connected electricity and 8760 (annual grid-connected electricity / 8760), and the power supplied to the grid is the quotient of the annual grid-disconnected electricity and 8760 (annual grid-disconnected electricity / 8760). The average grid-connected power at the microgrid connection point can be understood as the average of the grid-connected power and the power supplied to the grid. Correspondingly, the average load factor of the microgrid grid-connected transformer can be expressed as the ratio of the average grid-connected power at the microgrid connection point to the transformer capacity, facilitating the evaluation of the utilization efficiency of grid equipment.

[0037] Peak load deviation analysis is performed based on the peak load of the power grid before and after the construction of the microgrid to obtain the peak contribution rate of the power grid. The peak contribution rate can be expressed as the ratio of the peak load deviation between the peak load of the power grid before and after construction to the peak load of the power grid before construction, used to measure the peak-shaving contribution of industrial and commercial microgrids. It should be noted that the peak time can be understood as the time corresponding to the maximum power of the 220 kV substation. The peak load of the power grid before construction can be obtained from the user, while the peak load of the power grid after construction can be measured by the smart meter at the grid connection point of the industrial and commercial microgrid.

[0038] Based on the user load at the grid's valley time before microgrid construction and the microgrid load at the grid's valley time after microgrid construction, a valley load deviation analysis is performed to obtain the grid's valley contribution rate. The grid valley contribution rate can be expressed as the ratio of the valley load deviation between the user load at the grid's valley time before and after microgrid construction to the user load at the grid's valley time before microgrid construction. This ratio is used to measure the valley-filling contribution of industrial and commercial microgrids. It should be noted that the valley time can be understood as the time corresponding to the minimum power of a 220 kV substation. The user load at the grid's valley time before microgrid construction can be obtained from the user, while the microgrid load at the grid's valley time after microgrid construction can be measured through the smart meters at the industrial and commercial microgrid grid connection point.

[0039] The annual revenue of the power grid is obtained by comprehensively analyzing the annual user transmission and distribution fees and the annual user demand fees. The annual user transmission and distribution fees are the cumulative product of the monthly transmission and distribution price charged by the power grid to the user and the monthly user electricity consumption within a year. The annual user demand fees can be understood as the cumulative product of the user's maximum monthly load and the corresponding demand price within a year. The corresponding annual revenue of the power grid can be understood as the cumulative product of the annual user transmission and distribution fees and the annual user demand fees. It should be noted that both the monthly transmission and distribution price charged by the power grid to the user and the corresponding monthly demand price can be obtained from the monthly electricity purchase price list for industrial and commercial users published by the power grid company.

[0040] By constructing an evaluation index system for industrial and commercial microgrids that comprehensively considers the technical, economic, and impact-to-grid factors of industrial and commercial microgrids, the above-mentioned methods and steps are used to obtain comprehensive evaluation indicators for each microgrid scheme to be evaluated. This system can simultaneously achieve a reliable quantitative assessment of the characteristics of industrial and commercial microgrids themselves, as well as their impact on grid operation and economic benefits, thus providing a reliable analytical basis for the comprehensive evaluation of industrial and commercial microgrids.

[0041] S12. Based on subjective weighting, objective weighting and data weighting methods, obtain the first weight vector, second weight vector and third weight vector corresponding to the evaluation index system of the industrial and commercial microgrid, and perform fusion analysis on the first weight vector, second weight vector and third weight vector based on the alliance cooperative game theory to obtain the target weight vector.

[0042] Subjective weighting methods rely on the subjective judgment of experts or decision-makers to determine the weights of indicators, including the Delphi method and the analytic hierarchy process (AHP). Objective weighting methods determine the weights of indicators based on the inherent laws and characteristics of data, using mathematical models or statistical methods to avoid interference from human factors, including principal component analysis and entropy weighting. Data weighting methods utilize data mining, machine learning, and other technologies to automatically learn and discover the relationships between indicators from large amounts of data, and then determine the weights, including decision tree methods, random forest methods, and backpropagation (BP) neural network methods. Although subjective weighting, objective weighting, and data weighting methods can all be used to determine the weights of various indicators in multi-indicator comprehensive evaluation, each method has its own advantages and disadvantages and is applicable to different scenarios. Using a single weighting method or only combining subjective and objective weights can easily lead to large errors in indicator weights and weak adaptability to changes in scenarios. To maximize the objectivity and accuracy of the comprehensive evaluation indicator weight allocation, this embodiment preferably first calculates the weight vectors of the industrial and commercial microgrid evaluation indicator system using subjective, objective, and data weighting methods respectively, and then fuses and corrects the three sets of weight vectors obtained by the three weighting methods through cooperative game theory. Correspondingly, the target weight vector can be understood as a vector including the weights of each secondary indicator in the industrial and commercial microgrid evaluation indicator system. It is the optimal weight vector obtained by combining subjective, objective, and data weighting methods based on cooperative game theory. The specific acquisition process is as follows: 1) The first, second, and third weight vectors are calculated using three methods: subjective weighting, objective weighting, and data weighting. It should be noted that the subjective, objective, and data weighting methods in this embodiment can all be implemented using existing technologies. The following explanation uses the analytic hierarchy process (AHP) as the subjective weighting method, the entropy weighting method as the objective weighting method, and the backpropagation (BP) neural network method as the data weighting method to illustrate the process of obtaining the first, second, and third weight vectors: The steps for determining the first weight vector using the subjective weighting method (analytic hierarchy process) include: establishing a hierarchical index structure; establishing a judgment matrix based on expert opinions; finding the largest eigenvalue of the judgment matrix and calculating the eigenvector corresponding to the largest eigenvalue; then, normalizing the eigenvector and calculating the index weights to obtain the required first weight vector. The steps for determining the second weight vector using the objective weighting method (entropy weighting method) include: constructing a data matrix based on the sample's indicator data and normalizing it; calculating the information entropy of each indicator; calculating the difference coefficient of each indicator, and calculating the weight of each indicator based on the difference coefficient, thus obtaining the required second weight vector. The steps for determining the third weight vector using the data weighting method (BP neural network method) include: training and constructing the BP neural network weighting model; using normalized sample evaluation indicators as data input to the BP neural network weighting model; and outputting the weight values ​​corresponding to each indicator through the BP neural network weighting model to obtain the required third weight vector. The BP neural network weighting model learns and mines complex data relationships by simulating the information transmission method of neurons in the human brain. It can fully utilize a large amount of sample data and automatically adjust the network weights and thresholds, thereby more accurately determining the weights of each evaluation indicator. Compared with traditional weighting methods, the BP neural network method can avoid excessive interference from subjective factors, better reflect the objective laws behind the data, and comprehensively consider the nonlinear relationships between multiple indicators. For the evaluation indicator system of industrial and commercial microgrids with complex characteristics, it can provide a more reasonable and realistic weight allocation scheme, which is conducive to improving the accuracy and reliability of the comprehensive evaluation results.

[0043] It should be noted that the processes for obtaining the corresponding first, second, and third weight vectors based on the analytic hierarchy process (AHP), entropy weight method, and BP neural network method, respectively, can all be implemented with reference to relevant existing technologies. Of course, in practical applications, when determining indicator weights based on subjective weighting, objective weighting, and data weighting methods, the specific weighting method can be selected according to application requirements. After obtaining the first, second, and third weight vectors, the weight vectors obtained by these three weighting methods can be combined into an alliance. Treating the alliance as a whole, the optimal combined weights are determined through cooperative game theory to minimize the combined weight error. Specifically, the steps for fusing and analyzing the first, second, and third weight vectors based on alliance-type cooperative game theory to obtain the target weight vector include: Based on the aforementioned cooperative game theory, the first weight vector, the second weight vector, and the third weight vector are combined. Consistency correlation coefficients corresponding to the first weight vector, the second weight vector, and the third weight vector are obtained based on a preset consistency correlation coefficient expression. The preset consistency correlation coefficient expression can be understood as a coefficient that comprehensively considers the first weight vector, the second weight vector, and the third weight vector based on the cooperative game theory, measuring the degree of consistency between different weighting methods, and providing a basis for subsequently determining the combined weights. Specifically, the preset consistency correlation coefficient expression is expressed as follows: In the formula, L iLet L1 represent the consistency correlation coefficient between the weight vector obtained by the i-th weighting method and the combined weight vector obtained by weighting methods other than the i-th weighting method, where i = 1, 2, 3. If i = 1 represents the subjective weighting method and the corresponding weight vector is the first weight vector, then L1 represents the consistency correlation coefficient between the first weight vector and the combined weight vector of the second and third weight vectors. The combined weight vector of the second and third weight vectors can be understood as a vector composed of the average value of the corresponding elements in the second and third weight vectors. ij Let j represent the weight value of the j-th indicator in the weight vector obtained by the i-th weighting method, where j = 1, 2, ..., n; and n represents the number of indicators in the comprehensive evaluation index. This represents the average of all index weight values ​​in the weight vector obtained by the i-th weighting method, i.e. This represents the average of all index weight values ​​in the combined weight vector obtained by weighting methods other than the i-th weighting method. For example, i=1 represents the subjective weighting method, i=2 represents the objective weighting method, and i=3 represents the data weighting method. The second weight vector (w) calculated by the objective weighting method and the data weighting method 21 w 22 ,…,w 2n ) and the third weight vector (w 31 w 32 ,…,w 3n The average value corresponding to the weight combination is obtained by first combining the weights of the second and third weight vectors (adding them with equal weights), resulting in a vector element representation as follows. The combined weight vector is then calculated, and the average of all combined weights in the combined weight vector is calculated. And w -1j The combined weight of the j-th index in the combined weight vector corresponding to the second and third weight vectors; w -ij This represents the weight value of the j-th index in the combined weight vector obtained by weighting methods other than the i-th weighting method.

[0044] Based on the consistency correlation coefficients corresponding to the first weight vector, the second weight vector, and the third weight vector, a weighted fusion is performed on the first weight vector, the second weight vector, and the third weight vector to obtain a fused weight vector; wherein, the weights of each indicator in the fused weight vector can be expressed as: In the formula, w′ j L represents the weight value of the j-th index in the fusion weight vector; iw represents the consistency correlation coefficient between the weight vector obtained by the i-th weighting method and the combined weight vector obtained by weighting methods other than the i-th weighting method; ij This represents the weight value of the j-th index in the weight vector obtained by the i-th weighting method.

[0045] The fused weight vector is normalized to obtain the target weight vector; wherein, the normalization process can be implemented using existing normalization techniques, and no specific limitation is made here; the corresponding target weight vector can be understood as the standardized index combination weight obtained after normalizing the fused weight vector.

[0046] This embodiment comprehensively considers the complementarity among three different weighting methods. When calculating the consistency correlation coefficient, it measures the degree of consistency between different weighting methods based on the theory of cooperative game theory. This can effectively reduce expert bias in subjective weighting, data dependence in objective weighting, and weighting bias in single weighting methods. While balancing multi-dimensional information, it can also achieve adaptive optimization of multi-source weights through game theory, improve the dynamic adaptability and decision-making efficiency of weight analysis, and enhance the comprehensiveness, objectivity, and accuracy of weight allocation.

[0047] S13. Based on the comprehensive evaluation indicators of each of the microgrid schemes to be evaluated, construct a normalized decision matrix, and weight the normalized decision matrix according to the target weight vector to obtain a weighted normalized matrix; wherein, the normalized decision matrix can be understood as a multi-attribute normalized data matrix composed of the attribute values ​​of the comprehensive evaluation indicators of all microgrid schemes to be evaluated. Assume that the set E of microgrid schemes to be evaluated corresponding to the multi-attribute decision problem is {E... l ,l=1,2,…,m},E l Let F represent the l-th microgrid scheme to be evaluated, and let F be the set of index attributes {F}. j ,j=1,2,…n},F j Let x represent the j-th comprehensive evaluation index; that is, if there are m microgrid schemes to be evaluated (commercial and industrial microgrids to be evaluated) and n comprehensive evaluation indexes are used for evaluation, then the attribute value of the l-th microgrid scheme to be evaluated with respect to the j-th comprehensive evaluation index can be represented as x. lj (l = 1, 2, ..., m; j = 1, 2, ..., n), the corresponding initial multi-attribute data matrix is: Where X represents the initial multi-attribute data matrix composed of n comprehensive evaluation indicators of m microgrid schemes to be evaluated, arranged in rows.

[0048] Considering that the comprehensive evaluation indicators have different magnitudes and orders of magnitude, vector normalization is used to perform dimensionless processing on the initial multi-attribute data matrix to obtain the normalized decision matrix: In the formula, Where Y represents the normalized decision matrix; y lj and x lj These represent the matrix elements in the l-th row and j-th column of the normalized decision matrix and the initial multi-attribute data matrix, respectively.

[0049] After obtaining the normalized decision matrix for comprehensive evaluation analysis through the above steps, the target weight vector obtained based on the aforementioned cooperative game theory can be used to weight it, resulting in a weighted normalized matrix that highlights the differences in importance of different indicators in the evaluation, expressed as: V = [v lj ] m×n In the formula, v lj =w′ j ·y lj , l=1,2,…,m; j=1,2,…,n Where V represents the weighted normalization matrix; v lj and y lj represents the matrix element in the l-th row and j-th column of the weighted normalized matrix and the normalized decision matrix, respectively; m represents the total number of microgrid schemes to be evaluated; n represents the total number of comprehensive evaluation indicators.

[0050] S14. Based on the weighted normalization matrix, the various microgrid schemes to be evaluated are comprehensively ranked according to the approximation ideal point method to obtain a comprehensive evaluation result. The comprehensive evaluation result can be understood as the evaluation ranking result obtained by comprehensively evaluating the various microgrid schemes to be evaluated based on the ideal solution determined by the approximation ideal point method. In order to ensure the reliability of the determination of the ideal solution, this embodiment preferably divides all comprehensive evaluation indicators in the industrial and commercial microgrid evaluation index system into benefit-type indicators and cost-type indicators according to the principle of whether they have a positive impact on the comprehensive evaluation result of the microgrid. Then, the required positive ideal solution and negative ideal solution are calculated based on the approximation ideal point method, and the various microgrid schemes to be evaluated are comprehensively evaluated accordingly.

[0051] Specifically, the step of comprehensively ranking each of the microgrid schemes to be evaluated based on the weighted normalization matrix and the approximation of ideal points to obtain the comprehensive evaluation result includes: Based on preset classification rules, the indicators in the evaluation index system for industrial and commercial microgrids are divided into benefit-type indicators and cost-type indicators. The preset classification rules are to classify indicators that have a positive impact on the comprehensive evaluation results as benefit-type indicators and indicators that have a negative impact on the comprehensive evaluation results as cost-type indicators.

[0052] In this embodiment, the benefit-type indicators can be understood as indicators whose larger values ​​are more favorable for evaluating industrial and commercial microgrids and their impact on the power grid; the cost-type indicators can be understood as indicators whose smaller values ​​are more favorable for evaluation. In the industrial and commercial microgrid evaluation indicator system provided in this embodiment, the benefit-type indicators may include the proportion of renewable energy installed capacity within the microgrid, the self-sufficiency rate of renewable energy within the microgrid, the self-balancing degree of electricity within the microgrid, the local consumption rate of renewable energy, the source-load matching degree within the microgrid, the ratio of energy storage power to maximum load within the microgrid, the ratio of energy storage power to renewable energy power within the microgrid, the annual equivalent full charge and discharge times of energy storage within the microgrid, the peak-valley difference rate of power exchange at the microgrid grid connection point, the grid valley value contribution rate, and the annual revenue of the power grid. These indicators reflect the positive role of industrial and commercial microgrids in energy utilization, self-balancing, and grid support from the perspectives of technical evaluation and impact on the power grid; higher values ​​indicate better performance of industrial and commercial microgrids in these aspects. Correspondingly, the cost-type indicators include comprehensive electricity cost, primary investment cost, investment payback period, grid peak contribution rate, and the average load rate of the microgrid grid-connected transformer. The comprehensive electricity cost and the initial investment cost of the project directly reflect the cost of building and operating the microgrid for users. The investment payback period reflects the speed of cost recovery. The peak contribution rate of the power grid reflects the load situation measured at the microgrid connection point at the peak load moment of the power grid. The lower the contribution rate, the stronger the peak shaving effect of the microgrid. The average load rate of the microgrid grid-connected transformer indirectly reflects the impact of industrial and commercial microgrids on the utilization efficiency of power grid equipment. The lower the load rate, the smaller the negative impact on the utilization efficiency of power grid equipment. Therefore, the smaller the values ​​of these indicators, the better.

[0053] Based on the weighted normalization matrix, a positive ideal solution is obtained with the objectives of maximizing the benefit-type indicator and minimizing the cost-type indicator. In practical applications, for benefit-type indicators, the maximum value of the indicator column in the weighted normalization matrix is ​​taken as the corresponding indicator value in the positive ideal solution; for cost-type indicators, the minimum value of the indicator column is selected as the corresponding indicator value in the positive ideal solution. That is, the positive ideal solution can be expressed as: Among them, V + J represents the ideal solution; + and J - These represent the set of benefit-type indicators and the set of cost-type indicators, respectively. This represents the j-th index value in the positive ideal solution.

[0054] Based on the weighted normalization matrix, the negative ideal solution is obtained with the objectives of minimizing the benefit-type indicator and maximizing the cost-type indicator. In practical applications, for benefit-type indicators, the minimum value of the indicator column in the weighted normalization matrix is ​​taken as the corresponding indicator value in the negative ideal solution; for cost-type indicators, the maximum value of the indicator column is selected as the corresponding indicator value in the negative ideal solution. That is, the negative ideal solution can be expressed as: Among them, V - J represents the negative ideal solution; + and J - These represent the set of benefit-type indicators and the set of cost-type indicators, respectively. This represents the j-th index value in the negative ideal solution.

[0055] By dividing the indicators into benefit-type and cost-type indicators and then using the approximation-ideal-point method to determine the positive and negative ideal solutions, the scientific validity and rationality of the ideal solution determination can be effectively guaranteed. This can provide a reference standard for subsequently measuring the degree of closeness of each industrial and commercial microgrid to the ideal state, and thus provide a reliable guarantee for the effectiveness of the comprehensive evaluation of each industrial and commercial microgrid.

[0056] Based on the positive ideal solution and the negative ideal solution, the positive ideal solution gap and the negative ideal solution gap corresponding to each of the microgrid schemes to be evaluated are calculated respectively; wherein, the positive ideal solution gap can be understood as the Euclidean distance between the comprehensive evaluation index value corresponding to the microgrid scheme to be evaluated and the comprehensive evaluation index value corresponding to the positive ideal solution, and the negative ideal solution gap is the Euclidean distance between the comprehensive evaluation index value corresponding to the microgrid scheme to be evaluated and the comprehensive evaluation index value corresponding to the negative ideal solution, which can be expressed as: In the formula, and These represent the positive and negative ideal solution gaps for the l-th microgrid scheme to be evaluated, respectively; v lj This represents the j-th index value of the l-th microgrid scheme to be evaluated; and Let represent the j-th index value in the positive ideal solution and the negative ideal solution, respectively.

[0057] Based on the positive and negative ideal solution differences for each of the microgrid schemes to be evaluated, the corresponding positive ideal solution proximity is obtained; whereby the positive ideal solution proximity is expressed as: In the formula, C l This represents the proximity of the positive ideal solution to the l-th microgrid scheme to be evaluated. The smaller the value, the farther away it is from the positive ideal solution, and the larger the value, the closer it is to the positive ideal solution. It can reliably quantify the degree of proximity of each industrial and commercial microgrid to the ideal state.

[0058] A comprehensive evaluation result is obtained by ranking the positive ideal solutions of each microgrid scheme under evaluation based on their proximity to the ideal solution. This comprehensive evaluation result can be understood as a ranking of the merits of the industrial and commercial microgrid schemes by sorting their positive ideal solution proximity from largest to smallest. Based on the obtained comprehensive evaluation result, a comprehensive, systematic, objective, and scientific evaluation of different industrial and commercial microgrid schemes can be achieved, providing a reliable basis for decision-making regarding the construction conditions, operation strategies, and scheduling control optimization of industrial and commercial microgrids.

[0059] This invention provides a technical solution based on a pre-constructed evaluation index system for industrial and commercial microgrids, including technical evaluation indicators, economic benefit indicators, and grid impact indicators. This system obtains comprehensive evaluation indicators for each microgrid scheme to be evaluated. Using subjective weighting, objective weighting, and data weighting methods, it acquires a first weight vector, a second weight vector, and a third weight vector corresponding to the evaluation index system. Then, based on cooperative game theory, it performs a fusion analysis of the first, second, and third weight vectors to obtain a target weight vector. Next, it constructs a normalized decision matrix based on the comprehensive evaluation indicators of each microgrid scheme to be evaluated. Finally, it weights the normalized decision matrix according to the target weight vector to obtain a weighted normalized matrix. Based on the weighted normalized matrix, it ranks each microgrid scheme to be evaluated using the approximation of ideal points to obtain a comprehensive evaluation result. This technical solution effectively evaluates the characteristics of industrial and commercial microgrids while reliably quantifying their impact on grid operation and economic benefits. It effectively improves the comprehensiveness and accuracy of the comprehensive evaluation of industrial and commercial microgrids, thereby providing reliable analytical and decision-making basis for optimizing construction conditions, operation strategies, and dispatch control of industrial and commercial microgrids.

[0060] It should be noted that although the steps in the flowchart above are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order requirement for the execution of these steps, and they can be executed in other orders.

[0061] In one embodiment, such as Figure 2 As shown, a comprehensive evaluation system for industrial and commercial microgrids is provided, the system comprising: Evaluation index acquisition module 1 is used to acquire comprehensive evaluation indexes for each microgrid scheme to be evaluated based on a pre-constructed evaluation index system for industrial and commercial microgrids; the evaluation index system for industrial and commercial microgrids includes technical evaluation indexes, economic benefit indexes, and power grid impact indexes. The weight vector acquisition module 2 is used to acquire the first weight vector, the second weight vector, and the third weight vector corresponding to the evaluation index system of the industrial and commercial microgrid based on subjective weighting method, objective weighting method, and data weighting method, and to perform fusion analysis on the first weight vector, the second weight vector, and the third weight vector based on the alliance cooperative game theory to obtain the target weight vector. The normalized matrix acquisition module 3 is used to construct a normalized decision matrix based on the comprehensive evaluation index of each of the microgrid schemes to be evaluated, and to perform weighted processing on the normalized decision matrix according to the target weight vector to obtain a weighted normalized matrix. The evaluation result generation module 4 is used to comprehensively rank each of the microgrid schemes to be evaluated based on the weighted normalization matrix and the approximation ideal point method to obtain a comprehensive evaluation result.

[0062] Specific limitations regarding the comprehensive evaluation system for industrial and commercial microgrids can be found in the limitations of the comprehensive evaluation method for industrial and commercial microgrids mentioned above; the corresponding technical effects can be obtained equivalently and will not be repeated here. Each module in the aforementioned comprehensive evaluation system for industrial and commercial microgrids can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0063] Figure 3 An internal structural diagram of a computer device is shown in one embodiment. This computer device may specifically be a terminal or a server. Figure 3 As shown, the computer device includes a processor, memory, network interface, display, camera, and input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program can implement a comprehensive evaluation method for industrial and commercial microgrids. The display screen can be an LCD screen or an e-ink display screen. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0064] Those skilled in the art will understand that Figure 3The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device to which the present invention is applied. Specific computing devices may include more or fewer components than those shown in the figure, or combine certain components, or have the same component arrangement.

[0065] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described above.

[0066] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0067] In summary, the comprehensive evaluation method, system, equipment, and storage medium for industrial and commercial microgrids provided by this invention can effectively evaluate the characteristics of industrial and commercial microgrids while reliably quantifying their impact on grid operation and economic benefits. This effectively improves the comprehensiveness and accuracy of the comprehensive evaluation of industrial and commercial microgrids, thereby providing reliable analytical and decision-making basis for optimizing the construction conditions, operation strategies, and scheduling control of industrial and commercial microgrids.

[0068] The various embodiments in this specification are described in a progressive manner. For directly identical or similar parts of the embodiments, refer to each other. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. It should be noted that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0069] The above-described embodiments are merely preferred embodiments of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various improvements and substitutions without departing from the principles of the present invention, and these improvements and substitutions should also be considered within the scope of protection of the present invention. Therefore, the scope of protection of this invention should be determined by the scope of the claims.

Claims

1. A comprehensive evaluation method for industrial and commercial microgrids, characterized in that, The method includes the following steps: Based on a pre-constructed evaluation index system for industrial and commercial microgrids, comprehensive evaluation indexes for each microgrid scheme to be evaluated are obtained; the evaluation index system for industrial and commercial microgrids includes technical evaluation indexes, economic benefit indexes, and power grid impact indexes. Based on subjective weighting, objective weighting and data weighting methods, the first weight vector, the second weight vector and the third weight vector corresponding to the evaluation index system of the industrial and commercial microgrid are obtained, and the first weight vector, the second weight vector and the third weight vector are fused and analyzed based on the alliance cooperative game theory to obtain the target weight vector. Based on the comprehensive evaluation indicators of each microgrid scheme to be evaluated, a normalized decision matrix is ​​constructed, and the normalized decision matrix is ​​weighted according to the target weight vector to obtain a weighted normalized matrix. Based on the weighted normalization matrix, the various microgrid schemes to be evaluated are comprehensively ranked using the approximation of ideal points method to obtain a comprehensive evaluation result.

2. The comprehensive evaluation method for industrial and commercial microgrids as described in claim 1, characterized in that, The steps for constructing the evaluation index system for industrial and commercial microgrids include: Based on the operation data of renewable energy, load, and energy storage within the microgrid, the technical evaluation indicators are constructed. These indicators include the proportion of renewable energy installed capacity within the microgrid, the self-sufficiency rate of renewable energy within the microgrid, the power self-balance within the microgrid, the local consumption rate of renewable energy, the source-load matching degree within the microgrid, the ratio of energy storage power to maximum load within the microgrid, the ratio of energy storage power to renewable energy power within the microgrid, and the annual equivalent full charge and full discharge times of energy storage within the microgrid. Based on microgrid load cost per kilowatt-hour and microgrid investment return data, the aforementioned economic benefit indicators are constructed; the economic benefit indicators include comprehensive electricity cost, primary investment cost, and investment payback period. Based on the data on the impact of microgrids on the power grid, the power grid impact indicators are constructed. The power grid impact indicators include the peak-valley difference rate of the microgrid grid connection point's switching power, the average load rate of the microgrid grid-connected transformer, the peak value contribution rate of the power grid, the valley value contribution rate of the power grid, and the annual revenue of the power grid.

3. The comprehensive evaluation method for industrial and commercial microgrids as described in claim 2, characterized in that, The renewable energy operation data within the microgrid includes renewable energy installed capacity, annual self-generated and self-consumed renewable energy electricity, annual renewable energy power generation, renewable energy grid-connected electricity, microgrid off-grid electricity, annual renewable energy output curve, and annual maximum renewable energy power; the load operation data within the microgrid includes annual maximum load, annual load electricity consumption, and annual electricity load curve; the energy storage operation data within the microgrid includes maximum energy storage charging and discharging power, annual energy storage charging electricity consumption, and energy storage capacity. The step of constructing the technical evaluation index based on renewable energy operation data, load operation data, and energy storage operation data within the microgrid includes: Based on the installed capacity of renewable energy and the annual maximum load, the installed capacity ratio of renewable energy in the microgrid is obtained through installed capacity analysis. Based on the annual self-generated and self-consumed electricity of renewable energy and the annual load electricity consumption, the proportion of self-generated and self-consumed electricity is analyzed to obtain the self-sufficiency rate of renewable energy in the microgrid. Based on the renewable energy grid-connected electricity and the microgrid grid-disconnected electricity, the total grid-connected and grid-connected electricity of the microgrid is obtained. Based on the total grid-connected and grid-connected electricity of the microgrid and the annual load electricity consumption, the power balance capability is analyzed to obtain the power self-balance degree within the microgrid. Based on the annual self-generated and self-consumed electricity of the renewable energy and the annual power generation of the renewable energy, the grid connection and absorption capacity analysis is carried out to obtain the local absorption rate of the renewable energy; The source-load matching degree of the microgrid is obtained by superimposing and coupling analysis of the annual renewable energy output curve and the annual electricity load curve. The ratio of energy storage power to maximum load in the microgrid is obtained based on the maximum charging and discharging power of the energy storage and the maximum annual load. The ratio of energy storage power to renewable energy power in the microgrid is obtained based on the maximum charge and discharge power of the energy storage and the maximum annual power of the renewable energy. Based on the annual charging amount of the energy storage and the energy storage capacity, the annual equivalent number of full charge and full discharge cycles of the energy storage in the microgrid is obtained.

4. The comprehensive evaluation method for industrial and commercial microgrids as described in claim 2, characterized in that, The microgrid load cost per kilowatt-hour data includes annual value of basic investment, annual value of operation and maintenance costs, annual value of electricity charges, annual value of demand charges, annual value of grid connection revenue, and annual load electricity consumption; the microgrid investment revenue data includes microgrid basic investment, microgrid operation and maintenance costs, and microgrid annual revenue; The steps for constructing the economic benefit indicators based on microgrid load cost per kilowatt-hour data and microgrid investment return data include: The comprehensive electricity cost is obtained by analyzing the load cost per kilowatt-hour based on the annual value of the basic investment, the annual value of the operation and maintenance cost, the annual value of the electricity charge, the annual value of the demand charge, the annual value of the grid connection revenue, and the annual load electricity consumption. Based on the microgrid infrastructure investment and the microgrid operation and maintenance cost, a microgrid investment cost analysis is performed to obtain the primary investment cost. The investment payback period is obtained based on the initial investment cost and the annual revenue of the microgrid, using a preset investment cost model.

5. The comprehensive evaluation method for industrial and commercial microgrids as described in claim 2, characterized in that, The data on the impact of microgrids on the power grid include the maximum net load at the microgrid connection point, the minimum net load at the microgrid connection point, the average power transmitted through the grid at the microgrid connection point, the transformer capacity, the user load at the peak of the power grid before the construction of the microgrid, the microgrid load at the peak of the power grid after the construction of the microgrid, the user load at the valley of the power grid before the construction of the microgrid, the microgrid load at the valley of the power grid after the construction of the microgrid, the annual user transmission and distribution fees, and the annual user demand fees. The step of constructing the power grid impact index based on the microgrid's impact data includes: Peak-valley load deviation analysis is performed based on the maximum net load and the minimum net load of the microgrid connection point to obtain the peak-valley difference rate of the switching power at the microgrid connection point. Load analysis is performed based on the average power transmitted through the grid at the microgrid connection point and the transformer capacity to obtain the average load rate of the microgrid grid-connected transformer; Peak load deviation analysis is performed based on the user load at the peak time of the power grid before the construction of the microgrid and the microgrid load at the peak time of the power grid after the construction of the microgrid to obtain the peak contribution rate of the power grid. Based on the user load at the grid's off-peak time before the microgrid construction and the microgrid load at the grid's off-peak time after the microgrid construction, an off-peak load deviation analysis is performed to obtain the grid's off-peak contribution rate; The annual revenue of the power grid is obtained by comprehensively analyzing the annual user transmission and distribution fees and the annual user demand fees.

6. The comprehensive evaluation method for industrial and commercial microgrids as described in claim 1, characterized in that, The step of fusing and analyzing the first weight vector, the second weight vector, and the third weight vector based on cooperative game theory to obtain the target weight vector includes: Based on the aforementioned cooperative game theory, the first weight vector, the second weight vector, and the third weight vector are combined, and the consistency correlation coefficients corresponding to the first weight vector, the second weight vector, and the third weight vector are obtained respectively based on a preset consistency correlation coefficient expression; the preset consistency correlation coefficient expression is: In the formula, L i The w represents the consistency correlation coefficient between the weight vector obtained by the i-th weighting method and the combined weight vector obtained by weighting methods other than the i-th weighting method, where i = 1, 2, 3; ij Let j represent the weight value of the j-th indicator in the weight vector obtained by the i-th weighting method, where j = 1, 2, ..., n; and n represents the number of indicators in the comprehensive evaluation index. This represents the average value of all index weights in the weight vector obtained by the i-th weighting method; w represents the average of all index weights in the combined weight vector obtained by weighting methods other than the i-th weighting method; -ij This represents the weight value of the j-th index in the combined weight vector obtained by weighting methods other than the i-th weighting method; Based on the consistency correlation coefficients corresponding to the first weight vector, the second weight vector, and the third weight vector, the first weight vector, the second weight vector, and the third weight vector are weighted and fused to obtain a fused weight vector; The fusion weight vector is normalized to obtain the target weight vector.

7. The comprehensive evaluation method for industrial and commercial microgrids as described in claim 1, characterized in that, The step of comprehensively ranking the various microgrid schemes to be evaluated based on the weighted normalization matrix and the approximation of ideal points to obtain the comprehensive evaluation result includes: Based on preset classification rules, the indicators in the evaluation index system of industrial and commercial microgrids are divided into benefit-type indicators and cost-type indicators. The preset classification rules are to classify indicators that have a positive impact on the comprehensive evaluation results as benefit-type indicators and indicators that have a negative impact on the comprehensive evaluation results as cost-type indicators. Based on the weighted normalization matrix, a positive ideal solution is obtained with the objectives of maximizing the benefit-type index and minimizing the cost-type index; based on the weighted normalization matrix, a negative ideal solution is obtained with the objectives of minimizing the benefit-type index and maximizing the cost-type index; based on the positive ideal solution and the negative ideal solution, the positive ideal solution gap and negative ideal solution gap corresponding to each of the microgrid schemes to be evaluated are calculated respectively. Based on the positive ideal solution gap and negative ideal solution gap corresponding to each of the microgrid schemes to be evaluated, the corresponding positive ideal solution proximity is obtained; The comprehensive evaluation results are obtained by ranking the positive ideal solutions of each microgrid scheme to be evaluated based on their proximity.

8. A comprehensive evaluation system for industrial and commercial microgrids, characterized in that, The system includes: The evaluation index acquisition module is used to acquire comprehensive evaluation indexes for each microgrid scheme to be evaluated based on a pre-constructed evaluation index system for industrial and commercial microgrids; the evaluation index system for industrial and commercial microgrids includes technical evaluation indexes, economic benefit indexes, and power grid impact indexes. The weight vector acquisition module is used to acquire the first weight vector, the second weight vector, and the third weight vector corresponding to the evaluation index system of the industrial and commercial microgrid based on subjective weighting method, objective weighting method, and data weighting method, and to perform fusion analysis on the first weight vector, the second weight vector, and the third weight vector based on the alliance cooperative game theory to obtain the target weight vector. The normalized matrix acquisition module is used to construct a normalized decision matrix based on the comprehensive evaluation index of each of the microgrid schemes to be evaluated, and to perform weighted processing on the normalized decision matrix according to the target weight vector to obtain a weighted normalized matrix. The evaluation result generation module is used to comprehensively rank each of the microgrid schemes to be evaluated based on the weighted normalization matrix and the approximation ideal point method to obtain a comprehensive evaluation result.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

Images