Intelligent soft switching game planning method and device and computer equipment

Abstract

The invention relates to an intelligent soft switching game planning method and device and computer equipment. The method comprises the following steps: acquiring a power distribution network parameter, an electric energy transfer-in resource parameter, an electric energy transfer-out resource parameter and planning target configuration information of a target power distribution network; generating a comprehensive operation cost function of a power distribution network operator, and constructing an intelligent soft switching game planning model based on asymmetric Nash bargaining according to the comprehensive operation cost function and intelligent soft switching planning constraints; performing staged solving processing on the intelligent soft switching game planning model through an alternating direction multiplier method to obtain a first objective function for maximizing cooperative benefits and a second objective function for minimizing comprehensive cost; and carrying out solving processing of an alternating direction multiplier method to obtain a target planning scheme. According to the planning scheme, the utilization efficiency of flexible resources can be effectively improved.

Description

Technical Field

[0001] The present application relates to the technical field of power distribution networks, and in particular to an intelligent soft switch game planning method, device and computer equipment. Background Art

[0002] The widespread access to large-scale sources and loads such as distributed new energy and electric vehicles has brought severe challenges to the balance of supply and demand in the distribution system. Traditional control methods are difficult to achieve rapid response control of power flow, and the demand for flexible resource application has increased significantly. In recent years, the rapid development of power electronics technology has provided an opportunity to meet this challenge. New distribution devices represented by intelligent soft switches (Soft openpoint, SOP) have been endowed with flexible and diverse new control methods. By adjusting the transmission power between multiple feeders in the distribution network in real time, it can realize the mutual sharing of multiple types of resources and improve the operating status of the distribution system.

[0003] However, the current planning methods mainly focus on the intelligent soft switch planning under the traditional single-subject development model. However, with the addition of multiple selfish subjects, the planning method that only considers the interests of a single subject is no longer suitable for the development needs of distribution networks. The intelligent soft switch planning scheme implemented by the existing planning method has low utilization efficiency of flexibility resources. Summary of the invention

[0004] Based on this, it is necessary to provide an intelligent soft switch game planning method, device, computer equipment, computer readable storage medium and computer program product that can improve the efficiency of power resource utilization in response to the above technical problems.

[0005] In a first aspect, the present application provides an intelligent soft switch game planning method, comprising:

[0006] Obtaining the distribution network parameters, power transfer-in resource parameters, power transfer-out resource parameters and planning target configuration information of the target distribution network;

[0007] Based on the distribution network parameters, the power transfer-in resource parameters, the power transfer-out resource parameters and the planning target configuration information, a comprehensive operation cost function of the distribution network operator is generated, and according to the comprehensive operation cost function and the intelligent soft switch planning constraints, an intelligent soft switch game planning model based on asymmetric Nash bargaining is constructed;

[0008] The intelligent soft switch game planning model is solved in stages by an alternating direction multiplier method to obtain a first objective function that maximizes cooperation benefits and a second objective function that minimizes comprehensive costs;

[0009] An alternating direction multiplier method is used to solve the first objective function and the second objective function to obtain a target planning solution.

[0010] In a second aspect, the present application also provides an intelligent soft switch game planning device, comprising:

[0011] A data acquisition module is used to acquire the distribution network parameters, power transfer-in resource parameters, power transfer-out resource parameters and planning target configuration information of the target distribution network;

[0012] A model building module, for generating a comprehensive operation cost function of a distribution network operator based on the distribution network parameters, the power transfer-in resource parameters, the power transfer-out resource parameters and the planning target configuration information, and building a smart soft switch game planning model based on asymmetric Nash bargaining according to the comprehensive operation cost function and smart soft switch planning constraints;

[0013] A model data processing module, used for solving the intelligent soft switch game planning model in stages by using an alternating direction multiplier method to obtain a first objective function that maximizes the cooperation benefit and a second objective function that minimizes the comprehensive cost;

[0014] The model solving module is used to perform a solving process using an alternating direction multiplier method based on the first objective function and the second objective function to obtain a target planning solution.

[0015] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0016] Obtaining the distribution network parameters, power transfer-in resource parameters, power transfer-out resource parameters and planning target configuration information of the target distribution network;

[0017] Based on the distribution network parameters, the power transfer-in resource parameters, the power transfer-out resource parameters and the planning target configuration information, a comprehensive operation cost function of the distribution network operator is generated, and according to the comprehensive operation cost function and the intelligent soft switch planning constraints, an intelligent soft switch game planning model based on asymmetric Nash bargaining is constructed;

[0018] The intelligent soft switch game planning model is solved in stages by an alternating direction multiplier method to obtain a first objective function that maximizes cooperation benefits and a second objective function that minimizes comprehensive costs;

[0019] An alternating direction multiplier method is used to solve the first objective function and the second objective function to obtain a target planning solution.

[0020] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0021] Obtaining the distribution network parameters, power transfer-in resource parameters, power transfer-out resource parameters and planning target configuration information of the target distribution network;

[0022] Based on the distribution network parameters, the power transfer-in resource parameters, the power transfer-out resource parameters and the planning target configuration information, a comprehensive operation cost function of the distribution network operator is generated, and according to the comprehensive operation cost function and the intelligent soft switch planning constraints, an intelligent soft switch game planning model based on asymmetric Nash bargaining is constructed;

[0023] The intelligent soft switch game planning model is solved in stages by an alternating direction multiplier method to obtain a first objective function that maximizes cooperation benefits and a second objective function that minimizes comprehensive costs;

[0024] An alternating direction multiplier method is used to solve the first objective function and the second objective function to obtain a target planning solution.

[0025] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0026] Obtaining the distribution network parameters, power transfer-in resource parameters, power transfer-out resource parameters and planning target configuration information of the target distribution network;

[0027] Based on the distribution network parameters, the power transfer-in resource parameters, the power transfer-out resource parameters and the planning target configuration information, a comprehensive operation cost function of the distribution network operator is generated, and according to the comprehensive operation cost function and the intelligent soft switch planning constraints, an intelligent soft switch game planning model based on asymmetric Nash bargaining is constructed;

[0028] The intelligent soft switch game planning model is solved in stages by an alternating direction multiplier method to obtain a first objective function that maximizes cooperation benefits and a second objective function that minimizes comprehensive costs;

[0029] An alternating direction multiplier method is used to solve the first objective function and the second objective function to obtain a target planning solution.

[0030] The above-mentioned intelligent soft switch game planning method, device, computer equipment, computer-readable storage medium and computer program product first obtain the distribution network parameters, power transfer-in resource parameters, power transfer-out resource parameters and planning target configuration information of the target distribution network; then based on the distribution network parameters, power transfer-in resource parameters, power transfer-out resource parameters and planning target configuration information, generate the comprehensive operation cost function of the distribution network operator, and construct an intelligent soft switch game planning model based on asymmetric Nash bargaining according to the comprehensive operation cost function and the intelligent soft switch planning constraints; the intelligent soft switch game planning model is solved in stages by the alternating direction multiplier method to obtain the first objective function that maximizes the cooperative benefit and the second objective function that minimizes the comprehensive cost; the alternating direction multiplier method is used to solve the first objective function and the second objective function to obtain the target planning scheme. By integrating various types of information to determine the operating cost and planning constraints of the intelligent soft switch planning, the intelligent soft switch game planning model is constructed, and the intelligent soft switch game planning model is subjected to the target planning scheme that maximizes the cooperative benefit and minimizes the comprehensive cost. The obtained planning scheme can effectively improve the utilization efficiency of flexibility resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0032] Figure 1 An application environment diagram of an intelligent soft switch game planning method in one embodiment;

[0033] Figure 2 A schematic diagram of a process flow of an intelligent soft switch game planning method in one embodiment;

[0034] Figure 3 A schematic diagram of an improved actual power distribution network topology structure in one embodiment;

[0035] Figure 4 The curves of photovoltaic, wind turbine and load in a typical operation scenario in one embodiment;

[0036] Figure 5 A price curve for trading with a superior power grid in one embodiment;

[0037] Figure 6 A diagram showing a planning result of a distribution network intelligent soft switch in an embodiment;

[0038] Figure 7A diagram showing the return on investment of a distribution network operator in one embodiment;

[0039] Figure 8 A schematic diagram of a process for solving a target planning scheme in one embodiment;

[0040] Fig. 9 A schematic diagram of a flow chart of an intelligent soft switch game planning method in another embodiment;

[0041] Fig.10 is a structural block diagram of an intelligent soft switch game planning device in one embodiment;

[0042] Fig.11 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0044] With the deepening of the new round of power system reform, the opening of the power sales market has promoted the development of end-to-end power trading and expanded new space for the application of intelligent soft switches. In the weakly centralized end-to-end power trading market, a coordinator is usually responsible for organizing the trading activities of multiple self-interested entities. Therefore, the source-load matching function of new distribution devices such as intelligent soft switches has attracted further attention from scholars. Intelligent soft switches solve the problem of communication difficulties in power trading by providing a physical support platform for end-to-end power trading, and help supervise the compliance of each self-interested entity with market rules. However, considering the high investment cost of intelligent soft switches, in order to give full play to their role in the end-to-end power trading market, there is an urgent need for reasonable site selection and capacity determination.

[0045] The intelligent soft switch game planning method provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. When the user of the terminal 102 needs to plan a solution involving an intelligent soft switch, if the solution involves multiple self-interested entities and requires multiple distribution network operators to operate and construct, the intelligent soft switch game planning method of this application can be used to improve resource utilization efficiency. First, the user can submit various parameters such as distribution network parameters, power transfer-in resource parameters, power transfer-out resource parameters, and planning target configuration information to the server 104 through the terminal 102. The server 104 obtains the distribution network parameters, power transfer-in resource parameters, power transfer-out resource parameters and planning target configuration information of the target distribution network; based on the distribution network parameters, power transfer-in resource parameters, power transfer-out resource parameters and planning target configuration information, generates the comprehensive operation cost function of the distribution network operator, and constructs the intelligent soft switch game planning model based on asymmetric Nash bargaining according to the comprehensive operation cost function and the intelligent soft switch planning constraints; the intelligent soft switch game planning model is solved in stages by the alternating direction multiplier method to obtain the first objective function for maximizing the cooperation benefit and the second objective function for minimizing the comprehensive cost; the alternating direction multiplier method is used to solve the first objective function and the second objective function to obtain the target planning scheme. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, Internet of Things devices and portable wearable devices, and the Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car devices, projection devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The head mounted device may be a virtual reality (VR) device, an augmented reality (AR) device, smart glasses, etc. The server 104 may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0046] In an exemplary embodiment, Figure 2 As shown, an intelligent soft switch game planning method is provided, which is applied to Figure 1 The server 104 in the example is used as an example to illustrate, including the following steps 201 to 207. Among them:

[0047] Step 201, obtaining distribution network parameters, power transfer-in resource parameters, power transfer-out resource parameters and planning target configuration information of a target distribution network.

[0048] The distribution network parameters refer to the relevant parameter data based on the target distribution network to be planned, including the topological connection relationship of the distribution network, the size and location of load access, the capacity and location of distributed power access, and the annual operation data set of distributed power and load. The power transfer resource parameters and power transfer resource parameters are for power grid operators, among which the power transfer resource parameters refer to the power price purchased by the distribution network operator k from the upper power grid in time period t. The power transfer resource parameter refers to the electricity sales price. The planning target configuration information is the planning target that needs to be achieved in the distribution network planning process, including the safe operating voltage range of the distribution network, the expected voltage operating range, and the load loss cost conversion coefficient; the planning target configuration information sets the intelligent soft switch planning scheme and investment cost parameters; and sets the total transaction time within the operating day.

[0049] For example, in the process of intelligent soft switch game planning of the target distribution network, it is first necessary to obtain the target distribution network related data and planning target data. So as to carry out more effective planning processing on the basis of these basic planning data. Therefore, it is necessary to first obtain the distribution network parameters, power input resource parameters, power output resource parameters and planning target configuration information of the target distribution network. At the same time, it is also necessary to determine the various types of small items under each major item. In one embodiment, the actual distribution network topology of a certain place is as follows: Figure 3 Detailed parameters are shown in Table 1 and Table 2.

[0050] Table 1 The actual load access location and size of a certain distribution network

[0051]

[0052] Table 2 Line parameters of an actual distribution network example in a certain place

[0053]

[0054] The distribution system consists of three distribution areas, two of which are commercial loads and one is residential loads, with a voltage level of 10.5 kV. The total active power and reactive power of the loads are 20074 kW and 6587 kvar respectively. The specific configuration of the distributed power source is shown in Table 3, and the power factor is 0.9.

[0055] Table 3 Distributed power access and output data

[0056]

[0057] There are 6 nodes to be planned in the distribution network, which are located at nodes {6, 12, 18, 24, 32, 35}. The actual load, photovoltaic and wind turbine output data of the distribution network in a year are clustered to generate a typical daily operation scenario, and the following is obtained: Figure 4 The four typical daily operation scenarios are shown in Figure 1. The port loss coefficient of the intelligent soft switch converter is set to 0.01, and the safe operation range of the distribution network voltage is 0.90pu~1.10pu.

[0058] Step 203, based on the distribution network parameters, the power transfer-in resource parameters, the power transfer-out resource parameters and the planning target configuration information, a comprehensive operation cost function of the distribution network operator is generated, and according to the comprehensive operation cost function and the intelligent soft switch planning constraints, an intelligent soft switch game planning model based on asymmetric Nash bargaining is constructed.

[0059] Exemplarily, the comprehensive operation cost function refers to a function for calculating the input cost based on various basic data and the various inputs of the operator. Specifically, it can be constructed by integrating the distribution network parameters, the intelligent soft switch to be planned scheme and the investment cost parameters, taking into account the annual profit sharing of the intelligent soft switch construction, the annual investment and operation and maintenance cost of the intelligent soft switch, the annual site occupation cost, the annual cost of end-to-end power transaction between regions, the annual cost of transaction with the upper power grid, and the annual penalty cost of voltage deviation. In one embodiment, the comprehensive operation cost function Specifically expressed as:

[0060]

[0061] In the above formula, For the distribution network operator k, the profit share of the cooperation year is are the annual investment costs of the distribution network operator k participating in the intelligent soft switch and the interconnection line, is the annual cost of site occupancy for the distribution network operator k, The annual operation and maintenance cost of the smart soft switch that the distribution network operator k needs to pay, are the annual costs of distribution network operator k trading with the upper grid and participating in end-to-end power trading, The annual cost of voltage deviation penalty for distribution network operators k is given. After determining the comprehensive operating cost function, the intelligent soft switch investment planning constraints are taken into account, and a distribution network intelligent soft switch game planning model based on asymmetric Nash bargaining is constructed.

[0062] For the above data, among them:

[0063] a) The profit share of distribution network operator k participating in the cooperation is expressed as:

[0064]

[0065] Where N TS is the total number of typical operation scenarios, N T is the total number of time sections in a single scene; Ω R For distribution network operators, A collection of smart soft switches invested by distribution network operator k; is the cooperative surplus generated from the Mth smart soft switch, is the cooperative surplus generated from the mth smart soft switch in time period t in scenario ω, p ω is the probability of scene ω appearing, are the active and reactive power prices of distribution network operator k participating in end-to-end power trading in time period t in scenario ω, are the active and reactive power of the mth smart soft switch invested by the distribution network operator k in time period t in scenario ω, μ m,k is the investment proportion of distribution network operator k in the mth smart soft switch.

[0066] b) The annual investment cost of distribution network operator k participating in intelligent soft switching is expressed as:

[0067]

[0068] In the formula, is the total number of converter ports in the mth intelligent soft switch; is the investment cost of the mth intelligent soft switch, d is the discount rate, y is the life cycle of the intelligent soft switch, is the capacity of the mth intelligent soft switch converter at access node i; c AC is the unit capacity cost of the intelligent soft-switching converter.

[0069] c) The annual investment cost of the distribution network operator k participating in the interconnection line is expressed as:

[0070]

[0071] In the formula, is the set of interconnection lines invested by distribution network operator k; l ij is the length of line ij; Ψ is the correction coefficient, c L is the cost per unit length of the interconnection line.

[0072] d) The annual site occupation cost paid by distribution network operator k is expressed as:

[0073]

[0074] In the formula, The site occupation cost required to invest in the mth intelligent soft switch.

[0075] e) The annual operation and maintenance cost of the smart soft switch paid by the distribution network operator k is expressed as:

[0076]

[0077] In the formula, is the annual operation and maintenance cost required for the mth intelligent soft switch; η is the operation and maintenance coefficient of the intelligent soft switch.

[0078] f) The annual transaction cost between distribution network operator k and the upper grid is expressed as:

[0079]

[0080] In the formula, is the transaction cost between distribution network operator k and the upper-level power grid in time period t in scenario ω, They are the amount of electricity purchased and sold by distribution network operator k to the superior power grid in time period t in scenario ω; are the electricity purchase and sales prices from the distribution network operator k to the superior power grid in period t in scenario ω, respectively.

[0081] g) The annual cost of distribution network operator k participating in end-to-end power trading is expressed as:

[0082]

[0083] In the formula, is the cost of distribution network operator k participating in end-to-end power transaction in time period t in scenario ω, They are respectively the active and reactive power injected into the intelligent soft switch in node i in period t in scenario ω.

[0084] h) The annual cost of voltage deviation penalty for distribution network operator k is expressed as:

[0085]

[0086] In the formula, is the set of nodes in the distribution area operated by distribution network operator k; is the voltage deviation penalty cost of distribution network operator k in time period t in scenario ω, is the voltage deviation index of node i in period t in scenario ω, is the active power of the load at node i in period t in scenario ω; is the voltage offset penalty coefficient for period t in scenario ω. To improve the solution efficiency, linearization and relaxation methods are used to process the voltage offset index. The following results are obtained:

[0087]

[0088] Where V ω,i,t is the voltage of node i in time period t in scenario ω; V are the upper and lower limits of voltage safety operation, V t hr They are the upper and lower limits of the expected operating voltage respectively.

[0089] (2) The intelligent soft switch planning constraint is expressed as:

[0090]

[0091] In the formula, Ω SOP Plan a collection for all intelligent soft switches.

[0092] (3) Finally, the distribution network intelligent soft switch game planning model based on asymmetric Nash bargaining is expressed as:

[0093]

[0094] Where F is the objective function of the distribution network intelligent soft switch game planning model based on asymmetric Nash bargaining, is the annual comprehensive cost when the distribution network operator k does not participate in the cooperative game, τ k is the bargaining power of distribution network operator k in the cooperative game.

[0095] Step 205 , solving the intelligent soft switch game planning model in stages by using an alternating direction multiplier method to obtain a first objective function for maximizing the cooperation benefit and a second objective function for minimizing the comprehensive cost.

[0096] Step 207, performing a solution process using an alternating direction multiplier method based on the first objective function and the second objective function to obtain a target planning solution.

[0097] Among them, the Alternating Direction Method of Multipliers (ADMM) is a computational framework for solving constrained optimization problems, which is suitable for solving distributed convex optimization problems, especially statistical learning and image decomposition problems. ADMM decomposes the large global problem into multiple smaller and easier-to-solve local sub-problems through the decomposition and coordination process, and obtains the solution of the large global problem by coordinating the solution of the sub-problems. In the scheme of the present application, the intelligent soft switch game planning model is solved in stages by the alternating direction multiplier method to obtain the first objective function of maximizing the cooperative benefit and the second objective function of minimizing the comprehensive cost, so as to solve the intelligent soft switch game planning model under the conditions of maximizing the cooperative benefit and minimizing the comprehensive cost through the first objective function and the second objective function, and obtain the required planning scheme. As for the construction process of the first objective function and the second objective function, the first objective function can be specifically established with the first stage problem for the purpose of maximizing the cooperative benefit, and the intelligent soft switch and its interconnection line planning scheme can be decided; the second stage problem is to establish the second objective function for the purpose of minimizing the annual comprehensive cost of each distribution network operator, and the proportion of investment in the intelligent soft switch is decided according to the bargaining power of each distribution network operator in the cooperative game. Finally, based on the first objective function and the second objective function, 1 is performed to obtain the final target planning scheme. In one embodiment, the transaction price curve with the upper power grid can refer to Figure 5 The planning results of the intelligent soft switch of the distribution network in the target planning scheme can be referred to Figure 6 As shown; the return on investment diagram of each distribution network operator needs to be referred to Figure 7 .

[0098] The above-mentioned intelligent soft switch game planning method first obtains the distribution network parameters, power transfer-in resource parameters, power transfer-out resource parameters and planning target configuration information of the target distribution network; then, based on the distribution network parameters, power transfer-in resource parameters, power transfer-out resource parameters and planning target configuration information, generates the comprehensive operation cost function of the distribution network operator, and constructs the intelligent soft switch game planning model based on asymmetric Nash bargaining according to the comprehensive operation cost function and the intelligent soft switch planning constraints; the intelligent soft switch game planning model is solved in stages by the alternating direction multiplier method to obtain the first objective function that maximizes the cooperative benefit and the second objective function that minimizes the comprehensive cost; the alternating direction multiplier method is used to solve the first objective function and the second objective function to obtain the target planning scheme. By integrating various types of information to determine the operating cost and planning constraints of the intelligent soft switch planning, the intelligent soft switch game planning model is constructed, and the intelligent soft switch game planning model is subjected to the target planning scheme that maximizes the cooperative benefit and minimizes the comprehensive cost. The obtained planning scheme can effectively improve the utilization efficiency of flexibility resources.

[0099] In an exemplary embodiment, Figure 8 As shown, step 207 includes:

[0100] Step 801, based on the first objective function and taking the optimal operating cost of the target scenario as the target, a distribution network intelligent soft switch site selection and sizing model is constructed, and the distribution network intelligent soft switch site selection and sizing model is solved by a generalized Bendes decomposition algorithm to obtain an intelligent soft switch planning scheme.

[0101] Step 803 , performing a solution process of an alternating direction multiplier method based on the intelligent soft switch planning scheme and the second objective function to determine the operator investment ratio information of the distribution network operator.

[0102] Step 805: Obtain a target planning solution based on the intelligent soft switch planning solution and the operator investment ratio information.

[0103] Among them, the generalized Benders decomposition algorithm (GBD) is an optimization algorithm designed to solve mathematical programming problems with complex variables. It is mainly used to solve mixed integer programming problems (MIP), that is, extreme value problems that contain both continuous variables and integer variables. The generalized Benders decomposition method is an extension of the traditional Benders decomposition method, allowing sub-problems to be nonlinear problems, thereby increasing the scope of application of the algorithm. The basic principle of the generalized Benders decomposition algorithm includes two main parts: solving the main problem and sub-problems. The main problem is a linear programming problem, and its objective function is usually minimized or maximized. In the process of solving the main problem, it is necessary to introduce the information of the sub-problem to constrain the variables to ensure the feasibility and optimality of the main problem. The sub-problem is usually a linear or nonlinear programming problem, and the information of the main problem needs to be introduced in the process of solving it to obtain a more accurate solution. Through the information exchange between the main problem and the sub-problem, the whole problem is gradually solved until the convergence condition is met. In this application, a generalized Bendes decomposition algorithm is used to solve the intelligent soft switch site selection and capacity determination model of the distribution network, thereby obtaining an effective intelligent soft switch planning scheme.

[0104] For example, the present application converts the obtained distribution network intelligent soft switch game planning model into a two-stage problem and solves it sequentially using the alternating direction multiplier method, wherein the first stage problem establishes the objective function F with the purpose of maximizing the cooperation benefit. s1 , decision-making intelligent soft switch and its interconnection line planning scheme; the second stage problem is to establish the objective function F with the purpose of minimizing the annual comprehensive cost of each distribution network operator S2 , according to the bargaining power of each distribution network operator in the cooperative game, the proportion of investment in intelligent soft switches is determined;

[0105] (1) The first stage problem is expressed as:

[0106]

[0107] In the formula, F S1 is the objective function of the first stage problem model.

[0108] (2) The bargaining power of each distribution network operator in the cooperative game is expressed as:

[0109]

[0110] Where D m,k The power contribution of distribution network operator k in the mth smart soft switch is calculated as follows:

[0111]

[0112] In the formula, The power purchasing and selling capacity of the distribution network operator k using the mth smart soft switch are respectively: The capacity of the mth smart soft switch used by distribution network operator k in time period t in scenario ω is: is the active power loss of the mth smart soft switch converter connected to node i in time period t in scenario ω, The active power loss of the mth intelligent soft switch used by the distribution network operator k, g m,k , m,k They are respectively the energy contribution of distribution network operator k to the use of the mth smart soft switch under the exponential model.

[0113] (3) The second stage problem is expressed as:

[0114]

[0115] In the formula, is the optimal value of the objective function of the first stage problem, They are respectively the annual investment cost of the optimal interconnection line of the distribution network operator k obtained by solving the first stage problem model, the annual cost of trading with the upper power grid, the annual cost of participating in end-to-end power trading, and the annual cost of voltage deviation penalty. In this embodiment, by first constructing a distribution network intelligent soft switch site selection and capacity determination model, and then performing a one-stage solution through the generalized Bendes decomposition algorithm, and performing a solution processing of the alternating direction multiplier method based on the intelligent soft switch planning scheme and the second objective function, the operator investment ratio information of the distribution network operator is determined, thereby effectively ensuring the effectiveness of the planning scheme and the utilization efficiency of the flexibility resources.

[0116] As for the process of solving the intelligent soft switch game planning model in stages by the alternating direction multiplier method to obtain the first objective function of maximizing the cooperative benefit and the second objective function of minimizing the comprehensive cost, it specifically includes: initializing the decision variables, auxiliary variables and penalty factors based on the intelligent soft switch game planning model, and setting the maximum number of iterations, the initial number of iterations, the Lagrange multiplier and the convergence accuracy; iteratively solving the intelligent soft switch game planning model based on the decision variables, auxiliary variables and penalty factors, and updating the decision variable strategy set at fixed intervals according to the decision results of each distribution network operator in each iteration process; solving the decision variables of each distribution network operator in turn according to the auxiliary variables obtained in each iteration, and updating the Lagrange multiplier; in the iterative process, if the difference between the obtained auxiliary variables and the decision variables is less than the convergence accuracy, stop the iteration, and construct the first objective function of maximizing the cooperative benefit and the second objective function of minimizing the comprehensive cost according to the decision variable strategy set of the current iteration. In a specific embodiment, the processing process of the above-mentioned alternating direction multiplier method specifically includes 5 steps: Step 1: Initialize the decision variables M, the auxiliary variables and penalty factor ρ, set the maximum number of iterations Z max is 300, the initial iteration number Z is 0, the Lagrange multiplier λ is 0, and the convergence accuracy ξ is 5*10 -3 ; Step 2: According to the decision results of each distribution network operator in the Zth iteration, update the decision variable strategy set at a fixed interval; Step 3: According to the auxiliary variables obtained in the Zth iteration Solve the decision variables M of each distribution network operator in turn; Step 4: Set Z = Z + 1 and update the Lagrange multiplier λ; Step 5: Determine Is it less than the convergence precision ξ? If it is satisfied, stop the iteration and find the optimal solution; if it is not satisfied, jump back to step 2.

[0117] Based on the first objective function, the first stage problem model of the distribution network intelligent soft switch location and capacity determination model is constructed with the optimal operating cost of the target scenario as the target. As the goal, considering the operation constraints of intelligent soft switches, distribution network operation constraints, and end-to-end power trading price constraints, an intelligent soft switch operation model for distribution network driven by end-to-end power trading is established, and a distribution network intelligent soft switch location and capacity determination model is constructed in combination with the first stage problem model. The following is an explanation with specific embodiments:

[0118] (1) The optimal operating cost of distribution network operator k in time period t in scenario ω It is expressed as:

[0119]

[0120] In the formula, is the transaction cost between distribution network operator k and the upper-level power grid in time period t in scenario ω, is the cost of the distribution network operator k participating in the end-to-end power transaction in time period t in scenario ω, is the penalty cost of voltage deviation for distribution network operator k in time period t in scenario ω.

[0121] (2) The operation constraints of the intelligent soft switch are expressed as:

[0122]

[0123] In the formula, are the active and reactive power injected into node i by the mth smart soft switch in time period t in scenario ω; A AC is the loss coefficient of the intelligent soft-switching converter.

[0124] (3) The distribution network operation constraints are expressed as:

[0125]

[0126] In the formula, P is the set of distribution area lines operated by distribution network operator k; ω,ij,t , Q ω,ij,t are respectively the active and reactive power of line ij in time period t in scenario ω, P ω,i,t , Q ω,i,t are the active and reactive power of node i in time period t in scenario ω, I ω,ij,t is the square of the current of line ij in period t in scenario ω, are the active and reactive power injected by the distributed generation in node i in time period t in scenario ω, is the reactive load power in node i during period t in scenario ω; r ij 、x ij are the impedance and reactance of line ij respectively.

[0127] (4) The price constraint of end-to-end power trading is expressed as:

[0128]

[0129] In the formula, is the power supply and demand ratio of the distribution area operated by distribution network operator k in time period t in scenario ω.

[0130] (5) The location and capacity selection model of intelligent soft switches in distribution network is expressed as:

[0131] a) The main problem model of the site selection and capacity determination of intelligent energy storage soft switches in the distribution network can be sorted out as follows:

[0132]

[0133] st x≤S SOP,max

[0134] In the formula, Ω R N is the distribution network operator group; TS is the total number of typical operation scenarios; F MP The objective function of the main problem model for site selection and capacity determination of intelligent energy storage soft switches in distribution networks; a T The investment cost function of the distribution network in each region is The matrix transposition coefficients are: x is the set of decision variables in the main problem of intelligent soft switch location and capacity determination in the distribution network, including the installation location and capacity of the intelligent soft switch, the length and location of the interconnection line, and p ω is the probability of scene ω appearing, Λ ω is the auxiliary variable introduced, representing the lower limit of the objective function value of the distribution network operation optimization subproblem considering end-to-end power trading in scenario ω; S SOP,max Plan the maximum capacity for the intelligent soft switch.

[0135] b) Arranging the sub-problems of optimizing the operation of the distribution network considering end-to-end power trading can be obtained:

[0136]

[0137] ||W ω,n y ω ||≤w ω,n y ω :o ω，n ,n=1,...,N2

[0138] H ω,n y ω ≤h ω，n :ζ ω,n ,n=1,...,N3

[0139] Where N T is the number of time sections in a single scene; is the objective function of the distribution network operation optimization subproblem considering end-to-end power trading in scenario ω; ω Decision variables for running the optimization sub-problem in scenario ω; The location and capacity parameters of the main problem of location and capacity are passed to the running optimization subproblem.

[0140] When the location selection and sizing parameters transmitted by the main problem are considered in the operation optimization sub-problem, the first constraint is formed, which includes the inequality constraint in the intelligent soft switch operation constraint. ω,n 、j ω,n and g ω,n is the coefficient constant in the nth inequality constraint in scenario ω, σ ω,n , τω,n is the dual multiplier introduced in the nth inequality constraint in scenario ω, and N1 is the total number of inequality constraints in the intelligent soft switch operation constraint.

[0141] When only the decision variables in the operation optimization sub-problem are considered, the second and third constraints are formed. Among them, the second constraint is the standardized expression of the nonlinear inequality constraint that only considers the decision variables in the operation optimization sub-problem in the intelligent soft switch operation constraint. ω,n 、w ω,n is the coefficient constant in the nth nonlinear inequality constraint in scenario ω, o ω,n is the dual multiplier in the nth nonlinear equality constraint in scenario ω, N2 is the total number of nonlinear equality constraints contained; the second constraint is the standardized expression of the linear inequality constraint that only considers the decision variables of the operation optimization sub-problem in the intelligent soft switch operation constraint, distribution network operation constraint and end-to-end power transaction price constraint. H ω,n 、h ω,n is the coefficient constant in the nth linear inequality constraint in scenario ω, ζ ω,n is the dual multiplier in the nth linear inequality constraint in scenario ω, and N3 is the total number of linear inequality constraints contained.

[0142] Finally, on the basis of model construction, it is also necessary to split the distribution network intelligent soft switch site selection and sizing model into a main problem and operation optimization sub-problems under multiple independent scenarios, and use the generalized Bendes decomposition algorithm to iteratively solve the problem to obtain the intelligent soft switch planning scheme. The alternating direction multiplier method is further used to solve the second stage investment ratio negotiation problem to obtain the investment ratio of each distribution network operator.

[0143] (1) The generalized Bendes decomposition algorithm is expressed as:

[0144] Step 1: Set the initial number of iterations Z2 to 1 and the maximum number of iterations is 100, and the lower bound of the objective function for intelligent soft switch site selection and capacity determination is Λ L and upper bound Λ U are -∞ and +∞ respectively, and the iterative convergence accuracy ξ2 is 5*10 -3 ;

[0145] Step 2: Solve the main problem of intelligent soft switch location selection and capacity determination to obtain the optimal target value F MP , according to the formula Λ L =max(Λ L ,F MP ) Update the lower bound of the main problem and pass the location and capacity parameter x in the main problem to the running optimization subproblem;

[0146] Step 3: Solve the distribution network operation optimization sub-problems considering end-to-end power trading in each independent scenario in turn to obtain the optimal target value and the sub-problem decision variables y in each scenario ω ω Compare the optimal objective values ​​of the optimization sub-problems in each scenario and Λ obtained in the main problem ω ,like Then the Bendes optimality cut is returned and added as a new constraint to the main problem as follows;

[0147]

[0148] Step 4: If running the optimization subproblem in the Z2th iteration returns the Bends optimality cut to the main problem, update the upper bound Λ of the main problem U ;

[0149]

[0150] Step 5: If no Bends optimality cut is generated in the Z2th iteration, or the absolute value difference between the upper bound and the lower bound of the main problem |Λ U -Λ L |≤ξ2, then stop the iteration and obtain the optimal addressing and capacity setting scheme for the intelligent soft switch; otherwise, return to step 2 until convergence. In one embodiment, the complete intelligent soft switch game planning process of this application can be referred to Fig. 9 shown.

[0151] In order to fully verify the feasibility and effectiveness of the method of the present application, in this embodiment, the following three schemes are adopted for comparative analysis:

[0152] Solution I: No intelligent soft switch is planned, and the regional distribution network operators only trade with the upper-level power grid to obtain the initial operating status of the distribution network;

[0153] Solution II: Using the distribution network intelligent soft switch game planning method considering end-to-end power trading of the present invention, the distribution network operators in each region participate in the end-to-end power trading;

[0154] Solution III: Without considering end-to-end power trading and gaming, all distribution network operators form an alliance and plan intelligent soft switches with the goal of minimizing alliance costs;

[0155] The comparison of the annual comprehensive costs of Scheme I, Scheme II and Scheme III is shown in Table 4. To further illustrate the rationality of the annual total comprehensive cost, Table 5 is used to analyze the various costs of distribution network operators under Scheme I and Scheme II.

[0156] Table 4 Comparison of annual comprehensive costs under different schemes

[0157]

[0158] Table 5 Comparison of various costs between Scheme I and Scheme II

[0159]

[0160] Compared with Scheme I and Scheme II, it can be seen that the smart soft switch planned in Scheme III can effectively reduce the annual comprehensive cost of distribution network operator 3. However, it is noted that the annual comprehensive cost of distribution network operator 1 and distribution network operator 2 has increased and resulted in a negative cost reduction rate. If each distribution network operator is regarded as a self-interested subject, this situation is unacceptable. In addition, if the asymmetric Nash bargaining game theory is not adopted, all distribution network operators in Scheme III share the investment cost of the smart soft switch and its interconnection line, ignoring the interests of different self-interested subjects. Relatively speaking, the adoption of game theory in Scheme II and the introduction of end-to-end power trading avoid the situation where distribution network operator 1 and distribution network operator 2 are unprofitable. By comparing the three schemes, it can be seen that by using the distribution network smart soft switch game planning method considering end-to-end power trading proposed by the present invention, all distribution network operators can choose to flexibly invest in smart soft switches that are more beneficial to them in a cooperative game mode, thereby improving the economic efficiency of distribution network operation.

[0161] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0162] Based on the same inventive concept, the embodiment of the present application also provides an intelligent soft switch game planning device for implementing the intelligent soft switch game planning method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more intelligent soft switch game planning device embodiments provided below can refer to the limitations of the intelligent soft switch game planning method above, and will not be repeated here.

[0163] In an exemplary embodiment, Fig.10 As shown, an intelligent soft switch game planning device is provided, comprising:

[0164] The data acquisition module 1001 is used to acquire the distribution network parameters, power transfer-in resource parameters, power transfer-out resource parameters and planning target configuration information of the target distribution network.

[0165] The model building module 1003 is used to generate a comprehensive operating cost function of the distribution network operator based on the distribution network parameters, the power transfer-in resource parameters, the power transfer-out resource parameters and the planning target configuration information, and to build an intelligent soft switch game planning model based on asymmetric Nash bargaining according to the comprehensive operating cost function and the intelligent soft switch planning constraints.

[0166] The model data processing module 1005 is used to solve the intelligent soft switch game planning model in stages by using the alternating direction multiplier method to obtain a first objective function for maximizing the cooperation benefit and a second objective function for minimizing the comprehensive cost.

[0167] The model solving module 1007 is used to perform a solving process using an alternating direction multiplier method based on the first objective function and the second objective function to obtain a target planning solution.

[0168] In one embodiment, the model solving module 1007 is specifically used to: based on the first objective function, with the optimal operating cost of the target scenario as the target, construct a distribution network intelligent soft switch site selection and sizing model, solve the distribution network intelligent soft switch site selection and sizing model through the generalized Bendes decomposition algorithm to obtain an intelligent soft switch planning scheme; perform an alternating direction multiplier method solution processing based on the intelligent soft switch planning scheme and the second objective function to determine the operator investment ratio information of the distribution network operator; obtain a target planning scheme based on the intelligent soft switch planning scheme and the operator investment ratio information.

[0169] In one embodiment, the optimal operating cost of the target scenario It is expressed as:

[0170]

[0171] In the formula, is the energy transfer resource parameter of distribution network operator k in time period t in scenario ω, is the power transfer resource parameter of distribution network operator k in time period t in scenario ω, is the penalty cost of voltage deviation for distribution network operator k in time period t in scenario ω.

[0172] In one embodiment, the distribution network intelligent soft switch location and capacity determination model includes a distribution network intelligent energy storage soft switch location and capacity determination main problem model, and a distribution network operation optimization sub-problem model for end-to-end electric energy trading;

[0173] Among them, the main problem model of the site selection and capacity determination of the intelligent energy storage soft switch of the distribution network is expressed as:

[0174]

[0175] stx≤SSOP,max

[0176] In the formula, Ω R N is the distribution network operator group; TS is the total number of typical operation scenarios; F MP The objective function of the main problem model for site selection and capacity determination of intelligent energy storage soft switches in distribution networks; a T The investment cost function of the distribution network in each region is The matrix transposition coefficients are: x is the set of decision variables in the main problem of intelligent soft switch location and capacity determination in the distribution network, including the installation location and capacity of the intelligent soft switch, the length and location of the interconnection line, and p ω is the probability of scene ω appearing, Λ ω is the auxiliary variable introduced, representing the lower limit of the objective function value of the distribution network operation optimization subproblem considering end-to-end power trading in scenario ω; S SOP,max Plan the maximum capacity for the intelligent soft switch;

[0177] The distribution network operation optimization sub-problem model of end-to-end power trading is expressed as:

[0178]

[0179] ||W ω,n y ω ||≤w ω,n y ω :o ω,n ,n=1,...,N2

[0180] H ω,n y ω ≤h ω,n :ζ ω,n ,n=1,...,N3

[0181] Where N T is the number of time sections in a single scene; is the objective function of the distribution network operation optimization subproblem considering end-to-end power trading in scenario ω; ω Decision variables for running the optimization sub-problem in scenario ω; The location and capacity parameters of the main problem of location and capacity are passed to the running optimization subproblem.

[0182] In one embodiment, the model solving module 1007 is specifically used to: set the initial iteration number, the maximum iteration number, the lower bound Λ of the intelligent soft switch site selection and capacity determination objective function L and upper bound Λ U , and iterative convergence accuracy; solve the main problem model of intelligent soft switch location and capacity, and obtain the optimal target value F MP , according to the formula Λ L =max(Λ L ,FMP ) updates the lower bound of the main problem, and transfers the location and capacity parameters in the intelligent soft switch location and capacity main problem model to the distribution network operation optimization sub-problem model; solves the distribution network operation optimization sub-problem model considering end-to-end power trading in each independent scenario in turn, and obtains the optimal target value and the sub-problem decision variables y in each scenario ω ω Compare the optimal objective values ​​of the optimization sub-problems in each scenario and Λ obtained in the main problem ω ,like Then the Bendes optimality cut is returned and added as a new constraint to the main problem model of intelligent soft switch location and capacity determination, as shown in the following formula;

[0183]

[0184] When the optimization subproblem returns the Bendes optimality cut to the main problem in the iteration, the upper bound Λ is updated as follows U ;

[0185]

[0186] No Bends optimality cut or upper bound Λ is generated during the iteration U With the lower bound Λ L If the absolute value difference of is equal to the iterative convergence accuracy, the iteration is stopped and the intelligent soft switching planning scheme is obtained.

[0187] In one embodiment, the comprehensive operating cost function It is expressed as:

[0188]

[0189] In the above formula, For the distribution network operator k, the profit share of the cooperation year is are the annual investment costs of the distribution network operator k participating in the intelligent soft switch and the interconnection line, is the annual cost of site occupancy for the distribution network operator k, The annual operation and maintenance cost of the smart soft switch that the distribution network operator k needs to pay, are the annual costs of distribution network operator k trading with the upper grid and participating in end-to-end power trading, k is the annual cost of voltage excursion penalty for distribution network operators.

[0190] In one embodiment, the first objective function F S1 and the second objective function F S2 Respectively expressed as:

[0191]

[0192] In the formula, τ k is the bargaining power of distribution network operator k in the cooperative game, is the comprehensive cost of independent operation of the distribution network operator k before the cooperation, The optimal annual comprehensive cost of distribution network operator k is obtained by solving the first stage problem model.

[0193] In one embodiment, the model data processing module 1005 is specifically used to: initialize decision variables, auxiliary variables and penalty factors based on the intelligent soft switch game planning model, and set the maximum number of iterations, the initial number of iterations, the Lagrange multiplier and the convergence accuracy; iteratively solve the intelligent soft switch game planning model based on the decision variables, auxiliary variables and penalty factors, and update the decision variable strategy set at fixed intervals according to the decision results of each distribution network operator in each iteration process; solve the decision variables of each distribution network operator in turn according to the auxiliary variables obtained in each iteration, and update the Lagrange multiplier; during the iteration process, if the difference between the obtained auxiliary variables and the decision variables is less than the convergence accuracy, stop the iteration, and construct the first objective function of maximizing the cooperation benefit and the second objective function of minimizing the comprehensive cost based on the decision variable strategy set of the current iteration.

[0194] Each module in the above-mentioned intelligent soft switch game planning device can be implemented in whole or in part by software, hardware and their combination. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0195] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig.11 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data related to intelligent soft switch game planning. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an intelligent soft switch game planning method is implemented.

[0196] Those skilled in the art will understand that Fig.11 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0197] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0198] In one embodiment, a computer-readable storage medium is provided, storing a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0199] In one embodiment, a computer program product or computer program is provided, the computer program product or computer program includes computer instructions, the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps in the above-mentioned method embodiments.

[0200] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0201] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0202] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0203] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. An intelligent soft switch game planning method, characterized in that: The method comprises: Obtaining the distribution network parameters, power transfer-in resource parameters, power transfer-out resource parameters and planning target configuration information of the target distribution network; Based on the distribution network parameters, the power transfer-in resource parameters, the power transfer-out resource parameters and the planning target configuration information, a comprehensive operation cost function of the distribution network operator is generated, and according to the comprehensive operation cost function and the intelligent soft switch planning constraints, an intelligent soft switch game planning model based on asymmetric Nash bargaining is constructed; The intelligent soft switch game planning model is solved in stages by an alternating direction multiplier method to obtain a first objective function that maximizes cooperation benefits and a second objective function that minimizes comprehensive costs; An alternating direction multiplier method is used to solve the first objective function and the second objective function to obtain a target planning solution.

2. The method according to claim 1, characterized in that: The solving process of the alternating direction multiplier method based on the first objective function and the second objective function to obtain the target planning scheme includes: Based on the first objective function, taking the optimal operating cost of the target scenario as the target, constructing a distribution network intelligent soft switch location and capacity determination model, solving the distribution network intelligent soft switch location and capacity determination model by using a generalized Bendes decomposition algorithm, and obtaining an intelligent soft switch planning scheme; Performing a solution process of an alternating direction multiplier method based on the intelligent soft switch planning scheme and the second objective function to determine the operator investment ratio information of the distribution network operator; A target planning scheme is obtained based on the intelligent soft switch planning scheme and the operator investment ratio information.

3. The method according to claim 2, characterized in that The optimal operating cost of the target scenario It is expressed as: In the formula, is the energy transfer resource parameter of distribution network operator k in time period t in scenario ω, is the power transfer resource parameter of distribution network operator k in time period t in scenario ω, is the penalty cost of voltage deviation for distribution network operator k in time period t in scenario ω.

4. The method according to claim 2, characterized in that: The distribution network intelligent soft switch location and capacity determination model includes a distribution network intelligent energy storage soft switch location and capacity determination main problem model, and a distribution network operation optimization sub-problem model for end-to-end electric energy trading; Among them, the main problem model of the site selection and capacity determination of the intelligent energy storage soft switch of the distribution network is expressed as: In the formula, Ω R N is the distribution network operator group; TS is the total number of typical operation scenarios; F MP The objective function of the main problem model for site selection and capacity determination of intelligent energy storage soft switches in distribution networks; a T The investment cost function of the distribution network in each region is The matrix transposition coefficients are: x is the set of decision variables in the main problem of intelligent soft switch location and capacity determination in the distribution network, including the installation location and capacity of the intelligent soft switch, the length and location of the interconnection line, and p ω is the probability of scene ω appearing, Λ ω is the auxiliary variable introduced, representing the lower limit of the objective function value of the distribution network operation optimization subproblem considering end-to-end power trading in scenario ω; S SOP,max Plan the maximum capacity for the intelligent soft switch; The distribution network operation optimization sub-problem model of the end-to-end power transaction is expressed as: ||W ω，n the ω ||≤W ω,n the ω :oh ω,n ,n=1,...,N2 H ω，n y ω ≤h ω,n :ζ ω,n ,n=1,...,N3 Where N T is the number of time sections in a single scene; is the objective function of the distribution network operation optimization subproblem considering end-to-end power trading in scenario ω; ω Decision variables for running the optimization sub-problem in scenario ω; The location and capacity parameters of the main problem of location and capacity are passed to the running optimization subproblem.

5. The method according to claim 4, characterized in that The distribution network intelligent soft switch location and capacity determination model is solved by the generalized Bendes decomposition algorithm to obtain the intelligent soft switch planning scheme, which includes: Set the initial number of iterations, the maximum number of iterations, and the lower bound of the intelligent soft switch site selection and capacity determination objective function Λ L and upper bound Λ U , and iterative convergence accuracy; Solve the main problem model of intelligent soft switch location selection and capacity determination to obtain the optimal target value F MP , according to the formula Λ L =max(Λ L ,F MP ) updating the lower bound of the main problem, and transferring the location and capacity parameters in the intelligent soft switch location and capacity main problem model to the distribution network operation optimization sub-problem model; Solve the distribution network operation optimization sub-problem model considering end-to-end power trading in each independent scenario in turn to obtain the optimal target value and the sub-problem decision variables y in each scenario ω ω , compare the optimal objective values ​​of the optimization sub-problems in each scenario and Λ obtained in the main problem ω ,like Then the Bendes optimality cut is returned and added as a new constraint to the main problem model of intelligent soft switch location selection and capacity determination, as shown in the following formula: When the optimization subproblem returns the Bendes optimality cut to the main problem in the iteration, the upper bound Λ is updated as follows U ; No Bends optimality cut is generated during the iteration, or the upper bound Λ U With the lower bound Λ L If the absolute value difference of is equal to the iterative convergence accuracy, the iteration is stopped to obtain the intelligent soft switching planning scheme.

6. The method according to claim 1, characterized in that The comprehensive operating cost function It is expressed as: In the above formula, For the distribution network operator k, the profit share of the cooperation year is are the annual investment costs of the distribution network operator k participating in the intelligent soft switch and the interconnection line, is the annual cost of site occupancy for the distribution network operator k, The annual operation and maintenance cost of the smart soft switch that the distribution network operator k needs to pay, are the annual costs of distribution network operator k trading with the upper grid and participating in end-to-end power trading, k is the annual cost of voltage excursion penalty for distribution network operators.

7. The method according to claim 1, characterized in that The first objective function F S1 And the second objective function F S2 Respectively expressed as: In the formula, τ k is the bargaining power of distribution network operator k in the cooperative game, is the comprehensive cost of independent operation of the distribution network operator k before the cooperation, The optimal annual comprehensive cost of distribution network operator k is obtained by solving the first stage problem model.

8. The method according to any one of claims 1 to 7, characterized in that: The intelligent soft switch game planning model is solved in stages by the alternating direction multiplier method to obtain the first objective function of maximizing the cooperation benefit and the second objective function of minimizing the comprehensive cost, including: Initializing decision variables, auxiliary variables and penalty factors based on the intelligent soft switch game planning model, and setting the maximum number of iterations, the initial number of iterations, the Lagrange multiplier and the convergence accuracy; Iteratively solving the intelligent soft switch game planning model based on the decision variables, the auxiliary variables and the penalty factor, and updating the decision variable strategy set at fixed intervals according to the decision results of each distribution network operator in each iteration process; According to the auxiliary variables obtained in each iteration, the decision variables of each distribution network operator are solved in turn, and the Lagrange multiplier is updated; During the iteration process, when the difference between the auxiliary variable and the decision variable is less than the convergence accuracy, the iteration is stopped, and the first objective function of maximizing the cooperation benefit and the second objective function of minimizing the comprehensive cost are constructed based on the decision variable strategy set of the current iteration.

9. An intelligent soft switch game planning device, characterized in that: The device comprises: A data acquisition module is used to acquire the distribution network parameters, power transfer-in resource parameters, power transfer-out resource parameters and planning target configuration information of the target distribution network; A model building module, for generating a comprehensive operation cost function of a distribution network operator based on the distribution network parameters, the power transfer-in resource parameters, the power transfer-out resource parameters and the planning target configuration information, and building a smart soft switch game planning model based on asymmetric Nash bargaining according to the comprehensive operation cost function and smart soft switch planning constraints; A model data processing module, used for solving the intelligent soft switch game planning model in stages by using an alternating direction multiplier method to obtain a first objective function that maximizes the cooperation benefit and a second objective function that minimizes the comprehensive cost; The model solving module is used to perform a solving process using an alternating direction multiplier method based on the first objective function and the second objective function to obtain a target planning solution.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

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