Distributed energy storage power station site selection method and system based on node difference method

Abstract

The invention discloses a distributed energy storage power station site selection method and system based on a node difference method, and the method comprises the steps: constructing a multi-dimensional planning index system comprising distributed power supply output, load, power grid structure characteristics and the like, collecting the original index data of a to-be-selected node, and carrying out the data processing, objective weight endowing and weighted evaluation matrix construction. And applying an optimized approximate ideal solution sorting algorithm, introducing a node difference correction coefficient and a node correlation model, and comprehensively combining node characteristic difference and mutual influence to obtain a site selection potential sorting value of each node so as to determine a site selection scheme. The system comprises an original data acquisition unit, an index data processing unit, a weight determination unit, a weighting matrix construction unit, an algorithm calculation unit, a scheme determination unit and the like, all the units work cooperatively, scientific site selection of the distributed energy storage power station is achieved, the problems that a traditional method is single in index consideration, inaccurate in algorithm evaluation and the like are solved, and the accuracy and reliability of site selection are improved.

Description

Technical Field

[0001] The present invention relates to the field of energy storage power station site selection, and in particular to a distributed energy storage power station site selection method and system based on a node difference method. Background Art

[0002] As energy structure transformation accelerates, the site selection and planning of distributed energy storage power stations, key facilities for enhancing grid flexibility and stability, are crucial for optimizing energy allocation and reducing operating costs. Currently, traditional methods for selecting sites for distributed energy storage power stations rely heavily on empirical judgment or single-metric analysis, making them difficult to adapt to the multi-dimensional demands of complex grid environments.

[0003] On the one hand, existing methods have limitations in their indicator considerations. Some methods focus only on a single dimension, such as load demand or grid structure, while ignoring other key factors such as the output characteristics of distributed power sources and load fluctuations. As a result, the site selection plan cannot effectively address the challenges brought by the intermittent and random nature of distributed power sources, making it difficult to ensure stable grid operation and efficient energy utilization. On the other hand, traditional algorithms are insufficient in processing complex data and comprehensive evaluation. The simple sorting or fixed weight algorithms used cannot dynamically adjust indicator weights according to actual conditions, nor can they quantify the mutual influence between nodes. As a result, the site selection results lack accuracy and reliability, making it difficult to meet the requirements of modern power grids for the precise layout of distributed energy storage power stations. Summary of the Invention

[0004] In order to overcome the shortcomings and deficiencies of the prior art, the present invention provides a distributed energy storage power station site selection method and system based on a node difference method.

[0005] The technical solution adopted by the present invention is a distributed energy storage power station site selection method based on the node difference method, comprising the following steps: Step S1: Constructing a multi-dimensional planning indicator system for distributed energy storage, the indicator system includes different dimensions of distributed power output characteristics, load characteristics, and grid structure characteristics. The distributed power output characteristics include output changes based on temporal and spatial distribution, the load characteristics include load size and fluctuations in different time periods, and the grid structure characteristics include node voltage levels and line impedance parameters. Step S2: Based on the distributed energy storage multi-dimensional planning indicator system, for each candidate node, collecting original data corresponding to each indicator, and obtaining the original indicator data of each candidate node; Step S3: Processing the original indicator data, converting different types of indicator data into a form that can be uniformly compared according to the indicator type; Step S4: using preset rules, comprehensively considering the influence of each indicator in the distributed energy storage site selection and layout, and assigning objective weights to each indicator in the distributed energy storage multi-dimensional planning indicator system; Step S5: constructing a weighted evaluation matrix of the distributed energy storage power station of each candidate node based on the processed indicator data and corresponding indicator weights of each candidate node; Step S6: Using the optimized approximate ideal solution sorting algorithm, the weighted evaluation matrix of the distributed energy storage power station of each candidate node is calculated and processed to obtain the potential ranking value of the distributed energy storage site selection of each candidate node, and the optimal distributed energy storage site selection scheme is determined based on the potential ranking value.

[0006] Furthermore, in step S4, objective weights are assigned to each indicator using a weight determination model based on indicator variability, and the model formula is: ,in, Indicates the The weight of the indicator, , Indicates the indicators, , is the total number of indicators in the indicator system; For the The standard deviation of the original data of each indicator, For the The standard deviation of the original data of an indicator reflects the degree of variation of the indicator among the candidate nodes.

[0007] Furthermore, in step S5, a node comprehensive evaluation model is used to construct a weighted evaluation matrix, and the model formula is: ,in, Indicates the The candidate node is in The weighted evaluation value under each indicator, is the total number of nodes to be selected, , is the total number of indicators in the indicator system; For the The weight of each indicator; For the The candidate node is in The value of the original data under each indicator after processing.

[0008] Furthermore, in step S6, during the calculation process of the optimized approach-to-ideal solution sorting algorithm, a node difference correction coefficient is introduced to construct a node difference correction model, and the model formula is: ,in, Indicates the The corrected distance between the candidate nodes and the ideal solution, is the total number of nodes to be selected, is the total number of indicators in the indicator system; For the The candidate node is in The node difference correction coefficient under each indicator is determined based on the grid characteristics and load characteristics of the area where the node is located; For the Positive ideal solution of an index; For the The candidate node is in The weighted evaluation value under each indicator.

[0009] Furthermore, in step S3, for the conversion of interval-type indicator data, an interval mapping model is adopted, and the model formula is:

[0010] in, For the The candidate node is in The value after the conversion of the interval indicator, is the total number of nodes to be selected, , is the total number of indicators in the indicator system; For the The candidate node is in The original data under the interval-type indicators; Respectively The lower limit, optimal interval lower limit and upper limit of an interval-type indicator.

[0011] Furthermore, in step S6, during the calculation process of the optimized approximate ideal solution sorting algorithm, a node correlation model is constructed by combining the mutual influence between nodes. The model formula is: ,in, Indicates the The association value of the candidate nodes, is the total number of nodes to be selected; For the The candidate nodes and The correlation coefficient of the candidate nodes is determined based on the electrical distance and power transmission relationship between the nodes; For the The candidate nodes and The distance to the selected nodes.

[0012] Furthermore, the step S3 includes the following steps: Step S31: determining the indicator type of the original indicator data, and identifying whether the original indicator data belongs to a specific type of extremely small indicator, interval indicator, intermediate indicator, or extremely large indicator; Step S32: For the extremely small index data, a preset conversion formula is used to convert it into extremely large index data, so that the data is more consistent in subsequent comparisons; Step S33: For the intermediate indicator data, convert it into a very large indicator data format according to the preset conversion rules to facilitate unified analysis and comparison; Step S34: normalize all converted indicator data to make each indicator data in the same numerical range and eliminate the influence of dimension on the calculation results.

[0013] Furthermore, the step S4 includes the following steps: Step S41: Calculate the coefficient of variation of the original data of each indicator, and use the coefficient of variation to measure the degree of dispersion of each indicator data among each candidate node; Step S42: Preliminarily determine the weight of each indicator based on the coefficient of variation of each indicator and the preset weight distribution rules; Step S43: Based on the correlation between the indicators, the initially determined weights are modified so that the weights of the indicators reflect their importance in the energy storage site selection; Step S44: normalize the corrected weights so that the sum of all weights is 1, thereby obtaining the final objective weights of each indicator.

[0014] Furthermore, the step S6 includes the following steps: Step S61: Determine the positive ideal solution and negative ideal solution of each indicator according to the weighted evaluation matrix of each candidate node, which will serve as a reference standard for subsequent calculations; Step S62: using the optimized approach-to-ideal solution sorting algorithm, calculate the distance between each candidate node and the positive ideal solution and the negative ideal solution, and quantify the degree of difference between each node and the ideal state; Step S63: combining the node difference correction coefficient and the node correlation value, correcting the distance between each candidate node and the ideal solution to make the calculation result more in line with the actual site selection requirements; Step S64: sort the potential values ​​of the candidate nodes according to the corrected distances, thereby determining the best distributed energy storage site selection plan.

[0015] The distributed energy storage power station site selection system based on the node difference method includes: The raw data acquisition unit is used to collect the raw data corresponding to each indicator in the distributed energy storage multi-dimensional planning indicator system for each candidate node; An indicator data processing unit, connected to the raw data acquisition unit, is used to process the raw indicator data collected by the raw data acquisition unit and convert different types of indicator data into a form that can be uniformly compared; an indicator weight determination unit, connected to the indicator data processing unit, for comprehensively combining the influence of each indicator in the distributed energy storage site selection and layout, and assigning an objective weight to each indicator in the distributed energy storage multi-dimensional planning indicator system; A weighted matrix construction unit, connected to the index weight determination unit and the index data processing unit, for constructing a weighted evaluation matrix of a distributed energy storage power station for each candidate node based on the processed index data and corresponding index weight of each candidate node; an algorithm calculation unit connected to the weighted matrix construction unit, for calculating and processing the weighted evaluation matrix of the distributed energy storage power station of each candidate node using an optimized approximate ideal solution sorting algorithm; The scheme determination unit is connected to the algorithm calculation unit and is used to determine the best distributed energy storage site selection scheme based on the potential ranking value of the distributed energy storage site selection of each candidate node obtained by the algorithm calculation unit.

[0016] Beneficial Effects: This invention proposes a distributed energy storage power station site selection method and system based on the node difference method. By constructing a multidimensional distributed energy storage planning indicator system encompassing multiple dimensions, including distributed power generation output characteristics, load characteristics, and grid structure characteristics, the invention comprehensively collects raw data from each candidate node, overcoming the shortcomings of single-indicator analysis and fully integrating the intermittent and random characteristics of distributed power generation and the multidimensional needs of complex power grid environments. At the algorithm optimization level, a weight determination model based on indicator variability and a node comprehensive evaluation model are adopted. Weights are determined based on the degree of dispersion of indicator data, and node difference correction coefficients and inter-node interactions are incorporated into the calculation, eliminating the drawbacks of traditional fixed weights and simple ranking algorithms. By classifying and normalizing the raw indicator data and combining it with an optimized, near-ideal solution ranking algorithm, accurate ranking of the potential of each candidate node is achieved. The supporting site selection system's various units operate collaboratively, forming a complete process from data collection to solution determination, ultimately determining a scientific and reasonable site selection plan. This significantly improves the accuracy and reliability of distributed energy storage power station site selection, meets the modern power grid's demand for precise energy storage layout, effectively enhances grid flexibility and stability, optimizes energy allocation, and reduces operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flow chart of the method steps of the present invention; Figure 2 It is a diagram of the system unit composition of the present invention. DETAILED DESCRIPTION

[0018] It should be noted that, unless there is a conflict, the embodiments in this application and the features described in the embodiments can be combined with each other. The application is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] like Figure 1 As shown in FIG, the distributed energy storage power station site selection method based on the node difference method includes the following steps: Step S1: Constructing a multi-dimensional planning indicator system for distributed energy storage. The indicator system includes different dimensions such as distributed power output characteristics, load characteristics, and grid structure characteristics. Distributed power output characteristics include output changes based on temporal and spatial distribution, load characteristics include load size and fluctuations in different time periods, and grid structure characteristics involve parameters such as node voltage levels and line impedance. Specifically, this step aims to establish a comprehensive and systematic evaluation framework encompassing multiple dimensions, including distributed generation output characteristics, load characteristics, and grid structure characteristics. Distributed generation output characteristics reflect output variations over time and in different regions through spatiotemporal distribution parameters. Load characteristics quantify the dynamic changes in electricity demand through load magnitude and fluctuation parameters over different time periods. Grid structure characteristics characterize grid topology and transmission capacity through parameters such as node voltage levels and line impedance. These interrelated parameters together form a multidimensional indicator system, providing comprehensive data support for subsequent site selection assessments.

[0020] The index system is constructed using a hierarchical design. It first identifies the key factors influencing distributed energy storage site selection, then breaks each factor down into specific, quantifiable indicators, ensuring that the indicators are both independent and fully reflect site selection requirements. This structured design enables the index system to accurately capture the complex interactions between distributed energy storage and the power grid, providing a solid theoretical foundation for scientific site selection.

[0021] Step S2: Based on the distributed energy storage multi-dimensional planning indicator system, for each candidate node, collecting original data corresponding to each indicator, and obtaining the original indicator data of each candidate node; Specifically, this step collects raw data corresponding to each indicator for each candidate node based on the multidimensional indicator system constructed in step S1. This collection process follows standardized procedures to ensure data accuracy and consistency. For distributed generation output characteristic indicators, output data is collected at different time scales (e.g., hourly, daily, and seasonal). For load characteristic indicators, load curves and fluctuation data are collected over different time periods. For grid structure characteristic indicators, grid topology data such as node voltages and line parameters is collected.

[0022] Data collection utilizes a multi-source fusion approach, integrating data from multiple channels, including the power grid SCADA system, distributed power generation monitoring system, and meteorological data platform, to ensure data integrity and reliability. At the same time, the collected data undergoes preliminary quality screening to remove outliers and missing values, providing a high-quality raw data foundation for subsequent data processing.

[0023] Step S3: Processing the original indicator data, converting different types of indicator data into a form that can be uniformly compared according to the indicator type; Specifically, this step categorizes the raw indicator data, converting different types of indicator data (extremely small, interval, intermediate, and extremely large) into a uniformly comparable format. The process first identifies the indicator type and then selects a corresponding conversion method based on the characteristics of each type. For extremely small indicators, reverse conversion is used to convert them into extremely large indicators; for interval indicators, interval mapping is used to map the data to a uniform interval; and for intermediate indicators, specific conversion rules are used to convert them into extremely large indicators.

[0024] All converted data were normalized to eliminate dimensional differences and bring all indicators into the same numerical range. Normalization employed a standardization approach, calculating the mean and standard deviation to map the data to the interval [0, 1]. This process ensured comparability across indicators of varying nature and dimensions, providing a standardized data foundation for subsequent weighted evaluation.

[0025] Step S4: using preset rules, comprehensively considering the influence of each indicator in the distributed energy storage site selection and layout, and assigning objective weights to each indicator in the distributed energy storage multi-dimensional planning indicator system; Specifically, this step comprehensively considers the impact of each indicator on distributed energy storage site selection and uses an objective weighting method to determine indicator weights. First, the coefficient of variation of each indicator's raw data is calculated to quantify the degree of dispersion of the indicator across candidate nodes. A larger coefficient of variation indicates a more discriminatory indicator in site selection. Then, based on the coefficient of variation, a preliminary weight is assigned according to pre-set rules to ensure that the weight assignment matches the actual impact of the indicator.

[0026] The initial weights are further refined based on the correlations between indicators to avoid weight bias caused by indicator overlap. This revision process analyzes the correlation coefficients between indicators and adjusts the weights of indicators with stronger correlations, ensuring that the final weights more accurately reflect the independent contributions of each indicator. Finally, the revised weights are normalized to ensure that the sum of all weights is 1, forming a scientific and rational indicator weighting system.

[0027] Step S5: constructing a weighted evaluation matrix of the distributed energy storage power station of each candidate node based on the processed indicator data and corresponding indicator weights of each candidate node; Specifically, this step constructs a weighted evaluation matrix for distributed energy storage power plants for each candidate node based on the processed indicator data and the determined indicator weights. The rows of the matrix represent the candidate nodes, the columns represent the indicators, and the matrix elements are the weighted evaluation values ​​of each node under each indicator. By multiplying the processed indicator data with the corresponding weights, a quantitative assessment of the comprehensive performance of each node is achieved.

[0028] This matrix comprehensively reflects the comprehensive performance of each candidate node under a multi-dimensional indicator system, providing structured data input for subsequent optimization algorithm calculations. The matrix construction process strictly adheres to the results of data processing and weight allocation, ensuring the objectivity and accuracy of the evaluation results and providing a reliable quantitative basis for site selection decisions.

[0029] Step S6: Using the optimized approximate ideal solution sorting algorithm, the weighted evaluation matrix of the distributed energy storage power station of each candidate node is calculated and processed to obtain the potential ranking value of the distributed energy storage site selection of each candidate node, and the optimal distributed energy storage site selection scheme is determined based on the potential ranking value.

[0030] Specifically, this step uses an optimized approach-to-ideal solution sorting algorithm to calculate and process the weighted evaluation matrix. First, the positive and negative ideal solutions for each indicator are determined, representing the optimal and worst-case values ​​of the indicator, respectively. The distance between each candidate node and the positive and negative ideal solutions is then calculated to quantify the degree of difference between the node and the ideal state. Based on this, a node difference correction coefficient and node correlation value are introduced to correct the distance calculation results, making the evaluation more consistent with actual site selection requirements.

[0031] Finally, the potential ranking value of each candidate node is calculated based on the corrected distance. A higher ranking value indicates that the node is closer to the ideal location. By ranking the potential of all candidate nodes, the optimal distributed energy storage site selection scheme is determined. This process comprehensively combines the characteristics of the nodes themselves and the mutual influence between nodes, achieving scientific and refined site selection decisions.

[0032] Preferably, in step S4, objective weights are assigned to each indicator using a weight determination model based on indicator variability, and the model formula is: ,in, Indicates the The weight of the indicator, , Indicates the indicators, , is the total number of indicators in the indicator system; For the The standard deviation of the original data of each indicator, For the The standard deviation of the original data of each indicator reflects the degree of variation of the indicator among the candidate nodes. Through this model, the indicator weight is determined according to the discrete degree of the indicator data, so that the determination of the weight is more in line with the actual role of each indicator in energy storage site selection.

[0033] Specifically, through a weight determination model based on indicator variability, weights are objectively allocated according to the degree of discreteness reflected by the standard deviation of the original data of each indicator, so that the size of the weight is positively correlated with the degree of variation of the indicator among each candidate node, ensuring that indicators with high discrimination in energy storage site selection receive higher weights, improving the fit between weight allocation and actual site selection needs, and overcoming the defect that the traditional fixed weight method cannot be dynamically adjusted.

[0034] Preferably, in step S5, a node comprehensive evaluation model is used when constructing the weighted evaluation matrix, and the model formula is: ,in, Indicates the The candidate node is in The weighted evaluation value under each indicator, is the total number of nodes to be selected, , is the total number of indicators in the indicator system; For the The weight of each indicator; For the The candidate node is in The model constructs a weighted evaluation matrix that can reflect the comprehensive situation of each node by multiplying the indicator weight with the processed indicator data.

[0035] Specifically, a node comprehensive evaluation model is used to construct a weighted matrix, and the processed indicator data is multiplied by the corresponding weights to achieve a quantitative evaluation of the comprehensive performance of each node under multi-dimensional indicators. The model ensures the objectivity and comparability of the evaluation results by combining standardized data with weights, providing a structured data basis for subsequent algorithm calculations.

[0036] Preferably, in step S6, during the calculation process of the optimized approximate ideal solution sorting algorithm, a node difference correction coefficient is introduced to construct a node difference correction model, and the model formula is: ,in, Indicates the The corrected distance between the candidate nodes and the ideal solution, is the total number of nodes to be selected, is the total number of indicators in the indicator system; For the The candidate node is in The node difference correction coefficient under each indicator is determined based on factors such as the grid characteristics and load characteristics of the area where the node is located; For the Positive ideal solution of an index; For the The candidate node is in The weighted evaluation value under each indicator is used to correct the distance between the node and the ideal solution through this model, thereby improving the accuracy of the site selection plan.

[0037] Specifically, a node difference correction coefficient is introduced to construct a node difference correction model. The distance calculation between the node and the ideal solution is adjusted according to factors such as the grid characteristics and load characteristics of the node area, so that the evaluation results are more in line with the actual site selection needs. Especially when dealing with candidate nodes with significant differences in regional characteristics, this model can effectively improve the accuracy and adaptability of the site selection plan.

[0038] Preferably, in step S3, for the conversion of interval-type indicator data, an interval mapping model is adopted, and the model formula is:

[0039] in, For the The candidate node is in The value after the conversion of the interval indicator, is the total number of nodes to be selected, , is the total number of indicators in the indicator system; For the The candidate node is in The original data under the interval-type indicators; Respectively The lower limit, optimal interval lower limit and upper limit of each interval-type indicator are determined, and the interval-type indicator data are converted into a unified and comparable form through this model.

[0040] Specifically, an interval mapping model is used to transform interval-type indicator data. By setting three parameters: lower limit, lower limit of optimal interval, and upper limit, the original data is mapped to a unified interval to achieve comparability of different types of indicators. When processing indicators with specific interval requirements such as voltage qualification rate, this model can accurately reflect the degree of deviation of the data from the optimal interval, ensuring the scientific nature of the evaluation.

[0041] Preferably, in step S6, during the calculation process of the optimized approximate ideal solution sorting algorithm, a node correlation model is constructed by combining the mutual influence between nodes. The model formula is: ,in, Indicates the The association value of the candidate nodes, is the total number of nodes to be selected; For the The candidate nodes and The correlation coefficient of the candidate nodes is determined based on factors such as the electrical distance between nodes and the power transmission relationship; For the The candidate nodes and The model takes the mutual influence between nodes into account in the calculation, making the site selection result more consistent with the actual situation.

[0042] Specifically, a node correlation model is constructed, and the correlation coefficient is determined by comprehensively combining factors such as the electrical distance between nodes and the power transmission relationship. The mutual influence between nodes is quantified and this influence is incorporated into the site selection calculation. This makes the evaluation results more consistent with the actual operation of the power grid, avoids site selection deviations caused by ignoring the correlation between nodes, and improves the overall rationality of the plan.

[0043] Preferably, the step S3 includes the following steps: Step S31: determining the indicator type of the original indicator data, and identifying whether the original indicator data belongs to a specific type of extremely small indicator, interval indicator, intermediate indicator, or extremely large indicator; Step S32: For the extremely small index data, a preset conversion formula is used to convert it into extremely large index data, so that the data is more consistent in subsequent comparisons; Step S33: For the intermediate indicator data, convert it into a very large indicator data format according to the preset conversion rules to facilitate unified analysis and comparison; Step S34: normalize all converted indicator data to make each indicator data in the same numerical range and eliminate the influence of dimension on the calculation results.

[0044] Specifically, step S3 is refined into four sub-steps. Through indicator type identification, conversion of extremely small and intermediate indicators, and normalization processing, the unified comparability of indicator data of different properties is achieved. This structured processing flow ensures the accuracy and consistency of data conversion, provides a standardized data basis for subsequent weighted evaluation, and improves the reliability of evaluation results.

[0045] Preferably, the step S4 comprises the following steps: Step S41: Calculate the coefficient of variation of the original data of each indicator, and use the coefficient of variation to measure the degree of dispersion of each indicator data among each candidate node; Step S42: Preliminarily determine the weight of each indicator based on the coefficient of variation of each indicator and the preset weight distribution rules; Step S43: Based on the correlation between the indicators, the initially determined weights are revised so that the weights of the indicators can more accurately reflect their importance in energy storage site selection; Step S44: normalize the corrected weights so that the sum of all weights is 1, thereby obtaining the final objective weights of each indicator.

[0046] Specifically, the indicator weights are determined through four steps: coefficient of variation calculation, preset rule allocation, correlation correction, and normalization processing. This not only takes into account the discreteness of the indicator data, but also avoids the weight deviation caused by the correlation between indicators, forming a scientific and reasonable weight system, so that the weight of each indicator can more accurately reflect its actual importance in energy storage site selection.

[0047] Preferably, the step S6 comprises the following steps: Step S61: Determine the positive ideal solution and negative ideal solution of each indicator according to the weighted evaluation matrix of each candidate node, which will serve as a reference standard for subsequent calculations; Step S62: using the optimized approach-to-ideal solution sorting algorithm, calculate the distance between each candidate node and the positive ideal solution and the negative ideal solution, and quantify the degree of difference between each node and the ideal state; Step S63: combining the node difference correction coefficient and the node correlation value, correcting the distance between each candidate node and the ideal solution to make the calculation result more in line with the actual site selection requirements; Step S64: sort the potential values ​​of the candidate nodes according to the corrected distances, thereby determining the best distributed energy storage site selection plan.

[0048] Specifically, step S6 is decomposed into four sub-steps: positive / negative ideal solution determination, distance calculation, result correction, and potential ranking. By introducing the node difference correction coefficient and the node correlation value, the traditional approximate ideal solution ranking algorithm is optimized to achieve accurate evaluation and ranking of each candidate node, and finally determine the best site selection plan, thereby improving the scientificity and refinement of site selection decisions.

[0049] like Figure 2 As shown in the figure, the distributed energy storage power station site selection system based on the node difference method includes: The raw data acquisition unit is used to collect the raw data corresponding to each indicator in the distributed energy storage multi-dimensional planning indicator system for each candidate node; An indicator data processing unit, connected to the raw data acquisition unit, is used to process the raw indicator data collected by the raw data acquisition unit and convert different types of indicator data into a form that can be uniformly compared; an indicator weight determination unit, connected to the indicator data processing unit, for comprehensively combining the influence of each indicator in the distributed energy storage site selection and layout, and assigning an objective weight to each indicator in the distributed energy storage multi-dimensional planning indicator system; A weighted matrix construction unit, connected to the index weight determination unit and the index data processing unit, for constructing a weighted evaluation matrix of a distributed energy storage power station for each candidate node based on the processed index data and corresponding index weight of each candidate node; an algorithm calculation unit connected to the weighted matrix construction unit, for calculating and processing the weighted evaluation matrix of the distributed energy storage power station of each candidate node using an optimized approximate ideal solution sorting algorithm; The scheme determination unit is connected to the algorithm calculation unit and is used to determine the best distributed energy storage site selection scheme based on the potential ranking value of the distributed energy storage site selection of each candidate node obtained by the algorithm calculation unit.

[0050] The distributed energy storage power station site selection method and system based on the node difference method effectively overcomes the limitations of existing technologies in indicator consideration and algorithm evaluation through the construction of a multi-dimensional indicator system, the application of optimization algorithms, and the correction of node differences. Existing methods, which only focus on single-dimensional indicators or use simple ranking algorithms, are difficult to adapt to complex power grid environments and distributed power generation characteristics. This solution builds a multi-dimensional indicator system covering distributed power generation output characteristics, load characteristics, grid structure characteristics, and comprehensively collects raw data from each candidate node. This makes up for the shortcomings of traditional methods in terms of insufficient indicator considerations and can fully incorporate the impact of factors such as the intermittent and random characteristics of distributed power sources and load fluctuations on site selection.

[0051] At the algorithm optimization level, a weight determination model based on indicator variability is adopted, and the weights are dynamically adjusted according to the degree of discreteness of the indicator data. This abandons the mechanical nature of the traditional fixed-weight algorithm and makes the weight distribution more consistent with the actual role of each indicator in energy storage site selection. A weighted matrix is ​​constructed through a comprehensive node evaluation model, and an interval mapping model is combined to process different types of indicator data to ensure data comparability. A node difference correction coefficient and a node correlation model are introduced to improve the traditional approximate ideal solution sorting algorithm from the perspectives of node characteristic differences and mutual influence between nodes, respectively. By quantifying node characteristic differences and combining factors such as electrical distance and power transmission relationship between nodes, the accuracy and reliability of the site selection plan are significantly improved.

[0052] The various units of the supporting system operate collaboratively, forming a complete closed loop from raw data collection to final solution determination. The indicator data processing unit eliminates dimensionality effects through classification and normalization; the indicator weight determination unit scientifically assigns weights through steps such as coefficient of variation calculation and correlation correction; and the algorithm calculation unit achieves precise calculations by combining multiple optimization models. Ultimately, the solution determination unit outputs a scientific and reasonable site selection plan that meets the modern power grid's demand for precise energy storage layout, effectively improves grid flexibility and stability, optimizes energy allocation, and reduces operating costs, providing a comprehensive and innovative solution for the site selection of distributed energy storage power stations.

[0053] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "disposed," "installed," "connected," "connected," and "fixed" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; they may refer to mechanical connections or electrical connections; they may refer to direct connections or indirect connections through an intermediate medium; and they may refer to internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0054] While embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A distributed energy storage power station site selection method based on the node difference method is characterized by: The following steps are involved: Step S1: Constructing a multi-dimensional planning indicator system for distributed energy storage, the indicator system includes different dimensions of distributed power output characteristics, load characteristics, and grid structure characteristics. The distributed power output characteristics include output changes based on temporal and spatial distribution, the load characteristics include load size and fluctuations in different time periods, and the grid structure characteristics include node voltage levels and line impedance parameters. Step S2: Based on the distributed energy storage multi-dimensional planning indicator system, for each candidate node, collecting original data corresponding to each indicator, and obtaining the original indicator data of each candidate node; Step S3: Processing the original indicator data, converting different types of indicator data into a form that can be uniformly compared according to the indicator type; Step S4: using preset rules, comprehensively considering the influence of each indicator in the distributed energy storage site selection and layout, and assigning objective weights to each indicator in the distributed energy storage multi-dimensional planning indicator system; Step S5: constructing a weighted evaluation matrix of the distributed energy storage power station of each candidate node based on the processed indicator data and corresponding indicator weights of each candidate node; Step S6: Using the optimized approximate ideal solution sorting algorithm, the weighted evaluation matrix of the distributed energy storage power station of each candidate node is calculated and processed to obtain the potential ranking value of the distributed energy storage site selection of each candidate node, and the optimal distributed energy storage site selection scheme is determined based on the potential ranking value.

2. The distributed energy storage power station site selection method based on the node difference method according to claim 1 is characterized in that: In step S4, objective weights are assigned to each indicator using a weight determination model based on indicator variability. The model formula is: ,in, Indicates the The weight of the indicator, , Indicates the indicators, , is the total number of indicators in the indicator system; For the The standard deviation of the original data of each indicator, For the The standard deviation of the original data of an indicator reflects the degree of variation of the indicator among the candidate nodes.

3. The distributed energy storage power station site selection method based on the node difference method according to claim 1 is characterized in that: In step S5, a node comprehensive evaluation model is used to construct a weighted evaluation matrix, and the model formula is: ,in, Indicates the The candidate node is in The weighted evaluation value under each indicator, is the total number of nodes to be selected, , is the total number of indicators in the indicator system; For the The weight of each indicator; For the The candidate node is in The value of the original data under each indicator after processing.

4. The distributed energy storage power station site selection method based on the node difference method according to claim 1 is characterized in that: In step S6, during the calculation process of the optimized approach-to-ideal solution sorting algorithm, a node difference correction coefficient is introduced to construct a node difference correction model. The model formula is: ,in, Indicates the The corrected distance between the candidate nodes and the ideal solution, is the total number of nodes to be selected, is the total number of indicators in the indicator system; For the The candidate node is in The node difference correction coefficient under each indicator is determined based on the grid characteristics and load characteristics of the area where the node is located; For the Positive ideal solution of an index; For the The candidate node is in The weighted evaluation value under each indicator.

5. The distributed energy storage power station site selection method based on the node difference method according to claim 1 is characterized in that: In step S3, for the conversion of interval-type indicator data, an interval mapping model is adopted, and the model formula is: in, For the The candidate node is in The value after the conversion of the interval indicator, is the total number of nodes to be selected, , is the total number of indicators in the indicator system; For the The candidate node is in The original data under the interval-type indicators; Respectively The lower limit, optimal interval lower limit and upper limit of an interval-type indicator.

6. The distributed energy storage power station site selection method based on the node difference method according to claim 1 is characterized in that: In step S6, during the calculation process of the optimized approach-to-ideal solution sorting algorithm, a node correlation model is constructed by combining the mutual influence between nodes. The model formula is: ,in, Indicates the The association value of the candidate nodes, is the total number of nodes to be selected; For the The candidate nodes and The correlation coefficient of the candidate nodes is determined based on the electrical distance and power transmission relationship between the nodes; For the The candidate nodes and The distance to the selected nodes.

7. The distributed energy storage power station site selection method based on the node difference method according to claim 1 is characterized in that: The step S3 comprises the following steps: Step S31: determining the indicator type of the original indicator data, and identifying whether the original indicator data belongs to a specific type of extremely small indicator, interval indicator, intermediate indicator, or extremely large indicator; Step S32: For the extremely small index data, a preset conversion formula is used to convert it into extremely large index data, so that the data is more consistent in subsequent comparisons; Step S33: For the intermediate indicator data, convert it into a very large indicator data format according to the preset conversion rules to facilitate unified analysis and comparison; Step S34: normalize all converted indicator data to make each indicator data in the same numerical range and eliminate the influence of dimension on the calculation results.

8. The distributed energy storage power station site selection method based on the node difference method according to claim 1 is characterized in that: The step S4 comprises the following steps: Step S41: Calculate the coefficient of variation of the original data of each indicator, and use the coefficient of variation to measure the degree of dispersion of each indicator data among each candidate node; Step S42: Preliminarily determine the weight of each indicator based on the coefficient of variation of each indicator and the preset weight distribution rules; Step S43: Based on the correlation between the indicators, the initially determined weights are modified so that the weights of the indicators reflect their importance in the energy storage site selection; Step S44: normalize the corrected weights so that the sum of all weights is 1, thereby obtaining the final objective weights of each indicator.

9. The distributed energy storage power station site selection method based on the node difference method according to claim 1 is characterized in that: The step S6 comprises the following steps: Step S61: Determine the positive ideal solution and negative ideal solution of each indicator according to the weighted evaluation matrix of each candidate node, which will serve as a reference standard for subsequent calculations; Step S62: using the optimized approach-to-ideal solution sorting algorithm, calculate the distance between each candidate node and the positive ideal solution and the negative ideal solution, and quantify the degree of difference between each node and the ideal state; Step S63: combining the node difference correction coefficient and the node correlation value, correcting the distance between each candidate node and the ideal solution to make the calculation result more in line with the actual site selection requirements; Step S64: sort the potential values ​​of the candidate nodes according to the corrected distances, thereby determining the best distributed energy storage site selection plan.

10. A distributed energy storage power station site selection system based on the node difference method is characterized by: include: The raw data acquisition unit is used to collect the raw data corresponding to each indicator in the distributed energy storage multi-dimensional planning indicator system for each candidate node; An indicator data processing unit, connected to the raw data acquisition unit, is used to process the raw indicator data collected by the raw data acquisition unit and convert different types of indicator data into a form that can be uniformly compared; an indicator weight determination unit, connected to the indicator data processing unit, for comprehensively combining the influence of each indicator in the distributed energy storage site selection and layout, and assigning an objective weight to each indicator in the distributed energy storage multi-dimensional planning indicator system; A weighted matrix construction unit, connected to the index weight determination unit and the index data processing unit, for constructing a weighted evaluation matrix of a distributed energy storage power station for each candidate node based on the processed index data and corresponding index weight of each candidate node; an algorithm calculation unit connected to the weighted matrix construction unit, for calculating and processing the weighted evaluation matrix of the distributed energy storage power station of each candidate node using an optimized approximate ideal solution sorting algorithm; The scheme determination unit is connected to the algorithm calculation unit and is used to determine the best distributed energy storage site selection scheme based on the potential ranking value of the distributed energy storage site selection of each candidate node obtained by the algorithm calculation unit.

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