A method and system for evaluating the importance of nodes in energy storage planning of distribution network

Abstract

The present invention discloses a method and system for evaluating the importance of nodes in energy storage planning of distribution network. According to the topology information, load information, load weight information and line failure rate information of distribution network, it is assumed that nodes connected to energy storage configuration nodes or substation nodes do not lose load, and nodes not connected to energy storage configuration nodes or substation nodes lose all power supply loads; the N-1 fault scenarios of the system are traversed, and the total load loss expectation of the system under all N-1 faults is calculated, and based on this, the weight information of the distribution network node load is comprehensively analyzed to generate a candidate node set for distribution network energy storage planning, so as to realize the importance evaluation of distribution network energy storage planning nodes. The present invention can accurately and effectively detect nodes that are more important for improving system reliability for the analysis, construction, upgrading, transformation, operation optimization control and the like of the distribution network.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network analysis, and in particular relates to a method and system for evaluating the importance of distribution network energy storage planning nodes. Background Art

[0002] As the "nerve endings" of the power grid, the distribution network connects the transmission network and power users. It is an important part of the power system and an important infrastructure for ensuring and improving people's livelihood. In recent years, with the continuous development and maturity of energy storage related technologies, the cost of energy storage construction and operation and maintenance has continued to decrease. It is feasible to configure energy storage on a large scale on the distribution network side, and the energy storage planning issue on the distribution network side has therefore received widespread attention. Through the optimized scheduling of the energy storage system, not only can demand-side management and flexible load response be effectively achieved, and the peak-to-valley difference in load can be reduced, but also the reliability of power supply can be improved, and the full absorption and utilization of renewable energy power generation can be promoted. Therefore, appropriate energy storage configuration has a good supporting role for the distribution network, and appropriate energy storage scheduling also has a positive effect on reducing carbon emissions in the entire power production.

[0003] Distribution network energy storage planning needs to solve two problems. The first is the decision-making aspect, which requires answering questions such as which nodes to configure energy storage equipment, what kind of energy storage to configure, and how much energy storage to configure. The second is the optimization goal, which requires answering what is the goal of the optimization plan.

[0004] Regarding the optimization goal, a common method is to take cost optimization as the goal, including investment costs and (estimated) operation and maintenance costs. The main difficulty with this goal is that a large number of scenario simulations and a lot of cost parameter information that is difficult to obtain are required to obtain a more accurate estimate of the operation and maintenance costs, which can easily lead to inaccurate cost estimates, causing the planning scheme to deviate from the actual optimal. Another common method is to take maximizing the improvement of power supply reliability as the optimization goal. By choosing this goal, you only need to calculate the amount of load loss in each scenario without calculating the cost in each scenario. Therefore, this goal is more convenient than taking cost optimization as the goal. Of course, multi-objective optimization models can also be considered in distribution network energy storage planning problems.

[0005] No matter what is used as the optimization goal, node selection corresponds to discrete decisions, and there are some nonlinear factors in the cost or reliability calculation. Therefore, the energy storage planning problem is generally a nonlinear integer programming problem, which is not easy to solve. Reducing the number of integer variables is of great value in improving the efficiency of solving energy storage planning problems. An important idea is to select only a small number of key nodes as candidate nodes for energy storage configuration, which will greatly simplify the problem solving. In the distribution network, the power supply reliability of a few key nodes has a significant impact on the power supply reliability of the entire distribution network, so it is very necessary to design quantitative indicators for node importance.

[0006] Most existing methods identify important nodes in the power grid based on the grid topology or physical characteristics. They do not design importance indicators for energy storage planning problems, and do not consider node importance from the perspective of the improvement in power supply reliability after configuring energy storage at each node. Summary of the invention

[0007] The technical problem to be solved by the present invention is to provide a method and system for evaluating the importance of distribution network energy storage planning nodes in view of the deficiencies in the above-mentioned prior art. This method combines the actual operating characteristics of the power grid, takes the expectation of improving power supply reliability as the main consideration, integrates factors such as topological structure and load size, and on this basis, proposes a method for quickly identifying key nodes of energy storage configuration in the power grid.

[0008] The present invention adopts the following technical solutions:

[0009] A method for evaluating the importance of nodes in energy storage planning of a distribution network comprises the following steps:

[0010] S1. Construct a mathematical model of the distribution network based on the distribution network information;

[0011] S2, configuring energy storage operation at the kth node of the distribution network mathematical model constructed in step S1, then traversing all N-1 fault scenarios of the distribution network system after energy storage configuration, and calculating the expected system load loss power under each fault scenario;

[0012] S3, adding the expected load loss electricity of the system under each N-1 fault scenario in step S2, and obtaining the expected total load loss electricity of the distribution network system after the kth node performs the energy storage configuration operation;

[0013] S4. Execute steps S2 and S3 for all non-substation nodes in the distribution network system to obtain the expected total load loss of energy storage configured on each node; determine the importance evaluation index of energy storage planning nodes for reliability improvement of each node, screen out the candidate node set for energy storage power station planning, and then select the top m nodes with the highest reliability importance index as the candidate node set for energy storage planning, so as to realize the node importance evaluation of distribution network energy storage planning.

[0014] Specifically, in step S1, the distribution network information includes the topological structure (N, E) of the distribution network to be evaluated, the set N of all nodes of the distribution network, the set N0 of substation nodes of the distribution network, the set N\N0 of non-substation nodes of the distribution network, the active load capacity P of each node of the distribution network L,i , the critical weight w of each node load in the distribution network i , the distribution network line set E and the failure probability p of each branch l∈E l , scenario s where line l has N-1 faults l and all N-1 fault scenarios S of the line E.

[0015] Furthermore, step S2 is specifically as follows:

[0016] S201, performing energy storage configuration operation on the kth node of the distribution network to obtain a set of substation nodes and energy storage configuration nodes, the mathematical model of which is expressed as j∈N0∪{k};

[0017] S202, traverse N-1 branch fault scenarios for the distribution network after energy storage is configured in step S201, and in each fault scenario s l The topological adjacency matrix of the modified distribution network is M l ; By traversing M l Based on the data in, determine the connection relationship between node i and substation or energy storage access node j∈N0∪{k};

[0018] S203: Calculate N-1 fault scenario s when branch l fails l The probability of occurrence P l ;

[0019] S204, traverse the loads of non-substation nodes i∈N\{N0∪{k}} that are not configured with energy storage, and calculate the probability P of the disconnection fault scenario obtained in step S202 l Calculate disconnection scenarios l System load loss expectation under

[0020] S205, traversing disconnection scenarios l ∈S E , according to each disconnection scenario s obtained in step S204 l System load loss expectation under Traverse all disconnection scenarios l ∈S E Used to calculate the total expected load loss power of the system after node k is connected to energy storage.

[0021] Further, in step S201, any of the following methods is used to determine the connection relationship between node i and the substation node or energy storage access node j∈N0∪{k}:

[0022] According to the particularity of the distribution network topology, the source-load connection relationship judgment strategy can be simplified. If any branch is disconnected, the system will be divided into two subtrees. l , the independent subtree disconnected from the substation is recorded as T l , when the independent subtree T l When no energy storage power station or power supply device is connected, T l All load nodes in the will lose power, resulting in load loss, and the load loss flags of these nodes will be set to On the contrary, according to Tl The inclusion relationship with the energy storage access node k determines the adjacency matrix M l Is there a connected path C(i,j) in ? If so, then Otherwise, let

[0023] Traverse and count the topological adjacency matrix M l The data in the adjacency matrix M is determined l Is there a connected path C(i,j) in the network? If so, set the load loss flag of node i when node k is configured with energy storage. Set to Otherwise, let

[0024] Furthermore, in step S203, the fault scenario s l The probability of occurrence P l Specifically:

[0025]

[0026] Among them, p l is the failure probability of each branch, p m is the disconnection probability of the line other than branch l, and E is the line set of the distribution network.

[0027] Further, in step S204, the disconnection scenario s l System load loss expectation under Specifically:

[0028]

[0029] in, N is the set of all nodes in the distribution network, N0 is the substation node set of the distribution network, P l is the disconnection probability of branch l, The load loss flag of node i when node k is configured with energy storage, w i is the critical weight of each node load in the distribution network, P L,i It is the active load capacity of each node in the distribution network.

[0030] Specifically, in step S3, the total load loss expectation E of the distribution network system is k as follows:

[0031]

[0032] in, For disconnection scenes l The system loses load power.

[0033] Specifically, in step S4, the node importance index RI k It is expressed as:

[0034]

[0035] Among them, E k is the total load loss expectation of the distribution network system, N is the set of all nodes in the distribution network, and N0 is the set of substation nodes in the distribution network.

[0036] Specifically, in step S4, the candidate node set for energy storage power station planning is selected as follows:

[0037] Assume that the ratio between the number of candidate node set elements m and the number of distribution network nodes N is: represents the smallest integer greater than or equal to x, and the parameter δ∈[0,1] is defined as the candidate node ratio. First, the load node k∈N\N0 is selected according to the index RI k Arrange from large to small; then select the first m nodes with the highest reliability importance index to obtain the candidate node set Ψ0 for energy storage planning.

[0038] Another technical solution of the present invention is a distribution network energy storage planning node importance evaluation system, comprising:

[0039] Information module, constructs a mathematical model of the distribution network based on the distribution network information;

[0040] The calculation module configures the energy storage operation at the kth node of the distribution network mathematical model constructed by the information module, and then traverses all N-1 fault scenarios of the distribution network system after the energy storage is configured, and calculates the expected system load loss power under each fault scenario;

[0041] The accumulation module adds up the expected load loss electricity of the system under each N-1 fault scenario in the calculation module to obtain the expected total load loss electricity of the distribution network system after the kth node performs the energy storage configuration operation;

[0042] The evaluation module executes the calculation module and the accumulation module on all non-substation nodes in the distribution network system to obtain the expected total load loss of energy storage configured on each node; determines the importance evaluation index of energy storage planning nodes for reliability improvement of each node, screens out the candidate node set for energy storage power station planning, and then selects the top m nodes with the highest reliability importance index as the candidate node set for energy storage planning, realizing the importance evaluation of distribution network energy storage planning nodes.

[0043] Compared with the prior art, the present invention has at least the following beneficial effects:

[0044] The present invention provides a method for evaluating the importance of nodes in energy storage planning of a distribution network. According to the actual data of the distribution network system, a mathematical model of the distribution network is constructed, and by assuming that energy storage is configured on non-power supply nodes one by one, the total system load loss expectation after the N-1 line failure occurs in the system when energy storage is configured at each node is estimated. When estimating the total system load loss expectation, the present invention realizes a rapid estimation of the system load loss based on the principle that load nodes do not lose load when there is a topological connection with energy storage (equivalent to an emergency power supply). According to the total load loss size when energy storage is configured at different nodes, a relative index is formed by a normalization method, thereby realizing the evaluation of the importance of nodes in energy storage planning of distribution network for reliability improvement.

[0045] Furthermore, in the present invention, a concise and comprehensive mathematical model is used to describe the distribution network system, and the mathematical model includes the topological structure (N, E) of the distribution network to be evaluated, the set N of all nodes of the distribution network, the set N0 of substation nodes of the distribution network, the set N\N0 of non-substation nodes of the distribution network, the active load capacity P of each node of the distribution network L,i , the critical weight w of each node load in the distribution network i , the distribution network line set E and the failure probability p of each branch l∈E l , scenario s where line l has N-1 faults l and all N-1 fault scenarios S of the line E .

[0046] Furthermore, after setting the k-node configuration energy storage, based on the basic assumption that the load node does not lose load when there is a topological connection with the energy storage power source, the total load loss expectation of the system after the k-node configuration energy storage is realized. The present invention realizes fast calculation based on the basic methodology of probability and statistics.

[0047] Furthermore, the connection relationship between the judgment node i and the substation node or the energy storage access node j∈N0∪{k} is used to quickly judge the connection relationship between the energy storage node and the load node.

[0048] Furthermore, when calculating the total expected load loss of the system, based on the basic principles of probability, the binomial probability model is used to describe the fault scenario when a single line has N-1 disconnection faults: l probability of occurrence.

[0049] Furthermore, in calculating the disconnection scenario s l System load loss expectation under The present invention comprehensively considers multiple distribution network information such as the probability of disconnection failure of the distribution network system, the connection relationship between the load node and the power node, the load size of the load node, and the load importance of the load node, so as to realize the disconnection scenario of the distribution network system. lSystem load loss expectation under Calculation.

[0050] Furthermore, after configuring a node in the distribution network for energy storage, based on the additivity of probability expectations, the system load loss expectations under all single N-1 fault scenarios are added together to obtain the total load loss expectation under the N-1 fault scenario.

[0051] Furthermore, based on the basic principles of statistics, the total system load loss expectation when each node is configured with energy storage is normalized to obtain a relative indicator that reflects the degree of improvement in system reliability when each node is configured with energy storage, thereby realizing node importance assessment for distribution network energy storage planning aimed at improving reliability.

[0052] Furthermore, after obtaining the normalized index of system reliability improvement after configuring energy storage at each node, in order to meet the actual engineering needs, the first m nodes with the highest reliability importance index can be selected as the candidate node set for energy storage planning based on the evaluation index results. In engineering design, nodes in the candidate set can be selected to build energy storage, thereby realizing the combination of evaluation methods and engineering practice.

[0053] In summary, the present invention is based on the rapid calculation of the expected total system load loss power of the distribution network system under the N-1 fault scenario, and realizes the evaluation of the importance of distribution network energy storage planning nodes for reliability improvement. The present invention can accurately and effectively detect important nodes for the analysis, construction, upgrade, transformation, operation optimization control, etc. of the energy storage planning of the distribution network.

[0054] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a schematic diagram of the process of the present invention;

[0056] Figure 2 It is a schematic diagram of the distribution network plane topology diagram and the corresponding adjacency matrix and the loads and load weights on the nodes of the present invention. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0059] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0060] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0061] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0062] The present invention provides a method for evaluating the importance of energy storage planning nodes in a distribution network. The distribution network is abstracted into a plane topology diagram according to the actual wiring conditions of the distribution network, and the basis for distribution network analysis is established in combination with the load conditions, load weights and line failure probability of the distribution network; the total load loss expectation of all "N-1" failures when each node is configured with energy storage is calculated according to the abstract distribution network topology diagram and related load and line information; the importance of energy storage planning nodes in the distribution network is evaluated based on the total load loss expectation of each node, and a candidate node set for energy storage planning of the distribution network is generated. In this way, the identification of key energy storage configuration nodes of the distribution network can be realized, providing a reference for the construction and upgrading of the distribution network grid structure, energy storage construction, and maintaining the safe and reliable operation of the distribution network.

[0063] See also Figure 1 The present invention provides a method for evaluating the importance of nodes in energy storage planning of a distribution network, comprising the following steps:

[0064] S1, parameter input and initialization;

[0065] The information of the distribution network is obtained from the power system planning department. The input parameters for node importance evaluation include the topological structure of the distribution network to be evaluated (N, E), the substation node set N0 of the distribution network, the set of non-substation nodes of the distribution network N\N0, and the active load capacity P of each node in the distribution network. L,i , the critical weight w of each node load in the distribution network i , the line set E of the distribution network, the failure probability p of each branch l∈E l ; Initialize the node pointer to be evaluated k = 1.

[0066] The index of the non-substation node of the distribution network is i, and i∈N\N0, and the line index of the distribution network is l, and l∈E.

[0067] The critical weight w of each node load in the distribution network i , which is related to the type of power consumption of the node (for example, the critical weight of loads such as hospitals, railways, and data centers is usually higher than that of residential power loads). It can be determined by means of hierarchical analysis, fuzzy weighting, expert experience, etc. By introducing this weight coefficient, the load importance information can be organically combined with the quantitative indicators of the node's impact on reliability, thus realizing a comprehensive evaluation of node importance based on a combination of subjective and objective factors.

[0068] S2, evaluate the reliability importance of node k;

[0069] When node k is connected to the energy storage power station, the reliability importance index of the node is calculated by a double-layer traversal algorithm under the "N-1" fault assumption.

[0070] The N-1 fault scenario is a fault scenario in which only one line in the system is disconnected within a specific period of time.

[0071] The specific steps for calculating the expected system load loss power under each fault scenario are as follows:

[0072] S201. For the fault scenario where branch l is disconnected, the topological adjacency matrix of the distribution network is modified to M l ; Then by traversing M l , determine the connection relationship between node i and substation or energy storage access node j∈N0∪{k}. If there is a connected path C(i,j), set the load loss flag of the node to Otherwise, let

[0073] The following method is used to determine the connection relationship between node i and substation node or energy storage access node j∈N0∪{k}:

[0074] The strategy for determining the source-load connection relationship can be simplified according to the particularity of the distribution network topology. Since the distribution network usually adopts the power supply mode of "closed-loop design and open-loop operation" and in most cases presents a radial topology, any branch disconnection will split the system into two subtrees. For the disconnection scenario l, the independent subtree disconnected from the substation is recorded as T l Obviously, when there is no energy storage power station or power supply device connected to the distribution network, T l All load nodes in will lose power, resulting in load shedding; otherwise, only the load nodes in the l The inclusion relationship with the energy storage access node k can determine the power supply status of the corresponding load node. Since the subtree information corresponding to different disconnection scenarios can be stored in advance before the traversal algorithm, the above strategy will effectively reduce the computational complexity of node importance evaluation.

[0075] S202, Fault scenarios l The probability of occurrence P l ;

[0076] Failure scenarios l The probability of occurrence P l Calculated according to the following formula:

[0077]

[0078] Among them, p l is the failure probability of each branch, p m is the disconnection probability of the line other than branch l, and E is the line set of the distribution network.

[0079] S203, traverse the non-substation nodes i∈N\{N0∪{k}} that are not configured with energy storage, and calculate the expected system load loss power under the disconnection scenario l

[0080] System load loss power in disconnection scenario l The calculation is as follows:

[0081]

[0082] S204, traversing the disconnection scenarios l∈E, and calculating the expected system load loss power after node k is connected to the energy storage.

[0083] S3, output of evaluation results;

[0084] The total load loss expectation of the system after point k is connected to the energy storage is E k as follows:

[0085]

[0086] S4. Method for screening candidate nodes for energy storage planning.

[0087] S401, executing steps S2 and S3 for all non-substation nodes in the system to obtain the total load loss power expectation of the energy storage configuration at each node;

[0088] S402, normalizing the total load loss power expectation of all nodes, so as to obtain the importance evaluation index of the energy storage planning node for reliability improvement of each node;

[0089] Traverse the node k∈N\N0 to obtain the expected load loss power of all nodes, denoted as On this basis, it is normalized and the reliability importance index RI of each node is output k ;

[0090] Node importance index RI for reliability improvement when energy storage is configured at the kth node k It is expressed as:

[0091]

[0092] Index RI k The larger it is, the smaller the overall system load loss caused by node k being connected to the energy storage power station is, and the higher the reliability importance of the node is.

[0093] S403. According to the node importance evaluation index of step S402, the candidate node set for energy storage power station planning is screened out: the load nodes k∈N\N0 are arranged from large to small according to the importance index, and then the first m nodes with the highest reliability importance index are selected as the candidate node set Ψ0 for energy storage planning.

[0094] According to the above reliability importance index, the candidate node set for energy storage power station planning is screened out: Assume that the ratio of the number of candidate node set elements m to the number of distribution network nodes N is: in represents the smallest integer greater than or equal to x. The parameter δ∈[0,1] can be defined as the candidate node ratio. Based on the above premise, the load node k∈N\N0 is firstly classified according to the index RI k Arrange from large to small; then select the first m nodes with the highest reliability importance index to obtain the candidate node set Ψ0 for energy storage planning.

[0095] In one embodiment of the present invention, a distribution network energy storage planning node importance evaluation system is provided, which can be used to implement the above distribution network energy storage planning node importance evaluation method. Specifically, the distribution network energy storage planning node importance evaluation system includes an information module, a calculation module, an accumulation module and an evaluation module.

[0096] Among them, the information module constructs a mathematical model of the distribution network based on the distribution network information;

[0097] The calculation module configures the energy storage operation at the kth node of the distribution network mathematical model constructed by the information module, and then traverses all N-1 fault scenarios of the distribution network system after the energy storage is configured, and calculates the expected system load loss power under each fault scenario;

[0098] The accumulation module adds up the expected load loss electricity of the system under each N-1 fault scenario in the calculation module to obtain the expected total load loss electricity of the distribution network system after the kth node performs the energy storage configuration operation;

[0099] The evaluation module executes the calculation module and the accumulation module on all non-substation nodes in the distribution network system to obtain the expected total load loss of energy storage configured on each node; determines the importance evaluation index of energy storage planning nodes for reliability improvement of each node, screens out the candidate node set for energy storage power station planning, and then selects the top m nodes with the highest reliability importance index as the candidate node set for energy storage planning, realizing the importance evaluation of distribution network energy storage planning nodes.

[0100] In another embodiment of the present invention, a terminal device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the distribution network energy storage planning node importance evaluation method, including:

[0101] A mathematical model of the distribution network is constructed according to the distribution network information; an energy storage operation is configured at the kth node of the constructed mathematical model of the distribution network, and then all N-1 fault scenarios of the distribution network system after the energy storage is configured are traversed to calculate the expected system load loss in each fault scenario; the expected system load loss in each N-1 fault scenario is added together to obtain the expected total load loss of the distribution network system after the kth node executes the energy storage configuration operation; the above is performed on all non-substation nodes in the distribution network system to obtain the expected total load loss of the energy storage configured on each node; the importance evaluation index of the energy storage planning node for reliability improvement of each node is determined, the candidate node set for energy storage power station planning is screened out, and then the top m nodes with the highest reliability importance index are selected as the candidate node set for energy storage planning to realize the importance evaluation of the distribution network energy storage planning node.

[0102] In another embodiment of the present invention, a storage medium is also provided, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory, or a non-volatile memory (non-volatile memory), such as at least one disk storage.

[0103] One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the corresponding steps of the method for evaluating the importance of distribution network energy storage planning nodes in the above embodiment; one or more instructions in the computer-readable storage medium may be loaded and executed by a processor as follows:

[0104] A mathematical model of the distribution network is constructed according to the distribution network information; an energy storage operation is configured at the kth node of the constructed mathematical model of the distribution network, and then all N-1 fault scenarios of the distribution network system after the energy storage is configured are traversed to calculate the expected system load loss in each fault scenario; the expected system load loss in each N-1 fault scenario is added together to obtain the expected total load loss of the distribution network system after the kth node executes the energy storage configuration operation; the above is performed on all non-substation nodes in the distribution network system to obtain the expected total load loss of the energy storage configured on each node; the importance evaluation index of the energy storage planning node for reliability improvement of each node is determined, the candidate node set for energy storage power station planning is screened out, and then the top m nodes with the highest reliability importance index are selected as the candidate node set for energy storage planning to realize the importance evaluation of the distribution network energy storage planning node.

[0105] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0106] The specific meaning of node importance depends on its actual application scenario in power network planning and operation scheduling. Important nodes in the sense of power supply reliability refer to nodes that have a significant impact on the system operation safety and energy supply adequacy when a distribution network failure occurs. By adding energy storage power stations to reinforce, upgrade and protect such nodes, the reliability of the distribution network can be improved to the greatest extent, thereby ensuring the safe and stable operation of the system. Therefore, such important nodes can be used as priority candidate nodes for the planning of energy storage power stations on the distribution network side.

[0107] The primary challenge in selecting important nodes in terms of reliability is to quantitatively analyze the impact of any node on power supply reliability, that is, to reasonably define the node importance evaluation index. The distribution network is essentially a complex network with a non-homogeneous topological structure. Based on the concept of "point cut set" in graph theory, the reliability importance of the node can be reflected to a certain extent by calculating the degree of damage to the network connectivity after the evaluated node is deleted. After deleting the evaluated node, if the remaining network has more minimum spanning trees, it means that the degree of damage to the power grid is greater, and the importance of the node is higher.

[0108] However, node importance evaluation based solely on graph theory has two limitations:

[0109] (1) The degree of network topology damage is not necessarily equivalent to the degree of electrical structure damage, and the latter is the focus of distribution network reliability;

[0110] (2) The topological connection of the distribution network has a certain symmetry, which means that the impact of deleting some nodes on network connectivity is the same, and it is impossible to objectively reflect the importance of the nodes.

[0111] In order to make up for the above shortcomings, the present invention combines the idea of ​​"node deletion" with the source-load connection relationship of the distribution network, the load capacity of the node and the criticality of energy supply, and proposes a node importance index based on the power supply reliability criterion. For any node i, its reliability importance can be defined as the expected load loss power of the system under the "N-1" fault assumption after the node is connected to the energy storage.

[0112] However, accurate reliability assessment of all energy storage access nodes and fault scenarios will incur high computing time costs, which will weaken the practical value of the proposed node importance index. Since node importance is a relative measure, it is not necessary to accurately calculate the absolute amount of load loss in practical applications. Instead, the load loss situation is approximately estimated based on the connection relationship between each node and the substation node or energy storage node under the fault state, and the system reliability level under the "relative meaning" is quickly counted, so as to achieve the purpose of efficient judgment and selection of important nodes. It should be noted that reliability focuses on the safety of grid operation and energy supply adequacy under "high probability-low loss" events (such as component aging failure or human error). Since the possibility of "N-2" or more power failures in daily operation is very small, it is reasonable to only consider the "N-1" situation.

[0113] Supplementary explanation of the method of the present invention:

[0114] (1) According to the particularity of the distribution network topology, the judgment strategy for the source-load connection relationship in step S1 can be simplified. Since the distribution network usually adopts the power supply mode of "closed-loop design and open-loop operation", in most cases it has a radial topology, so the disconnection of any branch will split the system into two subtrees. For the disconnection scenario l, the independent subtree disconnected from the substation is recorded as T l .

[0115] Obviously, when there is no energy storage power station or power supply device connected to the distribution network, T l All load nodes in will lose power, resulting in load shedding; otherwise, only the load nodes in the lThe inclusion relationship with the energy storage access node k can determine the power supply status of the corresponding load node. Since the subtree information corresponding to different disconnection scenarios can be stored in advance before the traversal algorithm, the above strategy will effectively reduce the computational complexity of node importance evaluation.

[0116] (2) The criticality weight of the load is related to the type of power consumption of the node (for example, the criticality weight of loads such as hospitals, railways, and data centers is usually higher than that of residential power loads), and can be determined by methods such as hierarchical analysis, fuzzy weighting, and expert experience. By introducing this weight coefficient, the load importance information can be organically combined with the quantitative index of the node's impact on reliability, realizing a comprehensive evaluation of node importance based on a combination of subjective and objective factors.

[0117] 1) Example structure

[0118] To verify the effectiveness of the present invention, a real distribution network in Tibet is used as an example for testing. The distribution network includes two voltage levels, 110kV and 35kV, with a total of 21 nodes and 20 branches. The annual peak load is 24.47MW, which is supplied by the main substations at nodes 1, 19, and 20. The rated capacity of the three substations is 50MVA. The example test includes the following three steps:

[0119] 1) Selecting important energy storage candidate planning nodes using the node importance evaluation method of the present invention;

[0120] 2) Calculate the planned capacity of energy storage using a robust planning method based on candidate planning nodes;

[0121] 3) Conduct economic and reliability evaluation of energy storage planning schemes.

[0122] In the test, the node importance evaluation method of the present invention was compared with a general node importance evaluation method (a node importance evaluation method based on load and medium centrality).

[0123] 2) Example results

[0124] The node importance evaluation method of the present invention is used to evaluate the importance of the example, and the top 5 nodes in importance are selected to form a candidate node set for energy storage planning. The obtained candidate node results and indicators are as follows:

[0125] Table 1 Candidate nodes and importance index results (unit: normalized value)

[0126]

[0127] The robust planning method is used to plan energy storage on the candidate node set. The planned investment is set at 350 million yuan, the unit price of energy storage equipment is 5 million yuan / MWh, the charging and discharging power of 1MWh energy storage equipment is 0.6MW, the charging and discharging efficiency is 90%, and the maximum discharge depth is 90%. The planning results are as follows:

[0128] Table 2 Energy storage planning results of candidate node sets based on the method of the present invention (unit: MWh)

[0129]

[0130]

[0131] The candidate nodes and energy storage planning capacity of the general node importance evaluation method compared in the test are as follows:

[0132] Table 3 Energy storage planning results based on candidate node sets of general methods (unit: MWh)

[0133]

[0134] The reliability of the above planning results is evaluated, with the load loss energy period (EENS), load loss probability (LPSP), and autonomy (LA) as evaluation indicators. The comparison scheme is the system reliability level when there is no energy storage configuration. The comparison results are as follows:

[0135] Table 3 Comparison of reliability results between the planning scheme of the method of the present invention and the scheme without energy storage

[0136]

[0137] From the calculation results, it can be seen that when the failure probability is 80.2%, the EENS of the energy storage planning scheme based on the method of the present invention is 465.26MWh, and the LPSP is 0.275%. Compared with the EENS of 5953.27MWh and LPSP of 3.59% when there is no energy storage, the system reliability has been significantly improved, and the improvement has reached 92.2%. At the same time, the load loss probability of the general node importance evaluation method is 0.456%. In comparison, the load loss probability of the method of the present invention is only 60.3% of the general evaluation method, indicating that the method of this paper is superior to the general evaluation method in improving reliability. The calculation results show that the present invention has a significant effect on improving system reliability and is of great significance and value in ensuring the safe and reliable operation of the distribution network.

[0138] In summary, the method and system for evaluating the importance of energy storage planning nodes in a distribution network of the present invention can accurately and effectively detect important energy storage planning nodes in the distribution network, generate a candidate set of energy storage planning nodes, and provide a reference for upgrading the grid structure of the distribution network, constructing energy storage, and maintaining safe and reliable operation of the distribution network.

[0139] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0140] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0141] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0142] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0143] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A method for evaluating the importance of nodes in energy storage planning for a distribution network, characterized in that: The following steps are involved: S1. Construct a mathematical model of the distribution network based on the distribution network information. The distribution network information includes the topological structure of the distribution network to be evaluated. , the set of all nodes in the distribution network , the substation node set of the distribution network , the collection of non-substation nodes of the distribution network , Active load capacity of each node in the distribution network , the critical weight of each node load of the distribution network , the line collection of the distribution network and each branch The probability of failure ,line l Scenario of N-1 failure All N-1 fault scenarios of the line ; S2, the distribution network mathematical model constructed in step S1 k The energy storage operation is configured at each node, and then all N-1 fault scenarios of the distribution network system after the energy storage is configured are traversed to calculate the expected system load loss power under each fault scenario, which is specifically: S201, the distribution network k The nodes are configured for energy storage, and the set of substation nodes and energy storage configuration nodes is obtained. The mathematical model of this set is expressed as ; S202, traverse N-1 branch fault scenarios for the distribution network after energy storage is configured in step S201, and in each fault scenario The topological adjacency matrix of the modified distribution network is: ; By traversing Data in, judgment node i Connect to substation or energy storage node connection relationship; S203, calculation branch N-1 failure scenario when a failure occurs The probability of occurrence ; S204, traversing non-substation nodes that are not configured with energy storage The probability of the disconnection fault scenario obtained in step S203 is Calculate disconnection scenarios System load loss expectation under ; S205, traversing disconnection scenarios , according to each disconnection scenario obtained in step S204 System load loss expectation under , traverse all disconnection scenarios For computing nodes k The total load loss power expected for the system after connecting to the energy storage; S3, adding up the expected system load loss power under each N-1 fault scenario in step S2, to obtain the k The total load loss power of the distribution network system is expected after the nodes execute the energy storage configuration. as follows: in, For disconnection scenarios The system load loss power under S4, perform steps S2 and S3 on all non-substation nodes in the distribution network system to obtain the total load loss expectation of energy storage configuration on each node; determine the importance evaluation index of energy storage planning nodes for reliability improvement of each node, screen out the candidate node set for energy storage power station planning, and then select the top node with the highest reliability importance index. m The nodes are used as candidate node sets for energy storage planning to realize the importance evaluation of nodes in distribution network energy storage planning.

2. The method for evaluating the importance of nodes in energy storage planning of distribution network according to claim 1, characterized in that: In step S202, any of the following methods is used to determine the node i With substation node or energy storage access node Connection relationship: According to the particularity of the distribution network topology, the source-load connection relationship judgment strategy can be simplified. If any branch is disconnected, the system will be divided into two subtrees. , the independent subtree disconnected from the substation is recorded as , when the independent subtree When there is no energy storage power station or power supply device connected, All load nodes in the will lose power, resulting in load loss, and the load loss flags of these nodes will be set to On the contrary, according to Energy storage access node k The inclusion relationship determines the topological adjacency matrix Is there a connected path in , if it exists, then Otherwise, let ; Traverse and count the topological adjacency matrix Data in, determine the topological adjacency matrix Is there a connected path in , if it exists, will k When the node is configured with energy storage i Node load loss flag Set to Otherwise, let .

3. The method for evaluating the importance of nodes in energy storage planning of distribution network according to claim 1, characterized in that: In step S203, the fault scenario The probability of occurrence Specifically: in, is the failure probability of each branch, For branch l The probability of disconnection of lines other than A collection of lines in the distribution network.

4. The method for evaluating the importance of nodes in energy storage planning of distribution network according to claim 1, characterized in that: In step S204, disconnection scenario System load loss expectation under Specifically: in, is the set of all nodes in the distribution network, is the substation node set of the distribution network, For branch The probability of line failure is for k When the node is configured with energy storage i The node's load loss flag, is the critical weight of each node load in the distribution network, It is the active load capacity of each node in the distribution network.

5. The method for evaluating the importance of nodes in energy storage planning for a distribution network according to claim 1, characterized in that: In step S4, the node importance index It is expressed as: in, is the expected total load loss of the distribution network system, is the set of all nodes in the distribution network, It is a collection of substation nodes in the distribution network.

6. The method for evaluating the importance of nodes in energy storage planning for a distribution network according to claim 1, characterized in that: In step S4, the candidate node set for energy storage power station planning is selected as follows: Let the number of candidate node set elements be m Number of distribution network nodes N The proportional relationship is , Indicates greater than or equal to x The smallest integer parameter Defined as the candidate node ratio, first the load node Based on indicators Arrange from large to small; then select the top m nodes, and obtain the candidate node set for energy storage planning .

7. A distribution network energy storage planning node importance evaluation system, characterized in that: include: Information module, which builds a mathematical model of the distribution network based on the distribution network information. The distribution network information includes the topological structure of the distribution network to be evaluated. , the set of all nodes in the distribution network , the substation node set of the distribution network , the collection of non-substation nodes of the distribution network , Active load capacity of each node in the distribution network , the critical weight of each node load of the distribution network , the line collection of the distribution network and each branch The probability of failure ,line l Scenario of N-1 failure All N-1 fault scenarios of the line ; The calculation module is the first k The nodes are configured with energy storage operations, and then all N-1 fault scenarios of the distribution network system after energy storage configuration are traversed to calculate the expected system load loss power under each fault scenario, specifically: S201, the distribution network k The nodes are configured for energy storage, and the set of substation nodes and energy storage configuration nodes is obtained. The mathematical model of this set is expressed as ; S202, traverse N-1 branch fault scenarios for the distribution network after energy storage is configured in step S201, and in each fault scenario The topological adjacency matrix of the modified distribution network is: ; By traversing Data in, judgment node i Connect to substation or energy storage node connection relationship; S203, calculation branch N-1 failure scenario when a failure occurs The probability of occurrence ; S204, traversing non-substation nodes that are not configured with energy storage The probability of the disconnection fault scenario obtained in step S203 is Calculate disconnection scenarios System load loss expectation under ; S205, traversing disconnection scenarios , according to each disconnection scenario obtained in step S204 System load loss expectation under , traverse all disconnection scenarios For computing nodes k The total load loss power expected for the system after connecting to the energy storage; The accumulation module adds up the expected system load loss power under each N-1 fault scenario in the calculation module to obtain the k The total load loss power of the distribution network system is expected after each node performs the energy storage configuration operation. as follows: in, For disconnection scenarios The system load loss power under The evaluation module executes the calculation module and the accumulation module on all non-substation nodes in the distribution network system to obtain the total load loss expectation of the energy storage configuration on each node; determines the importance evaluation index of the energy storage planning node for reliability improvement of each node, screens out the candidate node set for energy storage power station planning, and then selects the top node with the highest reliability importance index. m The nodes are used as candidate node sets for energy storage planning to realize the importance evaluation of nodes in distribution network energy storage planning.

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