A method and system for configuring energy storage parameters in a distribution station area

Abstract

The present application relates to the technical field of electric power systems, and discloses a method and system for configuring energy storage parameters in a distribution station area, wherein the method comprises: obtaining power supply range information and power demand density information, and dividing the power supply area; identifying over-limit type nodes, and generating voltage over-limit parameters; determining voltage over-limit impact weights according to the topological connection relationship between the over-limit type nodes and the transformer, the voltage over-limit parameters, and the voltage regulation capability matrix, and generating voltage over-limit responsibility quantification results; based on the voltage over-limit responsibility quantification results, extracting load distribution characteristics, and determining energy storage system access points; optimizing the energy storage system capacity and optimizing the energy storage system access points according to the voltage sensitivity and load characteristics of the energy storage system access points, and obtaining a globally optimal energy storage system configuration solution, thereby solving the technical problem of optimizing the energy storage system access points and achieving the technical effect of maximizing the economic benefits of the energy storage system while ensuring the power supply quality.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to a method and system for configuring energy storage parameters in a distribution station area. Background Art

[0002] In the configuration of energy storage parameters in photovoltaic distribution substations, when multiple transformers supply power together, their power supply ranges and areas often overlap with each other, making it impossible to accurately identify and quantify the voltage over-limit parameters of each node, and it is also impossible to determine the power supply responsibility of each node, making it difficult to select the best access point for the energy storage system. How to maximize the economic benefits of the energy storage system while ensuring the quality of power supply has become a technical problem that needs to be solved urgently. Summary of the invention

[0003] The present application provides a method and system for configuring energy storage parameters in a distribution station area, which solves the technical problem of optimizing access points of an energy storage system and achieves the technical effect of maximizing the economic benefits of the energy storage system while ensuring the quality of power supply.

[0004] In order to achieve the above objectives, the main technical solutions adopted in this application include:

[0005] In a first aspect, an embodiment of the present application provides a method for configuring energy storage parameters in a distribution station area, the method comprising: obtaining power supply range information and power demand density information of multiple transformers, dividing the power supply area, and obtaining an overlapping area of ​​the transformer power supply range; identifying an over-limit node based on real-time voltage data of multiple nodes in the overlapping area of ​​the transformer power supply range, and generating a voltage over-limit parameter; determining a voltage over-limit influence weight of the transformer on the over-limit node according to a topological connection relationship between the over-limit node and the transformer, the voltage over-limit parameter, and a voltage regulation capability matrix; generating a voltage over-limit responsibility quantification result according to the voltage over-limit influence weight; based on the voltage over-limit responsibility quantification result, obtaining load distribution data of the corresponding distribution station area from a distribution management system, extracting load distribution characteristics from the load distribution data, and determining an energy storage system access point; optimizing the energy storage system capacity according to the voltage sensitivity and load characteristics of the energy storage system access point to obtain an initial value of the energy storage capacity of the access point; obtaining an energy storage system response characteristic according to the initial value of the energy storage capacity; optimizing the energy storage system access point according to the energy storage system response characteristic to obtain a globally optimal energy storage system configuration solution.

[0006] A method for configuring energy storage parameters in a distribution station area proposed in an embodiment of the present application obtains power supply range information and power demand density information of multiple transformers, divides the power supply area, and obtains overlapping areas of transformer power supply ranges; identifies over-limit nodes based on real-time voltage data of multiple nodes in the overlapping areas of transformer power supply ranges, and generates voltage over-limit parameters; determines the voltage over-limit influence weight of the transformer on the over-limit node based on the topological connection relationship between the over-limit node and the transformer, the voltage over-limit parameters, and the voltage regulation capability matrix; generates a voltage over-limit responsibility quantification result based on the voltage over-limit influence weight; and generates a voltage over-limit responsibility quantification result based on the voltage over-limit responsibility quantification result. As a result, the load distribution data of the corresponding distribution station area is obtained from the distribution management system, the load distribution characteristics in the load distribution data are extracted, and the access point of the energy storage system is determined; according to the voltage sensitivity and load characteristics of the access point of the energy storage system, the energy storage system capacity is optimized and configured to obtain the initial value of the energy storage capacity of the access point; according to the initial value of the energy storage capacity, the response characteristics of the energy storage system are obtained; according to the response characteristics of the energy storage system, the access point of the energy storage system is optimized to obtain the global optimal energy storage system configuration scheme, which solves the technical problem of optimizing the access point of the energy storage system and achieves the technical effect of maximizing the economic benefits of the energy storage system while ensuring the power supply quality.

[0007] Optionally, the obtaining of power supply range information and power demand density information of multiple transformers, dividing the power supply area, and obtaining the overlapping area of ​​transformer power supply ranges specifically includes: based on the power supply range information and power demand density information of the multiple transformers, dividing the power supply area using a GIS-based power grid space analysis tool to obtain an initial power supply range of each transformer; constructing a power grid structure model based on the initial power supply range, line impedance parameters and load distribution data in a distribution management system, performing power flow calculation on the power grid structure model, and obtaining a power grid operation status within the initial power supply range; determining whether the power grid operation status within the initial power supply range meets a first preset condition, and if not, optimizing and adjusting the initial power supply range to generate an actual power supply range; and identifying the overlapping area of ​​transformer power supply ranges based on the actual power supply range.

[0008] Optionally, identifying the overlapping area of ​​the transformer power supply range based on the actual power supply range specifically includes: processing the actual power supply range using spatial superposition operation to generate a preliminary overlapping area, and obtaining basic characteristic data of the preliminary overlapping area, including the corresponding area and number of users; obtaining power consumption characteristic data of the preliminary overlapping area, including user density, load type and power consumption pattern, generating an overlapping area characteristic report of the preliminary overlapping area based on the basic characteristic data and the power consumption characteristic data, and evaluating the voltage quality and line loss rate of the preliminary overlapping area based on the overlapping area characteristic report; formulating a power supply plan based on the voltage quality and line loss rate of the preliminary overlapping area, as well as the remaining capacity and power supply radius limit of the transformer, using a multi-objective optimization algorithm, and determining the ownership of the power supply transformer of each user, to generate the overlapping area of ​​the transformer power supply range.

[0009] Optionally, the real-time voltage data of multiple nodes in the overlapping area of ​​the transformer power supply range are used to identify the over-limit nodes and generate voltage over-limit parameters, which specifically includes: based on the distribution automation system, obtaining the real-time voltage data of multiple nodes in the overlapping area of ​​the transformer power supply range; using a preset sliding time window to pre-process the real-time voltage data of the multiple nodes to obtain voltage time series of the multiple nodes; based on the voltage time series of the multiple nodes, obtaining voltage statistical characteristics and voltage fluctuation spectrum characteristics; based on the voltage statistical characteristics and voltage fluctuation spectrum characteristics, identifying the over-limit nodes, and generating voltage over-limit parameters, including voltage over-limit amplitude and over-limit duration.

[0010] Optionally, the voltage over-limit influence weight of the transformer on the over-limit node is determined according to the topological connection relationship between the over-limit node and the transformer, the voltage over-limit parameters and the voltage regulation capability matrix, specifically including: obtaining the affinity between the over-limit node and the transformer according to the topological connection relationship between the over-limit node and the transformer and the minimum impedance distance; constructing the voltage regulation capability matrix according to the rated capacity, voltage regulation range and current load level of the transformer, and the node sensitivity coefficient; constructing a voltage over-limit responsibility allocation model according to the affinity between the over-limit node and the transformer, the voltage over-limit parameters and the voltage regulation capability matrix; wherein the voltage over-limit responsibility allocation model takes minimizing the total responsibility deviation and the responsibility balance as the objective function; and using the non-dominated sorting genetic algorithm NSGA-II to process the voltage over-limit responsibility allocation model to obtain the voltage over-limit influence weight of the transformer on the over-limit node.

[0011] Optionally, based on the voltage over-limit responsibility quantification result, the load distribution data of the corresponding distribution area is obtained from the distribution management system; the load distribution characteristics in the load distribution data are extracted to determine the access point of the energy storage system, specifically including: based on the voltage over-limit responsibility quantification result, the load distribution data of the corresponding distribution area is obtained from the distribution management system, and the load curve of each node in the distribution area is extracted; according to the load curve of each node, the load characteristics are extracted; based on the load characteristics and the power grid topology, a node evaluation index system is constructed, including load fluctuation rate, voltage sensitivity, line loss contribution rate and power supply radius; based on the node evaluation index system, a non-dominated sorting genetic algorithm NSGA-II is used to optimize the energy storage system access point and determine the optimal energy storage system access point.

[0012] Optionally, the energy storage system capacity is optimized and configured according to the voltage sensitivity and load characteristics of the energy storage system access point to obtain the initial value of the energy storage capacity of the access point, specifically including: based on the voltage sensitivity and load characteristics of the energy storage system access point, obtaining a voltage curve of the access point through power flow calculation; comparing the voltage curve with a standard voltage range to generate a voltage margin curve of the access point; optimizing the energy storage system capacity according to the voltage margin curve of the access point; judging whether the voltage improvement effect and load balance degree of the access point reach a set threshold based on the energy storage system capacity after the optimized configuration, and generating the initial value of the energy storage system capacity of the access point.

[0013] Optionally, the method of obtaining the response characteristics of the energy storage system according to the initial value of the energy storage capacity; optimizing the access point of the energy storage system according to the response characteristics of the energy storage system to obtain a globally optimal energy storage system configuration scheme specifically includes: obtaining the response characteristics of the energy storage system corresponding to the initial value of the energy storage capacity in the energy storage system parameter database; fitting the response characteristics of the energy storage system by the least squares method to obtain a mathematical model of the energy storage system; performing time series simulation on the mathematical model of the energy storage system, and connecting it to the distribution network simulation environment to perform power flow calculation to obtain power flow calculation results; generating an energy storage system access effect evaluation index according to the power flow calculation results; judging whether the energy storage system access effect evaluation index meets the preset target value, if not, generating the sensitivity of the energy storage system access effect evaluation index to the energy storage capacity by the central difference method; adjusting the energy storage capacity configuration scheme according to the sensitivity result; optimizing the access point of the energy storage system according to the adjusted energy storage capacity configuration scheme to obtain a globally optimal energy storage system configuration scheme.

[0014] Optionally, the method of optimizing the access point of the energy storage system according to the adjusted energy storage capacity configuration scheme to obtain a globally optimal energy storage system configuration scheme specifically includes: based on the adjusted energy storage capacity configuration scheme, obtaining the voltage sensitivity, load distribution characteristics, line loss rate, and power supply radius of each node, and constructing a multi-dimensional node evaluation index matrix; according to the multi-dimensional node evaluation index matrix, using an improved particle swarm optimization algorithm with adaptive inertia weight and chaotic perturbation to perform a global optimization search to obtain a global optimization search result; wherein, the global optimization search takes maximizing the voltage improvement effect and load balance, minimizing the line loss rate and investment cost as the objective function, and sets constraints on the access point of the energy storage system; using the Latin hypercube sampling method to generate typical scenarios, and evaluating the robustness of the global optimization search results to obtain a robustness evaluation result; evaluating the global optimization search results according to a hierarchical analysis evaluation system to obtain a comprehensive evaluation result, wherein the hierarchical analysis evaluation system includes voltage improvement effect, load balance, line loss rate, and investment cost; based on the robustness evaluation result and the comprehensive evaluation result, optimizing the access point of the energy storage system to obtain a globally optimal energy storage system configuration scheme.

[0015] In a second aspect, an embodiment of the present application provides a distribution station area energy storage parameter configuration system, the system comprising: a power supply range division module, used to obtain power supply range information and power demand density information of multiple transformers, divide the power supply area, and obtain the overlapping area of ​​the transformer power supply range; a voltage over-limit responsibility quantification module, used to identify over-limit nodes based on real-time voltage data of multiple nodes in the overlapping area of ​​the transformer power supply range, and generate voltage over-limit parameters; used to determine the voltage over-limit influence weight of the transformer on the over-limit node according to the topological connection relationship between the over-limit node and the transformer, the voltage over-limit parameters and the voltage regulation capability matrix; used A voltage over-limit responsibility quantification result is generated according to the voltage over-limit impact weight; an energy storage system configuration module is used to obtain the load distribution data of the corresponding distribution station area from the distribution management system based on the voltage over-limit responsibility quantification result, extract the load distribution characteristics in the load distribution data, and determine the energy storage system access point; is used to optimize the energy storage system capacity according to the voltage sensitivity and load characteristics of the energy storage system access point to obtain the initial value of the energy storage capacity; is used to obtain the energy storage system response characteristics according to the initial value of the energy storage capacity; and optimize the energy storage system access point according to the energy storage system response characteristics to obtain the global optimal energy storage system configuration plan.

[0016] In a third aspect, an embodiment of the present application provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, computer instructions are stored in the memory, and the processor executes the above method by executing the computer instructions.

[0017] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to enable a computer to execute the above method.

[0018] In a fifth aspect, an embodiment of the present application provides a computer program product, including computer instructions, which are used to enable a computer to execute the above method. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 A schematic diagram of a method for configuring energy storage parameters in a distribution station area provided in an embodiment of the present application;

[0021] Figure 2 A schematic diagram of a distribution station area energy storage parameter configuration system provided in an embodiment of the present application;

[0022] Figure 3 A schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0024] In the configuration of energy storage parameters in photovoltaic distribution substations, when multiple transformers are used for power supply, the transformer power supply ranges overlap with each other, making it difficult to clearly define the power supply responsibility of each node. At the same time, it is impossible to accurately identify and quantify the voltage over-limit parameters of each node, nor is it possible to determine the main voltage over-limit responsibility source. On this basis, it becomes extremely difficult to select the optimal energy storage system access point. Each access point may have an impact on multiple power supply areas, and these impacts are coupled and difficult to separate. In such a highly coupled system, finding the global optimal energy storage system configuration solution becomes a challenging problem. This requires not only considering the performance and cost of a single energy storage unit, but also weighing the synergy between multiple energy storage units. In addition, it is necessary to predict and evaluate the long-term impact of different configuration solutions on the entire power supply network. This complexity makes it difficult for traditional optimization algorithms to cope with it, and new mathematical models and algorithms need to be developed to deal with this high-dimensional, nonlinear, and dynamically changing optimization problem. How to maximize the economic benefits of the energy storage system while ensuring the quality of power supply has become a technical problem that needs to be solved urgently.

[0025] According to an embodiment of the present application, an embodiment of a method for configuring energy storage parameters in a distribution station area is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0026] In this embodiment, a method for configuring energy storage parameters in a distribution station area is provided. Figure 1 is a flow chart of a method for configuring energy storage parameters in a distribution station area according to an embodiment of the present application. Figure 1 As shown, the process includes the following steps:

[0027] Step S1, obtaining the power supply range information and power demand density information of multiple transformers, dividing the power supply area, and obtaining the overlapping area of ​​the transformer power supply range, specifically including:

[0028] Step S101, based on the power supply range information and power demand density information of the multiple transformers, the power supply area is divided using a GIS-based power grid space analysis tool to obtain an initial power supply range of each transformer.

[0029] The power supply range information includes geographical location coordinates, rated capacity and power supply radius information.

[0030] In some embodiments, the power supply range information of multiple transformers is obtained, including the geographic location coordinates, rated capacity and power supply radius information, and the power demand density data of the area is extracted from the power management system database. According to the power supply range information and the power demand density data, a GIS-based power grid spatial analysis tool is used to preliminarily divide the power supply area to obtain the initial power supply range of each transformer.

[0031] For example, transformer information is obtained from the power management system database, including geographic coordinates (such as longitude 121.5°E, latitude 31.2°N), rated capacity (such as 500kVA) and power supply radius (such as 500 meters). At the same time, power demand density data is extracted, such as the average power density of a certain area is 2kWh / m² / day. ArcGIS spatial analysis tools are used to preliminarily divide the power supply area and generate an initial power supply range layer.

[0032] Step S103, based on the initial power supply range, line impedance parameters and load distribution data in the distribution management system, construct a power grid structure model, perform power flow calculation on the power grid structure model, and obtain the power grid operation status of the initial power supply range; determine whether the power grid operation status of the initial power supply range meets the first preset condition, if not, optimize and adjust the initial power supply range to generate the actual power supply range.

[0033] In some embodiments, for the initial power supply range, a power grid structure model is constructed using a power grid topology analysis tool based on graph theory, line impedance parameters and load distribution data are obtained from the power distribution management system, power flow calculation is performed, and the power grid operation status is evaluated. If the power grid operation status does not meet the first preset condition, the initial power supply range boundary is optimized and adjusted to determine the actual power supply range of each transformer.

[0034] For example, line impedance parameters (such as 0.1+j0.3Ω / km) and load distribution data are obtained from the distribution management system, and the network structure model is constructed using the NetworkX graph theory library. The Newton-Raphson method is used to perform power flow calculations on the grid structure model to evaluate the grid operation status, including voltage deviation (such as ±5%) and line load rate (such as 80%). If the grid operation status does not meet the first preset condition, the Voronoi diagram algorithm is used to optimize and adjust the power supply range boundary to generate the actual power supply range of each transformer.

[0035] Step 105, based on the actual power supply range, identifying the overlapping area of ​​the transformer power supply range, specifically includes: using spatial superposition operation to process the actual power supply range to generate a preliminary overlapping area, and obtaining basic characteristic data of the preliminary overlapping area, including the corresponding area and number of users; obtaining power consumption characteristic data of the preliminary overlapping area, including user density, load type and power consumption pattern, generating an overlapping area characteristic report of the preliminary overlapping area based on the basic characteristic data and the power consumption characteristic data, and evaluating the voltage quality and line loss rate of the preliminary overlapping area based on the overlapping area characteristic report; based on the voltage quality and line loss rate of the preliminary overlapping area, as well as the remaining capacity and power supply radius limit of the transformer, using a multi-objective optimization algorithm to formulate a power supply plan, and determine the power supply transformer of each user, and generate the overlapping area of ​​the transformer power supply range.

[0036] For example, the QGIS spatial analysis tool is used to perform spatial overlay operations on the actual power supply range to identify preliminary overlapping areas. The basic characteristic data of the preliminary overlapping areas are calculated, including the area (such as 10,000 m²) and the number of users involved (such as 500 households). The power consumption characteristic data of the preliminary overlapping areas are obtained from the power consumption information collection system, such as user density (50 households / km²), load type (residential, commercial, and industrial ratios are 6:3:1) and power consumption rules (peak-to-valley difference 30%). Based on the basic characteristic data and the power consumption characteristic data, a preliminary overlapping area characteristic report is generated. According to the preliminary overlapping area characteristic report, the voltage sensitivity method is used to evaluate the voltage quality of the preliminary overlapping area, and the line loss rate (such as 3%) is calculated. Combined with the transformer remaining capacity (such as 20%) and the power supply radius limit, a multi-objective optimization model is constructed, and the NSGA-II multi-objective optimization algorithm is used to formulate a power supply plan. Among them, the optimization objectives include the power supply reliability index SAIDI (<2 hours / year / household), economic index (line loss rate <3%) and power quality index (voltage qualification rate >95%). Finally, the power supply transformer of each user is determined, and the overlapping area of ​​transformer power supply range is generated.

[0037] Step S3, based on the real-time voltage data of multiple nodes in the overlapping area of ​​the transformer power supply range, identifying the over-limit nodes and generating voltage over-limit parameters.

[0038] In some embodiments, based on the distribution automation system, the real-time voltage data of multiple nodes in the overlapping area of ​​the transformer power supply range are obtained; the real-time voltage data of the multiple nodes are preprocessed using a preset sliding time window to obtain the voltage time series of the multiple nodes; based on the voltage time series of the multiple nodes, the voltage statistical characteristics are calculated, including the voltage average, standard deviation and peak factor, and the voltage time series is multi-layered decomposed using a preset wavelet basis function to obtain the voltage fluctuation spectrum characteristics. If the voltage statistical characteristics and the voltage fluctuation spectrum characteristics meet the second preset condition, the support vector machine multi-class classification algorithm is used to divide the multiple nodes into stable nodes, fluctuating nodes and over-limit nodes. Among them, for stable nodes and fluctuating nodes, the generated voltage over-limit parameters include voltage margin; for over-limit nodes, the generated voltage over-limit parameters include voltage over-limit amplitude and over-limit duration.

[0039] For example, the real-time voltage data of each node in the overlapping area of ​​the power supply range is obtained from the distribution automation system, and the real-time voltage data is preprocessed using a sliding time window with a window size of 5 minutes and a sliding step of 1 minute. For example, for the 24-hour voltage data of node A, 1440 windows are generated, each containing 300 data points. The median absolute deviation method is used to identify outliers, and data points exceeding 3 times the standard deviation are marked as abnormal. For missing data, linear interpolation is used to fill in. After preprocessing, the statistical features of each window are calculated, such as the voltage mean (such as 220.5V), standard deviation (such as 1.2V), and crest factor (such as 1.05). The db4 wavelet basis function is used to perform a three-layer wavelet decomposition to obtain the approximate coefficient and detail coefficient. By analyzing the energy distribution of the detail coefficient, the periodicity and randomness of the voltage fluctuation are judged. For example, if the energy proportion of the first-layer detail coefficient exceeds 60%, it is considered that there is obvious high-frequency fluctuation. Based on these features, the support vector machine multi-class classification algorithm is used to classify the nodes. The voltage qualification rate threshold is set to 95%, and the fluctuation limit is set to ±5% of the rated voltage. The training data includes 1000 labeled samples, and the feature vector contains 10 statistics and wavelet coefficients. Using the radial basis kernel function, the optimal parameters C=10 and γ=0.1 are determined by grid search. For the over-limit node, the over-limit amplitude (such as 6.2V) and the over-limit duration (such as cumulative 30 minutes / day) are calculated. For stable and fluctuating nodes, the voltage margin (such as remaining 7.5V to the upper limit) is calculated.

[0040] In some embodiments, by constructing a 100x100 node voltage to injected power sensitivity matrix, the Newton-Raphson method is used to solve the load flow equation and calculate the partial derivatives of voltage to active and reactive power. Through sensitivity analysis, the three key factors that have the greatest impact on voltage over-limit can be identified, including load growth, distributed power fluctuations, and voltage regulator action.

[0041] Step S5, determining the voltage over-limit influence weight of the transformer on the over-limit node according to the topological connection relationship between the over-limit node and the transformer, the voltage over-limit parameters and the voltage regulation capability matrix; generating a voltage over-limit responsibility quantification result according to the voltage over-limit influence weight of the transformer on the over-limit node.

[0042] In some embodiments, the affinity between the over-limit node and the transformer is obtained according to the topological connection relationship between the over-limit node and the transformer and the minimum impedance distance; the voltage regulation capability matrix is ​​constructed according to the rated capacity, voltage regulation range and current load level of the transformer and the node sensitivity coefficient; the voltage over-limit responsibility allocation model is constructed according to the affinity between the over-limit node and the transformer, the voltage over-limit parameter and the voltage regulation capability matrix; wherein the voltage over-limit responsibility allocation model takes minimizing the total responsibility deviation and responsibility balance as the objective function; the non-dominated sorting genetic algorithm NSGA-II is used to process the voltage over-limit responsibility allocation model to obtain the voltage over-limit influence weight of the transformer on the over-limit node. The contribution of each transformer to the voltage over-limit is calculated according to the voltage over-limit influence weight, and the contribution is linearly combined and corrected by the weighted coefficient method to obtain the voltage over-limit responsibility quantification result.

[0043] Specifically, the topological connection relationship between the over-limit node and the transformer is obtained, and an over-limit node-transformer association matrix is ​​constructed according to the topological connection relationship; the shortest path algorithm is used to calculate the minimum impedance distance from the over-limit node to the transformer, and the affinity between the over-limit node and the transformer is obtained; the node sensitivity coefficient of the voltage of the over-limit node to each transformer is calculated, and a voltage regulation capability matrix is ​​constructed according to the node sensitivity coefficient, the rated capacity of the transformer, the voltage regulation range and the current load level; according to the affinity between the over-limit node and the transformer, the voltage over-limit parameter and the voltage regulation capability matrix, a voltage over-limit responsibility allocation model is constructed, and the voltage over-limit impact weight is solved by using the non-dominated sorting genetic algorithm NSGA-II; the contribution of each transformer to the voltage over-limit is calculated according to the voltage over-limit impact weight, and the contribution is linearly combined and corrected by the weighted coefficient method to obtain the voltage over-limit responsibility quantification result.

[0044] In some embodiments, the topological connection relationship between the over-limit node and the transformer is obtained from the distribution management system, and a 100x10 node-transformer association matrix is ​​constructed, including 100 nodes and 10 transformers. The minimum impedance distance is calculated using the Dijkstra algorithm, for example, the distance from node A to transformer 1 is 0.5 ohms, and the distance to transformer 2 is 0.8 ohms, and the affinity between the over-limit node and the transformer is obtained. The Newton-Raphson method is used for power flow calculation to obtain the voltage sensitivity coefficient, such as the sensitivity of node A to transformer 1 is 0.95, and the sensitivity to transformer 2 is 0.75. Combined with the rated capacity of the transformer (such as 500kVA), the voltage regulation range (±5%) and the current load level (80%), a 10x100 voltage regulation capability matrix is ​​constructed. According to the affinity between the over-limit node and the transformer, the voltage over-limit parameter and the voltage regulation capability matrix, a voltage over-limit responsibility allocation model is constructed. The NSGA-II algorithm is used to solve the voltage over-limit impact weight, with the population size set to 100, the number of iterations to 200, the crossover probability to 0.9, and the mutation probability to 0.1. The objective function of the voltage over-limit responsibility allocation model includes minimizing the total responsibility deviation and the responsibility balance, and the voltage over-limit impact weights of the 10 transformers are obtained. The contribution of each transformer to the voltage over-limit is calculated based on the voltage over-limit impact weight, such as the contribution of transformer 1 is 30%. The weighted coefficient method is used for correction, with the transformer load rate weight of 0.4 and the power supply radius weight of 0.6, and the correction coefficient is obtained after normalization. For example, the load rate of transformer 1 is 85%, the power supply radius is 2km, and the corrected contribution is 28%, which is the voltage over-limit responsibility quantification result. Finally, the rationality of the responsibility allocation is verified by comparing historical data or power flow simulation, and the correlation coefficient between the responsibility allocation result and the actual voltage change is calculated to ensure the accuracy of the quantitative result. For example, by comparing historical data, the Pearson correlation coefficient is calculated, and a correlation of 0.92 is obtained to verify the rationality of the responsibility allocation.

[0045] Step S7, based on the voltage over-limit responsibility quantification result, obtain the load distribution data of the corresponding distribution station area from the distribution management system; extract the load distribution characteristics in the load distribution data, and determine the access point of the energy storage system.

[0046] Based on the quantified result of voltage over-limit responsibility, the load distribution data of the corresponding distribution station area is obtained from the distribution management system, and the load curve of each node in the distribution station area is extracted; according to the load curve of each node, the load characteristics are extracted; based on the load characteristics and the grid topology, a node evaluation index system is constructed, including load fluctuation rate, voltage sensitivity, line loss contribution rate and power supply radius; based on the node evaluation index system, a non-dominated sorting genetic algorithm NSGA-II is used to optimize the potential access points of the energy storage system and determine the optimal access point of the energy storage system.

[0047] In some embodiments, based on the voltage over-limit responsibility quantification result, the load distribution data of the corresponding distribution substation in the distribution management system is obtained, the 24-hour load curve of each node in the distribution substation is extracted, and the load characteristics are extracted, including calculating the peak-to-valley difference and average load of the load curve, and using the ratio of the standard deviation to the average load to calculate the load fluctuation rate. In combination with the load characteristics and the grid topology, the power flow sensitivity method is used to calculate the influence of each node on the voltage, the node voltage sensitivity is obtained, and the line loss contribution rate of each node is calculated, and a node evaluation index system is constructed, including load fluctuation rate, voltage sensitivity, line loss contribution rate and power supply radius. Based on the node evaluation index system, the non-dominated sorting genetic algorithm NSGA-II is used to optimize the potential access points of the energy storage system, considering the load balance, voltage improvement effect and line loss reduction, and determining the optimal energy storage system access point. For example, according to the load characteristics and voltage over-limit parameters, the capacity of the energy storage system is preliminarily estimated, and the required active power and reactive power capacity are calculated, providing a reference basis for the subsequent detailed design of the energy storage system. Obtain the load data of the distribution area from the distribution management system, extract the 24-hour load curve of 100 nodes, calculate the peak-to-valley difference (such as 300kW), average load rate (75%) and load fluctuation rate (20%). Use the K-means clustering algorithm, set K=3, select the peak-to-valley difference, load rate and fluctuation rate as features to classify the load. After 50 iterations, the load distribution of residential (40%), commercial (35%) and industrial (25%) types is obtained. Using the power flow sensitivity method, a 100x100 Jacobian matrix is ​​constructed to calculate the node voltage sensitivity, such as the voltage sensitivity of node 1 to node 2 is 0.05pu / MW. Through line loss calculation, the line loss contribution rate of each node is obtained, such as the contribution rate of node 3 is 2.5%. Construct a node evaluation index system and normalize the four indicators. For example, the comprehensive score of node 4 is 0.85. The NSGA-II algorithm is used to optimize the energy storage system access point, with a population size of 100, an iteration number of 200, a crossover probability of 0.9, and a mutation probability of 0.1. The optimization objectives include a 10% increase in load balance, a 0.02 pu improvement in voltage, and a 3% reduction in line loss. After optimization, node 6 is determined to be the optimal energy storage system access point. Based on the load characteristics of the node (peak value 450kW, valley value 150kW) and the voltage over-limit parameter (0.03pu), the estimated energy storage capacity is 200kWh, the power is 300kW, and the reactive capacity is 150kvar.

[0048] Step S9, optimizing the capacity of the energy storage system according to the voltage sensitivity and load characteristics of the energy storage system access point to obtain an initial value of the energy storage capacity, specifically includes:

[0049] Based on the voltage sensitivity and load characteristics of the energy storage system access point, a voltage curve of the access point is obtained through power flow calculation; the voltage curve is compared with the standard voltage range to generate a voltage margin curve of the access point; according to the voltage margin curve of the access point, the capacity of the energy storage system is optimized; based on the energy storage system capacity after the optimized configuration, it is determined whether the voltage improvement effect and load balance degree of the access point reach the set threshold, and if so, it is determined as the initial value of the energy storage system capacity.

[0050] Specifically, the load curve of each access point is obtained, and the power flow calculation is performed according to the load curve to obtain the voltage curve of the access point; if the voltage curve exceeds the upper and lower limits of the standard voltage, the voltage margin curve is calculated. The energy storage capacity is optimized by using a particle swarm optimization algorithm, wherein the number of particles is set to a preset value, the maximum number of iterations is set to a preset value, and the learning factor is set to a preset value. The actual required capacity is calculated according to the global optimization search results, and the actual required capacity is obtained by multiplying the global optimization search results with the preset charge and discharge efficiency; the maximum charge and discharge power is calculated according to the actual required capacity and the preset power capacity ratio. The power flow calculation is performed based on the energy storage capacity after the optimized configuration, and the voltage improvement effect and load balance degree of each access point are judged; if the voltage improvement effect is not lower than the preset threshold, and the load balance degree is improved by not lower than the preset threshold, it is determined as the initial value of the energy storage capacity of the access point; if the voltage improvement effect is lower than the preset threshold, or the load balance degree is improved by lower than the preset threshold, it is returned for optimization. A global optimization objective function is constructed, and the global optimization objective function includes the mutual influence factors of each access point; and multiple access points are coordinated and optimized according to the global optimization objective function.

[0051] For example, according to the voltage sensitivity and load characteristics of the access point, the 24-hour load curve of each access point is extracted, and the Newton-Raphson method is used to calculate the power flow to obtain the 24-hour voltage curve. Compared with the upper and lower limits of the standard voltage (0.95~1.05pu), the voltage margin curve is calculated. For example, the minimum voltage margin of node A is 0.02pu. The particle swarm optimization algorithm is used to preliminarily optimize the energy storage capacity of each access point. The number of particles is set to 50, the maximum number of iterations is 100, and the learning factor c1=c2=2. The optimization goal is to maximize the voltage improvement effect and minimize the energy storage investment cost. The constraints include voltage margin requirements and upper and lower limits of energy storage capacity. The global optimization search results are evaluated, and the actual required capacity is calculated considering 90% of the charge and discharge efficiency; according to the power-to-capacity ratio of 0.5C, the maximum charge and discharge power of the energy storage system is determined, and the optimized energy storage capacity is corrected to obtain the corrected energy storage capacity value. Based on the corrected energy storage capacity, the flow calculation verification is carried out to check the voltage improvement effect and load balance of each access point. If the voltage improvement effect is not less than 2% and the load balance is not less than 10%, the initial value of the energy storage capacity of the access point is determined, otherwise the optimization step is returned for re-optimization. Coordinated optimization is carried out for multiple access points, and a global optimization objective function is constructed. The mutual influence of each access point is comprehensively considered to avoid the overall suboptimal problem caused by single-point optimization. The data of 5 access points are obtained from the distribution management system, and the 24-hour load curve is extracted. For example, the peak load of node A is 500kW and the valley load is 100kW. Coordinated optimization is carried out for the 5 access points, and a global objective function ∑(wi×fi) is constructed, where wi is the weight and fi is the single-point objective function. The Monte Carlo method is used to generate 1,000 sets of scenarios, and probabilistic flow calculation is performed to obtain the flow calculation results. If the voltage improvement effect of more than 95% of the scenarios is ≥2% and the load balance is improved by ≥10%, the initial value of the energy storage capacity is determined according to the flow calculation results. According to the results of the power flow calculation, the final energy storage capacity of node A is 230kWh and the power is 115kW, that is, the initial value of the energy storage capacity is 230kWh.

[0052] Step S11, obtaining the response characteristics of the energy storage system according to the initial value of the energy storage capacity; optimizing the access points of the energy storage system according to the response characteristics of the energy storage system to obtain a globally optimal energy storage system configuration solution, specifically including:

[0053] Obtain the energy storage system response characteristics corresponding to the initial value of the energy storage capacity in the energy storage system parameter database; use the least squares method to fit the energy storage system response characteristics to obtain the energy storage system mathematical model; perform time series simulation on the energy storage system mathematical model, and connect it to the distribution network simulation environment to perform power flow calculation to obtain the power flow calculation results; generate an energy storage system access effect evaluation index based on the power flow calculation results; determine whether the energy storage system access effect evaluation index meets the preset target value, and if not, use the central difference method to generate the sensitivity of the energy storage system access effect evaluation index to the energy storage capacity; adjust the energy storage capacity configuration plan based on the sensitivity result; optimize the access point of the energy storage system based on the adjusted energy storage capacity configuration plan to obtain the global optimal energy storage system configuration plan.

[0054] Specifically, the energy storage system response characteristics corresponding to the initial value of the energy storage capacity in the energy storage system parameter database are obtained, and the energy storage system response characteristics include charge and discharge efficiency curves, response speed, power characteristic curves and cycle life; the energy storage system response characteristics are fitted by the least squares method to obtain the energy storage system mathematical model; according to the energy storage system mathematical model, time series simulation is performed in the MATLAB / Simulink environment; the energy storage system mathematical model is connected to the distribution network simulation environment, and time-scale power flow calculation is performed to obtain the voltage distribution, load curve and line loss data before and after the connection; according to the power flow calculation results, Calculate the evaluation value of the energy storage system access effect evaluation index, the access effect evaluation index includes voltage improvement rate, load balance improvement ratio, line loss reduction rate and economic index; judge whether the evaluation value meets the preset target value, if the evaluation value does not meet the preset target value, use the central difference method to calculate the sensitivity of the access effect evaluation index to the energy storage capacity; according to the sensitivity result, adjust the energy storage capacity configuration plan, return to the time series simulation for iterative optimization; judge whether the evaluation value meets the preset target value or reaches the maximum number of iterations, if it meets the preset target value or reaches the maximum number of iterations, end the iterative optimization.

[0055] For example, according to the initial value of the energy storage capacity, the response characteristics of the energy storage system of the corresponding model are obtained from the energy storage system parameter database, including parameters such as the charging and discharging efficiency curve, response speed, power characteristic curve and cycle life. The equivalent circuit model is used to represent the charging and discharging characteristics of the energy storage system. The least squares method is used to fit the response characteristics of the energy storage system, and the mathematical model of the energy storage system is constructed. MATLAB / Simulink is used for time series simulation, and the time step is set to 15 minutes. The mathematical model of the energy storage system is connected to the distribution network simulation environment, and the power flow calculation of the 24-hour time scale is performed to obtain the voltage distribution, load curve and line loss data before and after the connection. Based on the simulation results, the evaluation values ​​of the energy storage system access effect evaluation indicators are calculated. The energy storage system access effect evaluation indicators include voltage improvement rate, load balance improvement ratio, line loss reduction rate and economic index. They are compared with the preset target values ​​to determine whether the access effect meets the requirements. The preset target values ​​are: voltage improvement rate ≥3%, load balance improvement ratio ≥15%, line loss reduction rate ≥5%, and investment payback period ≤8 years. If the access effect does not meet the requirements, the central difference method is used to calculate the sensitivity of the access effect evaluation index to the energy storage capacity. The step size is set to ±5% of the initial capacity. The energy storage capacity configuration plan is adjusted according to the sensitivity result, and the simulation step is returned to perform iterative optimization until the access effect requirements are met or the maximum number of iterations is reached.

[0056] For example, the parameters of lithium-ion batteries with an initial capacity of 500kWh are obtained from the energy storage system parameter database, including 95% charge and discharge efficiency, 10ms response speed, 0.5C-2C power characteristic curve and 4000 cycle life. The Thevenin equivalent circuit model is used to represent the energy storage system, and the Levenberg-Marquardt algorithm is used for parameter fitting to obtain a mathematical model of energy storage with an internal resistance of 0.05Ω, a polarization resistance of 0.02Ω and a polarization capacitance of 5000F. A distribution network model with 10 nodes is built in MATLAB / Simulink, the time step is set to 15 minutes, and the simulation is run for 24 hours to obtain the voltage distribution, load curve and line loss data before and after the energy storage is connected. The evaluation values ​​of the access effect evaluation indicators are calculated: the voltage improvement rate is 2.8%, the load balance is improved by 13%, the line loss reduction rate is 4.5%, and the investment recovery period is 8.5 years, which did not reach the preset target. The central difference method is used to calculate the sensitivity of the energy storage system access effect evaluation index to the energy storage capacity. For example, when the capacity increases by 5%, the voltage improvement rate increases by 0.2%. According to the sensitivity, the capacity is adjusted to 525kWh, and the simulation is repeated to obtain: voltage improvement rate 3.1%, load balance degree improvement 15.5%, line loss reduction rate 5.2%, and investment payback period 7.8 years, which meet the requirements. The charging and discharging strategy is optimized, and a scheduling algorithm based on fuzzy logic is adopted. Four fuzzy rules are set, such as "if the voltage is low and the load is light, high power discharge is performed", to further improve the access effect: voltage improvement rate 3.3%, load balance degree improvement 16.2%, line loss reduction rate 5.5%, and investment payback period 7.5 years.

[0057] Among them, according to the adjusted energy storage capacity configuration plan, the access point of the energy storage system is optimized to obtain the global optimal energy storage system configuration plan, which specifically includes: based on the adjusted energy storage capacity configuration plan, the voltage sensitivity, load distribution characteristics, line loss rate, and power supply radius of each node are obtained, and a multi-dimensional node evaluation index matrix is ​​constructed; according to the multi-dimensional node evaluation index matrix, an improved particle swarm optimization algorithm with adaptive inertia weight and chaotic perturbation is used to perform global optimization search to obtain global optimization search results; wherein, the global optimization search takes maximizing voltage improvement effect and load balance, minimizing line loss rate and investment cost as the objective function, and sets constraints on the access point of the energy storage system; the Latin hypercube sampling method is used to generate typical scenarios, and the robustness of the global optimization search results is evaluated to obtain robustness evaluation results; according to the hierarchical analysis evaluation system, the global optimization search results are evaluated to obtain comprehensive evaluation results, wherein the hierarchical analysis evaluation system includes voltage improvement effect, load balance, line loss rate and investment cost.

[0058] For example, according to the adjusted energy storage capacity configuration scheme, a distribution network flow calculation model is constructed to calculate the voltage sensitivity, load distribution characteristics, line loss rate and power supply radius of each node, and a 4×N-dimensional node evaluation index matrix is ​​constructed. The improved particle swarm optimization algorithm with adaptive inertia weight and chaotic perturbation is adopted to maximize the voltage improvement effect and load balance, minimize the line loss rate and investment cost as the objective function, set the constraints of the energy storage system access point, and conduct global optimization search. A single scheme evaluation is performed on the global optimization search results to calculate the voltage improvement rate, load balance improvement ratio, line loss reduction rate and investment payback period. The Latin hypercube sampling method is used to generate 100 groups of typical scenarios, including load fluctuations of ±20% and distributed power access of 0-30%, to evaluate the robustness of the energy storage system configuration scheme. Based on the results of multi-scenario analysis, the hierarchical analysis method is used to comprehensively evaluate the energy storage system configuration scheme, and a four-layer evaluation system is established, with the voltage improvement effect weight of 0.3, the load balance weight of 0.25, the line loss rate weight of 0.2, and the investment cost weight of 0.25, and the comprehensive score of each scheme is calculated. At the same time, the costs and benefits of the energy storage system over its entire life cycle, including equipment investment, operation and maintenance costs, electricity price benefits, and ancillary service income, are taken into account, the net present value and internal rate of return are calculated, and the solution with the highest comprehensive score and economic indicators that meet the requirements is selected as the globally optimal energy storage system configuration solution.

[0059] In some embodiments, based on the adjusted energy storage capacity configuration scheme, a distribution network flow model containing 100 nodes is constructed, and the parameters of each node are calculated using the Newton-Raphson method to obtain a 4×100-dimensional node evaluation index matrix. The voltage sensitivity range is 0.01-0.05pu / MW, the load distribution characteristics are represented by the peak-to-valley difference, ranging from 100-500kW, the line loss rate is 1-5%, and the power supply radius is 0.5-5km. The improved particle swarm algorithm is used for optimization, the population size is set to 100, the maximum number of iterations is 200, the adaptive inertia weight range is 0.4-0.9, and the chaotic mapping uses Logistic mapping. The global optimization search results show that the optimal access location is node 25, and the energy storage capacity is 2MWh. The evaluation results of a single scheme are as follows: the voltage improvement rate is 3.5%, the load balance degree is increased by 18%, the line loss reduction rate is 6%, and the investment payback period is 7.5 years. Latin hypercube sampling is used to generate 100 sets of scenarios, with a load fluctuation range of -20%~+20%, and a distributed power supply access ratio of 0-30%. Multi-scenario analysis shows that more than 95% of scenarios meet performance requirements. In the analytic hierarchy process evaluation, a four-layer structure of "goal-criteria-indicator-scheme" was established, and the consistency ratio CR=0.02<0.1 met the consistency requirements. The optimal scheme was calculated to have a comprehensive score of 0.85. Full life cycle economic analysis: equipment investment of 10 million yuan, annual operation and maintenance cost of 500,000 yuan, electricity price income of 2 million yuan / year, ancillary service income of 1 million yuan / year, net present value of 15 million yuan in the 15-year life cycle, and internal rate of return of 12%. Finally, node 25 was determined to be the optimal access location, and the solution with a storage capacity of 2MWh was the global optimal configuration.

[0060] The embodiment of the present application provides a method for configuring energy storage parameters in a distribution station area, which divides the power supply area by acquiring the power supply range information and power demand density information of multiple transformers to obtain the overlapping area of ​​the transformer power supply range; identifies the over-limit node based on the real-time voltage data of multiple nodes in the overlapping area of ​​the transformer power supply range, and generates voltage over-limit parameters; determines the voltage over-limit influence weight of the transformer on the over-limit node based on the topological connection relationship between the over-limit node and the transformer, the voltage over-limit parameters and the voltage regulation capability matrix; generates the voltage over-limit responsibility quantification result based on the voltage over-limit influence weight; and generates the voltage over-limit responsibility quantification result based on the voltage over-limit responsibility quantification. As a result, the load distribution data of the corresponding distribution station area is obtained from the distribution management system, the load distribution characteristics in the load distribution data are extracted, and the access point of the energy storage system is determined; according to the voltage sensitivity and load characteristics of the access point of the energy storage system, the energy storage system capacity is optimized and configured to obtain the initial value of the energy storage capacity of the access point; according to the initial value of the energy storage capacity, the response characteristics of the energy storage system are obtained; according to the response characteristics of the energy storage system, the access point of the energy storage system is optimized to obtain the global optimal energy storage system configuration scheme, which solves the technical problem of optimizing the access point of the energy storage system and achieves the technical effect of maximizing the economic benefits of the energy storage system while ensuring the power supply quality.

[0061] See also Figure 2 The embodiment of the present application provides a distribution station area energy storage parameter configuration system, the system comprising: a power supply range division module, used to obtain the power supply range information and power demand density information of multiple transformers, divide the power supply area, and obtain the transformer power supply range overlap area; a voltage over-limit responsibility quantification module, used to identify the over-limit type node based on the real-time voltage data of multiple nodes in the transformer power supply range overlap area, and generate voltage over-limit parameters; used to determine the voltage over-limit influence weight of the transformer on the over-limit type node according to the topological connection relationship between the over-limit type node and the transformer, the voltage over-limit parameters and the voltage regulation capability matrix ... The voltage over-limit impact weight generates a voltage over-limit responsibility quantification result; the energy storage system configuration module is used to obtain the load distribution data of the corresponding distribution station area from the distribution management system based on the voltage over-limit responsibility quantification result, extract the load distribution characteristics in the load distribution data, and determine the energy storage system access point; it is used to optimize the energy storage system capacity according to the voltage sensitivity and load characteristics of the energy storage system access point to obtain the initial value of the energy storage capacity; it is used to obtain the energy storage system response characteristics according to the initial value of the energy storage capacity; according to the energy storage system response characteristics, optimize the energy storage system access point to obtain the global optimal energy storage system configuration plan.

[0062] See also Figure 3 , Figure 3It is a structural diagram of a computer device provided by an embodiment of the present application, and the computer device includes: one or more processors 10, a memory 20, and an interface for connecting various components, including a high-speed interface and a low-speed interface. The various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 3 A processor 10 is taken as an example.

[0063] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0064] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0065] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0066] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0067] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0068] The embodiment of the present application also provides a computer-readable storage medium. The above method according to the embodiment of the present application can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0069] The embodiment of the present application provides a computer program product, which includes computer instructions, which are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the method of any embodiment of the present application.

[0070] Although the embodiments of the present application are described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations are all within the scope defined by the appended claims.

[0071] The systems or modules described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0072] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0073] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0074] The present application is described with reference to the flowcharts and / or block diagrams of the methods, systems, and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0075] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0076] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0077] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0078] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0079] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

[0080] Although the embodiments of the present application have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for configuring energy storage parameters in a distribution station area, characterized in that: The method comprises: Obtain the power supply range information and power demand density information of multiple transformers, divide the power supply area, and obtain the overlapping area of ​​the transformer power supply range; Based on the real-time voltage data of multiple nodes in the overlapping area of ​​the transformer power supply range, identifying the over-limit nodes and generating voltage over-limit parameters; Determine the voltage over-limit influence weight of the transformer on the over-limit node according to the topological connection relationship between the over-limit node and the transformer, the voltage over-limit parameter and the voltage regulation capability matrix; generate a voltage over-limit responsibility quantification result according to the voltage over-limit influence weight; Based on the voltage over-limit responsibility quantification result, the load distribution data of the corresponding distribution station area is obtained from the distribution management system, the load distribution characteristics in the load distribution data are extracted, and the access point of the energy storage system is determined; According to the voltage sensitivity and load characteristics of the access point, the capacity of the energy storage system is optimized to obtain an initial value of the energy storage capacity of the access point; According to the initial value of the energy storage capacity, the response characteristics of the energy storage system are obtained; according to the response characteristics of the energy storage system, the access points of the energy storage system are optimized to obtain a globally optimal energy storage system configuration solution.

2. The method according to claim 1, characterized in that The obtaining of the power supply range information and the power demand density information of the multiple transformers, dividing the power supply area, and obtaining the overlapping area of ​​the transformer power supply range specifically includes: Based on the power supply range information and power demand density information of the multiple transformers, the power supply area is divided using a GIS-based power grid spatial analysis tool to obtain an initial power supply range of each transformer; Based on the initial power supply range, and the line impedance parameters and load distribution data obtained from the power distribution management system, a power grid structure model is constructed, and power flow calculation is performed on the power grid structure model to obtain the power grid operation state of the initial power supply range; whether the power grid operation state of the initial power supply range meets the first preset condition is determined, and if not, the initial power supply range is optimized and adjusted to generate an actual power supply range; According to the actual power supply range, an overlapping area of ​​the transformer power supply range is identified.

3. The method according to claim 2, characterized in that The step of identifying the overlapping area of ​​the transformer power supply range according to the actual power supply range specifically includes: The actual power supply range is processed by using a spatial superposition operation to generate a preliminary overlapping area; Obtaining basic characteristic data of the preliminary overlapping area, including the corresponding area and number of users, and power consumption characteristic data of the preliminary overlapping area, including user density, load type and power consumption pattern; Based on the basic characteristic data and the power consumption characteristic data, generating an overlapping area characteristic report of the preliminary overlapping area, and evaluating the voltage quality and line loss rate of the preliminary overlapping area according to the overlapping area characteristic report; Based on the voltage quality and line loss rate of the preliminary overlapping area, as well as the remaining capacity of the transformer and the power supply radius limit, a multi-objective optimization algorithm is used to formulate a power supply plan, determine the power supply transformer of each user, and generate the overlapping area of ​​the transformer power supply range.

4. The method according to claim 1, characterized in that The method of identifying the over-limit node based on the real-time voltage data of multiple nodes in the overlapping area of ​​the transformer power supply range and generating the voltage over-limit parameter specifically includes: Based on the distribution automation system, real-time voltage data of multiple nodes in the overlapping area of ​​the transformer power supply range are obtained; Preprocessing the real-time voltage data of the plurality of nodes using a preset sliding time window to obtain voltage time series of the plurality of nodes; Based on the voltage time series, voltage statistical characteristics and voltage fluctuation spectrum characteristics are obtained; Based on the voltage statistical characteristics and voltage fluctuation spectrum characteristics, the over-limit node is identified, and voltage over-limit parameters are generated, including the voltage over-limit amplitude and over-limit duration.

5. The method according to claim 1, characterized in that The determining, according to the topological connection relationship between the over-limit node and the transformer, the voltage over-limit parameter, and the voltage regulation capability matrix, of the voltage over-limit influence weight of the transformer on the over-limit node specifically includes: Obtaining affinity between the over-limit node and the transformer according to the topological connection relationship and the minimum impedance distance between the over-limit node and the transformer; Construct a voltage regulation capability matrix based on the rated capacity, voltage regulation range, current load level, and node sensitivity coefficient of the transformer; According to the affinity between the over-limit node and the transformer, the voltage over-limit parameter and the voltage regulation capability matrix, a voltage over-limit responsibility allocation model is constructed; wherein the voltage over-limit responsibility allocation model takes minimizing the total responsibility deviation and the responsibility balance as the objective function; The non-dominated sorting genetic algorithm NSGA-II is used to process the voltage over-limit responsibility allocation model to obtain the voltage over-limit influence weight of the transformer on the over-limit node.

6. The method according to claim 1, characterized in that The step of obtaining the load distribution data of the corresponding distribution area from the distribution management system based on the voltage over-limit responsibility quantification result; extracting the load distribution characteristics in the load distribution data, and determining the energy storage system access point specifically includes: Based on the voltage over-limit responsibility quantification result, the load distribution data of the corresponding distribution substation is obtained from the distribution management system, and the load curve of each node in the corresponding distribution substation is extracted; Extract load characteristics according to the load curve of each node; Based on the load characteristics and grid topology, a node evaluation index system is constructed, including load fluctuation rate, voltage sensitivity, line loss contribution rate and power supply radius; Based on the node evaluation index system, a non-dominated sorting genetic algorithm NSGA-II is used to optimize the potential access points of the energy storage system and determine the optimal access point of the energy storage system.

7. The method according to claim 1, characterized in that The step of optimizing the energy storage system capacity according to the voltage sensitivity and load characteristics of the access point to obtain the initial value of the energy storage capacity of the access point specifically includes: Based on the voltage sensitivity and load characteristics of the access point, a load curve is extracted, and a voltage curve of the access point is obtained through power flow calculation; Comparing the voltage curve with a standard voltage range to generate a voltage margin curve of the access point; Optimizing the configuration of the energy storage system capacity according to the voltage margin curve of the access point; Based on the capacity of the energy storage system after the optimal configuration, it is determined whether the voltage improvement effect and load balance degree of the access point have reached the set threshold. If so, the initial value of the energy storage system capacity of the access point is determined.

8. The method according to claim 1, characterized in that The step of obtaining the response characteristics of the energy storage system according to the initial value of the energy storage capacity; and optimizing the access points of the energy storage system according to the response characteristics of the energy storage system to obtain a globally optimal energy storage system configuration solution specifically includes: Obtaining the energy storage system response characteristics corresponding to the initial value of the energy storage capacity in the energy storage system parameter database; fitting the energy storage system response characteristics using the least squares method to obtain a mathematical model of the energy storage system; Performing time series simulation on the mathematical model of the energy storage system, and connecting to the distribution network simulation environment to perform power flow calculation to obtain power flow calculation results; Using the energy storage system access effect evaluation index to evaluate the power flow calculation result, and generate an evaluation value; Determine whether the evaluation value meets the preset target value. If not, use the central difference method to generate the sensitivity of the energy storage system access effect evaluation index to the energy storage capacity; and adjust the energy storage capacity configuration plan according to the sensitivity result; According to the adjusted energy storage capacity configuration plan, the access points of the energy storage system are optimized to obtain the global optimal energy storage system configuration plan.

9. The method according to claim 8, characterized in that The method of optimizing the access points of the energy storage system according to the adjusted energy storage capacity configuration scheme to obtain the global optimal energy storage system configuration scheme specifically includes: Based on the adjusted energy storage capacity configuration scheme, the voltage sensitivity, load distribution characteristics, line loss rate, and power supply radius of each node are obtained to construct a multi-dimensional node evaluation index matrix; According to the multi-dimensional node evaluation index matrix, an improved particle swarm optimization algorithm with adaptive inertia weight and chaotic perturbation is used to perform global optimization search to obtain global optimization search results; wherein, the global optimization search takes maximizing voltage improvement effect and load balance, minimizing line loss rate and investment cost as objective functions, and sets constraints on energy storage system access points; A typical scenario is generated by using Latin hypercube sampling method, and the robustness of the global optimization search results is evaluated to obtain a robustness evaluation result; according to a hierarchical analysis evaluation system, the global optimization search results are evaluated to obtain a comprehensive evaluation result, wherein the hierarchical analysis evaluation system includes voltage improvement effect, load balance, line loss rate and investment cost; Based on the robustness evaluation result and the comprehensive evaluation result, the access point of the energy storage system is optimized to obtain a globally optimal energy storage system configuration solution.

10. A distribution station area energy storage parameter configuration system, characterized in that: The system comprises: The power supply range division module is used to obtain the power supply range information and power demand density information of multiple transformers, divide the power supply area, and obtain the overlapping area of ​​the transformer power supply range; A voltage over-limit responsibility quantification module is used to identify over-limit nodes based on the real-time voltage data of multiple nodes in the overlapping area of ​​the transformer power supply range, and generate voltage over-limit parameters; is used to determine the voltage over-limit influence weight of the transformer on the over-limit node according to the topological connection relationship between the over-limit node and the transformer, the voltage over-limit parameters and the voltage regulation capability matrix; and is used to generate a voltage over-limit responsibility quantification result according to the voltage over-limit influence weight; The energy storage system configuration module is used to obtain the load distribution data of the corresponding distribution station area from the distribution management system based on the voltage over-limit responsibility quantification result, extract the load distribution characteristics in the load distribution data, and determine the energy storage system access point; it is used to optimize the energy storage system capacity according to the voltage sensitivity and load characteristics of the energy storage system access point to obtain the initial value of the energy storage capacity; it is used to obtain the energy storage system response characteristics according to the initial value of the energy storage capacity; according to the energy storage system response characteristics, optimize the energy storage system access point to obtain the global optimal energy storage system configuration plan.

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