Cooperative configuration method of electric hydrogen energy storage and reactive power compensation for new energy distribution network

Abstract

The application discloses a kind of for new energy distribution network electric hydrogen energy storage and reactive power compensation collaborative configuration method, belong to new energy distribution network optimization operation technical field, comprising: obtaining the topological structure diagram of new energy distribution network;Electrical coupling strength matrix between nodes is constructed;Calculating partition electrical density, partition source load balance degree and partition scale configuration degree;Build partition comprehensive evaluation function and solve to obtain final partition set;Build the daily average comprehensive cost target of new energy distribution network, node source load fluctuation suppression target, node voltage support target and constraint condition;Collaborative configuration multi-objective optimization model is constructed;The non-dominated sorting nuts optimization algorithm is used to solve collaborative configuration multi-objective optimization model, and the Pareto candidate solution set is obtained;According to Pareto candidate solution set, entropy weight method is used to obtain electric hydrogen energy storage and reactive power compensation collaborative configuration result. The source load fluctuation suppression and node voltage active support capability of new energy distribution network can be improved, and the solving efficiency is high.

Description

Technical Field

[0001] This invention belongs to the field of new energy distribution network optimization operation technology, specifically involving a method for coordinated configuration of electric hydrogen energy storage and reactive power compensation in new energy distribution networks. Background Technology

[0002] As the proportion of new energy sources such as wind power and photovoltaics in the distribution network continues to increase, problems such as enhanced source-load fluctuations, complex power flow distribution, and insufficient voltage support capacity are becoming increasingly prominent during the operation of the distribution network. The output power of new energy sources is random and intermittent, while the load demand has obvious time-series variation characteristics. The superposition of the two can easily lead to source-load mismatch in local areas, aggravated fluctuations in the power exchange of the upper-level grid, node voltage deviation, and a decline in the capacity to absorb new energy sources.

[0003] Electric energy storage features fast response and flexible power regulation, making it suitable for smoothing short-term source-load fluctuations; hydrogen energy storage offers long-term energy storage and cross-period energy transfer capabilities, making it suitable for absorbing and reusing surplus electricity from new energy sources; reactive power compensation equipment can provide reactive power support and improve node voltage levels. Therefore, coordinating electric energy storage, hydrogen energy storage, and reactive power compensation equipment is beneficial for enhancing the active support capability of distribution networks with a high proportion of new energy sources connected to the grid.

[0004] However, existing methods often focus on the configuration of single energy storage devices or optimize only from the perspectives of economy and power balance, without fully considering the complementary relationship between electric energy storage, hydrogen energy storage, and reactive power compensation devices in terms of short-term power regulation, long-term energy transfer, and voltage support. Furthermore, directly selecting and quantifying the electric energy storage system, hydrogen energy storage system, and reactive power compensation device across the entire network can easily lead to problems such as a large number of candidate nodes, high dimensionality of decision variables, and low optimization efficiency.

[0005] Therefore, it is necessary to propose a method for the coordinated configuration of hydrogen energy storage and reactive power compensation in new energy distribution networks. Summary of the Invention

[0006] The purpose of this invention is to address the above-mentioned problems by proposing a method for the coordinated configuration of electric-hydrogen energy storage and reactive power compensation in new energy distribution networks. This method reduces the optimization dimensionality by partitioning the system and performs coordinated site selection and capacity determination of the electric energy storage system, hydrogen energy storage system and reactive power compensation equipment within each partition. This improves the ability to suppress source-load fluctuations and the ability to actively support node voltages, while also achieving high solution efficiency.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0008] The present invention proposes a method for coordinated configuration of hydrogen energy storage and reactive power compensation for new energy distribution networks, comprising the following steps:

[0009] S1. Obtain the topology diagram of the new energy distribution network;

[0010] S2. Construct the electrical coupling strength matrix between nodes of the new energy distribution network based on the topology diagram;

[0011] S3. Calculate the electrical density of the partition, the source-load balance of the partition, and the scale configuration of the partition based on the electrical coupling strength matrix between nodes;

[0012] S4. Construct and solve a comprehensive evaluation function for the partition based on the partition electrical density, partition source-load balance, and partition scale configuration to obtain the final partition set.

[0013] S5. Based on the final partition set, construct the daily average comprehensive cost target, node source-load fluctuation suppression target, node voltage support target, and constraints of the new energy distribution network.

[0014] S6. Construct a collaborative configuration multi-objective optimization model based on the daily average comprehensive cost target, the nodal source-load fluctuation suppression target, the nodal voltage support target, and constraints.

[0015] S7. Solve the cooperative configuration multi-objective optimization model using the non-dominated sorting nut optimization algorithm to obtain the Pareto candidate solution set;

[0016] S8. Based on the Pareto candidate solution set, the entropy weight method is used to obtain the coordinated configuration results of hydrogen energy storage and reactive power compensation.

[0017] Preferably, the electrical coupling strength matrix between nodes of the new energy distribution network is constructed based on the topology diagram, as follows:

[0018] S21. Construct a linearized power flow sensitivity equation based on the power flow Jacobian matrix;

[0019] S22. Obtain the power flow sensitivity matrix of voltage magnitude versus injected active power based on the linearized power flow sensitivity equation. To calculate the electrical dissimilarity matrix between nodes in a new energy distribution network, where nodes... With nodes Electrical differences The calculation is as follows:

[0020] S221, Computation Node For nodes Voltage influence factor :

[0021]

[0022] in, express The Middle Line 1 Column elements The absolute value; express The Middle Line 1 Column elements The absolute value, , This represents the total number of nodes in the node set of the topological graph. It is a positive number, used to avoid the denominator being zero;

[0023] S222, Computation Node With nodes Electrical differences :

[0024]

[0025] in, For nodes For nodes Voltage influence factor, For nodes For nodes Voltage influence factor, The set of nodes that receive loads in the node set of the topology graph;

[0026] S23. Transform the electrical difference matrix between nodes into the electrical coupling strength matrix between nodes, where, node With nodes Inter-electrical coupling strength The calculation is as follows:

[0027]

[0028] in, This represents the largest electrical difference in the inter-node electrical difference matrix.

[0029] Preferably, a linearized power flow sensitivity equation is constructed based on the power flow Jacobian matrix, as shown in the following formula:

[0030]

[0031] in, This is the vector of changes in the voltage phase angle of all nodes in the node set of the topology graph. This is a vector representing the change in voltage magnitude of all nodes in the node set of the topology graph. This is the vector of changes in injected active power for all nodes in the node set of the topology graph. This is the vector of changes in injected reactive power for all nodes in the node set of the topology graph. For the trend of Jacobi matrix The inverse matrix also represents the power flow sensitivity matrix. The power flow sensitivity matrix represents the voltage phase angle to injected active power. The power flow sensitivity matrix represents the voltage phase angle to injected reactive power. This represents the power flow sensitivity matrix to the injected reactive power. This represents the power flow sensitivity matrix to the injected active power. , , , All dimensions are .

[0032] Preferably, the electrical density of the partition, the source-load balance of the partition, and the size configuration of the partition are calculated based on the electrical coupling strength matrix between nodes, as follows:

[0033] 1) Zoned electrical tightness:

[0034]

[0035] in,

[0036]

[0037]

[0038] in, For the corresponding partition set The electrical density of the partitions, , For the corresponding partition set The A set of nodes for each partition. , The number of partitions, It is the set of nodes in the topological graph; The total electrical coupling strength of the new energy distribution network; For nodes The weighted connection strength, For nodes The weighted connection strength, and , , For nodes With nodes Inter-node electrical coupling strength, i.e., the first element in the inter-node electrical coupling strength matrix. Line 1 Column elements, This represents the total number of nodes in the node set of the topological graph. For nodes and nodes The partition assignment function; It is a positive number, used to avoid the denominator being zero;

[0039] 2) Regional source-load balance:

[0040]

[0041] Among them, the corresponding partition set The Each zone in time period Net load power The formula is as follows:

[0042]

[0043] in, For the corresponding partition set The partitioned source load balance, For the first time in the scheduling cycle The maximum value of the net load power amplitude of each zone. for amplitude, The number of time periods within the scheduling cycle. For nodes During the period The active power of the load, For nodes During the period The active power output of the new energy components;

[0044] 3) Partition size configuration:

[0045]

[0046] in,

[0047]

[0048] in, For the corresponding partition set The partition size configuration degree, and , For the corresponding partition set The partition size deviation, For the corresponding partition set The The number of nodes in each partition. This represents the ideal average number of nodes in a partition.

[0049] Preferably, a comprehensive evaluation function for each zone is constructed and solved based on the zone's electrical density, zone's source-load balance, and zone's scale configuration to obtain the final set of zones, as follows:

[0050] S41. Construct a comprehensive evaluation function for the partitioning:

[0051]

[0052] in, To find the maximum value in the entire partition set, For the corresponding partition set The overall evaluation value of the zone, For the corresponding partition set The electrical density of the partitions, For the corresponding partition set The partitioned source load balance, For the corresponding partition set The partition size configuration degree, , , The weighting coefficients are, in order: the weighting coefficient for the electrical density of the zone, the weighting coefficient for the source-load balance of the zone, and the weighting coefficient for the scale configuration of the zone.

[0053] S42. Solve for the final partition set using the improved snake optimization algorithm based on the partition comprehensive evaluation function.

[0054] Preferably, the daily average comprehensive cost target, nodal source-load fluctuation suppression target, nodal voltage support target, and constraints of the new energy distribution network are constructed based on the final partition set, as follows:

[0055] 5.1) The daily average comprehensive cost target is constructed as follows:

[0056]

[0057] in, This indicates taking the minimum value. Let the daily average comprehensive cost be the objective function value. The total life-cycle cost of the entire energy storage system. The total lifecycle cost of all hydrogen energy storage systems. The total life-cycle cost of all reactive power compensation equipment. The number of days per year;

[0058] 5.2) The target for suppressing nodal source load fluctuations is constructed as follows:

[0059]

[0060] in, The objective function value for suppressing nodal source load fluctuations; For the time period Active power exchange between the new energy distribution network and the upstream power grid. For the time period Active power exchange between the new energy distribution network and the upstream power grid; The number of time periods within the scheduling cycle;

[0061] 5.3) Node voltage support target, constructed as follows:

[0062]

[0063] in, The objective function value is used to support the node voltage; For nodes During the period The voltage amplitude; For nodes The average voltage amplitude during the scheduling period; This represents the total number of nodes in the node set of the topological graph.

[0064] 5.4) Constraints, including node injection power constraints, power flow constraints, capacity constraints, and device access location constraints.

[0065] Preferably, a collaborative configuration multi-objective optimization model is constructed based on the daily average comprehensive cost target, the nodal source-load fluctuation suppression target, the nodal voltage support target, and constraints, as follows:

[0066] S61. Constructing a collaborative configuration decision set :

[0067]

[0068] in,

[0069]

[0070] in, Configure decision variables for the devices in the k-th partition of the final partition set; For the final set of partitions The set of access nodes for the energy storage system in each partition; For the final set of partitions The set of access nodes for hydrogen energy storage systems in each partition; For the final set of partitions The set of access nodes for reactive power compensation equipment in each partition; For the final set of partitions The total investment capacity of the energy storage system in each zone; For the final set of partitions The rated power of the energy storage system in each zone; For the final set of partitions The capacity of hydrogen storage devices in each zone's hydrogen energy storage system; For the final set of partitions The rated power of the electrolyzers in the hydrogen energy storage system of each zone; For the final set of partitions The rated power of the fuel cells in the hydrogen energy storage system of each zone; For the final set of partitions The capacity of reactive power compensation equipment in each zone;

[0071] S62. Establish a multi-objective optimization model for collaborative configuration based on the collaborative configuration decision set, as follows:

[0072]

[0073] in, For traversal Take the minimum value. for The weighted composite objective function value; for The objective function value of the average daily comprehensive cost; for The objective function value for suppressing nodal source load fluctuations; for The node voltage supports the objective function value; The weighting coefficients are, in order: the weighting coefficient of the objective function value of average daily comprehensive cost, the weighting coefficient of the objective function value of nodal source-load fluctuation suppression, and the weighting coefficient of the objective function value of nodal voltage support.

[0074] Preferably, the non-dominated sorting nut optimization algorithm is used to solve the cooperative configuration multi-objective optimization model to obtain the Pareto candidate solution set, as follows:

[0075] S71. Treat each candidate configuration scheme as an individual in the non-dominated sorting nut optimization algorithm, and randomly generate... One candidate configuration scheme;

[0076] S72, Obtain the number of iterations in round 0. One candidate configuration scheme, ;

[0077] S73, Obtain the first according to the updated formula. The first iteration One candidate configuration scheme, , This is the second largest iteration round;

[0078] S74, the first Round iteration and the first All candidate configuration schemes in the round of iterations together constitute the first... The set to be selected in each round of iterations;

[0079] S75, according to the... The dominance relationship satisfied by any two candidate configuration schemes in the set to be screened in the round of iteration will be the first... In each iteration, all candidate configuration schemes in the set to be screened are divided into several non-dominated levels;

[0080] S76, respectively, the first The weighted comprehensive objective function values ​​of the device configuration decision variables of all partitions in the candidate configuration scheme in the set to be screened in the round of iteration are summed to form the corresponding total weighted objective value. After sorting all the total weighted objective values ​​from smallest to largest, the crowding distance of the corresponding candidate configuration scheme is calculated.

[0081] S77, the first In the round of iterations, the candidate configuration schemes in the set to be screened are sorted from smallest to largest according to the non-dominated level number. Then, within the same non-dominated level, they are sorted from largest to smallest according to the crowding distance, forming the th... Target sorting in rounds of iteration;

[0082] S78, Select the first In the target sorting of the round of iterations, the first Each candidate configuration scheme enters the next iteration, and the process returns to step S73 until the second maximum iteration round is reached. All candidate configuration schemes in the corresponding round when the iteration stops are then assigned to the Pareto candidate solution set.

[0083] Preferably, the formula is updated as follows:

[0084]

[0085] in, For the first The search state variables of each iteration; For the first The first iteration One candidate configuration scheme, For the first The first iteration One candidate configuration scheme, For the first The optimal candidate configuration scheme for each round of iterations; For the first The first reference cache location vector in each iteration, For the first The second reference cache position vector in the round of iteration; This indicates the foraging, storage, and update operator; This indicates the cache search recovery update operator;

[0086] According to the The dominance relationship satisfied by any two candidate configuration schemes in the set to be screened in the round of iteration will be the first... In each iteration, all candidate configuration schemes in the set to be screened are divided into several non-dominated levels, as follows:

[0087] For the In the set of candidates to be screened in the round of iteration, any two candidate configuration schemes are denoted as the first candidate configuration scheme and the second candidate configuration scheme. If the first candidate configuration scheme is less than or equal to the second candidate configuration scheme in terms of the objective function value of the average daily comprehensive cost, the objective function value of the node source load fluctuation suppression, and the objective function value of the node voltage support, and the first candidate configuration scheme is less than the second candidate configuration scheme in at least one of the objective function values ​​of the average daily comprehensive cost, the objective function value of the node source load fluctuation suppression, and the objective function value of the node voltage support, then it is considered that there is a dominance relationship, and the first candidate configuration scheme dominates the second candidate configuration scheme.

[0088] Will not be the first In each iteration, any candidate configuration scheme that is dominated by another candidate configuration scheme in the set to be screened is classified as the first non-dominated level and removed. In the remaining candidate configuration schemes, the process continues to screen and classify the candidate configuration schemes that are not dominated by other remaining candidate configuration schemes as the second non-dominated level and remove them. This process is repeated until all candidate configuration schemes are classified into the corresponding non-dominated levels, resulting in several non-dominated levels.

[0089] Preferably, the co-configuration result of electro-hydrogen energy storage and reactive power compensation is obtained using the entropy weight method based on the Pareto candidate solution set, as follows:

[0090] S81, the first Pareto candidate solution set The objective function values ​​of the q-th objective for each candidate configuration scheme are normalized to obtain the corresponding normalized values. , These correspond to the daily average comprehensive cost target, the nodal source-load fluctuation suppression target, and the nodal voltage support target, respectively.

[0091] S82. Based on the normalized values, the entropy weight method is used to calculate the entropy weights of the corresponding daily average comprehensive cost target, nodal source-load fluctuation suppression target, and nodal voltage support target, including:

[0092] S821. Calculate the first Pareto candidate solution set based on the normalized value. The candidate configuration scheme is in the... The proportion of the objective function value of each objective;

[0093] S822, According to the Pareto candidate solution set, the... The candidate configuration scheme is in the... The entropy weight is calculated based on the proportion of the objective function value of each objective.

[0094] S83. Calculate the comprehensive distance between each candidate configuration and the ideal configuration in the Pareto candidate solution set:

[0095]

[0096] in, For the Pareto candidate solution set, the first The overall distance between each candidate configuration and the ideal configuration; For the Pareto candidate solution set, the first The candidate configuration scheme is in the... Entropy weight of each target; For the Pareto candidate solution set, the first The normalized value of the objective function of each candidate configuration scheme for the qth objective; This is the ideal normalized value of the objective function for the q-th objective;

[0097] S84. Select the candidate configuration scheme with the smallest comprehensive distance as the final collaborative configuration scheme, and regard it as the collaborative configuration result of hydrogen energy storage and reactive power compensation.

[0098] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0099] This invention achieves coordinated configuration of electric energy storage systems, hydrogen energy storage systems, and reactive power compensation equipment through partitioning, candidate access node screening, multi-objective optimization, and decision-making. It enables the cluster (partition) partitioning results to simultaneously reflect node electrical connectivity, regional source-load matching capabilities, and cluster size balance, avoiding the problems of excessive candidate access nodes, high dimensionality of decision variables, and high computational complexity caused by direct full-network optimization. Specifically, after obtaining the topology diagram of the new energy distribution network, this invention first constructs an electrical coupling strength matrix between nodes based on the power flow sensitivity matrix. Based on the electrical coupling strength matrix, it calculates three indicators—partition electrical density, partition source-load balance, and partition size configuration—to establish a partition comprehensive evaluation function and solve it to obtain the final partition set. Then, based on the final partition set, it constructs the daily average comprehensive cost target, node source-load fluctuation suppression target, node voltage support target, and constraints of the new energy distribution network to form a coordinated configuration multi-objective optimization model. The non-dominated sorting nut optimization algorithm is used to solve the coordinated configuration multi-objective optimization model. After obtaining the Pareto candidate solution set, the entropy weight method is used to obtain the coordinated configuration results of electric-hydrogen energy storage and reactive power compensation. Based on the optimal cluster partitioning result (final partition set), this invention limits the candidate access locations of electric energy storage systems, hydrogen energy storage systems, and reactive power compensation equipment to within their respective clusters. It also establishes a multi-objective optimization model for proactive support and coordinated configuration, encompassing economic objectives, source-load fluctuation suppression objectives, and voltage support objectives. This model coordinates the short-term power regulation capability of electric energy storage systems, the long-term energy transfer capability of hydrogen energy storage systems, and the reactive power voltage support capability of reactive power compensation equipment. This enhances the source-load balance capability and proactive voltage support capability of high-proportion renewable energy distribution networks. Furthermore, it effectively addresses the coupling of discrete and continuous variables and the mutual constraints among multiple objectives during the site selection and capacity configuration of electric energy storage systems, hydrogen energy storage systems, and reactive power compensation equipment. While reducing overall configuration costs, it smooths out power fluctuations in the upstream power grid, improves node voltage operation levels, and enhances the absorption capacity and applicability of the renewable energy distribution network configuration scheme. Attached Figure Description

[0100] Figure 1 This is a flowchart of the method for coordinated configuration of hydrogen energy storage and reactive power compensation in a new energy distribution network according to the present invention.

[0101] Figure 2 This is a partitioning result diagram of the IEEE 69-node system of this invention;

[0102] Figure 3 The graphs show the changes in charging and discharging power and remaining power of the energy storage system in each partition of the IEEE 69-node system of this invention, where (a) corresponds to the first partition, (b) corresponds to the second partition, (c) corresponds to the third partition, and (d) corresponds to the fourth partition.

[0103] Figure 4 The graphs show the changes in hydrogen energy storage charging and discharging power and remaining hydrogen quantity in each partition of the IEEE 69-node system of this invention, where (a) corresponds to the first partition, (b) corresponds to the second partition, (c) corresponds to the third partition, and (d) corresponds to the fourth partition.

[0104] Figure 5 This is a comparison curve of the net load fluctuation of the IEEE 69-node system of the present invention under different scenarios.

[0105] Figure 6 This is a comparison curve of the average voltage of the IEEE 69-bus system of this invention under different scenarios;

[0106] Figure 7 This is a comparison curve of network active power loss in the IEEE 69-node system of this invention under different scenarios. Detailed Implementation

[0107] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0108] It should be noted that when a component is referred to as being "connected" to another component, it can be directly connected to the other component or there may be an intervening component. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application.

[0109] like Figures 1-7 As shown, a method for coordinated configuration of hydrogen energy storage and reactive power compensation in a new energy distribution network includes the following steps:

[0110] S1. Obtain the topology diagram of the new energy distribution network.

[0111] The node set of the topology graph is as follows: ,in, That is, the first The node number represents the total number of nodes in the node set of the topology diagram. The specific numbering can be set according to actual needs. Each node is an electrical connection point of the new energy distribution network, used to indicate the connection lines, loads, new energy components, electric energy storage systems, hydrogen energy storage systems, and reactive power compensation equipment. In this embodiment, new energy components include at least one of wind power components and photovoltaic components, or new energy components may also include other distributed power sources. The electric energy storage system includes battery energy storage units for regulating charging and discharging power. The hydrogen energy storage system includes a hydrogen storage device, an electrolyzer, and a fuel cell. The electrolyzer absorbs electrical energy and produces hydrogen, the hydrogen storage device stores hydrogen, and the fuel cell converts hydrogen energy into electrical energy to output active power to the new energy distribution network. Reactive power compensation equipment includes static var generators (SVG) for providing reactive power support. The node set of the topology diagram includes the set of load access nodes, the set of new energy component access nodes, the set of electric energy storage system access nodes, the set of hydrogen energy storage system access nodes, and the set of reactive power compensation equipment access nodes. This embodiment verifies the method for coordinated configuration of hydrogen energy storage and reactive power compensation in a new energy distribution network at the IEEE 69 node. =69. Loads and new energy components have been pre-configured. This invention primarily obtains the topology diagram of the new energy distribution network to await subsequent coordinated configuration of electric-hydrogen energy storage and reactive power compensation, i.e., the coordinated configuration of electric energy storage systems, hydrogen energy storage systems, and reactive power compensation equipment. Each node can be configured with at most one of the following: an electric energy storage system, a hydrogen energy storage system, and a reactive power compensation device. In this embodiment, each partition is configured with one electric energy storage system, one hydrogen energy storage system, and one reactive power compensation device, and its access node is determined by the subsequent multi-objective optimization model for coordinated configuration.

[0112] S2. Construct the electrical coupling strength matrix between nodes of the new energy distribution network based on the topology diagram.

[0113] In one embodiment, the electrical coupling strength matrix between nodes of the new energy distribution network is constructed based on the topology diagram, as follows:

[0114] S21. Construct a linearized power flow sensitivity equation based on the power flow Jacobian matrix;

[0115] S22. Obtain the power flow sensitivity matrix of voltage magnitude versus injected active power based on the linearized power flow sensitivity equation. To calculate the electrical dissimilarity matrix between nodes in a new energy distribution network, where nodes... With nodes Electrical differences The calculation is as follows:

[0116] S221, Computation Node For nodes Voltage influence factor :

[0117]

[0118] in, express The Middle Line 1 Column elements The absolute value; express The Middle Line 1 Column elements The absolute value, , This represents the total number of nodes in the node set of the topological graph. It is a positive number, used to avoid the denominator being zero;

[0119] S222, Computation Node With nodes Electrical differences :

[0120]

[0121] in, For nodes For nodes Voltage influence factor, For nodes For nodes Voltage influence factor, The set of nodes that receive loads in the node set of the topology graph;

[0122] S23. Transform the electrical difference matrix between nodes into the electrical coupling strength matrix between nodes, where, node With nodes Inter-electrical coupling strength The calculation is as follows:

[0123]

[0124] in, This represents the largest electrical difference in the inter-node electrical difference matrix.

[0125] In one embodiment, a linearized power flow sensitivity equation is constructed based on the power flow Jacobian matrix, as shown in the following formula:

[0126]

[0127] in, This is the vector of changes in the voltage phase angle of all nodes in the node set of the topology graph. This is a vector representing the change in voltage magnitude of all nodes in the node set of the topology graph. This is the vector of changes in injected active power for all nodes in the node set of the topology graph. This is the vector of changes in injected reactive power for all nodes in the node set of the topology graph. For the trend of Jacobi matrix The inverse matrix also represents the power flow sensitivity matrix. The power flow sensitivity matrix represents the voltage phase angle to injected active power. The power flow sensitivity matrix represents the voltage phase angle to injected reactive power. This represents the power flow sensitivity matrix to the injected reactive power. This represents the power flow sensitivity matrix to the injected active power. , , , All dimensions are .

[0128] Specifically, the electrical coupling strength matrix between nodes is constructed as follows:

[0129] S21. Construct a linearized power flow sensitivity equation based on the power flow Jacobian matrix, as shown in the following formula:

[0130]

[0131] in, This is the vector of changes in the voltage phase angle of all nodes in the node set of the topology graph. This is a vector representing the change in voltage magnitude of all nodes in the node set of the topology graph. This is the vector of changes in injected active power for all nodes in the node set of the topology graph. This is the vector of changes in injected reactive power for all nodes in the node set of the topology graph. For the trend of Jacobi matrix The inverse matrix also represents the power flow sensitivity matrix. The power flow sensitivity matrix represents the voltage phase angle to injected active power. The power flow sensitivity matrix represents the voltage phase angle to injected reactive power. This represents the power flow sensitivity matrix to the injected reactive power. This represents the power flow sensitivity matrix to the injected active power. , , , The dimensions of these four current sensitivity matrices are all... .

[0132] To characterize the electrical interconnections between nodes in a new energy distribution network, a voltage response difference index between nodes is established based on power flow sensitivity. That is, the relationship between changes in injected power and changes in node voltage state can be represented by a linearized power flow sensitivity equation constructed from the power flow Jacobian matrix.

[0133] S22. Obtain the power flow sensitivity matrix of voltage magnitude versus injected active power based on the linearized power flow sensitivity equation. To calculate the electrical dissimilarity matrix between nodes in a new energy distribution network, where nodes... With nodes Electrical differences The calculation is as follows:

[0134] S221, Computation Node For nodes Voltage influence factor :

[0135]

[0136] in, The power flow sensitivity matrix of voltage magnitude to injected active power The Middle Line 1 The elements of a column represent nodes. Injected active power to nodes The degree of influence of voltage amplitude, The power flow sensitivity matrix of voltage magnitude to injected active power The Middle Line 1 Column elements, , This represents the total number of nodes in the node set of the topological graph. express The absolute value; express The absolute value; The value is positive to avoid the denominator being zero, such as the range of values ​​being... .

[0137] S222, Computation Node With nodes Electrical differences :

[0138]

[0139] in, For nodes For nodes Voltage influence factor, For nodes For nodes Voltage influence factor, This represents the set of nodes that connect to the load in the node set of the topology graph. .node With nodes Electrical differences between The smaller the value, the more likely it is to be a node. With nodes The closer the voltage response characteristics, the better. The electrical differences between nodes form the node electrical difference matrix.

[0140] Since electrohydrogen energy storage mainly smooths out source load fluctuations through active power regulation, while reactive power compensation equipment is mainly used for voltage support, a sensitivity matrix of voltage amplitude to injected active power is selected. This serves as the foundational matrix for evaluating the electrical coupling of nodes.

[0141] S23. Transform the electrical difference matrix between nodes into the electrical coupling strength matrix between nodes, where, node With nodes Inter-electrical coupling strength The calculation is as follows:

[0142]

[0143] in, This represents the largest electrical difference in the inter-node electrical difference matrix. For example... , Indicates in Take within the interval The maximum value, A larger value indicates a stronger electrical connection between the two nodes, making them more suitable for subsequent assignment to the same partition. The electrical coupling strength between each node forms the inter-node electrical coupling strength matrix.

[0144] S3. Calculate the electrical density of the partition, the source-load balance of the partition, and the scale configuration of the partition based on the electrical coupling strength matrix between nodes.

[0145] In one embodiment, the electrical density of the partition, the source-load balance of the partition, and the size configuration of the partition are calculated based on the electrical coupling strength matrix between nodes, as follows:

[0146] 1) Zoned electrical tightness:

[0147]

[0148] in,

[0149]

[0150]

[0151] in, For the corresponding partition set The electrical density of the partitions, , For the corresponding partition set The A set of nodes for each partition. , The number of partitions, It is the set of nodes in the topological graph; The total electrical coupling strength of the new energy distribution network; For nodes The weighted connection strength, For nodes The weighted connection strength, and , , For nodes With nodes Inter-node electrical coupling strength, i.e., the first element in the inter-node electrical coupling strength matrix. Line number Column elements, This represents the total number of nodes in the node set of the topological graph. For nodes and nodes The partition assignment function; It is a positive number, used to avoid the denominator being zero;

[0152] 2) Regional source-load balance:

[0153]

[0154] Among them, the corresponding partition set The Each zone in time period Net load power The formula is as follows:

[0155]

[0156] in, For the corresponding partition set The partitioned source load balance, For the first time in the scheduling cycle The maximum value of the net load power amplitude of each zone. for amplitude, The number of time periods within the scheduling cycle. For nodes During the period The active power of the load, For nodes During the period The active power output of the new energy components;

[0157] 3) Partition size configuration:

[0158]

[0159] in,

[0160]

[0161] in, For the corresponding partition set The partition size configuration degree, and , For the corresponding partition set The partition size deviation, For the corresponding partition set The The number of nodes in each partition. This represents the ideal average number of nodes in a partition.

[0162] Specifically, the calculation of zone electrical density, zone source-load balance, and zone scale configuration is as follows:

[0163] 1) Partition electrical density: For any two distinct partitions, their intersection is an empty set, meaning there are no duplicate nodes between different partitions, and the following condition is met: The topology graph is divided into several non-overlapping partitions. To ensure strong electrical connections between nodes within the same partition, a partition electrical density parameter is constructed. . Used to evaluate the difference between the electrical coupling strength within a zone and the average electrical coupling strength of the entire new energy distribution network. The larger the value, the closer the electrical connection between nodes within the same partition, and the clearer the partition boundary.

[0164] 2) Regional source-load balance: For the first time in the scheduling cycle The maximum value of the net load power amplitude of each zone, i.e. , Represents the corresponding set of partitions The Each zone in time period The magnitude of net load power within the interval The maximum value, This represents the number of time periods within the scheduling cycle.

[0165] To ensure that the zoning results reflect the local source-load matching capability and avoid distortion caused by the cancellation of positive and negative net load power, the zoning source-load balance is constructed using the normalized average absolute net load power. . The larger the value, the higher the source-load matching degree within each zone, and the smaller the relative fluctuation of net load power within the zone, which is more conducive to subsequent local energy regulation by electric hydrogen energy storage. Net load fluctuation is the difference in net load power between adjacent time periods.

[0166] 3) Partition Size Configuration Degree: To avoid an imbalance in the subsequent configuration search space caused by too many or too few nodes in some partitions, a partition size configuration degree was constructed. This is used to ensure the partitioning results are balanced in scale, preventing any particular partition from becoming overly burdened with too many access nodes for electrical energy storage systems, hydrogen energy storage systems, and reactive power compensation equipment, leading to excessively high local optimization dimensionality. The closer the number of nodes in each partition is to the ideal average number of nodes in the partition, the better. The smaller, The larger it is, the more balanced it is.

[0167] S4. Construct a comprehensive evaluation function for the partition based on the partition electrical density, partition source-load balance, and partition scale configuration, and solve it to obtain the final partition set.

[0168] In one embodiment, a comprehensive evaluation function for each partition is constructed and solved based on the partition electrical density, partition source-load balance, and partition size configuration to obtain the final set of partitions, as detailed below:

[0169] S41. Construct a comprehensive evaluation function for the partitioned areas:

[0170]

[0171] in, To find the maximum value in the entire partition set, For the corresponding partition set The overall evaluation value of the zone, For the corresponding partition set The electrical density of the partitions, For the corresponding partition set The partitioned source load balance, For the corresponding partition set The partition size configuration degree, , , The weighting coefficients are, in order: the weighting coefficient for the electrical density of the zone, the weighting coefficient for the source-load balance of the zone, and the weighting coefficient for the scale configuration of the zone.

[0172] S42. Solve for the final partition set using the improved snake optimization algorithm based on the partition comprehensive evaluation function.

[0173] The final set of partitions is obtained as follows:

[0174] S41. Construct a comprehensive evaluation function for the partitioned areas:

[0175]

[0176] in,

[0177]

[0178] in, To find the maximum value in the entire partition set, For the corresponding partition set The overall evaluation value of the zone, , , The weighting coefficients for the electrical density of the zone, the source-load balance of the zone, and the scale configuration of the zone are, in order, all greater than or equal to 0.

[0179] Taking into account node electrical coupling, source-load balance, and the rationality of zoning scale, a comprehensive evaluation function for each zoning area is established. Depending on the planning focus, it can be adjusted according to actual needs. , , The three weighting coefficients are adjusted. Used to highlight the electrical coupling relationships between nodes within a partition. Used to highlight the need for source load fluctuation suppression Used to limit the differences in partition size.

[0180] S42. Based on the partition comprehensive evaluation function, the final partition set is obtained using the improved snake optimization algorithm, as follows:

[0181] S421. Several candidate partitioning schemes are initialized using a chaotic mapping function. Each candidate partitioning scheme is regarded as an individual position vector. Then the... Candidate partitioning schemes It is expressed as follows:

[0182]

[0183] in,

[0184]

[0185] in, Indicates the first Nodes in the candidate partitioning schemes The partition number, and satisfying , The number of partitions, , To improve the upper limit of the population size in the snake optimization algorithm; This is the lower limit of the partition number, i.e. =1; This is the upper limit of the partition number, i.e. ; It is a chaotic variable, i.e., a random natural number; This is a chaotic mapping function, such as the Tent chaotic mapping function, used to enhance the uniformity of the initial candidate partitioning scheme in the search space. According to the Nodes in the candidate partitioning schemes The initial encoding is converted into nodes using a chaotic mapping function. The partition number.

[0186] S422, according to the... Candidate partitioning schemes The partition numbers of each node form the first partition number. A set of partition schemes for each candidate partitioning scheme :

[0187]

[0188] in, Indicates the first Among the candidate partitioning schemes, the first... A set of nodes for each partition.

[0189] thus, This represents the partition number during the search process of the improved snake optimization algorithm. This indicates the node partitioning result determined by the partition number.

[0190] S423, Calculate the first Fitness of the partition set of candidate partitioning schemes The formula is as follows:

[0191]

[0192] in, The larger the value, the higher the value. The better the candidate partitioning scheme, the better. Represented as the first A set of partition schemes for each candidate partitioning scheme The overall evaluation value of the partitions is used to represent the fitness of the partition set.

[0193] S424, in the In the first iteration, the candidate partition scheme with the highest fitness in the partition set is selected as the second partition scheme. Optimal candidate partitioning scheme in rounds of iteration :

[0194]

[0195] in, Indicates traversing the first... All candidate partitioning schemes in the round of iteration are taken from the corresponding first partitioning scheme. The first iteration Candidate partitioning schemes fitness of the partition set Candidate partitioning schemes at maximum value, , This represents the first maximum iteration count, which is initialized to the 0th iteration.

[0196] S425, according to the... Optimal candidate partitioning scheme in round-by-round iteration Generate the first using the position update mechanism Candidate partitioning schemes in round-of-round iteration The location update mechanism is as follows:

[0197] Exploration phase:

[0198]

[0199] Development phase:

[0200]

[0201] in, Indicates from the first Candidate partitioning schemes randomly selected in rounds of iteration; The first random coefficient, The second random coefficient, The third random coefficient is taken as... ;when Less than or equal to the preset number of iterations At that time, it was in the exploratory stage, when Greater than the preset number of iterations It was in the development stage at the time. .

[0202] The exploration phase is used to expand the search range and reduce the probability of getting stuck in local optima; the development phase is used to conduct local searches around the optimal candidate partitioning scheme of the current iteration to improve convergence accuracy.

[0203] S426. Return to step S425 until the first maximum number of iterations is reached, and obtain the optimal candidate partitioning scheme for the corresponding round when the iteration stops as the final candidate partitioning scheme. :

[0204]

[0205] in, Indicates the nodes in the final candidate partitioning scheme The final partition number.

[0206] S427, According to the final candidate partitioning scheme The final partition set is formed by dividing each node into its final partition numbers. :

[0207]

[0208] in, For the final partition set, the first The final set of nodes for each partition.

[0209] According to the final candidate partitioning scheme The final partition number of each node is determined, and nodes with the same final partition number are grouped into the same partition to obtain the final node partitioning result. Final Partition Set This algorithm is used to define the range of access nodes for subsequent electrical energy storage systems, hydrogen energy storage systems, and reactive power compensation equipment. The improved snake optimization algorithm can improve the distribution uniformity of the initial candidate partitioning schemes, enhance the global search capability, and retain the optimal candidate partitioning schemes during the iteration process.

[0210] S5. Based on the final partition set, construct the daily average comprehensive cost target, node source-load fluctuation suppression target, node voltage support target, and constraints for the new energy distribution network.

[0211] In one embodiment, the daily average comprehensive cost target, nodal source-load fluctuation suppression target, nodal voltage support target, and constraints of the new energy distribution network are constructed based on the final partition set, as follows:

[0212] 5.1) The daily average comprehensive cost target is constructed as follows:

[0213]

[0214] in, This indicates taking the minimum value. Let the daily average comprehensive cost be the objective function value. The total life-cycle cost of the entire energy storage system. The total lifecycle cost of all hydrogen energy storage systems. The total life-cycle cost of all reactive power compensation equipment. This is the number of days per year;

[0215] 5.2) The target for suppressing nodal source load fluctuations is constructed as follows:

[0216]

[0217] in, The objective function value for suppressing nodal source load fluctuations; For the time period Active power exchange between the new energy distribution network and the upstream power grid. For the time period Active power exchange between the new energy distribution network and the upstream power grid; The number of time periods within the scheduling cycle;

[0218] 5.3) Node voltage support target, constructed as follows:

[0219]

[0220] in, The objective function value is supported by the node voltage; For nodes During the period The voltage amplitude; For nodes The average voltage amplitude during the scheduling period; This represents the total number of nodes in the node set of the topological graph.

[0221] 5.4) Constraints, including node injection power constraints, power flow constraints, capacity constraints, and device access location constraints.

[0222] Specifically, the daily average comprehensive cost target, nodal source-load fluctuation suppression target, nodal voltage support target, and constraints for the new energy distribution network are constructed based on the final partition set, as follows:

[0223] 5.1) The daily average comprehensive cost target is constructed as follows:

[0224]

[0225] in, This indicates taking the minimum value. Let the daily average comprehensive cost be the objective function value. The total life-cycle cost of the entire energy storage system. The total lifecycle cost of all hydrogen energy storage systems. The total life-cycle cost of all reactive power compensation equipment. This refers to the number of days per year. Specifically:

[0226] 5.1.1) Total life-cycle cost of energy storage systems The calculation is as follows:

[0227]

[0228] in, , , In order, the costs are: investment cost of the energy storage system, operation and maintenance cost of the energy storage system, and operating cost of the energy storage system.

[0229] Among them, the investment cost of the energy storage system :

[0230]

[0231] in, This is the annualized conversion factor for the energy storage system. Cost per unit energy capacity of an electric energy storage system; The unit power cost of an energy storage system; For the final set of partitions The total investment capacity of the energy storage system in each zone; For the final set of partitions The rated power of the energy storage system in each zone.

[0232] Operating and maintenance costs of energy storage systems :

[0233]

[0234] in, This is the ratio of operation and maintenance costs for the energy storage system.

[0235] Operating costs of electric energy storage systems :

[0236]

[0237] Where T is the number of time periods within the scheduling period; For time period The electricity purchase price; For time period The electricity price for sale; For the final set of partitions The energy storage systems in each zone during the time period The charging power; For the final set of partitions The energy storage systems in each zone during the time period The discharge power; This is the number of days per year.

[0238] 5.1.2) The total life-cycle cost of a hydrogen energy storage system is The calculation is as follows:

[0239]

[0240] in, , , In order, the costs are: investment costs of hydrogen energy storage systems, operation and maintenance costs of hydrogen energy storage systems, and operating costs of hydrogen energy storage systems; Revenue from hydrogen sales.

[0241] Among them, the investment cost of hydrogen energy storage systems :

[0242]

[0243] in, This is the annualized conversion factor for hydrogen energy storage systems; The unit capacity cost of hydrogen storage devices in a hydrogen energy storage system; The unit power cost of the electrolyzer for the hydrogen energy storage system; The unit power cost of fuel cells for hydrogen energy storage systems; For the final set of partitions The capacity of hydrogen storage devices in each zone's hydrogen energy storage system; For the final set of partitions The rated power of the electrolyzers in the hydrogen energy storage system of each zone; For the final set of partitions The rated power of the fuel cells in the hydrogen energy storage system of each zone.

[0244] Operation and maintenance costs of hydrogen energy storage systems :

[0245]

[0246] in, This is the ratio of operation and maintenance costs for hydrogen energy storage systems.

[0247] Operating costs of hydrogen energy storage systems :

[0248]

[0249] in, For the final set of partitions The hydrogen energy storage system in each zone during the time period The absorption power, such as the power consumption of the electrolyzer in the corresponding hydrogen energy storage system; For the final set of partitions The hydrogen energy storage system in each zone during the time period The output power, such as the power generation of the fuel cell in the corresponding hydrogen energy storage system.

[0250] Revenue from hydrogen sales Represented as:

[0251]

[0252]

[0253] in, For the final set of partitions Hydrogen sales volume in each district; This refers to the unit price of hydrogen. For the final set of partitions The amount of hydrogen stored in the hydrogen storage devices of each zone's hydrogen energy storage system at the end of the scheduling cycle. This refers to the lower limit of safety for hydrogen storage, such as the rated hydrogen storage capacity of the hydrogen storage device in a hydrogen energy storage system. 10%; For the final set of partitions The capacity of the hydrogen storage devices in the hydrogen energy storage system of each zone, To obtain the maximum value.

[0254] 5.1.3) Total life-cycle cost of reactive power compensation equipment The calculation is as follows:

[0255]

[0256] in, The annualized equivalent investment cost of reactive power compensation equipment; The operating and maintenance costs of reactive power compensation equipment.

[0257] Among them, the annualized equivalent investment cost of reactive power compensation equipment :

[0258]

[0259] in, For the final set of partitions The capacity of reactive power compensation equipment in each zone; for The unit capacity investment price; The annualized conversion factor for reactive power compensation equipment satisfies... .

[0260] further, unit capacity investment price :

[0261]

[0262] in, Currency conversion factor; , , The coefficients are, in order, the quadratic coefficient, the linear coefficient, and the constant coefficient of the quadratic fitting function for the unit capacity investment price of reactive power compensation equipment. These coefficients characterize the relationship between the unit capacity investment price of reactive power compensation equipment and the change in the capacity of the reactive power compensation equipment.

[0263] The annualized conversion factors for electric energy storage systems, hydrogen energy storage systems, and reactive power compensation equipment are calculated as follows:

[0264]

[0265] in, This refers to the annualized conversion factor for electrical energy storage systems, hydrogen energy storage systems, or reactive power compensation equipment. Let be the discount rate, satisfying... ; This refers to the service life of the corresponding electric energy storage system, hydrogen energy storage system, or reactive power compensation equipment.

[0266] Operation and maintenance costs of reactive power compensation equipment :

[0267]

[0268] in, The ratio of operation and maintenance costs for reactive power compensation equipment should meet the following requirements. .

[0269] The optimization targets for electric energy storage systems are formulated by combining investment costs, operation and maintenance costs, and operating costs. For hydrogen energy storage systems, the optimization targets are formulated by combining investment costs, operation and maintenance costs, operating costs, and hydrogen sales revenue. For reactive power compensation equipment, the optimization targets are formulated by combining annualized equivalent investment costs and operation and maintenance costs. Minimizing these optimization targets yields the daily average comprehensive cost target for the new energy distribution network, which helps ensure the economic efficiency of the coordinated configuration of electric energy storage systems, hydrogen storage systems, and reactive power compensation equipment.

[0270] 5.2) The target for suppressing nodal source load fluctuations is constructed as follows:

[0271]

[0272] in, The objective function value for suppressing nodal source load fluctuations (i.e., net load fluctuations) is determined. For the time period Active power exchange between the new energy distribution network and the upstream power grid; For the time period In this embodiment, the active power exchange between the new energy distribution network and the upstream power grid is scheduled intraday at 1-hour intervals. ,Right now .

[0273] During the period Active power exchange between new energy distribution networks and upstream power grids The calculation is as follows:

[0274]

[0275] in, For the new energy distribution network during the time period Total active power of the load; For the new energy distribution network during the time period The total output power of the new energy components; For the final set of partitions The energy storage systems in each zone during the time period The charging power, For the final set of partitions The energy storage systems in each zone during the time period The discharge power; For the final set of partitions The hydrogen energy storage system in each zone during the time period Absorption power, For the final set of partitions The hydrogen energy storage system in each zone during the time period . output power.

[0276] To evaluate the improvement effect of reactive power compensation equipment on the active power operation level of nodes, a node active power fluctuation target is established as the node source load fluctuation suppression target. The target function value for this node source load fluctuation suppression is ( The smaller the value, the smoother the change in active power exchange between the new energy distribution network and the external upstream power grid, and the better the suppression effect of source-load fluctuations. In this embodiment, the new energy distribution network is a distribution network that connects to loads, new energy components, electric energy storage systems, hydrogen energy storage systems, and reactive power compensation equipment, wherein the electric energy storage systems, hydrogen energy storage systems, and reactive power compensation equipment are yet to be planned; the upstream power grid is an external power grid connected to the new energy distribution network through substations or points of common coupling. The new energy distribution network can exchange active power with the upstream power grid according to its own load, the power generation of new energy components, and the charging and discharging status of energy storage equipment (electric energy storage system, hydrogen energy storage system). When the power within the new energy distribution network is insufficient, it purchases electricity from the upstream power grid; when the power generation of new energy components or the discharge power of energy storage equipment (electric energy storage system, hydrogen energy storage system) within the new energy distribution network is surplus, it can feed active power back to the upstream power grid.

[0277] 5.3) Node voltage support target, constructed as follows:

[0278]

[0279] Among them, nodes Average voltage amplitude during the scheduling period Defined as:

[0280]

[0281] in, The objective function value (i.e., voltage fluctuation) is used to support the node voltage. For nodes During the period The voltage amplitude.

[0282] To evaluate the improvement effect of reactive power compensation equipment on node voltage operation, a node voltage fluctuation target is established as a node voltage support target. The smaller the value, the smaller the node voltage fluctuation and the better the voltage support effect.

[0283] 5.4) Constraints are constructed as follows:

[0284] 5.4.1) Node Injection Power Constraints:

[0285]

[0286]

[0287] in, For nodes During the period The equivalent active power injected, For nodes During the period The equivalent reactive power injection power; For nodes During the period The active power output of the new energy components, For nodes During the period The reactive power output of the new energy components; For nodes During the period The active power of the load, For nodes During the period The reactive power of the load; For nodes During the period The equivalent active power injected into the electric energy storage system; For nodes During the period The equivalent active power injection of the hydrogen energy storage system; For nodes During the period The equivalent reactive power injection power of the reactive power compensation equipment.

[0288] Among them, nodes During the period The equivalent active power injection of the electric energy storage system :

[0289]

[0290] node During the period Equivalent active power injection of hydrogen energy storage system :

[0291]

[0292] node During the period The equivalent reactive power injection of the reactive power compensation equipment :

[0293]

[0294] in, For the final set of partitions The energy storage system in each zone is at the node. Access status, For the final set of partitions The hydrogen energy storage system in each zone is at the node. Access status, For the final set of partitions The reactive power compensation equipment in each partition is at the node. Access status, For the final set of partitions The reactive power compensation equipment in each zone during the time period The reactive power output. , representing the th partition of the final partition set Each zone's energy storage system access node , , representing the th partition of the final partition set The energy storage systems in each zone are not connected to the nodes. ; , representing the th partition of the final partition set Hydrogen energy storage system access nodes in each zone , , representing the th partition of the final partition set The hydrogen energy storage system in each zone is not connected to the node. ; , representing the th partition of the final partition set Reactive power compensation equipment access nodes in each partition , , representing the th partition of the final partition set The reactive power compensation equipment in each zone is not connected to the node. .

[0295] Based on the connection location and operating power of the electric energy storage system, hydrogen energy storage system, and reactive power compensation equipment, establish node injection power constraints for the nodes after the corresponding equipment is connected.

[0296] 5.4.2) Current flow constraints:

[0297] The injected power at the node should satisfy the AC power flow constraints:

[0298]

[0299] in, The first node in the admittance matrix Line number The conductivity of the column, The first node in the admittance matrix Line number The susceptance of the column; For nodes With nodes During the period The voltage phase angle difference; For nodes During the period voltage amplitude, For nodes During the period The voltage amplitude.

[0300] node During the period voltage amplitude Should meet:

[0301]

[0302] in, This is the lower limit of the node voltage amplitude. This represents the upper limit of the node voltage amplitude.

[0303] Time period The branch capacity should meet the following requirements:

[0304]

[0305] in, branch road During the period The active power; branch road During the period reactive power; The maximum permissible apparent power of branch ij; branch Represents a node and nodes The connection lines.

[0306] Time period Active power exchange between the new energy distribution network and the upper-level power grid Should meet:

[0307]

[0308] in, This is the lower limit of active power exchange. This represents the upper limit of active power exchange.

[0309] 5.4.3) Capacity Constraints:

[0310] The configuration capacity and power of the electric energy storage system meet the following requirements:

[0311]

[0312] in, For the final set of partitions The total investment capacity of the energy storage system in each zone, This is the lower limit of the rated energy capacity of the electric energy storage system. This is the upper limit of the rated energy capacity of the electric energy storage system; For the final set of partitions The rated power of the energy storage system in each zone, This is the lower limit of the rated power of the energy storage system. This is the upper limit of the rated power of the energy storage system.

[0313] The operating power of the energy storage system meets the following requirements:

[0314]

[0315] in, Let be the charging power of the energy storage system in the k-th partition of the final partition set during time period t. Let be the discharge power of the energy storage system in the kth partition of the final partition set during time period t.

[0316] The configuration capacity and power of the hydrogen energy storage system meet the following requirements:

[0317]

[0318] in, For the final set of partitions The capacity of hydrogen storage devices in each zone's hydrogen energy storage system. This represents the lower limit of the capacity of hydrogen storage devices in a hydrogen energy storage system. This refers to the upper limit of the capacity of hydrogen storage devices in a hydrogen energy storage system. For the final set of partitions The rated power of the electrolyzers in the hydrogen energy storage system of each zone, This is the lower limit of the rated power of the electrolyzer in a hydrogen energy storage system. This is the upper limit of the rated power of the electrolyzer in the hydrogen energy storage system; For the final set of partitions The rated power of the fuel cells in the hydrogen energy storage system of each zone, This represents the lower limit of the rated power of fuel cells in hydrogen energy storage systems. This represents the upper limit of the rated power of the fuel cell in the hydrogen energy storage system.

[0319] The operating power of the hydrogen energy storage system meets the following requirements:

[0320]

[0321] in, For the final set of partitions The hydrogen energy storage system in each zone during the time period Absorption power, For the final set of partitions The hydrogen energy storage system in each zone during the time period . output power.

[0322] The configuration capacity of the reactive power compensation equipment meets the following requirements:

[0323]

[0324] in, For the final set of partitions The capacity of reactive power compensation equipment in each zone, This represents the lower limit of the apparent capacity of reactive power compensation equipment. This refers to the upper limit of the apparent capacity of reactive power compensation equipment.

[0325] The operating power of the reactive power compensation equipment meets the following requirements:

[0326]

[0327] in, For the final set of partitions The reactive power compensation equipment in each zone during the time period reactive power output The absolute value; This is the lower limit of the reactive power output power of the reactive power compensation equipment. This is the upper limit of the reactive power output of the reactive power compensation equipment.

[0328] 5.4.4) Equipment access location constraints:

[0329]

[0330] in, For the final set of partitions The A set of nodes for each partition. For the final set of partitions The set of access nodes for the energy storage system in each partition; For the final set of partitions The set of access nodes for hydrogen energy storage systems in each partition; For the final set of partitions A set of access nodes for reactive power compensation devices in each partition.

[0331] Final partition set The The access nodes for electrical energy storage, hydrogen energy storage, and reactive power compensation equipment in each zone respectively meet the requirements. , , This constraint means that each type of device selects an access node within its respective partition. By using this constraint, the device location range can be limited to the corresponding partition, reducing the search dimensions across the entire network.

[0332] S6. Construct a collaborative configuration multi-objective optimization model based on the daily average comprehensive cost target, the node source-load fluctuation suppression target, the node voltage support target, and constraints.

[0333] In one embodiment, a collaborative configuration multi-objective optimization model is constructed based on the daily average comprehensive cost target, the nodal source-load fluctuation suppression target, the nodal voltage support target, and constraints, as detailed below:

[0334] S61. Constructing a collaborative configuration decision set :

[0335]

[0336] in,

[0337]

[0338] in, Configure decision variables for the devices in the k-th partition of the final partition set; For the final set of partitions The set of access nodes for the energy storage system in each partition; For the final set of partitions The set of access nodes for hydrogen energy storage systems in each partition; For the final set of partitions The set of access nodes for reactive power compensation equipment in each partition; For the final set of partitions The total investment capacity of the energy storage system in each zone; For the final set of partitions The rated power of the energy storage system in each zone; For the final set of partitions The capacity of hydrogen storage devices in each zone's hydrogen energy storage system; For the final set of partitions The rated power of the electrolyzers in the hydrogen energy storage system of each zone; For the final set of partitions The rated power of the fuel cells in the hydrogen energy storage system of each zone; For the final set of partitions The capacity of reactive power compensation equipment in each zone;

[0339] S62. Establish a multi-objective optimization model for collaborative configuration based on the collaborative configuration decision set, as follows:

[0340]

[0341] in, For traversal Take the minimum value. for The weighted composite objective function value; for The objective function value of the average daily comprehensive cost; for The objective function value for suppressing nodal source load fluctuations; for The node voltage supports the objective function value; The weighting coefficients are, in order: the weighting coefficient of the objective function value of average daily comprehensive cost, the weighting coefficient of the objective function value of nodal source-load fluctuation suppression, and the weighting coefficient of the objective function value of nodal voltage support.

[0342] Specifically, a collaborative configuration decision set is formed by constructing the device configuration decision variables of each partition in the final partition set. The collaborative configuration decision set is obtained by combining the device configuration decision variables of all partitions in the final partition set. The collaborative configuration decision set includes the access node indexes of the electric energy storage system, hydrogen energy storage system, and reactive power compensation equipment in each zone, as well as their corresponding capacities and rated power. Then, a collaborative configuration multi-objective optimization model is established based on the collaborative configuration decision set. For traversal Take the minimum value, that is, traverse the collaborative configuration decision set. The device configuration decision variables for all partitions are minimized. All are greater than or equal to 0, and satisfy the following conditions: .

[0343] Taking into account economic efficiency, source-load fluctuation suppression, and voltage support effects, a multi-objective optimization model for the coordinated configuration of an electric energy storage system, a hydrogen energy storage system, and reactive power compensation equipment is established. The constraints of this coordinated configuration multi-objective optimization model include node injection power constraints, power flow constraints, capacity constraints, and equipment access location constraints.

[0344] S7. Solve the co-configuration multi-objective optimization model using the non-dominated sorting nut optimization algorithm to obtain the Pareto candidate solution set.

[0345] In one embodiment, the non-dominated sorting nut optimization algorithm is used to solve the cooperative configuration multi-objective optimization model to obtain the Pareto candidate solution set, as follows:

[0346] S71. Treat each candidate configuration scheme as an individual in the non-dominated sorting nut optimization algorithm, and randomly generate... One candidate configuration scheme;

[0347] S72, Obtain the number of iterations in round 0. One candidate configuration scheme, ;

[0348] S73, Obtain the first according to the updated formula. The first iteration One candidate configuration scheme, , This is the second largest iteration round;

[0349] S74, the first Round iteration and the first All candidate configuration schemes in the round of iterations together constitute the first... The set to be selected in each round of iterations;

[0350] S75, according to the... The dominance relationship satisfied by any two candidate configuration schemes in the set to be screened in the round of iteration will be the first... In each iteration, all candidate configuration schemes in the set to be screened are divided into several non-dominated levels;

[0351] S76, respectively, the first The weighted comprehensive objective function values ​​of the device configuration decision variables of all partitions in the candidate configuration scheme in the set to be screened in the round of iteration are summed to form the corresponding total weighted objective value. After sorting all the total weighted objective values ​​from smallest to largest, the crowding distance of the corresponding candidate configuration scheme is calculated.

[0352] S77, the first In the round of iterations, the candidate configuration schemes in the set to be screened are sorted from smallest to largest according to the non-dominated level number. Then, within the same non-dominated level, they are sorted from largest to smallest according to the crowding distance, forming the th... Target sorting in rounds of iteration;

[0353] S78, Select the first In the target sorting of the round of iterations, the first Each candidate configuration scheme enters the next iteration, and the process returns to step S73 until the second maximum iteration round is reached. All candidate configuration schemes in the corresponding round when the iteration stops are then assigned to the Pareto candidate solution set.

[0354] In one embodiment, the formula is updated as follows:

[0355]

[0356] in, For the first The search state variables of each iteration; For the first The first iteration One candidate configuration scheme, For the first The first iteration One candidate configuration scheme, For the first The optimal candidate configuration scheme for each round of iterations; For the first The first reference cache location vector in each iteration, For the first The second reference cache position vector in the round of iteration; This indicates the foraging, storage, and update operator; This indicates the cache search recovery update operator;

[0357] According to the The dominance relationship satisfied by any two candidate configuration schemes in the set to be screened in the round of iteration will be the first... In each iteration, all candidate configuration schemes in the set to be screened are divided into several non-dominated levels, as follows:

[0358] For the first In the set of candidates to be screened in the round of iteration, any two candidate configuration schemes are denoted as the first candidate configuration scheme and the second candidate configuration scheme. If the first candidate configuration scheme is less than or equal to the second candidate configuration scheme in terms of the objective function value of the average daily comprehensive cost, the objective function value of the node source load fluctuation suppression, and the objective function value of the node voltage support, and the first candidate configuration scheme is less than the second candidate configuration scheme in at least one of the objective function values ​​of the average daily comprehensive cost, the objective function value of the node source load fluctuation suppression, and the objective function value of the node voltage support, then it is considered that there is a dominance relationship, and the first candidate configuration scheme dominates the second candidate configuration scheme.

[0359] Will not be the first In each iteration, any candidate configuration scheme that is dominated by another candidate configuration scheme in the set to be screened is classified as the first non-dominated level and removed. In the remaining candidate configuration schemes, the process continues to screen and classify the candidate configuration schemes that are not dominated by other remaining candidate configuration schemes as the second non-dominated level and remove them. This process is repeated until all candidate configuration schemes are classified into the corresponding non-dominated levels, resulting in several non-dominated levels.

[0360] Specifically, the Pareto candidate solution set is obtained as follows:

[0361] S71. Treat each candidate configuration scheme as an individual in the non-dominated sorting nut optimization algorithm, and randomly generate... One candidate configuration scheme.

[0362] The Non-Dominated Sorting Nut Optimization Algorithm (NSNOA) is used to handle the multi-objective optimization problem in the coordinated configuration of electric energy storage systems, hydrogen energy storage systems, and reactive power compensation equipment, and obtains a Pareto candidate solution set. Each candidate configuration scheme in the NNOA represents a complete equipment configuration scheme, including the access nodes and parameters of the electric energy storage system, hydrogen energy storage system, and reactive power compensation equipment within each partition.

[0363] S72, Obtain the number of iterations in round 0. One candidate configuration scheme :

[0364]

[0365]

[0366] in, In order to be with the first Each candidate configuration scheme is a random vector of the same dimension, and the values ​​of its elements range from... , , The population size is the value for the non-dominated sorting nut optimization algorithm. This indicates element-wise multiplication; The lower bound of the variable for the candidate configuration scheme. This represents the upper limit of the candidate configuration schemes; this formula is used to randomly generate initial candidate configuration schemes within the upper and lower limits of the variables. The lower limits of the candidate access node indices are, in order, those for electric energy storage systems, hydrogen energy storage systems, and reactive power compensation equipment; The upper limits of the candidate access node indices for electric energy storage systems, hydrogen energy storage systems, and reactive power compensation equipment are, in order. This is the lower limit of the rated energy capacity of the electric energy storage system. This is the upper limit of the rated energy capacity of the electric energy storage system; This is the lower limit of the rated power of the energy storage system. This is the upper limit of the rated power of the energy storage system; This represents the lower limit of the capacity of hydrogen storage devices in a hydrogen energy storage system. This refers to the upper limit of the capacity of hydrogen storage devices in a hydrogen energy storage system. This is the lower limit of the rated power of the electrolyzer in a hydrogen energy storage system. This is the upper limit of the rated power of the electrolyzer in the hydrogen energy storage system; This represents the lower limit of the rated power of fuel cells in hydrogen energy storage systems. This is the upper limit of the rated power of the fuel cell in the hydrogen energy storage system; This represents the lower limit of the apparent capacity of reactive power compensation equipment. This represents the upper limit of the apparent capacity of the reactive power compensation equipment. Each candidate configuration scheme satisfies the upper and lower bound constraints of the variables, such as... Initially =0, , This is the second largest iteration round, and the others are similar. ,in, For the first The first iteration The first of the candidate configuration schemes Device configuration decision variables for each partition.

[0367] S73, Obtain the first according to the updated formula. The first iteration The specific update formula for the candidate configuration schemes is as follows:

[0368]

[0369] Among them, the Search state variables in round iterations :

[0370]

[0371] in, for Random numbers within, For the first The first iteration One candidate configuration scheme, For the first The first iteration One candidate configuration scheme; For the first The optimal candidate configuration scheme for each round of iterations; For the first The first reference cache location vector in each iteration, For the first The second reference cache position vector in each iteration is used to provide a reference search direction for the cache search recovery update operator; This indicates the foraging, storage, and update operator; This indicates the cached search recovery update operator. When... When the non-dominated sorting nut optimization algorithm uses an exploratory search method to expand the search range of candidate configuration schemes; when At this time, the non-dominated sorting nut optimization algorithm uses an open search approach to perform a local search around the current better candidate configuration scheme.

[0372] S74, the first All candidate configuration schemes in the round of iteration and the first All candidate configuration schemes in the round of iterations together constitute the first... The set to be selected in round iteration :

[0373] .

[0374] S75, when the The set to be selected in round iteration If any two candidate configuration schemes satisfy a preset condition, it is considered that there is a dominance relationship between the corresponding two candidate configuration schemes, and the first one is assigned to the next configuration scheme based on all existing dominance relationships. The set to be selected in round iteration All candidate configuration schemes are divided into several non-dominated levels, among which:

[0375] The specific preset conditional expressions are as follows:

[0376] For the The set to be selected in round iteration Any two candidate configuration schemes and If the objective function values ​​for daily average comprehensive cost, nodal source load fluctuation suppression, and nodal voltage support are... Not inferior to And at least one of the following is true: daily average comprehensive cost objective function value, nodal source load fluctuation suppression objective function value, and nodal voltage support objective function value. Superior If so, then a dominant relationship is considered to exist. Dominate ,Right now:

[0377]

[0378] And at least one exists ,satisfy:

[0379]

[0380] in, For the first The set to be selected in round iteration The Middle One candidate configuration scheme, For the first The set to be selected in round iteration The Middle One candidate configuration scheme, ,and ; Indicates the first The objective function value of each objective. ,but , , In order, they are the objective function values ​​for average daily comprehensive cost, nodal source-load fluctuation suppression, and nodal voltage support; Indicates the first The objective function value of each objective. This indicates the target index that makes the strict priority over the relation hold, and Since all three objectives are minimization objectives, Indicates the first The set to be selected in round iteration The Middle One candidate configuration scheme In the The first objective is no worse than the second. The set to be selected in round iteration The Middle One candidate configuration scheme , Satisfy the first The set to be selected in round iteration The Middle One candidate configuration scheme Superior to the first in at least one objective The set to be selected in round iteration The Middle One candidate configuration scheme .

[0381] Specifically, no. The multi-objective function values ​​of each candidate configuration scheme in the round of iterations are calculated as follows:

[0382]

[0383] in, Indicates the first The set to be selected in round iteration The Middle One candidate configuration scheme The multi-objective function value; Indicates the first The set to be selected in round iteration The Middle One candidate configuration scheme The objective function value of the average daily comprehensive cost is used to evaluate the economic efficiency of the corresponding candidate configuration scheme; Indicates the first The set to be selected in round iteration The Middle One candidate configuration scheme The objective function value for suppressing nodal source-load fluctuations is used to evaluate the degree of fluctuation in active power exchange between the new energy distribution network and the upper-level power grid. Indicates the first The set to be selected in round iteration The Middle One candidate configuration scheme The node voltage support objective function value is used to evaluate the improvement effect of the node voltage operation level, and the rest are similar.

[0384] Based on the above-mentioned dominance relationship, the first The set to be selected in round iteration The candidate configuration schemes are divided into several non-dominant levels. First, those not dominated... Candidate configuration schemes dominated by any other candidate configuration scheme are classified as the first non-dominated level; then, candidate configuration schemes in the first non-dominated level are removed, and candidate configuration schemes that are not dominated by any other remaining candidate configuration schemes are further selected and classified as the second non-dominated level; this process is repeated until all candidate configuration schemes are classified into the corresponding non-dominated level. The higher the non-dominated level of the candidate configuration, the better the overall performance of the candidate configuration in terms of economy, source load fluctuation suppression and voltage support.

[0385] S76, respectively, the first The set to be selected in round iteration The weighted comprehensive objective function values ​​of the device configuration decision variables for all partitions of the corresponding candidate configuration scheme are summed to form the total weighted objective value of the corresponding candidate configuration scheme. For the th... The set to be selected in round iteration All candidate configuration schemes are sorted in ascending order of total weighted objective value. The congestion distance of the corresponding candidate configuration scheme is calculated based on the daily average comprehensive cost objective function value, the node source load fluctuation suppression objective function value, and the node voltage support objective function value of the adjacent candidate configuration schemes.

[0386]

[0387] in, For the first The set to be selected in round iteration The Middle The congestion distance of each candidate configuration scheme; and The order is number 1 The preceding and following adjacent candidate configuration schemes of the nth candidate configuration scheme The objective function value of each objective; and The order is number 1 The maximum and minimum values ​​of the objective function for each objective; the larger the crowding distance, the sparser the distribution of candidate solutions around the candidate configuration, which is more conducive to maintaining the diversity of the Pareto candidate solution set.

[0388] For the first The set to be selected in round iteration For each candidate configuration scheme, the weighted comprehensive objective function value of the equipment configuration decision variables for all partitions is calculated. Then, the weighted comprehensive objective function values ​​of the equipment configuration decision variables for all partitions of the corresponding candidate configuration scheme are summed and sorted in ascending order. The first candidate configuration scheme after sorting is denoted as... The second candidate configuration scheme is denoted as .

[0389] S77, the first The set to be selected in round iteration After sorting the candidate configuration schemes in ascending order of non-dominance level, the schemes within the same non-dominance level are then sorted in descending order of congestion distance, forming the [number missing]... The target sorting in rounds of iteration.

[0390] Specifically, candidate configuration schemes with smaller non-dominance level numbers are prioritized; when candidate configuration schemes are at the same non-dominance level, candidate configuration schemes with larger congestion distances are prioritized, and the candidate configuration scheme with the highest overall ranking is selected as the first... Optimal candidate configuration scheme in round iteration Candidate configuration schemes are selected according to the rule of prioritizing non-dominated level and then congestion distance. The smaller the non-dominated level, the better the candidate configuration scheme. When the non-dominated levels are the same, the larger the congestion distance, the better the distribution of the candidate configuration scheme in the Pareto candidate solution set.

[0391] S78, Select the first In the target sorting of the round of iterations, the first Each candidate configuration scheme is selected as a candidate configuration scheme for the next iteration, and the process returns to step S73 until the second maximum iteration round is reached. All candidate configuration schemes for the corresponding round at the time of stopping iteration are classified into the Pareto candidate solution set. :

[0392]

[0393] in, For the Pareto candidate solution set, the first One candidate configuration scheme, This represents the number of candidate configuration schemes in the Pareto candidate solution set.

[0394] S8. Based on the Pareto candidate solution set, the entropy weight method is used to obtain the coordinated configuration results of hydrogen energy storage and reactive power compensation.

[0395] In one embodiment, the co-configuration result of electro-hydrogen energy storage and reactive power compensation is obtained using the entropy weight method based on the Pareto candidate solution set, as detailed below:

[0396] S81, the first Pareto candidate solution set The objective function values ​​of the q-th objective for each candidate configuration scheme are normalized to obtain the corresponding normalized values. , These correspond to the daily average comprehensive cost target, the nodal source-load fluctuation suppression target, and the nodal voltage support target, respectively.

[0397] S82. Based on the normalized values, the entropy weight method is used to calculate the entropy weights of the corresponding daily average comprehensive cost target, nodal source-load fluctuation suppression target, and nodal voltage support target, including:

[0398] S821. Calculate the first Pareto candidate solution set based on the normalized value. The candidate configuration scheme is in the... The proportion of the objective function value of each objective;

[0399] S822, According to the Pareto candidate solution set, the... The candidate configuration scheme is in the... The entropy weight is calculated based on the proportion of the objective function value of each objective.

[0400] S83. Calculate the comprehensive distance between each candidate configuration and the ideal configuration in the Pareto candidate solution set:

[0401]

[0402] in, For the Pareto candidate solution set, the first The overall distance between each candidate configuration and the ideal configuration; For the Pareto candidate solution set, the first The candidate configuration scheme is in the... Entropy weight of each target; For the Pareto candidate solution set, the first The normalized value of the objective function of each candidate configuration scheme for the qth objective; This is the ideal normalized value of the objective function for the q-th objective;

[0403] S84. Select the candidate configuration scheme with the smallest comprehensive distance as the final collaborative configuration scheme, and regard it as the collaborative configuration result of hydrogen energy storage and reactive power compensation.

[0404] Specifically, the results of the coordinated configuration of hydrogen energy storage and reactive power compensation are as follows:

[0405] S81, the first Pareto candidate solution set The objective function value of each candidate configuration scheme for the q-th objective is normalized using the following formula:

[0406]

[0407] in, For the Pareto candidate solution set, the first The objective function value of the candidate configuration schemes for the q-th objective The normalized value; In order to be in Take the maximum value within the interval. In order to be in Take the minimum value within the interval; The value is positive to avoid a denominator of zero. This is due to the daily average comprehensive cost target, the nodal source-load fluctuation suppression target, and the nodal voltage support target (corresponding to...). In all cases, the goal is to minimize the objective. The smaller the value, the higher the value in the Pareto candidate solution set. The better the performance of each candidate configuration for the q-th objective, the better. To avoid positive numbers with a denominator of zero, such as satisfying In this embodiment, we take .

[0408] S82. The entropy weights for the daily average comprehensive cost target, the nodal source-load fluctuation suppression target, and the nodal voltage support target are calculated using the entropy weight method, as detailed below:

[0409] S821, Calculate the first Pareto candidate solution set. The candidate configuration scheme is in the... The proportion of the objective function value of each objective :

[0410]

[0411] S822, according to Calculate the first Pareto candidate solution set The candidate configuration scheme is in the... Entropy weight of each target :

[0412]

[0413] in,

[0414]

[0415] in, For the Pareto candidate solution set, the first The candidate configuration scheme is in the... The information entropy of each target.

[0416] The more significant the differences between different candidate configurations on a certain objective, the more effective decision-making information that objective contains, and the greater its entropy weight.

[0417] S83. Calculate the comprehensive distance between each candidate configuration and the ideal configuration in the Pareto candidate solution set:

[0418]

[0419] in, For the Pareto candidate solution set, the first The overall distance between each candidate configuration and the ideal configuration; This is the idealized normalized value of the objective function for the q-th objective. Since all three objectives are minimization objectives, it is usually taken as... .

[0420] S84. Select the candidate configuration scheme with the smallest overall distance as the final collaborative configuration scheme, and regard it as the collaborative configuration result of hydrogen energy storage and reactive power compensation. :

[0421]

[0422] in, This represents the minimum comprehensive distance between the candidate configuration and the ideal configuration in the Pareto candidate solution set. This is the final collaborative configuration scheme.

[0423] After obtaining the final set of partitions Subsequently, based on the results of the coordinated configuration of hydrogen energy storage and reactive power compensation... The optimal access nodes, capacity, and rated power configuration results for electric energy storage systems, hydrogen energy storage systems, and reactive power compensation equipment within each zone can be obtained, completing the coordinated site selection and capacity determination for these systems. This process comprehensively considers equipment investment and operating costs, source-load fluctuation mitigation capabilities, and node voltage support effects. Under the constraints of the new energy distribution network, a multi-objective optimization model for coordinated configuration is constructed and solved. Based on the Pareto candidate solution set, the entropy weight method is used to decide on the candidate configuration schemes in the Pareto candidate solution set, determining the coordinated configuration result of electric-hydrogen energy storage and reactive power compensation to complete the coordinated configuration of electric-hydrogen energy storage and reactive power compensation.

[0424] The performance of the present invention will be verified through specific experiments.

[0425] To verify the effectiveness of the proposed method for the coordinated configuration of an electric energy storage system, a hydrogen energy storage system, and a reactive power compensation device (SVG), a simulation analysis (using Python 3.14 software) was conducted on the IEEE 69-bus system as the new energy distribution network in this embodiment. Considering the output power fluctuations of distributed wind power and photovoltaics, as well as load timing changes, the IEEE 69-bus system was used to set up a baseline scenario, scenario 1, scenario 2, scenario 3, and scenario 4 for comparison. The baseline scenario is the original scenario without the configuration of an electric energy storage system, a hydrogen energy storage system, and reactive power compensation equipment (daily comprehensive cost is 0 yuan); Scenario 1 is a scenario where an electric energy storage system, a hydrogen energy storage system, and reactive power compensation equipment are configured collaboratively based on the method of this invention; Scenario 2 is a scenario where an electric energy storage system and a hydrogen energy storage system are configured collaboratively based on the method of this invention, but without reactive power compensation equipment; Scenario 3 is a scenario where only an electric energy storage system is configured based on the method of this invention; Scenario 4 is a scenario where steps S2-S4 are not executed and only an electric energy storage system is configured based on the method of this invention, wherein the number and location of the electric energy storage system are consistent with those in Scenario 3. The simulation results mainly include the node partition set, the output power (charge and discharge power) and remaining charge (state of charge, SOC) of the electric energy storage system, the output power (charge and discharge power) and remaining hydrogen (LOH) of the hydrogen energy storage system, net load fluctuation, node average voltage, and the comparison results of network active power loss of the new energy distribution network, as shown in the figure. Figures 2 to 7 As shown.

[0426] first, Figure 2 The cluster partitioning results for the IEEE 69-node system are presented, i.e., a set of partitions, with one partition corresponding to one cluster. Figure 2 As can be seen, this invention divides the IEEE 69-node system into four clusters based on the topology diagram of the new energy distribution network. Figure 2 This is a partitioning diagram of the IEEE 69-node system. Figure 2The circles in the diagram represent the nodes of the renewable energy distribution network, totaling 69 nodes. The lines connecting the nodes represent the connecting lines. The renewable energy distribution network is divided into four zones (clusters). The nodes in the green, red, blue, and purple boxes correspond to the nodes in the four zones, respectively. The load is set according to the standard configuration of the IEEE-69 node system. Renewable energy components are located at nodes 31, 39, 54, and 56, with maximum peak power generation of 0.3MW, 0.2MW, 0.2MW, and 0.3MW respectively. Energy storage systems are located at nodes 6, 9, 13, and 29; hydrogen energy storage systems are located at nodes 4, 17, 45, and 60; and reactive power compensation equipment is located at nodes 8, 18, 27, and 36. All nodes in the zones together form the node set of the renewable energy distribution network, which contains 69 nodes, numbered from 0 to 68, or alternatively from 1 to 69, depending on actual needs. For example, the node numbers in the blue boxes include 11 to 26, 56, and 57, totaling 18 nodes; similarly, the node numbers in the other colored boxes are determined according to the corresponding boxes in the figure, and will not be repeated here. The above partitioning results provide a basis for the candidate access node range for subsequent electric energy storage systems, hydrogen energy storage systems, and reactive power compensation equipment. Nodes within each cluster have strong electrical connections, and their source-load characteristics are relatively coordinated. This partitioning result provides a local optimization range for the site selection and capacity determination of subsequent electric energy storage systems, hydrogen energy storage systems, and reactive power compensation equipment, avoiding unconstrained searches across the entire network, thereby improving the targeting and optimization efficiency of equipment configuration.

[0427] Figure 3 The results show the changes in charging and discharging power and remaining power (SOC) of the energy storage systems within each cluster of the IEEE 69-node system. The line graph represents the remaining power of the energy storage system, and the bar chart represents the charging and discharging power. Positive values ​​correspond to the charging power, and negative values ​​correspond to the discharging power. Figure 3 It can be seen that the energy storage system charges when the output power of the new energy components is relatively abundant or the load is low. At this time, the output power of the energy storage system is positive, corresponding to the charging power, and the State of Charge (SOC) increases during the charging process. Conversely, it discharges when the load increases or the power deficit widens. At this time, the input power of the energy storage system is negative, corresponding to the discharging power, and the SOC decreases during the discharging process. The output power amplitude, duration, and SOC trends of the energy storage systems in different clusters are not entirely the same, indicating that there are differences in the source-load fluctuation characteristics within each cluster, and that the energy storage system can respond differently according to local power demand. Therefore, Figure 3 This indicates that the energy storage system mainly plays a role in short-term power balancing, peak shaving and valley filling, and quickly smoothing out net load fluctuations.

[0428] Figure 4 The results of charge / discharge power and remaining hydrogen (LOH) variations of hydrogen energy storage systems within each cluster of the IEEE 69-node system are presented. The line graph represents the remaining hydrogen of the hydrogen energy storage system, and the bar chart represents the charge / discharge power of the hydrogen energy storage system; positive values ​​correspond to the charging power, and negative values ​​correspond to the discharging power. Figure 4 It can be seen that during periods of low load or surplus of renewable energy components, the hydrogen energy storage system absorbs electrical energy through an electrolyzer to produce hydrogen. At this time, the output power of the hydrogen energy storage system is positive, corresponding to the charging power, and the LOH (Lower Hourly Rate) increases with the hydrogen storage process. When the load is high or active power support is required, hydrogen energy is released through a fuel cell and supplied to the renewable energy distribution network. At this time, the output power of the hydrogen energy storage system is negative, corresponding to the discharging power, and the LOH decreases with the hydrogen release process. Figure 3 Compared to electric energy storage systems, Figure 4 The hydrogen energy storage system exhibits a longer output power duration and a relatively smoother change process, indicating that it is more suitable for undertaking inter-period energy transfer and renewable energy consumption tasks. Figure 3 and Figure 4 The comparison shows that electric energy storage systems and hydrogen energy storage systems form a complementary relationship of short-term rapid regulation and long-term energy support.

[0429] The role of varistors (SVG) is mainly reflected in voltage support and reactive power flow improvement. Although SVG does not undertake reactive power transfer, it can output capacitive reactive power during periods of low voltage or high reactive power demand in renewable energy distribution networks, thereby improving node voltage levels and power flow distribution in renewable energy distribution network branches. This effect can be achieved by... Figure 6 and Figure 7 Further manifestation: Figure 6 This reflects the improvement in the average voltage level of the new energy distribution network after the participation of SVG. Figure 7 This reflects the reduction in active power losses in the network after the participation of SVG. Therefore, a synergistic relationship of active power regulation and reactive power support is formed between the reactive power compensation device (SVG) and the electrical energy storage system and the hydrogen energy storage system.

[0430] Figure 5 The results of net load fluctuation comparison for the IEEE 69-bus system under different scenarios are presented. Figure 5 In the graph, the horizontal axis represents the scheduling period in hours; the vertical axis represents the net load fluctuation value, dimensionless; and the right-hand side represents different configured scenarios. Different colored lines correspond to different scenarios: red for scenario 1, orange for scenario 2, green for scenario 3, cyan for scenario 4, and purple for the original scenario. Figure 5It can be seen that, compared to the original scenario, the net load fluctuations in scenarios 1 to 4 were suppressed to varying degrees. The overall amplitude of the net load fluctuation in scenario 1 was smaller, indicating that the coordinated configuration of the electric energy storage system, hydrogen energy storage system, and reactive power compensation equipment can more effectively smooth out the active power exchange fluctuations between the renewable energy distribution network and the upstream power grid, thereby improving the operational stability of the renewable energy distribution network. Figure 5 It can be seen that net load fluctuations are quite significant under the baseline scenario, especially during periods of change in the output power of new energy sources and load peak-valley transitions, where the amplitude of net load fluctuations is large. With only an electric energy storage system configured, short-term fluctuations are somewhat suppressed; with the further configuration of a hydrogen energy storage system, the cross-period energy balancing capability is enhanced, and net load fluctuations continue to decrease; when electric energy storage systems, hydrogen energy storage systems, and reactive power compensation equipment are configured in a coordinated manner, net load fluctuations are further improved. Figure 5 As can be seen from Table 1, Figure 5 This paper presents the hourly variation trend of net load fluctuation in the renewable energy distribution network under different scenarios. Table 1 summarizes the net load fluctuation data for each time period throughout the day, obtaining the net load fluctuation index. Based on the full-day statistical results in Table 1, the net load fluctuation index is derived from the baseline scenario. kW / day reduced to scenario 1 kW / day, a reduction of approximately This demonstrates that the coordinated configuration scheme of the electric energy storage system, hydrogen energy storage system, and reactive power compensation equipment adopted in this invention can effectively mitigate the impact of power output fluctuations and load timing changes on the active power balance of the new energy distribution network, thereby improving the operational stability of the new energy distribution network.

[0431] Table 1

[0432]

[0433] Figure 6 The average voltage comparison results of the IEEE 69-bus system under different scenarios are presented. Figure 6 It can be seen that the average voltage fluctuates greatly under the baseline scenario, and the voltage level is low in some periods. After configuring the electric energy storage system and the hydrogen energy storage system, the voltage level is improved due to the improved distribution of active power. After further introducing reactive power compensation equipment, the average voltage curve is more stable overall, and the voltage level is significantly increased during low voltage periods. Figure 6 In the graph, the horizontal axis represents the scheduling period in hours; the vertical axis represents the average voltage in units of pu (units per unit), which is dimensionless; the right-hand side represents different configuration scenarios. Different colored curves correspond to different scenarios: red line for scenario 1, orange line for scenario 2, green line for scenario 3, blue line for scenario 4, and black line for the original scenario. Figure 6As can be seen, the line graph in the figure reflects the hourly trend of average voltage changes with the scheduling period under various scenarios; the average voltage index and minimum node voltage in Table 1 are comprehensive results obtained based on the statistical data of voltage at each time period and node throughout the day. Therefore, Figure 6 The data in Table 1 is not a one-to-one correspondence with the actual voltage levels. Instead, the voltage improvement effect is evaluated from two perspectives: time-series trends and daily statistical indicators. According to the simulation data in Table 1, the average voltage index decreased from 0.238 pu / day in the baseline scenario to 0.154 pu / day, a reduction of approximately 35.3%; the minimum node voltage increased from 0.907 pu to 0.963 pu. These results demonstrate that the coordinated operation of reactive power compensation equipment with electrical energy storage systems and hydrogen energy storage systems can significantly enhance voltage support capabilities and improve the operational safety margin of the new energy distribution network.

[0434] Figure 7 The results of comparing the active power loss of the IEEE 69-node system under different scenarios are presented. Figure 7 In the graph, the horizontal axis represents the scheduling period in hours; the vertical axis represents network active power loss in kW; and the right-hand side represents different configuration scenarios. Different colored curves correspond to different scenarios: red line corresponds to scenario 1, blue line to scenario 2, green line to scenario 3, pink line to scenario 4, and black line to the original scenario. Figure 7 As can be seen, the line graph in the figure reflects the hourly trend of network active power loss under different scenarios and scheduling periods; the network active power loss in Table 1 is a comprehensive result obtained based on the statistical data of network active power loss at all times of the day, thus obtaining the network active power loss index. Figure 7 It can be seen that the network active power loss is relatively high in the baseline scenario, and it increases significantly during periods of heavy load. After configuring only the electrical energy storage system, the active power transmission pressure on the lines is reduced. With the addition of the hydrogen energy storage system, the energy distribution over time is more rational, further reducing the network active power loss. Further configuration of reactive power compensation equipment improves reactive power flow and voltage distribution, leading to an even greater reduction in network active power loss. According to simulation data, the network active power loss index decreased from 3518.6 kW / day in the baseline scenario to 2368.7 kW / day, a reduction of approximately 32.7%. This result demonstrates that the present invention can reduce line losses through the synergistic effect of active power regulation and reactive power compensation.

[0435] comprehensive Figures 5 to 7It can be seen that different equipment combinations improve the operating performance of the IEEE 69-node system to varying degrees. With only an electric energy storage system, the IEEE 69-node system can reduce short-term net load fluctuations to some extent, but its ability to support long-term energy transfer and continuous power is limited. Introducing a hydrogen energy storage system further enhances the source-load balancing capability of the IEEE 69-node system. Further configuring reactive power compensation equipment on top of the electric-hydrogen energy storage (electric and hydrogen energy storage systems) further reduces voltage fluctuations and network losses, and increases the minimum node voltage. Compared to the electric-hydrogen energy storage scheme without reactive power compensation equipment, adding reactive power compensation equipment further reduces net load fluctuations, voltage fluctuations, and active power losses by approximately 4.6%, 13.5%, and 5.4%, respectively, indicating that reactive power compensation equipment has a significant benefit in improving voltage quality and reducing line losses.

[0436] Compared with the prior art, the beneficial effects of the present invention are mainly reflected in the following aspects: 1) As Figure 2 As shown, this invention reduces the search range for device location and capacity determination through cluster partitioning, making the configuration results more consistent with the local source-load characteristics and electrical coupling relationships of the new energy distribution network; 2) As Figure 3 and Figure 4 As shown, this invention integrates electrical energy storage systems and hydrogen energy storage systems into a unified configuration framework, achieving complementarity between short-term power regulation and long-term energy transfer; 3) As Figure 6 and Figure 7 As shown, this invention further introduces reactive power compensation equipment for dynamic reactive power support, which can improve the voltage level of the new energy distribution network and reduce network losses; 4) As Figures 5 to 7 As shown, the present invention can achieve a good overall effect in terms of net load fluctuation suppression, voltage support and network loss reduction.

[0437] In summary, combining Figures 2 to 7 Simulation results show that this invention, through a technical approach of cluster partitioning, coordinated configuration of electric-hydrogen energy storage, reactive power compensation equipment for reactive power support, and multi-objective decision-making, achieves coordinated configuration of multiple types of regulating resources in the new energy distribution network. Experimental results demonstrate that this method can effectively reduce net load fluctuations and network active power losses, improve node voltage levels, and enhance the adaptability of the new energy distribution network to new energy fluctuations, thus possessing significant engineering application value.

[0438] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0439] The embodiments described above are merely specific and detailed examples of the embodiments described in this application, and should not be construed as limiting the scope of the application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these modifications and improvements all fall within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the appended claims.

Claims

1. A method for coordinated configuration of hydrogen energy storage and reactive power compensation in a new energy distribution network, characterized in that: Includes the following steps: S1. Obtain the topology diagram of the new energy distribution network; S2. Construct the electrical coupling strength matrix between nodes of the new energy distribution network based on the topology diagram; S3. Calculate the electrical density of the partition, the source-load balance of the partition, and the scale configuration of the partition based on the electrical coupling strength matrix between nodes; S4. Construct and solve a comprehensive evaluation function for the partition based on the partition electrical density, partition source-load balance, and partition scale configuration to obtain the final partition set. S5. Based on the final partition set, construct the daily average comprehensive cost target, node source-load fluctuation suppression target, node voltage support target, and constraints of the new energy distribution network. S6. Construct a collaborative configuration multi-objective optimization model based on the daily average comprehensive cost target, the nodal source-load fluctuation suppression target, the nodal voltage support target, and constraints; S7. Solve the cooperative configuration multi-objective optimization model using the non-dominated sorting nut optimization algorithm to obtain the Pareto candidate solution set; S8. Based on the Pareto candidate solution set, the entropy weight method is used to obtain the coordinated configuration results of hydrogen energy storage and reactive power compensation.

2. The method for coordinated configuration of hydrogen energy storage and reactive power compensation for new energy distribution networks as described in claim 1, characterized in that: The construction of the inter-node electrical coupling strength matrix of the new energy distribution network based on the topology diagram is as follows: S21. Construct a linearized power flow sensitivity equation based on the power flow Jacobian matrix; S22. Obtain the power flow sensitivity matrix of voltage magnitude versus injected active power based on the linearized power flow sensitivity equation. To calculate the electrical dissimilarity matrix between nodes in a new energy distribution network, where nodes... With nodes Electrical differences The calculation is as follows: S221, Computation Node For nodes Voltage influence factor : in, express Middle Line number Column elements The absolute value; express Middle Line number Column elements The absolute value, , This represents the total number of nodes in the node set of the topological graph. It is a positive number, used to avoid the denominator being zero; S222, Computation Node With nodes Electrical differences : in, For nodes For nodes Voltage influence factor, For nodes For nodes Voltage influence factor, The set of nodes that receive loads in the node set of the topology graph; S23. Transform the electrical difference matrix between nodes into the electrical coupling strength matrix between nodes, where, node With nodes Inter-electrical coupling strength The calculation is as follows: in, This represents the largest electrical difference in the inter-node electrical difference matrix.

3. The method for coordinated configuration of hydrogen energy storage and reactive power compensation for new energy distribution networks as described in claim 2, characterized in that: The linearized power flow sensitivity equation based on the power flow Jacobian matrix is ​​as follows: in, This is the vector of changes in the voltage phase angle of all nodes in the node set of the topology graph. This is a vector representing the change in voltage magnitude of all nodes in the node set of the topology graph. This is the vector of changes in injected active power for all nodes in the node set of the topology graph. This is the vector of changes in injected reactive power for all nodes in the node set of the topology graph. For the trend of Jacobi matrix The inverse matrix also represents the power flow sensitivity matrix. The power flow sensitivity matrix represents the voltage phase angle to injected active power. The power flow sensitivity matrix represents the voltage phase angle to injected reactive power. This represents the power flow sensitivity matrix to the injected reactive power. This represents the power flow sensitivity matrix to the injected active power. , , , All dimensions are .

4. The method for coordinated configuration of hydrogen energy storage and reactive power compensation for new energy distribution networks as described in claim 1, characterized in that: The calculation of partition electrical density, partition source-load balance, and partition size configuration based on the inter-node electrical coupling strength matrix is ​​as follows: 1) Zoned electrical tightness: in, in, For the corresponding partition set The electrical density of the partitions, , For the corresponding partition set The A set of nodes for each partition. , The number of partitions, It is the set of nodes in the topological graph; The total electrical coupling strength of the new energy distribution network; For nodes The weighted connection strength, For nodes The weighted connection strength, and , , For nodes With nodes Inter-node electrical coupling strength, i.e., the first element in the inter-node electrical coupling strength matrix. Line number Column elements, This represents the total number of nodes in the node set of the topological graph. For nodes and nodes The partition assignment function; It is a positive number, used to avoid the denominator being zero; 2) Regional source-load balance: Among them, the corresponding partition set The Each zone in time period Net load power The formula is as follows: in, For the corresponding partition set The partitioned source load balance, For the first time in the scheduling cycle The maximum value of the net load power amplitude of each zone. for amplitude, The number of time periods within the scheduling cycle. For nodes During the period The active power of the load, For nodes During the period The active power output of the new energy components; 3) Partition size configuration: in, in, For the corresponding partition set The partition size configuration degree, and , For the corresponding partition set The partition size deviation, For the corresponding partition set The The number of nodes in each partition. This represents the ideal average number of nodes in a partition.

5. The method for coordinated configuration of hydrogen energy storage and reactive power compensation for new energy distribution networks as described in claim 1, characterized in that: The process involves constructing and solving a comprehensive evaluation function for each zone based on its electrical density, source-load balance, and scale configuration, to obtain the final set of zones, as detailed below: S41. Construct a comprehensive evaluation function for the partitioning: in, To find the maximum value in the entire partition set, For the corresponding partition set The overall evaluation value of the zone, For the corresponding partition set The electrical density of the partitions, For the corresponding partition set The partitioned source load balance, For the corresponding partition set The partition size configuration degree, , , The weighting coefficients are, in order: the weighting coefficient for the electrical density of the zone, the weighting coefficient for the source-load balance of the zone, and the weighting coefficient for the scale configuration of the zone. S42. Solve for the final partition set using the improved snake optimization algorithm based on the partition comprehensive evaluation function.

6. The method for coordinated configuration of hydrogen energy storage and reactive power compensation for new energy distribution networks as described in claim 1, characterized in that: The daily average comprehensive cost target, nodal source-load fluctuation suppression target, nodal voltage support target, and constraints for constructing the new energy distribution network based on the final partition set are as follows: 5.1) The daily average comprehensive cost target is constructed as follows: in, This indicates taking the minimum value. Let the daily average comprehensive cost be the objective function value. The total life-cycle cost of the entire energy storage system. The total lifecycle cost of all hydrogen energy storage systems. The total life-cycle cost of all reactive power compensation equipment. The number of days per year; 5.2) The target for suppressing nodal source load fluctuations is constructed as follows: in, The objective function value for suppressing nodal source load fluctuations; For the time period Active power exchange between the new energy distribution network and the upstream power grid. For the time period Active power exchange between the new energy distribution network and the upstream power grid; This represents the number of time periods within the scheduling cycle. 5.3) Node voltage support target, constructed as follows: in, The objective function value is supported by the node voltage; For nodes During the period The voltage amplitude; For nodes The average voltage amplitude during the scheduling period; This represents the total number of nodes in the node set of the topological graph. 5.4) Constraints, including node injection power constraints, power flow constraints, capacity constraints, and device access location constraints.

7. The method for coordinated configuration of hydrogen energy storage and reactive power compensation for new energy distribution networks as described in claim 6, characterized in that: The collaborative configuration multi-objective optimization model, based on the daily average comprehensive cost target, the nodal source-load fluctuation suppression target, the nodal voltage support target, and constraints, is constructed as follows: S61. Constructing a collaborative configuration decision set : in, in, Configure decision variables for the devices in the k-th partition of the final partition set; For the final set of partitions The set of access nodes for the energy storage system in each partition; For the final set of partitions The set of access nodes for hydrogen energy storage systems in each partition; For the final set of partitions The set of access nodes for reactive power compensation equipment in each partition; For the final set of partitions The total investment capacity of the energy storage system in each zone; For the final set of partitions The rated power of the energy storage system in each zone; For the final set of partitions The capacity of hydrogen storage devices in each zone's hydrogen energy storage system; For the final set of partitions The rated power of the electrolyzers in the hydrogen energy storage system of each zone; For the final set of partitions The rated power of the fuel cells in the hydrogen energy storage system of each zone; For the final set of partitions The capacity of reactive power compensation equipment in each zone; S62. Establish a multi-objective optimization model for collaborative configuration based on the collaborative configuration decision set, as follows: in, For traversal Take the minimum value. for The weighted composite objective function value; for The objective function value of the average daily comprehensive cost; for The objective function value for suppressing nodal source load fluctuations; for The node voltage supports the objective function value; The weighting coefficients are, in order: the weighting coefficient of the objective function value of average daily comprehensive cost, the weighting coefficient of the objective function value of nodal source-load fluctuation suppression, and the weighting coefficient of the objective function value of nodal voltage support.

8. The method for coordinated configuration of hydrogen energy storage and reactive power compensation for new energy distribution networks as described in claim 7, characterized in that: The non-dominated sorting nut optimization algorithm is used to solve the cooperative configuration multi-objective optimization model to obtain the Pareto candidate solution set, as detailed below: S71. Treat each candidate configuration scheme as an individual in the non-dominated sorting nut optimization algorithm, and randomly generate... One candidate configuration scheme; S72, Obtain the number of iterations in round 0. One candidate configuration scheme, ; S73, Obtain the first according to the updated formula. The first iteration One candidate configuration scheme, , This is the second largest iteration round; S74, the first Round iteration and the first All candidate configuration schemes in the round of iterations together constitute the first... The set to be selected in each round of iterations; S75, according to the... The dominance relationship satisfied by any two candidate configuration schemes in the set to be screened in the round of iteration will be the first... In each iteration, all candidate configuration schemes in the set to be screened are divided into several non-dominated levels; S76, respectively, the first The weighted comprehensive objective function values ​​of the device configuration decision variables of all partitions in the candidate configuration scheme in the set to be screened in the round of iteration are summed to form the corresponding total weighted objective value. After sorting all the total weighted objective values ​​from smallest to largest, the crowding distance of the corresponding candidate configuration scheme is calculated. S77, the first In the round of iterations, the candidate configuration schemes in the set to be screened are sorted from smallest to largest according to the non-dominated level number. Then, within the same non-dominated level, they are sorted from largest to smallest according to the crowding distance, forming the th... Target sorting in rounds of iteration; S78, Select the first In the target sorting of the round of iterations, the first Each candidate configuration scheme enters the next iteration, and the process returns to step S73 until the second maximum iteration round is reached. All candidate configuration schemes in the corresponding round when the iteration stops are then assigned to the Pareto candidate solution set.

9. The method for coordinated configuration of hydrogen energy storage and reactive power compensation for new energy distribution networks as described in claim 8, characterized in that: The update formula is as follows: in, For the first The search state variables of each iteration; For the first The first iteration One candidate configuration scheme, For the first The first iteration One candidate configuration scheme, For the first The optimal candidate configuration scheme for each round of iterations; For the first The first reference cache location vector in each iteration, For the first The second reference cache position vector in the round of iteration; This indicates the foraging, storage, and update operator; This indicates the cache search recovery update operator; According to the first The dominance relationship satisfied by any two candidate configuration schemes in the set to be screened in the round of iteration will be the first... In each iteration, all candidate configuration schemes in the set to be screened are divided into several non-dominated levels, as follows: For the In the set of candidates to be screened in the round of iteration, any two candidate configuration schemes are denoted as the first candidate configuration scheme and the second candidate configuration scheme. If the first candidate configuration scheme is less than or equal to the second candidate configuration scheme in terms of the objective function value of the average daily comprehensive cost, the objective function value of the node source load fluctuation suppression, and the objective function value of the node voltage support, and the first candidate configuration scheme is less than the second candidate configuration scheme in at least one of the objective function values ​​of the average daily comprehensive cost, the objective function value of the node source load fluctuation suppression, and the objective function value of the node voltage support, then it is considered that there is a dominance relationship, and the first candidate configuration scheme dominates the second candidate configuration scheme. Will not be the first In each iteration, any candidate configuration scheme that is dominated by another candidate configuration scheme in the set to be screened is classified as the first non-dominated level and removed. In the remaining candidate configuration schemes, the process continues to screen and classify the candidate configuration schemes that are not dominated by other remaining candidate configuration schemes as the second non-dominated level and remove them. This process is repeated until all candidate configuration schemes are classified into the corresponding non-dominated levels, resulting in several non-dominated levels.

10. The method for coordinated configuration of hydrogen energy storage and reactive power compensation for new energy distribution networks as described in claim 8, characterized in that: The specific details are as follows: The entropy weight method is used to obtain the coordinated configuration results of hydrogen energy storage and reactive power compensation based on the Pareto candidate solution set. S81, the first Pareto candidate solution set The objective function values ​​of the q-th objective for each candidate configuration scheme are normalized to obtain the corresponding normalized values. , These correspond to the daily average comprehensive cost target, the nodal source-load fluctuation suppression target, and the nodal voltage support target, respectively. S82. Based on the normalized values, the entropy weight method is used to calculate the entropy weights of the corresponding daily average comprehensive cost target, nodal source-load fluctuation suppression target, and nodal voltage support target, including: S821. Calculate the first Pareto candidate solution set based on the normalized value. The candidate configuration scheme is in the... The proportion of the objective function value of each objective; S822, According to the Pareto candidate solution set, the... The candidate configuration scheme is in the... The entropy weight is calculated based on the proportion of the objective function value of each objective. S83. Calculate the comprehensive distance between each candidate configuration and the ideal configuration in the Pareto candidate solution set: in, For the Pareto candidate solution set, the first The overall distance between each candidate configuration and the ideal configuration; For the Pareto candidate solution set, the first The candidate configuration scheme is in the... Entropy weight of each target; For the Pareto candidate solution set, the first The normalized value of the objective function of each candidate configuration scheme for the qth objective; This is the ideal normalized value of the objective function for the q-th objective; S84. Select the candidate configuration scheme with the smallest comprehensive distance as the final collaborative configuration scheme, and regard it as the collaborative configuration result of hydrogen energy storage and reactive power compensation.

Images