A method for optimizing the configuration of energy storage capacity based on the shared energy storage mode

Abstract

The present invention relates to the technical field of energy storage management, and particularly to a method for optimizing the configuration of energy storage capacity based on a shared energy storage mode. The method includes the following steps: extracting load characteristic indicators of a target area by using a power dispatching center to generate energy storage area load characteristic data; performing node association strength processing on the energy storage area load characteristic data to generate power grid node association topology data; obtaining regional power supply plan data; performing spatio-temporal coupling supply-demand balance analysis on the regional power supply plan data based on the energy storage area load characteristic data to generate corrected inter-regional complementary potential data; and performing optimization on the capacity of shared energy storage nodes according to the corrected inter-regional complementary potential data to generate dynamic energy storage capacity configuration data. The present invention realizes the optimized configuration of shared energy storage capacity through refined cross-regional power energy scheduling management, effectively solves the problem of power supply-demand matching, and improves the acceptance capacity of the power grid for renewable energy.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage management, and particularly to a method for optimizing the configuration of energy storage capacity based on a shared energy storage mode. Background Art

[0002] With the large-scale grid connection of renewable energy power generation, its intermittency and volatility have brought huge challenges to the stable operation of the power system. Energy storage technology, as an effective solution, can smooth the output fluctuations of renewable energy, improve the stability and reliability of the power grid, and promote the consumption of renewable energy. Shared energy storage refers to multiple regions or multiple entities jointly investing in, constructing, and using energy storage facilities. Through cross-regional power dispatching and market trading mechanisms, the optimal allocation and efficient utilization of energy storage resources can be achieved. Through cross-regional dispatching, shared energy storage can serve the power demands of multiple regions simultaneously, improve the utilization rate of energy storage facilities, and reduce the investment cost per unit of energy storage capacity. For example, when a region is in the peak electricity consumption period, the energy storage resources of other regions can be called for peak shaving; when the output of renewable energy in a region is excessive, the excess electric energy can be stored in the energy storage facilities of other regions to avoid curtailment of wind and solar power. However, traditional methods for optimizing the configuration of energy storage capacity are often limited to resource allocation within a single region and lack overall consideration of the cross-regional power market. This limitation makes it impossible to effectively coordinate and manage the power demand and supply between different regions, resulting in increased power transmission losses and unreasonable resource allocation. Summary of the Invention

[0003] Based on this, the present invention provides a method for optimizing the configuration of energy storage capacity based on a shared energy storage mode to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for optimizing the configuration of energy storage capacity based on a shared energy storage mode includes the following steps:

[0005] Step S1: Use the power dispatching center to divide the shared energy storage regions to obtain shared energy storage region data; extract the target region load characteristic indexes from the shared energy storage region data to generate regional load characteristic data; perform weighted load characteristic value processing on the regional load characteristic data to generate energy storage region load characteristic data;

[0006] Step S2: Construct the grid topology structure for the shared energy storage region data to generate cross-regional grid topology structure data; perform node association strength processing on the cross-regional grid topology structure data through the energy storage region load characteristic data to generate grid node association topology data;

[0007] Step S3: Obtain the regional power supply plan data; based on the energy storage area load characteristic data, use the regional power supply plan data to conduct a spatio-temporal coupled supply-demand balance analysis on the grid node associated topology data, and generate the regional supply-demand gap matrix data;

[0008] Step S4: Evaluate the potential of inter-regional power mutual assistance based on the regional supply-demand gap matrix data, and generate the corrected inter-regional complementary potential data; use the regional power supply plan data to conduct an inter-regional shared scheduling analysis on the corrected inter-regional complementary potential data, and obtain the spatio-temporal power flow scheduling map data;

[0009] Step S5: Configure the regional energy storage capacity according to the spatio-temporal power flow scheduling map data, and generate the regional energy storage capacity quota data; optimize the capacity of the shared energy storage nodes according to the regional energy storage capacity quota data, and generate the dynamic energy storage capacity configuration data.

[0010] Through the professional division method of the power dispatching center, the present invention establishes a shared energy storage area, providing a clear geographical scope for the integration and optimal allocation of cross-regional energy storage resources. On this basis, the load characteristic indicators of the target area are extracted and regional load characteristic data are generated, which helps to deeply understand the power consumption patterns and load characteristics of each region. The weighted load characteristic value processing further strengthens the identification of key load characteristics, ensuring that the energy storage configuration can more accurately match the actual needs of the region. Through the node correlation strength processing, not only the mutual dependence of the internal nodes of the power grid is revealed, but also the power interaction relationship between different regions is reflected. Combining the regional power supply plan data and the load characteristic data of the energy storage area, a spatio-temporal coupled supply-demand balance analysis is carried out on the power grid node correlation topology data. By considering the load changes in the time dimension and the energy distribution in space, the matching of power supply and demand is made more accurate, reducing the situation of overproduction or shortage, and accurately reflecting the power supply-demand imbalance of each region at different time scales. Through the evaluation of the potential of inter-regional power mutual assistance and the shared dispatching analysis, the possibility of relieving the power supply-demand contradictions of each region by mutual support between different regions is revealed, promoting the efficient flow and utilization of resources, reducing the demand for the overall reserve capacity, further refining the specific plan of the mutual assistance action, ensuring that the power flow can flow to the place where it is most needed at the right time, and enhancing the collaborative operation ability of the entire power grid system. According to the spatio-temporal power flow dispatching map data, the regional energy storage capacity is configured to ensure the efficient utilization of energy storage resources and dynamically adapt to the demand changes of the power system, realize the reasonable allocation of energy storage resources, be able to flexibly respond to the power demand changes, and improve the operation efficiency of the energy storage system. Therefore, an energy storage capacity optimization configuration method based on the shared energy storage mode of the present invention extracts and weights the load characteristics, considers the time and space dimensions of power supply and demand, accurately identifies the supply-demand gaps of each region at different time periods, utilizes the supply-demand complementarity between different regions, realizes the dynamic and flexible configuration of energy storage resources, and can adapt to the real-time changes of the power system.

[0011] Preferably, step S1 includes the following steps:

[0012] Step S11: Use the power dispatching center to divide the shared energy storage area to obtain the shared energy storage area data;

[0013] Step S12: Collect the historical power load data according to the shared energy storage area data to obtain the original historical power load data;

[0014] Step S13: Detect the outliers in the original historical power load data and fill in the missing values to generate the regional historical power load data;

[0015] Step S14: Extract the load characteristic indicators of the target area according to the regional historical power load data to generate the regional load characteristic data;

[0016] Step S15: Map the regional characteristic indicators of the shared energy storage area data through the regional load characteristic data to generate regional multi-dimensional characteristic matrix data;

[0017] Step S16: Perform Z-sore standardization processing on the regional multi-dimensional characteristic matrix data to obtain a standardized regional characteristic matrix; evaluate the weights of the characteristic indicators according to the standardized regional characteristic matrix to obtain characteristic indicator weight vector data;

[0018] Step S17: Use the characteristic indicator weight vector data to perform weighted load eigenvalue processing on the standardized regional characteristic matrix to generate energy storage area load characteristic data.

[0019] The present invention uses the power dispatching center to divide the shared energy storage area, which can reasonably divide the service scope of energy storage resources. Collecting historical power load data according to the shared energy storage area data can reflect the electricity consumption characteristics and change trends of each region, providing a reliable data basis for the optimal allocation of energy storage capacity. Detecting outliers and filling missing values in the original historical power load data can eliminate the noise and errors in the data, ensuring the accuracy of subsequent analysis. Extracting the load characteristic indicators of the target area according to the regional historical power load data can deeply understand the power demand characteristics and change laws of each region, reflecting the electricity consumption patterns and demand characteristics of each region. Mapping the regional characteristic indicators of the shared energy storage area data through the regional load characteristic data forms a data structure that comprehensively reflects the regional characteristics, which helps to comprehensively evaluate the energy storage demand from multiple perspectives. Through Z-score standardization processing, the influence of the dimension between different indicators is eliminated. Using the characteristic indicator weight vector to perform weighted processing on the standardized regional characteristic matrix provides an accurate guidance for the optimal allocation of energy storage capacity, ensuring that the resource allocation highly matches the actual needs of the region.

[0020] Preferably, step S15 includes the following steps:

[0021] Step S151: Perform dynamic time warping processing on the regional load characteristic data, and perform inter-regional characteristic comparison to generate inter-regional characteristic difference data;

[0022] Step S152: Calculate the similarity of the time series characteristics according to the inter-regional characteristic difference data to generate regional load similarity data;

[0023] Step S153: Perform load pattern clustering analysis according to the regional load similarity data, and perform regional cluster division to generate regional load cluster data;

[0024] Step S154: Extract the cluster typical load curve from the regional load cluster data to obtain the cluster typical load curve data;

[0025] Step S155: Perform regional mapping on the shared energy storage area data through the cluster typical load curve data to generate region-cluster feature mapping data;

[0026] Step S156: Perform multi-dimensional feature vector processing based on the region-cluster feature mapping data to obtain multi-dimensional feature vector data;

[0027] Step S157: Construct a regional feature matrix for the regional load characteristic data through the multi-dimensional feature vector data to generate regional multi-dimensional feature matrix data.

[0028] In the present invention, by performing dynamic time warping on the regional load characteristic data, the non-linear time distortion effect in the time series data can be effectively eliminated, making the load characteristics between different regions comparable on the time scale. By calculating the temporal feature similarity of the regional load, the similarity degree of the load behaviors between regions is quantified, and this data helps to identify those regions with similar load patterns. Based on the similarity data, the regions are logically grouped to form load clusters with common characteristics, which can be more targeted at specific types of load demand groups. By extracting the typical load curve data of each clustering cluster, it represents the basic pattern of the load behaviors within each cluster. By performing regional mapping on the shared energy storage area data through the cluster typical load curve data, the corresponding relationship between the region and the load pattern cluster is established. Through multi-dimensional feature vector matrix processing, the electricity consumption characteristics of each region can be comprehensively described.

[0029] Preferably, step S2 includes the following steps:

[0030] Step S21: Use the power dispatching center to construct the power grid topological structure for the shared energy storage area data and initialize the line loss weight to generate cross-regional power grid topological structure data;

[0031] Step S22: Assign node power loads to the cross-regional power grid topological structure data through the energy storage area load characteristic data to generate load-weighted power grid topological data;

[0032] Step S23: Perform node betweenness centrality statistics based on the load-weighted power grid topological data to obtain node betweenness centrality data;

[0033] Step S24: Calculate the electrical distance between regions based on the load-weighted power grid topological data to generate inter-regional electrical distance data;

[0034] Step S25: Based on the inter-regional electrical distance data, perform node association strength processing on the load-weighted power grid topological data through the node betweenness centrality data to generate power grid node association topological data.

[0035] The present invention constructs the power grid topology of the shared energy storage area through the professional technology of the power dispatching center, and initializes the line loss weights, ensuring the reality and efficiency of resource scheduling and configuration. By combining the load characteristic data of the energy storage area to assign node power loads to the power grid topology, it essentially assigns an importance level to each node in the power grid that matches the power load it bears. Statistical node betweenness centrality reveals which nodes play a bridging or hub role in the power grid, helping to prioritize these key nodes in energy storage configuration to ensure the efficient and stable flow of electricity. The electrical distance not only considers the geographical distance but also incorporates power grid structure and impedance factors, and is an important parameter for evaluating the cost and feasibility of power exchange between regions. Through node association strength processing, it can better reflect the power interaction relationship between different regions and reflects the tightness of load transfer and power flow between each node.

[0036] Preferably, step S3 includes the following steps:

[0037] Step S31: Perform multi-step prediction of regional time-series load based on the load characteristic data of the energy storage area to obtain corrected load prediction curve data;

[0038] Step S32: Obtain regional power supply plan data, including regional power production plans and estimated renewable energy output data;

[0039] Step S33: Use the regional power supply plan data to evaluate the regional power supply capacity of the shared energy storage area data and generate regional power supply data;

[0040] Step S34: Conduct spatio-temporal coupled supply-demand balance analysis on the regional power supply data through the corrected load prediction curve data to generate regional spatio-temporal supply-demand balance data;

[0041] Step S35: Calculate the probability of supply-demand gap based on the regional spatio-temporal supply-demand balance data to generate regional supply-demand gap probability data;

[0042] Step S36: Conduct regional supply-demand gap risk assessment based on the regional supply-demand gap probability data and mark the regional supply-demand gap for the regional spatio-temporal supply-demand balance data to generate regional supply-demand gap matrix data.

[0043] The multi-step time-series load prediction based on the regional load characteristic data in the present invention provides power demand predictions for each energy storage region within a certain period in the future. Obtaining the regional power supply plan data, especially the power production plans and the estimated output of renewable energy in each region, provides detailed information on the supply side for the energy storage regions. The assessment of the power supply capacity of the shared energy storage regions using the power supply plan data generates regional power supply data that reveals the power supply potential and limitations of each region at specific times. By performing spatio-temporal coupling supply-demand balance analysis on the regional power supply data by correcting the load prediction curve data, the supply-demand matching conditions of each region at different times can be accurately identified, and the specific time and space distributions of the supply-demand imbalance can be revealed. Calculating the probability of the supply-demand gap based on the regional spatio-temporal supply-demand balance data quantifies the risk level of the supply-demand imbalance. By calculating the probability of the supply-demand gap, the reliability of the power supply in each region can be evaluated. Conducting a regional supply-demand gap risk assessment based on the regional supply-demand gap probability data visually displays the supply-demand risk status of each region and can clearly identify the regions and time periods that require key attention.

[0044] Preferably, step S31 includes the following steps:

[0045] Step S311: Perform variational mode decomposition on the load characteristic data of the energy storage region to generate regional load mode component data;

[0046] Step S312: Use a preset time series prediction model to perform intelligent prediction on the regional load mode component data to generate load mode component prediction data;

[0047] Step S313: Perform superposition of regional component predictions based on the load mode component prediction data and perform dynamic reconstruction of the load curve to generate regional load prediction curve data;

[0048] Step S314: Perform single-region load confidence estimation based on the regional load prediction curve data to generate a single-region load prediction confidence interval;

[0049] Step S315: Use the single-region load prediction confidence interval to perform correlation analysis of the load prediction error on the regional load prediction curve data to generate prediction error correlation data;

[0050] Step S316: Perform curve confidence correction on the regional load prediction curve data through the prediction error correlation data to generate corrected load prediction curve data.

[0051] In the present invention, by performing variational mode decomposition (VMD) on the load characteristic data of the energy storage area, the complex load data is effectively decomposed into several relatively independent modal components with physical meanings, which helps to deeply understand the internal laws and driving factors of load changes. Using a preset time series prediction model (such as ARIMA, LSTM, etc.) to predict the regional load modal component data, the generated load modal component prediction data can capture the future change trends of each mode, improving the prediction accuracy and adaptability. By decomposing and separately predicting each part, it is possible to better handle the nonlinear, periodic, and trend characteristics of the load data. Combining the prediction results of each modal component to form a complete load prediction curve, through dynamic reconstruction, it can better reflect the overall trend and local characteristics of load changes. According to the regional load prediction curve data, single-region load confidence estimation is carried out, which can quantify the uncertainty of the prediction results. By calculating the confidence interval, the reliability of the prediction can be evaluated. Using the single-region load prediction confidence interval to conduct load prediction error correlation analysis reveals the mutual relationship between prediction errors in different regions or at different time points. Through curve confidence correction of the regional load prediction curve data using the prediction error correlation data, not only the uncertainty of the prediction itself is considered, but also the prediction results are corrected through correlation analysis, improving the robustness and practicality of the prediction.

[0052] Preferably, step S4 includes the following steps:

[0053] Step S41: Evaluate the potential of inter-regional power mutual assistance based on the regional supply-demand gap matrix data and the grid node association topology data, and generate corrected inter-regional complementary potential data;

[0054] Step S42: Construct typical supply-demand scenarios for the corrected inter-regional complementary potential data through the regional power supply plan data to obtain multi-time scale supply-demand scenario data;

[0055] Step S43: Use the grid node association topology data to construct a power network simulation model;

[0056] Step S44: Use the power network simulation model to conduct shared power dispatching simulation for the multi-time scale supply-demand scenario data and perform dispatching power flow calculation to generate multi-scenario power dispatching simulation data;

[0057] Step S45: Conduct inter-regional shared dispatching analysis based on the multi-scenario power dispatching simulation data and perform spatio-temporal atlas processing of the power flow to obtain spatio-temporal power flow dispatching atlas data.

[0058] By comprehensively considering the supply-demand gap and network topology, the present invention not only identifies which regions have supply-demand imbalance, but also quantifies the ability of regions to support each other, and can accurately identify the feasibility and potential of power mutual assistance between regions. By constructing typical supply-demand scenarios for the corrected complementary potential data between regions through regional power supply plan data, various supply-demand situations can be simulated. By constructing supply-demand scenarios with multiple time scales, the power supply-demand changes in different time periods can be comprehensively considered. The power network simulation model constructed using the grid node association topology data can accurately reflect the structural characteristics and operation rules of the power grid. Through the power network simulation model, the shared power dispatch simulation of the supply-demand scenario data with multiple time scales is carried out, revealing the operation state and power flow pattern of the power grid under different supply-demand situations. The inter-regional shared dispatch analysis based on the multi-scenario power dispatch simulation data intuitively shows the power flow pattern in time and space, and can clearly present the power transmission situation in different times and regions.

[0059] Preferably, step S41 includes the following steps:

[0060] Step S411: Perform regional gap time series decomposition on the regional supply-demand gap matrix data to generate regional gap time series data;

[0061] Step S412: Conduct supply-demand fluctuation correlation analysis based on the regional gap time series data to generate the supply-demand gap fluctuation correlation coefficient;

[0062] Step S413: Calculate the regional supply-demand mutual information for the grid node association topology data through the supply-demand gap fluctuation correlation coefficient, and conduct power complementary potential evaluation to obtain the supply-demand complementary potential data between regions;

[0063] Step S414: Conduct transmission capacity constraint analysis based on the grid node association topology data to generate regional transmission capacity constraint data; conduct distance attenuation factor processing based on the grid node association topology data to generate the inter-regional distance attenuation factor;

[0064] Step S415: Use the inter-regional distance attenuation factor and the regional transmission capacity constraint data to correct the supply-demand complementary potential data between regions to obtain the corrected supply-demand complementary potential data between regions.

[0065] The present invention decomposes the regional supply-demand gap matrix data in time series, transforming complex supply-demand gap information into a time series form. Through time series decomposition, the time-varying characteristics of the supply-demand gap in each region can be presented more clearly, which helps to identify the supply-demand complementarity opportunities between different regions. Based on the time series data of regional gaps, correlation analysis of supply-demand fluctuations is carried out to quantify the degree of association between the changes in supply-demand gaps in different regions. By calculating the correlation coefficient, regional pairs with complementary potential can be identified. Through the correlation coefficient of supply-demand gap fluctuations, the regional supply-demand mutual information of the grid node association topology data is calculated, comprehensively considering the network topology and supply-demand fluctuation characteristics, and the actual complementary ability between regions is evaluated. By analyzing the transmission capacity constraint and distance attenuation factors, the feasibility and efficiency of power transmission can be reflected more realistically. The complementary potential value of the regional supply-demand complementary potential data is corrected by using the inter-regional distance attenuation factor and the regional transmission capacity constraint data. By combining the theoretical complementary potential with the actual grid limitations and making corrections by considering distance attenuation and transmission capacity constraints, a more practical and feasible complementary potential evaluation result can be obtained.

[0066] Preferably, step S5 includes the following steps:

[0067] Step S51: Configure the regional energy storage capacity for the corrected inter-regional complementary potential data through the spatio-temporal power flow scheduling atlas data to generate regional energy storage capacity quota data;

[0068] Step S52: Use the regional energy storage capacity quota data to plan the minimum loss path of cross-regional power transmission for the spatio-temporal power flow scheduling atlas data to obtain the energy storage area scheduling path data;

[0069] Step S53: Use the preset model predictive control algorithm to optimize the state of the energy storage nodes in the control period for the energy storage area scheduling path data and the regional energy storage capacity quota data to obtain the dynamic energy storage node control strategy;

[0070] Step S54: Optimize the capacity of the shared energy storage nodes according to the dynamic energy storage node control strategy to obtain the optimized energy storage capacity configuration data;

[0071] Step S55: Evaluate the operation benefits according to the optimized energy storage capacity configuration data, and make a rolling adjustment of the capacity configuration to generate the dynamic energy storage capacity configuration data.

[0072] The present invention configures the regional energy storage capacity for the corrected inter-regional complementary potential data through the spatio-temporal power flow scheduling map data. It can reasonably regulate the energy storage capacity of each region according to the actual situation of power flow and complementary potential, and can better match the spatio-temporal distribution of power demand and supply. Using the regional energy storage capacity quota data to plan the minimum loss path of cross-regional power transmission for the spatio-temporal power flow scheduling map data can reduce the energy loss during power transmission and improve the overall efficiency of the system. By adopting the preset model predictive control algorithm and comprehensively considering the energy storage area scheduling path data and the regional energy storage capacity quota data, the refined management and dynamic scheduling of energy storage resources are realized, the response speed and regulation ability of the energy storage system are improved, and the stable operation of the power system and the real-time balance of energy supply and demand are ensured. According to the dynamic energy storage node control strategy, the capacity of the shared energy storage node is optimized to ensure that the capacity setting of each energy storage node can not only meet the power regulation requirements within the region and across regions, but also adapt to the changing power market environment, avoiding the problems of overcapacity or insufficient capacity and improving the utilization efficiency of energy storage resources.

[0073] Preferably, step S51 includes the following steps:

[0074] Step S511: Identify the target energy storage node according to the spatio-temporal power flow scheduling map data to obtain the target energy storage optimization node data;

[0075] Step S512: Collect the energy storage capacity constraint parameters according to the target energy storage optimization node data and set the energy storage capacity constraint conditions to obtain the energy storage capacity constraint condition data;

[0076] Step S513: Use the energy storage capacity constraint condition data to initially configure the regional energy storage capacity for the corrected inter-regional complementary potential data to generate the initial regional energy storage capacity configuration data;

[0077] Step S514: Perform shared energy storage capacity allocation processing on the corrected inter-regional complementary potential data through the spatio-temporal power flow scheduling map data to obtain the shared energy storage capacity allocation problem data;

[0078] Step S515: Perform Markov decision processing on the shared energy storage capacity allocation problem data and optimize the dynamic capacity quota for the initial regional energy storage capacity configuration data to generate the regional energy storage capacity quota data.

[0079] The present invention identifies target energy storage nodes based on spatio-temporal power flow scheduling atlas data, which can accurately locate the energy storage nodes that need to be optimized. By analyzing the spatio-temporal characteristics of power flow, it can identify the energy storage positions that are most critical to system balance and efficiency. According to the target energy storage optimization node data, the energy storage capacity constraint parameters are collected. By setting appropriate constraint conditions, it can avoid capacity configuration schemes that exceed physical or economic limits, ensuring that the final optimization result meets both technical requirements and economic benefits. Using the energy storage capacity constraint condition data, the regional energy storage capacity is initially configured for the corrected inter-regional complementary potential data. Through this initial configuration, it can provide a reasonable starting point for the subsequent optimization process, accelerate the optimization convergence speed, and ensure the feasibility of the initial scheme. The shared energy storage capacity is allocated and processed for the corrected inter-regional complementary potential data through spatio-temporal power flow scheduling atlas data, transforming the energy storage capacity allocation problem into a quantifiable optimization problem. By considering spatio-temporal power flow and regional complementary potential, it can more accurately describe the capacity allocation requirements in the shared energy storage mode. Through Markov decision processing, it can consider the randomness and time-variability of the power system and achieve a more flexible and adaptable capacity configuration. The dynamic capacity quota optimization can adjust the energy storage capacity allocation of each region in real time according to the changes in the system state, maximizing the benefits of the entire system. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 It is a schematic flowchart of the steps of a method for optimizing the energy storage capacity based on a shared energy storage mode according to the present invention;

[0081] Figure 2 is Figure 1 a detailed implementation step flowchart of step S3 in

[0082] Figure 3 is Figure 1 a detailed implementation step flowchart of step S4 in

[0083] The realization, functional characteristics and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0084] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0085] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0086] It should be understood that although terms such as "first" and "second" may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0087] To achieve the above object, please refer to Figures 1 to 3 , the present invention provides an energy storage capacity optimization configuration method based on a shared energy storage mode, including the following steps:

[0088] Step S1: Use the power dispatching center to divide the shared energy storage area to obtain shared energy storage area data; extract the target area load characteristic indexes from the shared energy storage area data to generate area load characteristic data; perform weighted load characteristic value processing on the area load characteristic data to generate energy storage area load characteristic data;

[0089] Step S2: Construct the power grid topology structure for the shared energy storage area data to generate cross-regional power grid topology structure data; perform node association strength processing on the cross-regional power grid topology structure data through the energy storage area load characteristic data to generate power grid node association topology data;

[0090] Step S3: Obtain the regional power supply plan data; perform spatio-temporal coupling supply-demand balance analysis on the power grid node association topology data by using the regional power supply plan data based on the energy storage area load characteristic data to generate regional supply-demand gap matrix data;

[0091] Step S4: Evaluate the potential of inter-regional power mutual assistance according to the regional supply-demand gap matrix data to generate corrected inter-regional complementary potential data; perform inter-regional shared scheduling analysis on the corrected inter-regional complementary potential data by using the regional power supply plan data to obtain spatio-temporal power flow scheduling map data;

[0092] Step S5: Configure the regional energy storage capacity according to the spatio-temporal power flow scheduling atlas data to generate regional energy storage capacity quota data; optimize the capacity of the shared energy storage nodes according to the regional energy storage capacity quota data to generate dynamic energy storage capacity configuration data.

[0093] In the embodiment of the present invention, referring to Figure 1 As described above, it is a schematic diagram of the step flow of a method for optimizing the configuration of energy storage capacity based on a shared energy storage mode in the present invention. In this embodiment, the method for optimizing the configuration of energy storage capacity based on a shared energy storage mode includes the following steps:

[0094] Step S1: Use the power dispatching center to divide the shared energy storage area to obtain shared energy storage area data; extract the load characteristic indexes of the target area from the shared energy storage area data to generate regional load characteristic data; perform weighted load characteristic value processing on the regional load characteristic data to generate energy storage area load characteristic data.

[0095] In the embodiment of the present invention, using the regional power data provided by the power dispatching center, such as the load curve, power generation output data, geographical location, grid structure, etc. of each region, the entire power system is divided into several shared energy storage areas through clustering analysis or graph theory methods. For example, the K-Means clustering algorithm can be used, and the similarity of the regional load curve, the capacity of the transmission lines between regions, etc. are used as the clustering basis to divide the power system into 3 shared energy storage areas: Region A, Region B, and Region C. Analyze the historical load data of each shared energy storage area, and extract the load characteristic indexes of the target area, including peak load, valley load, load rate, load growth rate, load volatility, etc. For example, use the Pandas library in Python to read the load curve data of Region A, calculate its daily peak load and daily valley load, and calculate the peak-valley difference and daily average load, and then obtain the load rate and load growth rate of Region A. Organize the load characteristic index data of all regions into a table to form regional load characteristic data. According to the importance of each index, for example, set the weight of the daily peak-valley difference rate to 0.5, the weight of the load volatility to 0.3, and the weight of the average load to 0.2, and perform weighted processing on the regional load characteristic data to obtain energy storage area load characteristic data.

[0096] Step S2: Construct the grid topology structure for the shared energy storage area data to generate cross-regional grid topology structure data; perform node association strength processing on the cross-regional grid topology structure data through the energy storage area load characteristic data to generate grid node association topology data.

[0097] In the embodiments of the present invention, based on the power grid topology data provided by the power dispatching center, such as the voltage levels of each node, line parameters, etc., the cross-regional power grid topology structure data is constructed. For example, power system simulation software such as PowerWorld or Matpower is used to input the power grid line and node information to construct a cross-regional power grid model including Region A, Region B, and Region C. According to the energy storage area load characteristic data generated in step S1, such as the maximum load difference between regions, load correlation degree, etc., node association strength processing is performed on the cross-regional power grid topology structure data. For example, the PageRank algorithm is used to calculate the importance of each node in the power grid, and combined with the load correlation degree between regions, weights are assigned to the connections between nodes. The greater the weight, the higher the node association strength. Finally, the power grid node association topology data is generated, which reflects the power grid connection strength and load correlation degree between different regions. For example, if the load correlation degree between Region A and Region B is high, the weights of the nodes and lines connecting these two regions will be high.

[0098] Step S3: Obtain the regional power supply plan data; based on the energy storage area load characteristic data, use the regional power supply plan data to perform spatio-temporal coupling supply-demand balance analysis on the power grid node association topology data, and generate regional supply-demand gap matrix data;

[0099] In the embodiments of the present invention, the regional power supply plan data is obtained, such as the power generation plan, load forecast, renewable energy output forecast, etc. of each region. For example, the power supply plan data for the next week of Regions A, B, and C is obtained from the power dispatching center, including the power generation plan, load forecast, curtailment of wind and solar data, etc. of each region. Based on the energy storage area load characteristic data and the regional power supply plan data, spatio-temporal coupling supply-demand balance analysis is performed on the power grid node association topology data by using a linear programming or mixed integer programming model. For example, an optimization model is constructed, the objective function is to minimize the system operation cost, and the constraint conditions include node power balance, line power flow limit, energy storage charge and discharge constraints, etc. By solving the optimization model, the supply-demand gap of each region at different time periods is obtained and organized into regional supply-demand gap matrix data. For example, the model will calculate the supply-demand gap of Region A for each hour of the next week and store it in the matrix.

[0100] Step S4: Evaluate the potential of inter-regional power mutual assistance according to the regional supply-demand gap matrix data, and generate corrected inter-regional complementary potential data; use the regional power supply plan data to perform inter-regional shared scheduling analysis on the corrected inter-regional complementary potential data to obtain spatio-temporal power flow scheduling map data;

[0101] In the embodiments of the present invention, based on the regional supply-demand gap matrix data, for example, analyzing the supply-demand gap situations in different regions at different time periods to evaluate the potential of inter-regional power mutual assistance. For example, if there is a power surplus in region A during a certain period and a power shortage in region B, the potential of region A transmitting power to region B can be evaluated. On this basis, considering factors such as the capacity of inter-regional transmission lines and grid stability, the complementary potential between regions is corrected to generate corrected inter-regional complementary potential data. Using the regional power supply plan data and the corrected inter-regional complementary potential data, inter-regional shared scheduling analysis is carried out. For example, dynamic programming or genetic algorithms are used to optimize the power exchange plan between regions to maximize system benefits, such as reducing system operation costs and improving the consumption capacity of new energy. Finally, spatio-temporal power flow scheduling map data is obtained, which reflects the power exchange situations between different regions at different time periods and the schemes of energy storage systems participating in grid scheduling. For example, through optimization algorithms, the amount of power transmitted from region A to region B per hour in the next week can be obtained, as well as the charge-discharge power of the energy storage system at different time periods, and this information is presented in the form of a map. For example, the Matplotlib library in Python is used to draw a map showing the direction and magnitude of the power flow changing with time.

[0102] Step S5: Configure the regional energy storage capacity according to the spatio-temporal power flow scheduling map data to generate regional energy storage capacity quota data; optimize the capacity of shared energy storage nodes according to the regional energy storage capacity quota data to generate dynamic energy storage capacity configuration data.

[0103] In the embodiments of the present invention, based on the spatio-temporal power flow scheduling map data, for example, analyzing the charge-discharge requirements of energy storage in different regions at different time periods to configure the regional energy storage capacity. For example, according to the charge-discharge requirements of energy storage in region A during peak periods, the energy storage capacity quota of region A is determined. Finally, regional energy storage capacity quota data is generated. According to the regional energy storage capacity quota data, the capacity of shared energy storage nodes is optimized. For example, considering factors such as the geographical location of shared energy storage nodes, grid connection conditions, and energy storage technology types, integer programming is used to optimize the capacity configuration scheme of shared energy storage nodes. For example, the energy storage capacity of region A is allocated to multiple shared energy storage nodes within region A, and the capacity of each node is adjusted according to the characteristics of the nodes. Finally, dynamic energy storage capacity configuration data is generated, which reflects the capacity allocation scheme of shared energy storage nodes in different regions and the change of node capacity over time. For example, the optimization algorithm will calculate the optimal capacity of each energy storage node in region A and formulate the charge-discharge plan for each node according to the load prediction and renewable energy output prediction in the next week to form dynamic energy storage capacity configuration data.

[0104] Preferably, step S1 includes the following steps:

[0105] Step S11: Use the power dispatching center to divide the shared energy storage area and obtain the shared energy storage area data;

[0106] Step S12: Collect the historical power load data according to the shared energy storage area data to obtain the original historical power load data;

[0107] Step S13: Detect outliers in the original historical power load data and fill in the missing values to generate the regional historical power load data;

[0108] Step S14: Extract the target area load characteristic indicators according to the regional historical power load data to generate the regional load characteristic data;

[0109] Step S15: Map the regional characteristic indicators to the shared energy storage area data through the regional load characteristic data to generate the regional multi-dimensional characteristic matrix data;

[0110] Step S16: Perform Z-sore standardization on the regional multi-dimensional characteristic matrix data to obtain the standardized regional characteristic matrix; evaluate the weights of the characteristic indicators according to the standardized regional characteristic matrix to obtain the characteristic indicator weight vector data;

[0111] Step S17: Use the characteristic indicator weight vector data to perform weighted load eigenvalue processing on the standardized regional characteristic matrix to generate the energy storage area load characteristic data.

[0112] In the embodiments of the present invention, load data, generation data, grid topology structure and other information of each region are obtained by using the SCADA system of the power dispatching center. For example, power load data, generation data and transmission line information of each province and city in East China are obtained. Then, based on the K-means clustering algorithm, clustering analysis is performed on the regional load data, and regions with similar load characteristics are divided into a shared energy storage region to obtain shared energy storage region data. Historical power load data of each region in this region is collected from the historical database of the power dispatching center. For example, hourly power load data in the past three years is collected. The collected data includes information such as time stamps and load values. Outlier detection is performed on the original historical power load data. For example, using the 3σ criterion, data outside the range of the mean ± 3 times the standard deviation is identified as an outlier and removed or corrected. For example, if the power load data of a certain hour suddenly soars to several times the normal value, then this data is considered an outlier and needs to be removed or corrected. At the same time, missing values in the data are filled. For example, using the linear interpolation method or the mean filling method, the missing data is supplemented. According to the regional historical power load data, load characteristic indicators of each region are extracted, such as peak load, valley load, load rate, load growth rate, load volatility, etc. For example, using the Pandas library in Python to read the historical load data of Region A, calculate its daily peak load and daily valley load, and calculate the peak-to-valley difference and daily average load, and then obtain the load rate and load growth rate of Region A. The characteristic indicators such as the load rate and load growth rate of Region A are associated with information such as the geographical location and grid structure of Region A, and finally multi-dimensional characteristic matrix data of the region is generated. For example, a matrix is generated, where each row represents a shared energy storage region and each column represents a load characteristic indicator, such as the daily peak-to-valley difference rate, load volatility, etc. Z-score standardization processing is performed on the multi-dimensional characteristic matrix data of the region, that is, the value of each characteristic indicator is subtracted from its mean and then divided by its standard deviation to obtain a standardized regional characteristic matrix. Then, based on the standardized regional characteristic matrix, for example, using the entropy weight method or the principal component analysis method, the weights of the characteristic indicators are evaluated to obtain characteristic indicator weight vector data. For example, calculate the weights of indicators such as the daily peak-to-valley difference rate and load volatility. For example, the weight of the daily peak-to-valley difference rate is 0.5, and the weight of the load volatility is 0.3. For example, multiply the standardized daily peak-to-valley difference rate of each shared energy storage region by its weight of 0.5, multiply the standardized load volatility by its weight of 0.3, and then add all the weighted characteristic values to obtain the weighted load characteristic value of this shared energy storage region. Finally, energy storage region load characteristic data is generated. For example, a data set containing the weighted load characteristic values of each shared energy storage region is generated.

[0113] Preferably, step S15 includes the following steps:

[0114] Step S151: Perform dynamic time warping processing on the regional load characteristic data, and conduct inter-regional feature comparison to generate inter-regional feature difference data;

[0115] Step S152: Calculate the similarity of time series features based on the inter-regional feature difference data to generate regional load similarity data;

[0116] Step S153: Conduct load pattern clustering analysis based on the regional load similarity data, and perform regional cluster division to generate regional load cluster data;

[0117] Step S154: Extract the cluster typical load curve from the regional load cluster data to obtain the cluster typical load curve data;

[0118] Step S155: Perform regional mapping on the shared energy storage area data through the cluster typical load curve data to generate regional-cluster feature mapping data;

[0119] Step S156: Conduct multi-dimensional feature vector processing based on the regional-cluster feature mapping data to obtain multi-dimensional feature vector data;

[0120] Step S157: Construct a regional feature matrix for the regional load characteristic data through the multi-dimensional feature vector data to generate regional multi-dimensional feature matrix data.

[0121] In the embodiments of the present invention, using the regional load characteristic data obtained in step S14, such as the peak load, valley load, load rate, etc. of each region, the dynamic time warping (DTW) algorithm is adopted for time series alignment and comparison. The DTW algorithm can effectively handle problems such as inconsistent lengths of time series data and time offsets, and calculate the similarity between load curves in different regions. For example, the dtw library in Python can be used to calculate the DTW distance between the historical load curves of Region A and Region B. Then, based on the DTW distance, inter-regional feature comparison is performed. For example, calculate the DTW distance between each region and all other regions, and generate inter-regional feature difference data. For example, generate a matrix where each element represents the DTW distance between two regions. Adopting a distance-based similarity calculation method, for example, 1 minus the ratio of the DTW distance to the maximum DTW distance can be used as the similarity. The higher the similarity, the more similar the regional load characteristics are. Finally, generate regional load similarity data. For example, generate a matrix where each element represents the load similarity score between two regions. Using the K-Means clustering algorithm, all regions are used as the initial data points, and the number of clustering clusters is set to 3. Through iterative calculation, the regions are divided into three different clustering clusters, and the regional clustering cluster division result is obtained. For example, Region A and Region B are divided into the same clustering cluster, and Region C is divided into another clustering cluster. Organize the clustering cluster division result into a table form to generate regional load cluster data. For example, the cluster information to which the region belongs is used to extract the typical load curve of the cluster. For each cluster, the average or median of the historical load curves of all regions within the cluster can be calculated as the typical load curve of the cluster. For example, for Cluster 1, the average of the historical load curves of all regions within the cluster can be calculated as the typical load curve of Cluster 1. Finally, obtain the typical load curve data of the cluster. For example, obtain 3 typical load curves, which respectively represent the load patterns of 3 clusters. For each shared energy storage region, calculate the DTW distance between its historical load curve and the typical load curve of each cluster, and map this region to the cluster with the smallest DTW distance. For example, if the DTW distance between the historical load curve of Region A and the typical load curve of Cluster 2 is the smallest, then Region A is mapped to Cluster 2. Finally, generate the region-cluster feature mapping data. Extract the multi-dimensional feature vectors of each region. Information such as the geographical location, power grid structure, load type, and clustering cluster number of the region can be used as features to form a multi-dimensional feature vector. For example, splice the regional load characteristic data (such as the daily peak-to-valley difference rate, load volatility, etc.) with the one-hot encoding of the cluster to which the region belongs to generate a new matrix, where each row represents a shared energy storage region, and each column represents a feature, including load characteristics such as the daily peak-to-valley difference rate, load volatility, etc. and the one-hot encoding of the cluster to which the region belongs.

[0122] Preferably, step S2 includes the following steps:

[0123] Step S21: The power dispatching center is used to construct the grid topology structure for the data of the shared energy storage area, and initialize the line loss weights to generate the cross-regional grid topology structure data;

[0124] Step S22: The cross-regional grid topology structure data is weighted with the node power load through the load characteristic data of the energy storage area to generate the load-weighted grid topology data;

[0125] Step S23: The node betweenness centrality is statistically analyzed according to the load-weighted grid topology data to obtain the node betweenness centrality data;

[0126] Step S24: The inter-regional electrical distance is calculated according to the load-weighted grid topology data to generate the inter-regional electrical distance data;

[0127] Step S25: Based on the inter-regional electrical distance data, the node association strength of the load-weighted grid topology data is processed through the node betweenness centrality data to generate the grid node association topology data.

[0128] In the embodiments of the present invention, data such as the geographical location information, line connection information, and line parameters (such as resistance and reactance) of each node in the shared energy storage area are obtained by using the SCADA system of the power dispatching center. Then, a cross-regional power grid topology structure is constructed using a graph theory library such as NetworkX, where nodes represent power facilities such as substations or power plants, and edges represent transmission lines. Next, the line loss weight of each line is initialized. For example, the resistance and reactance of the line are calculated according to parameters such as line length and conductor type, and the resistance value is used as the line loss weight. The average load value of each node can be used as the weight of the node, and this weight is assigned to the corresponding node in the power grid topology graph. For example, if the average load of node A is 100 MW, the weight of node A in the graph structure is set to 100. Finally, load-weighted power grid topology data is generated, such as a graph structure data containing node information, edge information, line loss weight, and node load weight. Network analysis libraries such as NetworkX can be used to take the load-weighted power grid topology data as input, calculate the betweenness centrality of each node, and obtain the betweenness centrality data of the nodes. For example, the betweenness centrality of node 1 is 0.5, and the betweenness centrality of node 2 is 0.3, etc. The Dijkstra algorithm can be used to calculate the weighted average of the line loss weights on the shortest path between two regions, where the weight is the line loss weight. For example, if the line loss weights on the shortest path between region A and region B are 0.1, 0.2, and 0.3 respectively, the electrical distance between region A and region B is (0.1 + 0.2 + 0.3) / 3 = 0.2. Finally, inter-regional electrical distance data is generated, such as a matrix, where each element represents the electrical distance between two regions. For example, if the electrical distance between node A and node B is 0.2, the betweenness centrality of node A is 0.5, and the betweenness centrality of node B is 0.3, then the association strength between node A and node B is 1 / 0.2×0.5×0.3 = 0.75. The calculated association strength is updated as the weight of the edge to the load-weighted power grid topology data. Finally, power grid node association topology data is generated, such as a graph structure data containing node information, edge information, line loss weight, node load weight, and node association strength.

[0129] As an example of the present invention, refer to Figure 2 shown in Figure 1 the detailed implementation step flow diagram of step S3 in

[0130] Step S31: Perform multi-step prediction of regional time-series load according to the load characteristic data of the energy storage area to obtain corrected load prediction curve data;

[0131] In the embodiments of the present invention, for example, historical load data, meteorological data, etc. of each region are used to perform multi-step prediction of regional time-series load using time-series prediction models such as LSTM, Prophet, etc. For example, a time-series prediction model can be constructed using the statsmodels library or the prophet library in Python, and historical load data and meteorological data are used as inputs to predict the power load of each region within a future period (e.g., the next 24 hours). The prediction results can be the load prediction values at each time step and the confidence intervals of the prediction values. Since there are errors in the prediction results, it is necessary to correct the prediction results, for example, calibrate the prediction results according to historical prediction errors. Finally, corrected load prediction curve data is obtained, such as a dataset containing the load prediction values of each region for the next 24 hours.

[0132] Step S32: Obtain regional power supply plan data, including power production plans for each region and estimated renewable energy output data;

[0133] In the embodiments of the present invention, regional power supply plan data is obtained from the power dispatching center or relevant departments, including power production plans for each region, such as power generation plans for thermal power, hydropower, nuclear power, etc., and estimated renewable energy output data, such as predicted power generation data for solar energy, wind energy, etc. For example, the planned power generation of thermal power, hydropower, nuclear power, etc. for each region within the next 24 hours, as well as the predicted power generation of solar energy, wind energy, etc., can be obtained. For example, obtain the regional power supply plan data for regions A, B, and C for the next week from the power dispatching center, including power generation plans, load predictions, curtailment data of wind and solar power, etc. for each region.

[0134] Step S33: Use the regional power supply plan data to evaluate the regional power supply capacity of the shared energy storage area data and generate regional power supply data;

[0135] In the embodiments of the present invention, the regional power supply capacity of the shared energy storage area data is evaluated using the regional power supply plan data, such as the planned power generation and predicted renewable energy power generation of each region. For example, the planned power generation of each region and the predicted renewable energy power generation can be added together to obtain the total power supply capacity of the region. Considering the volatility and uncertainty of renewable energy output, the regional power supply capacity can be estimated within an interval according to the confidence interval of the predicted renewable energy power generation. Finally, regional power supply data is generated, such as a data containing the power supply capacity (e.g., maximum value, minimum value, average value) of each region for the next 24 hours.

[0136] Step S34: Perform spatio-temporal coupled supply-demand balance analysis on the regional power supply data through the corrected load prediction curve data to generate regional spatio-temporal supply-demand balance data;

[0137] In the embodiment of the present invention, by correcting the load prediction curve data, such as the load prediction values for the next 24 hours, a spatio-temporal coupling supply-demand balance analysis is performed on the regional power supply data. For example, the load prediction value of each region can be compared with the power supply capacity of that region to determine whether the region is in a state of supply exceeding demand, supply falling short of demand, or supply-demand balance at each time step. For example, if the load prediction value of Region A at a certain time step is 100 MW and the power supply capacity is 120 MW, then the region is in a state of supply exceeding demand at that time step. Finally, regional spatio-temporal supply-demand balance data is generated, such as a dataset containing the supply-demand balance state (e.g., supply exceeding demand, supply falling short of demand, supply-demand balance) of each region at each time step within the next 24 hours.

[0138] Step S35: Calculate the probability of supply-demand gap according to the regional spatio-temporal supply-demand balance data, and generate regional supply-demand gap probability data;

[0139] In the embodiment of the present invention, according to the regional spatio-temporal supply-demand balance data, such as the supply-demand balance state of each region at each time step within the next 24 hours, the probability of supply-demand gap is calculated. For example, the number of time steps in which each region is in a state of supply falling short of demand within the next 24 hours can be counted, and this number is divided by the total number of time steps (e.g., 24) to obtain the supply-demand gap probability of the region. For example, if Region A has 4 time steps in a state of supply falling short of demand within the next 24 hours, then the supply-demand gap probability of Region A is 4 / 24 = 1 / 6. Finally, regional supply-demand gap probability data is generated.

[0140] Step S36: Conduct a risk assessment of the regional supply-demand gap based on the regional supply-demand gap probability data, and mark the regional supply-demand gap for the regional spatio-temporal supply-demand balance data to generate regional supply-demand gap matrix data.

[0141] In the embodiment of the present invention, a risk assessment of the regional supply-demand gap is performed according to the regional supply-demand gap probability data, such as the supply-demand gap probability of each region. For example, according to a pre-set risk threshold, regions with a supply-demand gap probability higher than the threshold can be identified as high-risk regions. For example, if the risk threshold is set at 0.2, regions with a supply-demand gap probability higher than 0.2 are identified as high-risk regions. Then, the regional supply-demand gap is marked for the regional spatio-temporal supply-demand balance data. For example, the supply-demand balance state of high-risk regions at time steps when supply falls short of demand is marked as "gap", and the supply-demand balance states of other regions and time steps are marked as "balanced". Finally, regional supply-demand gap matrix data is generated, such as a matrix where each row represents a region, each column represents a time step, and the elements in the matrix are "gap" or "balanced", indicating whether there is a supply-demand gap in that region at that time step.

[0142] Preferably, step S31 includes the following steps:

[0143] Step S311: Perform variational mode decomposition on the load characteristic data of the energy storage area to generate regional load mode component data;

[0144] Step S312: Use a preset time series prediction model to perform intelligent prediction of the regional load mode components on the regional load mode component data to generate load mode component prediction data;

[0145] Step S313: Perform regional component prediction superposition based on the load mode component prediction data and perform dynamic reconstruction of the load curve to generate regional load prediction curve data;

[0146] Step S314: Perform single-region load confidence estimation based on the regional load prediction curve data to generate a single-region load prediction confidence interval;

[0147] Step S315: Use the single-region load prediction confidence interval to perform load prediction error correlation analysis on the regional load prediction curve data to generate prediction error correlation data;

[0148] Step S316: Perform curve confidence correction on the regional load prediction curve data through the prediction error correlation data to generate corrected load prediction curve data.

[0149] In the embodiments of the present invention, the historical load curve data of region A is used as input by using the PyVMD library in Python. The number of modes for VMD decomposition is set, for example, 5, and VMD decomposition is performed to obtain 5 modal components of the load curve of region A. Each modal component represents the information of different frequency components in the load curve. For example, the low-frequency component represents the trend change of the load, and the high-frequency component represents the fluctuation change of the load. Each modal component is stored as a separate file, such as a CSV file, to generate regional load modal component data. Using the LSTM model, the historical data of each modal component of region A is used as training data to train the LSTM model, and the trained model is used to predict the change trend of each modal component in the future for a period of time (for example, the next week), generating load modal component prediction data. According to the load modal component prediction data, regional component prediction superposition is performed. For example, the predicted values of multiple IMF components of each region are added together to obtain the total load prediction value of the region. Then, dynamic reconstruction of the load curve is performed. For example, the total load prediction values at each time step are connected to form a complete load prediction curve, and finally regional load prediction curve data is generated. The Bootstrap method can be used to resample the load prediction curve of each region and calculate the confidence interval of the load prediction value at each time step. For example, the historical load data of each region can be resampled 1000 times, and after each resampling, a load prediction curve is generated using the method of steps S311 - S313, and then the confidence interval of 1000 load prediction curves at each time step is calculated. Finally, the single-region load prediction confidence interval is generated. The correlation coefficient of the load prediction error between every two regions can be calculated. For example, the Pearson correlation coefficient can be used to calculate the correlation coefficient between the load prediction errors at each time step within the next 24 hours in region A and region B, and finally prediction error correlation data is generated. According to the correlation coefficient of the inter-regional load prediction error, the load prediction confidence interval of each region is adjusted. For example, if the load prediction errors of region A and region B are highly positively correlated, the load prediction confidence interval of region A can be expanded to reflect the influence of the load prediction error of region B on region A, and finally corrected load prediction curve data is generated.

[0150] As an example of the present invention, refer to Figure 3 shown in Figure 1 is a schematic diagram of the detailed implementation steps of step S4 in

[0151] Step S41: Evaluate the potential of inter-regional power mutual assistance based on the regional supply-demand gap matrix data and the power grid node association topology data, and generate corrected inter-regional complementary potential data;

[0152] In the embodiments of the present invention, based on the regional supply-demand gap matrix data, such as whether there is a supply-demand gap at each time step within the next 24 hours in each region, and the grid node association topology data, such as the graph structure data including node information, line information, line loss weights, node load weights, and node association strengths, the potential for inter-regional power mutual assistance is evaluated. For example, based on information such as the electrical distance between regions, line transmission capacity, and node betweenness centrality, the maximum power support that each region can obtain from other regions at each time step can be evaluated. Considering the grid transmission losses and stability limitations, the initial evaluation results are corrected. For example, the maximum power support is reduced according to the line transmission capacity and node betweenness centrality, and finally, the corrected inter-regional complementary potential data is generated.

[0153] Step S42: Construct typical supply-demand scenarios for the corrected inter-regional complementary potential data through the regional power supply plan data to obtain multi-time scale supply-demand scenario data;

[0154] In the embodiments of the present invention, typical supply-demand scenarios are constructed for the corrected inter-regional complementary potential data through the obtained regional power supply plan data, such as the planned power generation and renewable energy predicted power generation in each region. For example, different supply-demand scenarios can be constructed based on the output predictions of different power generation types (such as wind power, photovoltaic power, thermal power) and load predictions in the regional power supply plan data. For example, scenarios with high wind power output and low load, low wind power output and high load, etc. Each scenario corresponds to a set of regional supply-demand data and covers different time scales, such as daily, weekly, monthly, etc. All supply-demand scenario data is organized into a data set to obtain multi-time scale supply-demand scenario data.

[0155] Step S43: Construct a power network simulation model using the grid node association topology data;

[0156] In the embodiments of the present invention, a power network simulation model is constructed using the grid node association topology data, such as the graph structure data including node information, line information, line loss weights, node load weights, and node association strengths. For example, power system simulation software such as Matpower and PSS / E can be used to construct a power network simulation model, and the node information, line information, line parameters, etc. in the grid node association topology data are imported into the simulation software.

[0157] Step S44: Use the power network simulation model to perform a shared power dispatch simulation on the multi-time scale supply-demand scenario data, and perform a dispatch power flow calculation to generate multi-scenario power dispatch simulation data;

[0158] In the embodiments of the present invention, in each supply-demand scenario, PowerWorld or Matpower software is used to perform power flow calculation and optimal dispatching of the power system. For example, control strategies such as generator output setting and load distribution are set to simulate the process of the energy storage system participating in grid dispatching, and the simulation results are recorded. The load data, generation data, and inter-regional power exchange plan in each supply-demand scenario are input into the power network simulation model, and power flow calculation for dispatching is performed. For example, the Newton-Raphson method is used to calculate the voltage, current, and power flow distribution on each line at each node. Finally, multi-scenario power dispatching simulation data is generated.

[0159] Step S45: Perform inter-regional shared dispatching analysis based on the multi-scenario power dispatching simulation data, and perform power flow spatio-temporal atlas processing to obtain spatio-temporal power flow dispatching atlas data.

[0160] In the embodiments of the present invention, based on the multi-scenario power dispatching simulation data, such as data on the voltage, current, and power flow distribution at each node in each supply-demand scenario, inter-regional shared dispatching analysis is performed. For example, indicators such as the power exchange volume between regions, line transmission loss, and node voltage stability in each supply-demand scenario can be analyzed. Then, power flow spatio-temporal atlas processing is performed. For example, the power flow distribution on each line in each supply-demand scenario is graphically displayed. For example, arrows are used to represent the power flow direction, and the thickness of the arrows represents the power flow magnitude. Finally, spatio-temporal power flow dispatching atlas data is obtained. For example, how much power is transmitted from region A to region B, and during which time periods the energy storage system charges and discharges.

[0161] Preferably, step S41 includes the following steps:

[0162] Step S411: Perform regional gap time series decomposition on the regional supply-demand gap matrix data to generate regional gap time series data;

[0163] Step S412: Perform supply-demand fluctuation correlation analysis based on the regional gap time series data to generate a supply-demand gap fluctuation correlation coefficient;

[0164] Step S413: Perform regional supply-demand mutual information calculation on the grid node association topology data through the supply-demand gap fluctuation correlation coefficient, and perform power complementary potential evaluation to obtain inter-regional supply-demand complementary potential data;

[0165] Step S414: Perform transmission capacity constraint analysis based on the grid node association topology data to generate regional transmission capacity constraint data; perform distance attenuation factor processing based on the grid node association topology data to generate an inter-regional distance attenuation factor;

[0166] Step S415: Use the inter - regional distance attenuation factor and the regional power transmission capacity constraint data to correct the inter - regional supply - demand complementary potential data, and obtain the corrected inter - regional complementary potential data.

[0167] In the embodiment of the present invention, for the regional supply - demand gap matrix data, such as whether there is a supply - demand gap at each time step within the next 24 hours for each region, perform regional gap time - series decomposition. Methods such as empirical mode decomposition (EMD), variational mode decomposition (VMD), etc. can be used to decompose the supply - demand gap time - series data of each region into several modal components with different frequency characteristics. The Pearson correlation coefficient of the corresponding IMF components between every two regions can be calculated, or the cross - correlation function between the supply - demand gap time - series data of two regions can be calculated. For example, the Pearson correlation coefficient between the IMF components of two regions can be calculated using the numpy library in Python, or the cross - correlation function between the supply - demand gap time - series data of two regions can be calculated using the scipy library. The correlation coefficient of the supply - demand gap fluctuations between region A and region B, as well as the grid connection strength between region A and region B, can be calculated, and the supply - demand complementary potential between region A and region B can be calculated using methods such as mutual information. For example, it is evaluated whether the power surplus in region A can effectively make up for the power gap in region B. Organize the supply - demand complementary potential between all pairs of regions into a matrix form to obtain the inter - regional supply - demand complementary potential data. Analyze the capacity limits of the transmission lines between different regions to generate regional power transmission capacity constraint data. For example, the transmission line capacity from region A to region B is 100 MW. According to the grid node association topology data, calculate the distance between different regions, and use the distance attenuation factor model, such as the exponential attenuation model, to calculate the inter - regional distance attenuation factor. For example, the distance between region A and region B is 100 km, and the distance attenuation factor is 0.8. Multiply the supply - demand complementary potential between region A and region B by the inter - regional distance attenuation factor and consider the transmission line capacity limit from region A to region B to obtain the corrected inter - regional complementary potential. For example, the corrected complementary potential from region A to region B is 80 MW. Organize the corrected complementary potential between all pairs of regions into a matrix form to obtain the corrected inter - regional complementary potential data.

[0168] Preferably, step S5 includes the following steps:

[0169] Step S51: Configure the regional energy storage capacity for the corrected inter - regional complementary potential data through the spatio - temporal power flow scheduling atlas data to generate regional energy storage capacity quota data;

[0170] Step S52: Use the regional energy storage capacity quota data to plan the minimum - loss path of cross - regional power transmission for the spatio - temporal power flow scheduling atlas data to obtain the energy storage area scheduling path data;

[0171] Step S53: Use the preset model prediction control algorithm to optimize the state of energy storage nodes in the control cycle for the energy storage area scheduling path data and the regional energy storage capacity quota data, and obtain the dynamic energy storage node control strategy;

[0172] Step S54: Optimize the capacity of the shared energy storage node according to the dynamic energy storage node control strategy to obtain the optimized configuration data of the energy storage capacity;

[0173] Step S55: Evaluate the operation benefits according to the optimized configuration data of the energy storage capacity, and perform rolling adjustment of the capacity configuration to generate the dynamic energy storage capacity configuration data.

[0174] In the embodiments of the present invention, according to the spatio-temporal power flow scheduling atlas data, it is analyzed that the energy storage system needs to discharge during the peak period in Region A, and needs to charge during the valley period. Combining with the corrected complementary potential data between regions, for example, Region A can provide energy storage services to Region C, and the energy storage capacity quota of Region A is determined. For example, the energy storage capacity quota of Region A is 100 MWh. Using shortest path algorithms such as Dijkstra algorithm or Floyd algorithm, calculate the minimum loss path for realizing power mutual assistance between regions under the condition of meeting the energy storage capacity constraint. For example, if Region A needs to transmit 100 MWh of power to Region B, the Dijkstra algorithm can be used to calculate the minimum loss path from Region A to Region B, and the actual power that can be transmitted is determined according to the line loss on the path and the energy storage capacity constraint. Set the control period to 1 hour, and use the MPC algorithm to optimize the charge and discharge strategy of the energy storage system in Region A. The objective function can be set to minimize the operating cost of the energy storage system or maximize the revenue of the energy storage system. The constraint conditions can include the charge and discharge power limit of the energy storage system, the energy storage capacity limit, the grid voltage stability limit, etc. According to the optimization results, generate the charge and discharge power and status of each energy storage node in each time period, and obtain the dynamic energy storage node control strategy. According to the charge and discharge status of each energy storage node at different time steps and the line loss on the power transmission path, optimize the capacity of each energy storage node to minimize the total cost of the energy storage system or maximize the benefit of the energy storage system. Analyze the charge and discharge power and status of the energy storage system in Region A at different time periods, and optimize the capacity of the energy storage node according to indicators such as the charge and discharge frequency and charge and discharge depth of the energy storage node. For example, increase the capacity of a certain node and reduce the capacity of another node to meet the energy storage demand in Region A and reduce the total cost of the energy storage system. According to information such as the charge and discharge times, charge and discharge amounts, and power transmission losses of each energy storage node, calculate indicators such as the operating cost of the energy storage system, the reduced carbon emissions, and the improved power system reliability. Then, perform rolling adjustment of the capacity configuration. For example, according to the operation benefit evaluation results, adjust the capacity of the energy storage node. For example, increase the capacity of the energy storage node with higher benefit and reduce the capacity of the energy storage node with lower benefit. According to the evaluation results, perform rolling adjustment of the energy storage capacity configuration. For example, according to the operation situation of the energy storage system, adjust the capacity of the energy storage node to adapt to the changes in the grid load and the fluctuations in the output of renewable energy. Finally, generate dynamic energy storage capacity configuration data, which contains information such as the capacity and charge and discharge strategy of each energy storage node, and is updated over time.

[0175] Preferably, step S51 includes the following steps:

[0176] Step S511: Identify the target energy storage node according to the spatio-temporal power flow scheduling atlas data to obtain the target energy storage optimization node data;

[0177] Step S512: Collect energy storage capacity constraint parameters based on the target energy storage optimization node data, and set energy storage capacity constraint conditions to obtain energy storage capacity constraint condition data;

[0178] Step S513: Use the energy storage capacity constraint condition data to perform preliminary allocation of regional energy storage capacity for the corrected inter-regional complementary potential data, and generate preliminary regional energy storage capacity allocation data;

[0179] Step S514: Perform shared energy storage capacity allocation processing on the corrected inter-regional complementary potential data through the spatio-temporal power flow scheduling map data to obtain shared energy storage capacity allocation problem data;

[0180] Step S515: Perform Markov decision processing on the shared energy storage capacity allocation problem data, and perform dynamic capacity quota optimization on the preliminary regional energy storage capacity allocation data to generate regional energy storage capacity quota data.

[0181] In the embodiments of the present invention, based on information such as the power supply-demand balance state, power flow direction, and node importance of each node at different time steps, nodes that need to be configured with energy storage are identified, such as nodes with a large power supply-demand gap, nodes with frequent changes in power flow direction, nodes with a high betweenness centrality, etc. Analyze the spatio-temporal power flow scheduling atlas data to determine which nodes play important roles in the cross-regional power transmission process. For example, which nodes have a large change in power flow, which nodes connect multiple regions, which nodes are close to renewable energy power generation plants or load centers, etc. Extract these key nodes and record information such as the geographical location and voltage level of the nodes to obtain the target energy storage optimization node data. According to the geographical location, environmental conditions, etc. of the target energy storage optimization nodes, query the technical parameters of the energy storage system, such as the type, rated power, energy storage capacity, charge-discharge efficiency, etc. of the energy storage system. Combining the operating requirements of the power grid, such as power grid voltage stability, frequency stability, etc., set energy storage capacity constraint conditions, such as the energy storage capacity cannot exceed a certain upper limit, and the charge-discharge power cannot exceed a certain upper limit, etc. According to the power supply-demand gap of each region at different time steps and the potential for power mutual assistance between regions, and on the premise of meeting the energy storage capacity constraint conditions, initially determine the energy storage capacity that needs to be configured in each region. Optimization methods such as linear programming can be used to solve the preliminary configuration plan of the regional energy storage capacity that meets the constraint conditions, and finally generate the preliminary regional energy storage capacity configuration data. Analyze the power exchange situation between different regions in the spatio-temporal power flow scheduling atlas data and the process of the energy storage system participating in power grid scheduling. Combining the corrected potential data for regional complementarity, transform the energy storage capacity allocation problem into an optimization problem. For example, how to allocate the 100 MWh energy storage capacity of region A to maximize the power complementarity benefit between regions and meet the energy storage capacity constraint conditions. Model the shared energy storage capacity allocation problem as a Markov decision process. The state space can be the energy storage capacity states of different regions, the action space can be the energy storage capacity allocation strategies between different regions, and the reward function can be the operating benefit of the energy storage system, such as reducing the system operating cost, increasing the new energy consumption rate, etc. Use reinforcement learning algorithms, such as the Q-learning algorithm, to solve the Markov decision process, obtain the optimal energy storage capacity allocation strategy, and optimize the preliminary regional energy storage capacity configuration data according to this strategy to obtain the final energy storage capacity quota for each region. For example, the energy storage capacity quota of region A is adjusted to 80 MWh.

[0182] The beneficial effects of this application are as follows. Through fine regional division and load characteristic analysis, the power demand characteristics of each region are accurately grasped, laying a data foundation for cross-regional collaboration. Subsequently, the construction of the cross-regional power grid topology structure and the analysis of node association strength provide an optimized path from a network perspective for the efficient flow of power resources, reducing transmission losses. Through supply-demand balance analysis and gap assessment, as well as the precise quantification of the potential for power mutual assistance, the supply-demand mismatch problems between different regions are effectively identified and coordinated, promoting the optimal allocation of resources. Further, by using the spatio-temporal power flow scheduling map and dynamic energy storage capacity configuration, the flexible scheduling and optimal layout of energy storage resources are realized, ensuring the real-time matching of power demand and supply, greatly enhancing the overall response speed and adaptability of the system. The optimal configuration of cross-regional energy storage capacity is achieved, improving the new energy consumption capacity, reducing the system operation cost, and enhancing the stability and reliability of the power system operation.

[0183] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be encompassed within the present invention.

[0184] The above are only specific implementation manners of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing energy storage capacity configuration based on a shared energy storage mode, characterized in that: The following steps are involved: Step S1: using the power dispatching center to divide the shared energy storage area and obtain the shared energy storage area data; Extract the target area load characteristic index from the shared energy storage area data to generate regional load characteristic data; process the weighted load characteristic value according to the regional load characteristic data to generate energy storage area load characteristic data; Step S2: constructing a power grid topology structure for the shared energy storage area data to generate cross-regional power grid topology structure data; performing node association strength processing on the cross-regional power grid topology structure data using the energy storage area load characteristic data to generate power grid node association topology data; Step S3: Acquire regional power supply plan data; Based on the energy storage regional load characteristic data, use the regional power supply plan data to perform spatiotemporal coupling supply and demand balance analysis on the grid node associated topology data to generate regional supply and demand gap matrix data; Step S4: Evaluate the inter-regional power mutual assistance potential according to the regional supply-demand gap matrix data to generate corrected inter-regional complementary potential data; perform inter-regional shared scheduling analysis on the corrected inter-regional complementary potential data using the regional power supply plan data to obtain spatiotemporal power flow scheduling map data; Step S5: configuring regional energy storage capacity according to the spatiotemporal power flow dispatching graph data, and generating regional energy storage capacity quota data; The shared energy storage node capacity is optimized according to the regional energy storage capacity quota data, and dynamic energy storage capacity configuration data is generated.

2. The energy storage capacity optimization configuration method based on the shared energy storage mode according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: using the power dispatching center to divide the shared energy storage area and obtain shared energy storage area data; Step S12: collecting historical power load data according to the shared energy storage area data to obtain original historical power load data; Step S13: performing outlier detection on the original historical power load data and filling in missing values ​​to generate regional historical power load data; Step S14: extracting the load characteristic index of the target area according to the historical power load data of the area, and generating regional load characteristic data; Step S15: Mapping regional characteristic indicators to the shared energy storage regional data through regional load characteristic data to generate regional multi-dimensional characteristic matrix data; Step S16: performing Z-sore standardization on the regional multi-dimensional feature matrix data to obtain a standardized regional feature matrix; performing feature index weight evaluation based on the standardized regional feature matrix to obtain feature index weight vector data; Step S17: Use the characteristic index weight vector data to perform weighted load characteristic value processing on the standardized regional characteristic matrix to generate energy storage regional load characteristic data.

3. The energy storage capacity optimization configuration method based on the shared energy storage mode according to claim 2 is characterized in that: Step S15 includes the following steps: Step S151: Perform dynamic time warping on the regional load characteristic data, and compare the characteristics between regions to generate characteristic difference data between regions; Step S152: Calculate the time series feature similarity based on the feature difference data between regions to generate regional load similarity data; Step S153: performing load pattern clustering analysis based on regional load similarity data, and performing regional clustering division to generate regional load cluster data; Step S154: extracting cluster typical load curves from regional load cluster data to obtain cluster typical load curve data; Step S155: performing regional mapping on the shared energy storage regional data through the cluster typical load curve data to generate regional-cluster characteristic mapping data; Step S156: performing multi-dimensional feature vector processing according to the region-cluster feature mapping data to obtain multi-dimensional feature vector data; Step S157: constructing a regional characteristic matrix for the regional load characteristic data through the multi-dimensional characteristic vector data to generate regional multi-dimensional characteristic matrix data.

4. The energy storage capacity optimization configuration method based on the shared energy storage mode according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: using the power dispatching center to construct the grid topology structure for the shared energy storage area data, and initialize the line loss weight to generate cross-regional grid topology structure data; Step S22: weighting node power loads on cross-regional power grid topology data using energy storage area load characteristic data to generate load-weighted power grid topology data; Step S23: performing node betweenness centrality statistics according to the load-weighted power grid topology data to obtain node betweenness centrality data; Step S24: Calculate the inter-regional electrical distance according to the load-weighted power grid topology data to generate inter-regional electrical distance data; Step S25: Based on the inter-regional electrical distance data, the load-weighted power grid topology data is processed for node association strength through the node betweenness centrality data to generate power grid node association topology data.

5. The energy storage capacity optimization configuration method based on the shared energy storage mode according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: performing a multi-step forecast of regional time series load according to the energy storage regional load characteristic data to obtain corrected load forecast curve data; Step S32: Acquire regional power supply plan data, including power production plans for each region and renewable energy output estimation data; Step S33: using the regional power supply plan data to evaluate the regional power supply capacity of the shared energy storage regional data, and generating regional power supply data; Step S34: performing spatiotemporal coupled supply and demand balance analysis on regional power supply data by modifying load forecast curve data to generate regional spatiotemporal supply and demand balance data; Step S35: Calculate the supply-demand gap probability based on the regional spatiotemporal supply-demand balance data to generate regional supply-demand gap probability data; Step S36: Perform regional supply and demand gap risk assessment based on regional supply and demand gap probability data, and mark regional supply and demand gaps on regional spatiotemporal supply and demand balance data to generate regional supply and demand gap matrix data.

6. The energy storage capacity optimization configuration method based on the shared energy storage mode according to claim 5 is characterized in that: Step S31 includes the following steps: Step S311: performing variational modal decomposition on the energy storage regional load characteristic data to generate regional load modal component data; Step S312: using a preset time series prediction model to perform intelligent prediction of modal components on regional load modal component data to generate load modal component prediction data; Step S313: performing regional component prediction superposition according to the load modal component prediction data, and dynamically reconstructing the load curve to generate regional load prediction curve data; Step S314: performing single-region load confidence estimation according to the regional load forecast curve data to generate a single-region load forecast confidence interval; Step S315: using the single-region load forecast confidence interval to perform load forecast error correlation analysis on the regional load forecast curve data to generate forecast error correlation data; Step S316: Perform curve confidence correction on the regional load forecast curve data using the forecast error correlation data to generate corrected load forecast curve data.

7. The method for optimizing energy storage capacity configuration based on a shared energy storage mode according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: evaluating the inter-regional power mutual assistance potential according to the regional supply-demand gap matrix data and the grid node association topology data, and generating corrected inter-regional complementary potential data; Step S42: constructing typical supply and demand scenarios for the modified inter-regional complementary potential data through regional power supply plan data to obtain multi-time scale supply and demand scenario data; Step S43: constructing a power network simulation model using the grid node associated topology data; Step S44: using the power network simulation model to perform shared power dispatch simulation on the multi-time scale supply and demand scenario data, and perform dispatch flow calculation to generate multi-scenario power dispatch simulation data; Step S45: Perform inter-regional shared scheduling analysis based on the multi-scenario power scheduling simulation data, and perform power flow spatiotemporal graph processing to obtain spatiotemporal power flow scheduling graph data.

8. The energy storage capacity optimization configuration method based on the shared energy storage mode according to claim 7 is characterized in that: Step S41 includes the following steps: Step S411: performing regional gap time series decomposition on the regional supply-demand gap matrix data to generate regional gap time series data; Step S412: performing supply-demand fluctuation correlation analysis based on the regional gap time series data to generate a supply-demand gap fluctuation correlation coefficient; Step S413: Calculate the regional supply and demand mutual information of the grid node associated topology data through the supply and demand gap fluctuation correlation coefficient, and evaluate the power complementarity potential to obtain the inter-regional supply and demand complementarity potential data; Step S414: performing transmission capacity constraint analysis based on the grid node associated topology data to generate regional transmission capacity constraint data; performing distance attenuation factor processing based on the grid node associated topology data to generate inter-regional distance attenuation factors; Step S415: using the inter-regional distance attenuation factor and the regional transmission capacity constraint data to correct the inter-regional supply-demand complementary potential value, and obtain corrected inter-regional complementary potential data.

9. The method for optimizing energy storage capacity configuration based on a shared energy storage mode according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: performing regional energy storage capacity configuration on the modified inter-regional complementary potential data through the spatiotemporal power flow dispatching graph data to generate regional energy storage capacity quota data; Step S52: using the regional energy storage capacity quota data to perform cross-regional power transmission minimum loss path planning on the spatiotemporal power flow scheduling map data, and obtaining energy storage regional scheduling path data; Step S53: Optimizing the state of energy storage nodes in a control cycle by using a preset model predictive control algorithm for the energy storage area scheduling path data and the regional energy storage capacity quota data to obtain a dynamic energy storage node control strategy; Step S54: optimizing the capacity of the shared energy storage node according to the dynamic energy storage node control strategy to obtain energy storage capacity optimization configuration data; Step S55: Perform an operation benefit evaluation based on the energy storage capacity optimization configuration data, and perform rolling adjustment on the capacity configuration to generate dynamic energy storage capacity configuration data.

10. The energy storage capacity optimization configuration method based on the shared energy storage mode according to claim 1 is characterized in that: Step S51 includes the following steps: Step S511: identifying target energy storage nodes according to the spatiotemporal power flow dispatch graph data to obtain target energy storage optimization node data; Step S512: collecting energy storage capacity constraint parameters according to the target energy storage optimization node data, and setting energy storage capacity constraint conditions to obtain energy storage capacity constraint condition data; Step S513: using the energy storage capacity constraint condition data to perform preliminary configuration of regional energy storage capacity on the modified inter-regional complementary potential data, and generating preliminary regional energy storage capacity configuration data; Step S514: performing shared energy storage capacity allocation processing on the modified inter-regional complementary potential data through the spatiotemporal power flow dispatching graph data to obtain shared energy storage capacity allocation problem data; Step S515: Markov decision processing is performed on the shared energy storage capacity allocation problem data, and dynamic capacity quota optimization is performed on the preliminary regional energy storage capacity configuration data to generate regional energy storage capacity quota data.

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