Power distribution network asset planning method based on game theory and terminal

Abstract

The invention discloses a power distribution network asset planning method based on a game theory and a terminal, and the method comprises the steps: determining the interruption time and the interruption frequency of the line installed capacity according to collected operation data, calculating the performance index of a power distribution network based on the interruption time and the interruption frequency, determining a cost function corresponding to a topological structure of the power distribution network, and carrying out the asset planning of the power distribution network. According to the method, the power distribution network performance index minimization and the cost function minimization serve as targets, the topological structure of the power distribution network is optimized through the game theory, the optimal topological structure is obtained, multiple indexes of the power supply quality are comprehensively considered, the topological structure is optimized through the game theory method with the power distribution network performance index minimization and the cost function minimization serve as targets, and the optimal power supply quality is obtained. The accuracy and efficiency of power distribution network asset planning are ensured, the reliability and stability of the power distribution network are improved, and the power supply quality of the power distribution network is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network planning, and in particular to a distribution network asset planning method and terminal based on game theory. Background Art

[0002] Nowadays, the reliability and stability of power supply are crucial to the normal operation of all walks of life. However, with the growth of energy demand and the increasing complexity of power systems, improving the power supply quality of distribution networks has become an important technical challenge. Traditional distribution network asset planning methods often only consider technical indicators and economic indicators, but lack comprehensive consideration of power supply quality, which may lead to power outages, voltage and frequency fluctuations and other problems in actual operation. The existing technologies mainly include traditional power system planning methods and some distribution network asset planning methods based on optimization algorithms. Traditional methods are usually based on experience and simplified models, and it is difficult to fully consider the comprehensiveness of power supply quality. Although the optimization algorithm can provide a certain degree of improvement, it still has problems such as insufficient impact on power supply quality and high computational complexity. In addition, the existing technology also has certain limitations in solving power outages and power supply quality instability. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a distribution network asset planning method and terminal based on game theory, which can improve the power supply quality of the distribution network.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0005] A distribution network asset planning method based on game theory comprises the following steps:

[0006] Collecting operation data of the distribution network, and determining the interruption time of the line installed capacity and the number of interruptions of the line installed capacity according to the operation data;

[0007] Calculating a distribution network performance indicator based on the interruption time and the number of interruptions;

[0008] Determining a cost function corresponding to the topological structure of the distribution network;

[0009] With the goal of maximizing the performance index of the distribution network and minimizing the cost function, the topological structure of the distribution network is optimized using game theory to obtain the optimal topological structure.

[0010] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0011] A distribution network asset planning terminal based on game theory includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented:

[0012] Collecting operation data of the distribution network, and determining the interruption time of the line installed capacity and the number of interruptions of the line installed capacity according to the operation data;

[0013] Calculating a distribution network performance indicator based on the interruption time and the number of interruptions;

[0014] Determining a cost function corresponding to the topological structure of the distribution network;

[0015] With the goal of maximizing the performance index of the distribution network and minimizing the cost function, the topological structure of the distribution network is optimized using game theory to obtain the optimal topological structure.

[0016] The beneficial effects of the present invention are as follows: the interruption time of the installed capacity of the line and the number of interruptions of the installed capacity of the line are determined according to the collected operation data, the distribution network performance index is calculated based on the interruption time and the number of interruptions, the cost function corresponding to the topological structure of the distribution network is determined, and the topological structure of the distribution network is optimized by using game theory with the goal of minimizing the performance index of the distribution network and minimizing the cost function to obtain the optimal topological structure, which comprehensively considers multiple indicators of power supply quality, and uses game theory methods to optimize the topological structure with the goal of minimizing the performance index of the distribution network and minimizing the cost function, thereby ensuring the accuracy and efficiency of the distribution network asset planning, improving the reliability and stability of the distribution network, and thus improving the power supply quality of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flowchart of a distribution network asset planning method based on game theory according to an embodiment of the present invention;

[0018] Figure 2 The present invention is a schematic diagram of the structure of a distribution network asset planning terminal based on game theory according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in combination with the implementation modes and the accompanying drawings.

[0020] Please refer to Figure 1 , a distribution network asset planning method based on game theory, comprising the steps of:

[0021] Collecting operation data of the distribution network, and determining the interruption time of the line installed capacity and the number of interruptions of the line installed capacity according to the operation data;

[0022] Calculating a distribution network performance indicator based on the interruption time and the number of interruptions;

[0023] Determining a cost function corresponding to the topological structure of the distribution network;

[0024] With the goal of maximizing the performance index of the distribution network and minimizing the cost function, the topological structure of the distribution network is optimized using game theory to obtain the optimal topological structure.

[0025] From the above description, it can be seen that the beneficial effects of the present invention are: determining the interruption time and the number of interruptions of the line installed capacity according to the collected operating data, calculating the distribution network performance index based on the interruption time and the number of interruptions, determining the cost function corresponding to the topological structure of the distribution network, minimizing the distribution network performance index and minimizing the cost function as the goal, using game theory to optimize the topological structure of the distribution network, and obtaining the optimal topological structure, comprehensively considering multiple indicators of power supply quality, and using game theory methods to optimize the topological structure with the goal of minimizing the distribution network performance index and minimizing the cost function, ensuring the accuracy and efficiency of distribution network asset planning, improving the reliability and stability of the distribution network, and thus improving the power supply quality of the distribution network.

[0026] Further, determining the interruption time of the line installed capacity and the number of interruptions of the line installed capacity according to the operation data includes:

[0027]

[0028]

[0029] In the formula, γ l represents the interruption time of the installed capacity of line l, n represents the number of fault location or repair stages, P j represents the non-power supply capacity during the fault location or repair phase j, t j (S) represents the duration of non-power supply capacity during the fault location or repair phase j, P l represents the full power capacity of line l, σ l represents the number of interruptions of the installed capacity of line l, and S represents the line topology.

[0030] From the above description, it can be seen that calculating the interruption time of the line installed capacity and the number of interruptions of the line installed capacity can measure the impact of power failures in the distribution lines, and can better understand the operating status of the distribution network in order to achieve better asset planning.

[0031] Further, the calculating of the distribution network performance index based on the interruption time and the number of interruptions includes:

[0032] Calculate a current performance indicator of the line based on the interruption time and the full power capacity;

[0033] calculating expected performance indicators of the line based on the full power capacity;

[0034] A distribution network performance indicator is calculated according to the current performance indicator and the expected performance indicator.

[0035] From the above description, it can be seen that the current performance indicators of the line are calculated according to the interruption time and the full power capacity, the expected performance indicators of the line are calculated according to the full power capacity, and finally the distribution network performance indicators are calculated according to the current performance indicators and the expected performance indicators. The distribution network performance indicators can ensure the power supply quality of the distribution network during subsequent asset planning.

[0036] Further, the calculating the current performance index of the line according to the interruption time and the full power capacity includes:

[0037]

[0038] In the formula, δ l represents the current performance index of line l, Ψ represents the set of all lines, γ s represents the interruption time of the installed capacity of line s, P s represents the full power capacity of line s;

[0039] The expected performance index of the line calculated according to the full power capacity includes:

[0040]

[0041] In the formula, ρ l represents the expected performance index of line l, α represents the overhead length of line l, and α s represents the overhead length of line s, β l represents the length of the underground section of line l, β s represents the length of the underground section of line s, C l represents the number of transformer and distribution centers on line l, C s represents the number of transformer and distribution centers of line s, |Ψ| represents the cardinality of the set of all lines;

[0042] The calculating of the distribution network performance index according to the current performance index and the expected performance index comprises:

[0043]

[0044] Where η l Represents the performance index of the distribution network.

[0045] From the above description, it can be seen that by calculating the current performance index of the line and the expected performance index of the line, the performance of the distribution line can be effectively improved in the subsequent optimization.

[0046] Further, the cost function corresponding to the topological structure of the distribution network is determined to include:

[0047] Determine the remote control equipment installation cost and maintenance cost of the topology of the power distribution network;

[0048] determining locations and repair costs of average power outages in a topology of the power distribution network;

[0049] A cost function is obtained according to the installation cost and maintenance cost of the remote control device and the location and repair cost of the average power outage.

[0050] From the above description, it can be seen that the cost function takes into account the installation cost and maintenance cost of the remote control equipment as well as the location and repair cost of the average power outage, ensuring the economy of the optimal topology structure finally obtained.

[0051] Furthermore, the installation cost and maintenance cost of the remote control device for determining the topological structure of the power distribution network include:

[0052] J ins (S) = c ins S;

[0053] J man (S) = c man S(T c -1);

[0054] In the formula, J ins (S) represents the installation cost of remote control equipment, c ins represents the average installation cost of the remote control equipment, S represents the line topology, J man (S) represents the maintenance cost of remote control equipment, c man represents the average annual maintenance cost of remote control equipment, T c Indicates the preset installment time domain.

[0055] From the above description, it can be seen that using remote control devices to replace all local devices will provide the best results for customers, but also incur the highest cost for the company. Therefore, for a given line, the best location of the remote control device should be determined while balancing the needs of both. Therefore, for a given topology, the distribution network investment will depend on the cost of installing and maintaining the remote control equipment to ensure economy.

[0056] Further, the determining the average power outage location and maintenance cost of the topological structure of the distribution network includes:

[0057]

[0058]

[0059] E rep =P n ×t r ;

[0060] In the formula, J loc (S) represents the average location cost of power outage, c ene represents the energy cost per kVA-hour, r represents the annual average failure rate per kilometer of line, α l represents the overhead length of line l, β l represents the length of the underground section of line l, E loc (S) represents the non-power supply energy during power fault location, J rep represents the average outage maintenance cost, E rep represents the non-power supply energy during power failure maintenance, n represents the number of fault location or repair stages, P j represents the non-power supply capacity during the fault location or repair phase j, t j (S) represents the duration of non-power supply capacity during the fault location or repair phase j, P n represents the non-power supply capacity during the final stage of fault repair, t r It represents the average time to repair the fault during the final stage of fault repair;

[0061] The cost function obtained according to the installation cost and maintenance cost of the remote control device and the average power outage location and repair cost includes:

[0062] J(S)=J ins (S)+J man (S)+J loc (S)+J rep ;

[0063] Where J(S) represents the cost function.

[0064] From the above description, it can be seen that since customer satisfaction is related to the average power outage location and maintenance cost, the average power outage location and maintenance cost of the topology of the distribution network are also determined to ensure that the final asset planning result improves customer satisfaction while ensuring the economy of the distribution network.

[0065] Furthermore, the topological structure of the distribution network is optimized using game theory with the goal of maximizing the performance index of the distribution network and minimizing the cost function, and the optimal topological structure includes:

[0066] With the goal of maximizing the performance index of the distribution network and minimizing the cost function, the topological structure of the distribution network is optimized using cooperative game theory to obtain the optimal topological structure.

[0067] From the above description, it can be seen that using cooperative game theory to optimize the topological structure can better balance the interests of all parties and formulate a more reasonable asset planning plan. It can not only take into account the competition and cooperation relationship between the subjects, but also identify the optimal strategy, making the planning plan more operational and feasible, promoting the smooth implementation of distribution network asset planning, and improving the overall efficiency and operation level of the system.

[0068] Furthermore, the topology of the distribution network is optimized using cooperative game theory with the goal of maximizing the performance index of the distribution network and minimizing the cost function, and the optimal topology includes:

[0069] With the goal of maximizing the distribution network performance index and minimizing the cost function, using cooperative game theory to calculate the Shapley value of the local equipment in the topology of the distribution network;

[0070] The local device with a negative Shapley value is determined, and the local device with a negative Shapley value is replaced by a remote control device to obtain an optimal topology structure.

[0071] From the above description, it can be seen that with the goal of maximizing the distribution network performance indicators and minimizing the cost function, cooperative game theory is used to calculate the Shapley value of local equipment in the topology of the distribution network, and remote control devices are used to replace local devices with negative Shapley values ​​to obtain the optimal topology. This can significantly reduce the number of distribution lines with supply shortages, which is very effective in lines that require a lot of manual braking time, such as those in difficult-to-access areas. The optimized topology can effectively increase the net benefits of the company and customers, while improving the power supply quality of the distribution network.

[0072] Please refer to Figure 2 Another embodiment of the present invention provides a distribution network asset planning terminal based on game theory, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, each step in the above-mentioned distribution network asset planning method based on game theory is implemented.

[0073] The above-mentioned distribution network asset planning method and terminal based on game theory of the present invention can be applied to distribution network asset planning scenarios, which are described below through specific implementation methods:

[0074] Please refer to Figure 1 , Embodiment 1 of the present invention is:

[0075] A distribution network asset planning method based on game theory comprises the following steps:

[0076] S1. Collecting the operation data of the distribution network, and determining the interruption time and the number of interruptions of the line installed capacity according to the operation data, specifically:

[0077]

[0078]

[0079] In the formula, γ l represents the interruption time of the installed capacity of line l, n represents the number of fault location or repair stages, P j represents the non-power supply capacity during the fault location or repair phase j, t j (S) represents the duration of non-power supply capacity during the fault location or repair phase j, P l represents the full power capacity of line l, σ l represents the number of interruptions of the installed capacity of line l, S represents the line topology, where, represents the set of line topology structures, P j ≤P l , γ l is measured in hours, σ l It is dimensionless.

[0080] Both the interruption time of the line installed capacity and the number of interruptions of the line installed capacity can be extended to a set of faults involved in the selected line within any time interval (usually one year) by summing up the individual indicators caused by each fault. In addition, a substation can obtain an index related to the entire area by adding up the individual indices of its lines and transformers.

[0081] In an optional embodiment, after collecting the operation data of the distribution network, it also includes: cleaning, format conversion and data mining of the operation data to obtain processed operation data. Cleaning removes erroneous or invalid data, and format conversion can convert the data into time series data or spatial data. Data mining ensures the accuracy and real-time nature of the data. Only accurate data can support subsequent planning and decision-making. The operation data includes various key operation data, such as voltage, current, frequency and other parameters, as well as information such as the operating status of the equipment and load conditions.

[0082] The concept of power supply quality consists in the distribution of electricity, keeping its voltage and frequency values ​​within desired limits while minimizing the number and duration of power interruptions, without neglecting the interaction between supplier and customer, the most critical aspect from the consumer's point of view being the continuity of supply, therefore the present invention measures the impact of power failures in distribution lines by defining the interruption time of the line's installed capacity and the number of interruptions of the line's installed capacity.

[0083] S2. Calculating the distribution network performance index based on the interruption time and the number of interruptions, specifically including S21-S23:

[0084] S21. Calculate the current performance index of the line according to the interruption time and the full power capacity, specifically:

[0085]

[0086] In the formula, δ l represents the current performance index of line l, Ψ represents the set of all lines, γ s represents the interruption time of the installed capacity of line s, P s Represents the full power capacity of line s.

[0087] In order to improve the performance of distribution lines, it is possible to detect which lines need more improvement from the perspective of power supply continuity, due to γ l The relative power capacity of line l is not considered, so all γ in the target area are defined as l The index is a weighted measure of the power capacity of the line, i.e. the current performance indicator of the line.

[0088] Lines can be classified according to their current performance indicators, but it is also necessary to consider their expected performance. To this end, the expected performance indicator of each line will be calculated as the weighted sum of the following pairs of Ψ using the historical average failure data of the distribution lines, as follows:

[0089] S22. Calculating expected performance indicators of the line according to the full power capacity, specifically including:

[0090]

[0091] In the formula, ρ l represents the expected performance index of line l, α l represents the overhead length of line l, α s represents the overhead length of line s, β l represents the length of the underground section of line l, β s represents the length of the underground section of line s, C l represents the number of transformer and distribution centers on line l, C srepresents the number of transformer and distribution centers of line s, and |Ψ| represents the cardinality of the set of all lines.

[0092] S23, calculating the distribution network performance index according to the current performance index and the expected performance index, specifically including:

[0093]

[0094] Where η l Represents the performance index of the distribution network.

[0095] In addition to considering the continuity of power supply, the distribution network performance index also takes into account the relative power capacity in the relevant area and the physical and electrical characteristics of the relevant lines. This index calculates the γ of the distribution line relative to the reference range. l Impact and expectations based on historical data¶ l If the current performance is consistent with the expected performance, the better the continuity of the line voltage, η l The closer the value of is to 1, the distribution network performance index can be used to perform effective sorting of power lines within the distribution network.

[0096] S3, determining a cost function corresponding to the topological structure of the distribution network, specifically including S31-S33:

[0097] Replacing all local equipment with remote control equipment will provide the best results for customers, but also incur the highest cost for the company. For a given line, the best location for the remote control equipment should be determined by balancing the needs of both. For a given topology, the distribution network investment will depend on the installation and maintenance costs, as follows:

[0098] S31, determining the installation cost and maintenance cost of the remote control equipment of the topological structure of the distribution network, specifically:

[0099] J ins (S) = c ins S;

[0100] J man (S) = c man S(T c -1);

[0101] In the formula, J ins (S) represents the installation cost of remote control equipment, c ins represents the average installation cost of the remote control equipment, S represents the line topology, J man (S) represents the maintenance cost of remote control equipment, c man represents the average annual maintenance cost of remote control equipment, T c Indicates the preset installment time domain.

[0102] Customer satisfaction was also related to the average outage location and repair cost, as follows:

[0103] S32, determining the average power outage location and maintenance cost of the topological structure of the distribution network, specifically:

[0104]

[0105]

[0106] E rep =P n ×t r ;

[0107] In the formula, J loc (S) represents the average location cost of power outage, c ene represents the energy cost per kVA hour, r represents the annual average failure rate per kilometer of line, E loc (S) represents the non-power supply energy during power fault location, J rep represents the average outage maintenance cost, E rep Represents the non-power supply energy during power failure maintenance, P n represents the non-power supply capacity during the final stage of fault repair, t r Indicates the average time to repair a fault during the final stage of fault repair.

[0108] S33, obtaining a cost function according to the installation cost and maintenance cost of the remote control device and the average power outage location and repair cost, specifically:

[0109] J(S)=J ins (S)+J man (S)+J loc (S)+J rep ;

[0110] Where J(S) represents the cost function.

[0111] This cost function can be used to evaluate the topology Different possibilities for upgrading a local device to a remote control device.

[0112] S4. With the goal of maximizing the distribution network performance index and minimizing the cost function, the topological structure of the distribution network is optimized using game theory to obtain an optimal topological structure.

[0113] Specifically, with the goal of maximizing the performance index of the distribution network and minimizing the cost function, the topological structure of the distribution network is optimized using cooperative game theory to obtain the optimal topological structure. Definition, where represents the set of participants containing the possible locations of remote control devices in a given distribution line, and the vector The cost function formula is used to calculate each topology, and 2 can be obtained according to the function of the upgrade position in the research route. N The cost of each possible configuration. In order to find the average contribution of participants in a large amount of information, the Shapley value will be considered. This is the most commonly used evaluation rule in cooperative game theory, including S41-S42:

[0114] S41, with the goal of maximizing the distribution network performance index and minimizing the cost function, using cooperative game theory to calculate the Shapley value of the local device in the topological structure of the distribution network, specifically including:

[0115] The Shapley value will pay off the vector Assign to game For all The payment vector is:

[0116]

[0117] In the formula, {i} represents a value that does not belong to the set S but belongs to the set The elements in N represent the set The maximum value in . The marginal contribution of each player in is the average of all possible permutations in which it may participate, with a weight depending on the size of each set S, i.e.

[0118] The efficiency property states that the value of the full set of benefits will be fully distributed among the participants, specifically:

[0119]

[0120] When computing the cost function, it is assumed that the parameters are fixed so that the characteristic function (for each element i, its payment vector Become its corresponding characteristic function) depends only on the topological structure, but in fact the parameters may change. In the planning process, various uncertain factors, such as weather changes, equipment failures, etc., are fully considered, and corresponding risk management measures are taken to reduce their impact on the planning scheme. This can improve the feasibility and reliability of the planning scheme and ensure that the distribution network can provide stable and reliable power supply under various circumstances. To this end, the average failure rate r and energy cost c are considered. ene can vary within a pre-specified interval, so the characteristic function will be reformulated as J(S,r), and the Shapley value of the local device can be calculated by taking a large number of samples of these parameters using the Monte Carlo method, specifically:

[0121]

[0122] In the formula, represents the Shapley value of the local device, which is a measure of the performance of each location, L represents the number of samples in the probability distribution, and p represents the index flag, which represents the characteristic function arrive Sum.

[0123] S42: Determine the local device with a negative Shapley value, and use a remote control device to replace the local device with a negative Shapley value to obtain an optimal topology structure.

[0124] This approach of finding locations for remote control equipment can significantly reduce the number of distribution lines with supply shortages. This will be particularly effective in lines that require a lot of manual braking time, such as those in difficult-to-access areas. All local equipment with negative Shapley values ​​will be recommended to be replaced with remote control equipment. This upgrade means an increase in net benefits for the company and its customers. The process can be repeated in a cycle to handle the changing needs of customers, so the installed infrastructure can achieve dynamic changes and cope well with uncertainty.

[0125] Traditional methods often only focus on a single technical indicator or economic indicator, while the present invention makes the distribution network asset planning more comprehensive and accurate by comprehensively considering multiple indicators of power supply quality. It not only considers the continuity of power, but also effectively controls factors such as voltage and frequency stability. For example, considering the number of power outages and the duration of outages, it can ensure that a stable power supply can be provided under various working conditions, thereby significantly improving the power supply quality. In addition, the present invention uses game theory methods to better balance the interests of all parties and formulate a more reasonable planning scheme. The application of game theory methods can not only take into account the competition and cooperation relationship between subjects, but also identify the optimal strategy, making the planning scheme more operational and feasible. For example, through game theory methods, agreements can be reached between different stakeholders to promote the smooth implementation of distribution network asset planning and improve the overall efficiency and operation level of the system.

[0126] Please refer to Figure 2 , Embodiment 2 of the present invention is:

[0127] A distribution network asset planning terminal based on game theory comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, each step of the distribution network asset planning method based on game theory in the first embodiment is implemented.

[0128] In summary, the present invention provides a distribution network asset planning method and terminal based on game theory, which determines the interruption time of the line installed capacity and the number of interruptions of the line installed capacity according to the collected operation data, calculates the distribution network performance index based on the interruption time and the number of interruptions, determines the cost function corresponding to the topological structure of the distribution network, and optimizes the topological structure of the distribution network using game theory with the goal of minimizing the distribution network performance index and minimizing the cost function to obtain the optimal topological structure, comprehensively considers multiple indicators of power supply quality, and optimizes the topological structure using game theory with the goal of minimizing the distribution network performance index and minimizing the cost function, thereby ensuring the accuracy and efficiency of distribution network asset planning, improving the reliability and stability of the distribution network, and thus improving the power supply quality of the distribution network; in addition, using cooperative game theory to optimize the topological structure can better balance the interests of all parties and formulate a more reasonable asset planning scheme, which can not only take into account the competition and cooperation relationship between the subjects, but also identify the optimal strategy, so that the planning scheme is more operational and feasible, promotes the smooth implementation of distribution network asset planning, and improves the overall efficiency and operation level of the system.

[0129] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's specification and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A distribution network asset planning method based on game theory, characterized in that: Includes steps: Collecting operation data of the distribution network, and determining the interruption time of the line installed capacity and the number of interruptions of the line installed capacity according to the operation data; Calculating a distribution network performance indicator based on the interruption time and the number of interruptions; Determining a cost function corresponding to the topological structure of the distribution network; With the goal of maximizing the performance index of the distribution network and minimizing the cost function, the topological structure of the distribution network is optimized using game theory to obtain the optimal topological structure.

2. A distribution network asset planning method based on game theory according to claim 1, characterized in that: Determining the interruption time and the number of interruptions of the line installed capacity according to the operation data includes: In the formula, γ l represents the interruption time of the installed capacity of line l, n represents the number of fault location or repair stages, P j represents the non-power supply capacity during the fault location or repair phase j, t j (S) represents the duration of non-power supply capacity during the fault location or repair phase j, P l represents the full power capacity of line l, σ l represents the number of interruptions of installed capacity of line l, and S represents the line topology.

3. A distribution network asset planning method based on game theory according to claim 2, characterized in that: The calculating of the distribution network performance index based on the interruption time and the number of interruptions includes: Calculate a current performance indicator of the line based on the interruption time and the full power capacity; calculating expected performance indicators of the line based on the full power capacity; A distribution network performance indicator is calculated according to the current performance indicator and the expected performance indicator.

4. A distribution network asset planning method based on game theory according to claim 3, characterized in that: The calculating of the current performance index of the line according to the interruption time and the full power capacity comprises: In the formula, δ l represents the current performance index of line l, Ψ represents the set of all lines, γ s represents the interruption time of the installed capacity of line s, P s represents the full power capacity of line s; The expected performance index of the line calculated according to the full power capacity includes: In the formula, ρ l represents the expected performance index of line l, α l represents the overhead length of line l, α s represents the overhead length of line s, β l represents the length of the underground section of line l, β s represents the length of the underground section of line s, C l represents the number of transformer and distribution centers on line l, C s represents the number of transformer and distribution centers of line s, Ψ| represents the cardinality of the set of all lines; The calculating of the distribution network performance index according to the current performance index and the expected performance index comprises: Where η l Represents the performance index of the distribution network.

5. The distribution network asset planning method based on game theory according to claim 1 is characterized in that: The cost function corresponding to the topological structure of the distribution network is determined to include: Determine the remote control equipment installation cost and maintenance cost of the topology of the power distribution network; determining locations and repair costs of average power outages in a topology of the power distribution network; A cost function is obtained according to the installation cost and maintenance cost of the remote control device and the location and repair cost of the average power outage.

6. A distribution network asset planning method based on game theory according to claim 5, characterized in that: The installation cost and maintenance cost of the remote control device for determining the topology of the power distribution network include: J ins (S)=c ins S; J man (S)=c man S(T c -1); In the formula, J ins (S) represents the installation cost of remote control equipment, c ins represents the average installation cost of the remote control equipment, S represents the line topology, J man (S) represents the maintenance cost of remote control equipment, c man represents the average annual maintenance cost of remote control equipment, T c Indicates the preset installment time domain.

7. A distribution network asset planning method based on game theory according to claim 6, characterized in that: Determining the average power outage location and maintenance cost of the topology of the power distribution network includes: AND rep =P n ×t r ; In the formula, J loc (S) represents the average location cost of power outage, c ene represents the energy cost per kVA-hour, r represents the annual average failure rate per kilometer of line, α l represents the overhead length of line l, β l represents the length of the underground section of line l, E loc (S) represents the non-power supply energy during power fault location, J rep represents the average outage maintenance cost, E rep represents the non-power supply energy during power failure maintenance, n represents the number of fault location or repair stages, P j represents the non-power supply capacity during the fault location or repair phase j, t j (S) represents the duration of non-power supply capacity during the fault location or repair phase j, P n represents the non-power supply capacity during the final stage of fault repair, t r It represents the average time to repair the fault during the final stage of fault repair; The cost function obtained according to the installation cost and maintenance cost of the remote control device and the average power outage location and repair cost includes: J(S)=J ins (S)+J man (S)+J loc (S)+J rep ; Where J(S) represents the cost function.

8. The distribution network asset planning method based on game theory according to claim 1 is characterized in that: The goal of maximizing the distribution network performance index and minimizing the cost function is to use game theory to optimize the topology of the distribution network to obtain the optimal topology, which includes: With the goal of maximizing the performance index of the distribution network and minimizing the cost function, the topological structure of the distribution network is optimized using cooperative game theory to obtain the optimal topological structure.

9. A distribution network asset planning method based on game theory according to claim 8, characterized in that: The goal of maximizing the distribution network performance index and minimizing the cost function is to use cooperative game theory to optimize the topology of the distribution network to obtain the optimal topology, which includes: With the goal of maximizing the distribution network performance index and minimizing the cost function, using cooperative game theory to calculate the Shapley value of the local equipment in the topology of the distribution network; The local device with a negative Shapley value is determined, and the local device with a negative Shapley value is replaced by a remote control device to obtain an optimal topology structure.

10. A distribution network asset planning terminal based on game theory, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, each step in the distribution network asset planning method based on game theory described in any one of claims 1 to 9 is implemented.

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