Intelligent soft switching planning method and device for power distribution network, equipment, medium and product

Abstract

The invention relates to an intelligent soft switching planning method and device for a power distribution network, equipment, a medium and a product. The method comprises the steps of generating an intelligent soft switch planning strategy set according to power distribution network parameters, to-be-selected positions of intelligent soft switches in the power distribution network and unit capacity cost of each planning period by obtaining power distribution network parameters of each planning period of the power distribution network, and determining the intelligent soft switch according to the power distribution network parameters and the intelligent soft switch planning strategy set. According to the method, a first income model of an operator and a second income model of a power distribution network are constructed, and a target intelligent soft switch planning strategy of each planning period is determined according to the first income model and the second income model, so that different requirements of different interest subjects in intelligent soft switch planning can be considered; medium and long term planning can be carried out on the intelligent soft switch in different periods, and the characteristic that the source load increases year by year in the power distribution network is considered, so that the utilization efficiency of equipment resources is improved, and the return on investment of the intelligent soft switch is improved.

Description

Technical Field

[0001] The present application relates to the field of smart grid technology, and in particular to a method, device, equipment, medium and product for planning smart soft switches in a distribution network. Background Art

[0002] With the continuous improvement of the power system, the participation of multiple stakeholders in the power market competition has become a development trend. At the same time, the continuous increase in the installed capacity of distributed renewable energy has made it difficult for traditional discrete control methods to effectively cope with the rapid changes in distribution network trends. The demand for the application of new flexible distribution equipment with rapid response capabilities has increased significantly. Among them, new flexible distribution equipment represented by intelligent soft switches (Soft open point, SOP) can adjust the transmission power between interconnected feeders in real time, providing support for the application of diversified and flexible control methods. Intelligent soft switches not only help promote the flexible matching of distributed sources and loads, but also meet the flexible operation needs of multiple stakeholders, supporting the economical and efficient operation of the distribution network.

[0003] However, current intelligent soft switching planning methods in distribution networks suffer from low resource utilization. Summary of the Invention

[0004] Based on this, it is necessary to provide a method, device, equipment, medium and product for planning intelligent soft switches in distribution networks, which can carry out medium- and long-term planning of intelligent soft switches in distribution networks while minimizing comprehensive costs, in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for intelligent soft switching planning of a distribution network. The method comprises:

[0006] Obtaining distribution network parameters for each planning period of the distribution network;

[0007] generating a smart soft switch planning strategy set according to the distribution network parameters, the candidate locations of each smart soft switch in the distribution network, and the unit capacity cost of each planning period;

[0008] Constructing a first revenue model for the operator and a second revenue model for the distribution network based on the distribution network parameters and the intelligent soft switch planning strategy set;

[0009] A target intelligent soft switch planning strategy for each planning period is determined according to the first profit model and the second profit model.

[0010] In a second aspect, the present application also provides an intelligent soft switch planning device for a distribution network. The device includes:

[0011] An acquisition module is used to obtain distribution network parameters of each planning period of the distribution network;

[0012] A generation module, configured to generate a smart soft switch planning strategy set based on the distribution network parameters, the candidate locations of each smart soft switch in the distribution network, and the unit capacity cost of each planning period;

[0013] A construction module, configured to construct a first revenue model for the operator and a second revenue model for the distribution network based on the distribution network parameters and the intelligent soft switch planning strategy set;

[0014] The determination module is configured to determine a target intelligent soft switch planning strategy for each planning period according to the first profit model and the second profit model.

[0015] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any of the above methods when executing the computer program.

[0016] In a fourth aspect, the present application also provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of any of the above methods when executed by a processor.

[0017] In a fifth aspect, the present application also provides a computer program product, comprising a computer program, which implements the steps of any of the above methods when executed by a processor.

[0018] The above-mentioned intelligent soft switch planning method, device, equipment, medium and product for the distribution network obtains the distribution network parameters of each planning period of the distribution network, generates an intelligent soft switch planning strategy set based on the distribution network parameters, the candidate locations of each intelligent soft switch in the distribution network and the unit capacity cost of each planning period, constructs the first profit model of the operator and the second profit model of the distribution network based on the distribution network parameters and the intelligent soft switch planning strategy set, and determines the target intelligent soft switch planning strategy for each planning period based on the first profit model and the second profit model, thereby taking into account the different needs of different stakeholders in the intelligent soft switch planning, and can also carry out medium- and long-term planning of the intelligent soft switches by period, taking into account the characteristics of the annual growth of sources and loads in the distribution network, thereby improving the efficiency of equipment resource utilization and improving the return on investment of intelligent soft switches. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a diagram of the internal structure of a computer device provided in an embodiment of the present application;

[0020] Figure 2 This is a flow chart of a method for planning intelligent soft switches for a distribution network provided in an embodiment of the present application;

[0021] Figure 3 This is a flow chart of a method for medium- and long-term dynamic game planning of intelligent soft switches in a distribution network under source-load growth provided by an embodiment of the present application;

[0022] Figure 4 This is a schematic diagram of a distributed power source connected to a distribution network in different planning periods provided by an embodiment of the present application;

[0023] Figure 5 This is a schematic diagram of a typical operating scenario provided by an embodiment of the present application;

[0024] Figure 6 This is a schematic diagram of an expansion process of an intelligent soft switch planning solution provided in an embodiment of the present application;

[0025] Figure 7 This is a price diagram of transactions with the main network during different time periods provided by an embodiment of the present application;

[0026] Figure 8 This is a schematic diagram of a medium- and long-term dynamic planning result of an intelligent soft switch based on a hybrid game provided in an embodiment of the present application;

[0027] Figure 9 This is a flow chart of a method for determining a target intelligent soft switch planning strategy provided by an embodiment of the present application;

[0028] Figure 10 This is a flowchart of another method for determining a target intelligent soft switch planning strategy provided by an embodiment of the present application;

[0029] Figure 11 This is a flow chart of a mid- to long-term dynamic game planning method for an intelligent soft switch provided in an embodiment of the present application;

[0030] Figure 12 This is a structural block diagram of an intelligent soft switch planning device for a distribution network provided in an embodiment of the present application. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0032] With the continuous improvement of the power system, the participation of multiple stakeholders in the power market competition has become a development trend. At the same time, the continuous increase in the installed capacity of distributed renewable energy has made it difficult for traditional discrete control methods to effectively cope with the rapid changes in distribution network trends. The demand for the application of new flexible distribution equipment with rapid response capabilities has increased significantly. Among them, new flexible distribution equipment represented by intelligent soft switches (Soft open point, SOP) can adjust the transmission power between interconnected feeders in real time, providing support for the application of diversified and flexible control methods. Intelligent soft switches not only help promote the flexible matching of distributed sources and loads, but also meet the flexible operation needs of multiple stakeholders, supporting the economical and efficient operation of the distribution network.

[0033] However, current methods for planning intelligent soft switches in distribution networks suffer from low resource utilization. Given the high cost of intelligent soft switches, minimizing overall costs while implementing medium- and long-term planning for intelligent soft switches in distribution networks has become a pressing technical challenge.

[0034] The intelligent soft switch planning method for the distribution network provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Figure 1 This is an internal structure diagram of a computer device provided in an embodiment of the present application. The computer device may be a server, and its internal structure diagram may be as follows: Figure 1 As shown. The computer device includes a processor, memory, and a network interface connected via a system bus. 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 computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When executed by the processor, the computer program implements a method for intelligent soft switching planning of a distribution network.

[0035] Those skilled in the art will understand that Figure 1 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 shown in the figure, or combine certain components, or have a different component arrangement.

[0036] In one embodiment, Figure 2 As shown, Figure 2 This is a flow chart of a method for planning an intelligent soft switch for a distribution network provided by an embodiment of the present application. This method can be applied to Figure 1 In a computer device, the method comprises the following steps:

[0037] S201: Obtain distribution network parameters for each planning period of the distribution network.

[0038] Optionally, the distribution network parameters for each planning period of the distribution network may include: the topology of the distribution network, the deployment location and capacity of distributed power sources and loads in each planning period, the annual operating parameters of distributed power sources and loads, the price of electricity sold from distribution network area k to the main grid in time period t in scenario ω within planning period p, and the price of electricity sold from distribution network area k to the main grid in time period t in scenario ω within planning period p. and electricity purchase price The voltage safe operation range, economic operation range and voltage deviation penalty coefficient of the distribution network, the total transaction time N in a typical operation day T wait.

[0039] Reference Figure 4 , Figure 4 This is a schematic diagram of a distributed power supply connected to a distribution network in different planning periods provided by an embodiment of the present application. For example, assuming that the total planning period is 20 years, the entire planning period can be divided into 4 planning periods, each planning period lasting 5 years, and then intelligent soft switching planning is performed for each planning period. Distributed power supplies in different planning periods are arranged as follows: Figure 4 The distribution network in Region A is connected in the manner shown. The active and reactive load access parameters for Region A's distribution network can be shown in Table 1 below. The line parameters for Region A's distribution network can be shown in Table 2 below. The voltage level is 10.5 kV. In this example, there are two commercial areas and one residential area. The average annual load growth rates for each area over the four cycles are 2.0%, 1.5%, 1.0%, and 0.5%, respectively.

[0040]

[0041] Table 1

[0042]

[0043] Table 2

[0044] Reference Figure 5 , Figure 5 This is a schematic diagram of a typical operating scenario provided by an embodiment of the present application. In the embodiment of the present application, cluster analysis can be performed on the annual operating data set of distributed renewable energy, residential and commercial loads in the distribution network to generate typical daily scenarios in four seasons, such as Figure 5 The operating loss coefficient of each port of the intelligent soft switch is set to 0.01, and the upper and lower limits of the distribution network safety voltage are set to 0.90 pu and 1.10 pu respectively. The planned investment unit price for each cycle can be shown in Table 3 below:

[0045]

[0046] Table 3

[0047] S202 : Generate a smart soft switch planning strategy set based on distribution network parameters, candidate locations of each smart soft switch in the distribution network, and unit capacity cost of each planning period.

[0048] Optionally, the candidate position of each intelligent soft switch in the distribution network is the accessible position of the intelligent soft switch, which may also be referred to as the plannable position of the intelligent soft switch.

[0049] In an embodiment of the present application, a smart soft switch planning strategy set can be generated based on the distribution network parameters, the candidate locations of each smart soft switch in the distribution network, and the unit capacity cost of each planning period, taking into account the expansion planning constraints of the smart soft switch in each planning period.

[0050] For example, the intelligent soft switch planning strategy set can be expressed as the following formulas (1)-(5):

[0051]

[0052] In formulas (1)-(5), represents the set of nodes that can be connected to the intelligent soft switch in the planning scheme m, represents the set of nodes accessible to the intelligent soft switch in the planning scheme m′, represents the set of nodes that can be connected to the intelligent soft switch in the planning scheme m″; N represents the total number of intelligent soft switch access nodes included in the planning scheme m. PL Indicates the total number of intelligent soft switch planning schemes, N C represents the maximum total number of expandable ports of the intelligent soft switch, ψ represents the feasible set of intelligent soft switch topologies that includes all planning schemes (i.e., the set of intelligent soft switch planning strategies), ψ(m) represents the feasible set of intelligent soft switch topologies expanded from scheme m, and ψ(m, τ) represents the feasible set of intelligent soft switch topologies with τ ports expanded from scheme m; β p，m′ It represents a binary variable used to determine whether the m′th intelligent soft switching planning scheme is adopted within period p, and is used to constrain the uniqueness of the planning scheme.

[0053] Reference Figure 6 , Figure 6 Schematic diagram of the expansion process of an intelligent soft switch planning scheme provided by the embodiment of the present application. The expansion process of the above intelligent soft switch planning scheme can be as follows Figure 6 shown.

[0054] S203: Constructing a first revenue model for the operator and a second revenue model for the distribution network based on the distribution network parameters and the intelligent soft switch planning strategy set.

[0055] In one embodiment, a first revenue objective function for the operator can be established based on distribution network parameters and a set of smart soft switch planning strategies, taking into account the operator's investment costs, operating costs, and profit sharing costs within each planning period. Furthermore, a first revenue model can be constructed by taking into account the smart soft switch investment constraints and smart soft switch operating constraints within each planning period. The operator can, for example, be a third-party operator.

[0056] Optionally, based on the distribution network parameters and the intelligent soft switch planning strategy set, the investment cost, operating cost and profit sharing of each regional distribution network in each period can be considered to establish a second profit objective function of the regional distribution network. In addition, the investment constraints of the intelligent soft switches and the distribution network operation constraints in each planning period can be taken into account to construct a second profit model for each regional distribution network.

[0057] Among them, the first income model and the second income model are both medium- and long-term investment income models.

[0058] For example, the first profit objective function of the operator's first profit model, that is, the third-party operator's medium- and long-term profit objective function F SOP It can be expressed as the following formulas (6)-(7):

[0059]

[0060] In formulas (6)-(7), N PE represents the total number of planning cycles, represents the investment payback period; yp The investment recovery factor for each planning period is used to spread the investment generated each time to the base year; represents the operator’s end-to-end transaction revenue within the planning period p, They represent the smart soft switch investment (i.e., the first investment cost) and the first operation and maintenance cost paid by the operator within the planning period p, respectively. They represent the first civil construction cost and the first tie line reconstruction cost paid by the operator within the planning period p, It represents the profit sharing cost paid by the operator during the planning period.

[0061] End-to-end transaction revenue of the operator within the planning period p It can be expressed as the following formulas (8)-(10):

[0062]

[0063] In formulas (8)-(10), represents the total number of years that the planning period p lasts, N S Represents the total number of typical day scenes, N T Indicates the total number of time sections under each typical day scenario; ΩN Represents a collection of nodes; represents the electricity sales revenue of the SOP operator in the planning scheme m within the planning period p, represents the end-to-end electricity transaction cost in planning scheme m under scenario ω in time period p; They represent the active and reactive end-to-end transaction prices of node i in planning scheme m under scenario ω in planning period p, They represent the active and reactive injection powers of the SOP converter connected to node i in the planning scheme m under the scenario ω in the planning period p, respectively. ω represents the probability of scene ω occurring.

[0064] The smart soft switch investment (i.e., the first investment cost) paid by the operator within the planning period p can be expressed as the following formulas (11)-(12):

[0065]

[0066] In formulas (11)-(12), represents the total investment cost of intelligent soft switching in planning scheme m within planning period p; represents the SOP capacity of the planning scheme m within the planning period p, represents the investment strategy of the third-party operator in the planning scheme m within the planning period p; It represents the unit capacity price of the intelligent soft-switching converter in the planning period p.

[0067] The first operation and maintenance cost paid by the operator within the planning period p can be expressed as the following formulas (13)-(14):

[0068]

[0069] In formulas (13)-(14), represents the total operation and maintenance cost of the planning scheme m within the planning period p; It represents the operation and maintenance cost coefficient within the planning period p.

[0070] The first civil construction cost paid by the operator within the planning period p can be expressed as the following formulas (15)-(16):

[0071]

[0072] In formulas (15)-(16), represents the total civil construction cost of planning scheme m within planning period p; Represents the civil construction cost within the planning period p.

[0073] The cost of the first tie line reconstruction paid by the operator within the planning period p can be expressed as the following formulas (17)-(18):

[0074]

[0075] In formulas (17)-(18), represents the total cost of the tie line reconstruction in the planning scheme m within the planning period p; represents the cost of unit length interconnection line reconstruction within the planning period p; l p，m It represents the length of the newly built tie line in the planning scheme m within the planning period p.

[0076] The profit sharing cost paid by the operator during the planning period p can be expressed as the following formula (19):

[0077]

[0078] In one embodiment, the first constraint condition of the first revenue model includes: an intelligent soft switch investment constraint and an intelligent soft switch operation constraint within each planning period.

[0079] For example, the smart soft switch investment constraint in each planning period can be expressed as the following formula (20):

[0080]

[0081] The intelligent soft switch operation constraints within each planning period can be expressed as the following formulas (21)-(23):

[0082]

[0083] In formulas (21)-(23), Indicates the total number of intelligent soft switches planned in planning scheme m within planning period p; A represents the nth intelligent soft switch port set in the planning scheme m within the planning period p; AC Represents the loss coefficient of the intelligent soft-switching converter.

[0084] In another possible implementation, the second revenue objective function of the second revenue model of the distribution network, that is, the medium- and long-term revenue objective function of the distribution network in region k, is It can be expressed as the following formula (24):

[0085]

[0086] In formula (24), represents the profit share of the distribution network in region k within the planning period p, They represent the second investment cost and the second operation and maintenance cost of the smart soft switch paid by the distribution network in area k within the planning period p, They represent the second civil construction cost and the second interconnection line reconstruction cost paid by the distribution network in area k during the planning period p, They represent the electricity purchase cost paid by the distribution network in region k to the main grid and the cost of participating in end-to-end transactions within the planning period p, represents the voltage deviation penalty cost paid by the distribution network in region k during the planning period p.

[0087] The profit share of the distribution network in region k within the planning period p can be expressed as the following formula (25):

[0088]

[0089] The second investment cost of the smart soft switch paid by the distribution network in area k within the planning period p can be expressed as the following formula (26):

[0090]

[0091] The second operation and maintenance cost of the smart soft switch paid by the distribution network in area k within the planning period p can be expressed as the following formula (27):

[0092]

[0093] The second civil construction cost paid by the distribution network in area k during planning period p can be expressed as the following formula (28):

[0094]

[0095] The cost of the second tie line transformation paid by the distribution network in area k during the planning period p can be expressed as the following formula (29):

[0096]

[0097] The electricity purchase cost paid by the distribution network in region k to the main grid during the planning period p can be expressed as the following formulas (30)-(31):

[0098]

[0099] In formulas (30)-(31), represents the cost of electricity purchased from the main grid by the distribution network in region k in planning scheme m during time period t under scenario ω within planning period p; They represent the electricity purchase and sales prices of the distribution network in region k to the main grid in the planning period p under the scenario ω during time period t, They represent the active power purchased and sold by the distribution network in area k from the main grid in planning scheme m during period t under scenario ω within planning period p.

[0100] The end-to-end transaction cost paid by the distribution network in region k during the planning period p can be expressed as the following formulas (32)-(33):

[0101]

[0102] In formulas (32)-(33), represents the set of nodes in the distribution network of region k, represents the set of smart soft switch access nodes of the distribution network in area k in planning scheme m within planning period p; It represents the total amount of end-to-end electric energy transactions participated by the distribution network in region k in planning scheme m during time period t under scenario ω within planning period p.

[0103] The voltage deviation penalty cost paid by the distribution network in region k during the planning period p can be expressed as follows:

[0104]

[0105] In formulas (34)-(35), represents the total amount of voltage deviation penalty paid by the distribution network in area k in planning scheme m under scenario ω during planning period p; Γ U Indicates the load loss cost conversion coefficient when the voltage exceeds the limit. It represents the voltage deviation index of node i in planning scheme m during time period t under scenario ω within planning period p; represents the active load at node i in time period t under scenario ω within planning period p, φ p,ω,m,t,k In order to solve the model efficiently, the voltage deviation index Linearization processing is shown in the following formulas (36)-(38):

[0106]

[0107] In formulas (36)-(38), V Represent the square of the upper and lower limits of the safety voltage, V thr Respectively represent the square of the upper and lower limits of economic operation voltage. p,ω,m,t,i In the economic operating range If the internal is not optimized, no voltage deviation penalty fee will occur, otherwise it will occur. denote the dual variables introduced by constraints (36)-(38) respectively.

[0108] For example, the second constraint of the second benefit model includes: smart soft switch investment constraint and distribution network operation constraint within each planning period. The distribution network operation constraint within each planning period can be expressed as:

[0109]

[0110] Where, represents the set of lines in the distribution network of region k, is the set of substation access nodes in the distribution network of region k, represents the set of lines connecting the distribution network in region k to the substation access node, represents the set of distributed renewable energy sources in region k; They represent the active and reactive outputs of the connected distributed renewable energy sources at node i in planning scheme m during time period t under scenario ω within planning period p, They represent the active and reactive loads at node i in time period t under scenario ω within planning period p, respectively. p,ω,m,t,ij , Q p,ω,m,t,ij They represent the active and reactive power of line ij in planning scheme m during time period t under scenario ω within planning period p; R ij 、X ij Represents the resistance and reactance in branch ij, P T,MAX Indicates the maximum value of electricity sold to the main network. They represent the upper and lower limits of the active power output of distributed renewable energy in node i in time period t in scenario ω within planning period p. and denote the dual variables introduced by constraints (39)-(48) respectively.

[0111] Reference Figure 7 , Figure 7 This is a price diagram of transactions with the main network in different time periods provided by an embodiment of the present application.

[0112] S204: Determine a target intelligent soft switch planning strategy for each planning period according to the first profit model and the second profit model.

[0113] Optionally, refer to Figure 3 , Figure 3 This is a flow chart of a method for medium- and long-term dynamic game planning of intelligent soft switches in distribution networks under source-load growth provided by an embodiment of the present application. Figure 3 As shown in the figure, based on the master-slave game and asymmetric Nash bargaining theory, with the distribution network as the lower follower and the operator as the upper leader, a hybrid game dynamic programming model can be constructed according to the first and second profit models. Then, the target intelligent soft switching planning strategy for each planning period can be determined according to the hybrid game dynamic programming model.

[0114] For example, refer to Figure 8 , Figure 8 This is a schematic diagram of a mid- to long-term dynamic planning result of an intelligent soft switch based on a hybrid game provided by an embodiment of the present application. The target intelligent soft switch planning strategy of each planning cycle, that is, the mid- to long-term dynamic planning result of the intelligent soft switch can be as follows: Figure 8 shown.

[0115] In an embodiment of the present application, by obtaining the distribution network parameters of each planning period of the distribution network, a smart soft switch planning strategy set is generated according to the distribution network parameters, the candidate positions of each smart soft switch in the distribution network and the unit capacity cost of each planning period, and based on the distribution network parameters and the smart soft switch planning strategy set, a first profit model of the operator and a second profit model of the distribution network are constructed. According to the first profit model and the second profit model, the target smart soft switch planning strategy for each planning period is determined, thereby taking into account the different needs of different stakeholders in the smart soft switch planning, and also making medium- and long-term planning for the smart soft switches by period, taking into account the characteristics of the annual growth of source and load in the distribution network, thereby improving the efficiency of equipment resource utilization and improving the return on investment of the smart soft switch.

[0116] Reference Figure 9 , Figure 9 This is a flow chart of a method for determining a target intelligent soft switch planning strategy provided by an embodiment of the present application. This embodiment involves a possible implementation method for determining a target intelligent soft switch planning strategy for each planning period based on a first profit model and a second profit model. Based on the above embodiment, S204 includes the following steps:

[0117] S901, based on the master-slave game and asymmetric Nash bargaining theory, a hybrid game dynamic programming model is constructed according to the first and second profit models.

[0118] Alternatively, a hybrid game dynamic programming model can be constructed based on the master-slave game and asymmetric Nash bargaining theory, with the distribution network as the lower-level follower and the operator as the upper-level leader, according to the first and second revenue models. The hybrid game dynamic programming model includes a lower-level follower model, an upper-level leader model, and a cooperative game model.

[0119] S902: Determine the target intelligent soft switch planning strategy for each planning period according to the hybrid game dynamic programming model.

[0120] Optionally, when solving the hybrid game dynamic programming model, the hybrid game dynamic programming model can be decomposed into two stages: master-slave game and cooperative game, and solved in order. The master-slave game stage problem is processed using the McCormick envelope and equilibrium constraint mathematical programming method to solve the investment planning strategies of third-party operators and multi-regional distribution network alliances in each cycle; the cooperative game stage aims to maximize the medium- and long-term benefits of the distribution networks in each region within the alliance, and relies on the alternating direction multiplier method to clarify the investment responsibilities of each region within the alliance.

[0121] Exemplarily, the McCormick envelope includes the following steps: 1. Introducing the decision variable w ij , used to replace the nonlinear product term x in the form of multiplication of two continuous decision variables i x j 2. Consider x i Lower limit x i and upper limit x j Lower limit x j and upper limit Construct the following inequality constraints:

[0122]

[0123] 3. Based on the inequality constraints constructed in step 2, the convex relaxation technique is used to obtain the following results:

[0124]

[0125] In one embodiment, the equilibrium constraint mathematical programming method may include the following steps: 1. Introducing the Lagrange multiplier λ i Construct the Lagrangian augmented function L of the distribution network investment income model of each lower layer region, and calculate the partial derivative as shown below;

[0126] L=b T x+λ i (A T xc) (57)

[0127]

[0128] Where b T and A T represents the coefficient matrix, and c represents the coefficient.

[0129] 2. Establish the Carroll-Kuhn-Tucker optimality conditions for the investment and return model of the distribution network in each lower layer

[0130] 3. For the inequality constraints in the distribution network investment and return model of each lower layer region, the complementary relaxation condition 0≤(qx-w)⊥e≥0 is added and the large M method is used. The results are shown below;

[0131] qx-w≤ζM (59)

[0132] e≤(1-ζ)M (60)

[0133] Where M represents a sufficiently large constant and ζ represents a binary variable.

[0134] 4. Use the strong duality principle to transform the two-layer master-slave game planning model into a single-layer one.

[0135] In one possible implementation, the alternating direction multiplier method includes the following steps: Step 1, set the convergence accuracy ξ = 2*10-5, introduce the penalty factor ρ and set it to 0.001, and maximize the number of iterations Z max = 200 and set the number of iterations Z to 0, and initialize the investment ratio of the distribution network in area k in the planning scheme m within the period p With auxiliary variables Step 2: According to the auxiliary variables of the distribution network in area k in the planning scheme m within the period p obtained in the Zth iteration Solve the decision variables of the distribution network in area k in planning scheme m within period p Step 3: Convergence judgment, if the residual meets the convergence conditions Stop the iteration; otherwise, set Z = Z + 1, update the Lagrange multiplier λ, and return to step 2.

[0136] In the embodiment of the present application, based on the master-slave game and asymmetric Nash bargaining theory, a hybrid game dynamic programming model is constructed according to the first and second revenue models. The target intelligent soft switch planning strategy for each planning period is determined according to the hybrid game dynamic programming model, thereby achieving a master-slave game between all regional distribution networks and third-party operators in the first stage of the hybrid game to decide on the investment and end-to-end transaction plans for intelligent soft switches in different periods. In the second stage of the hybrid game, each regional distribution network conducts a cooperative game based on the alliance's investment strategy in different periods, decides on the investment proportion to be undertaken by each within the alliance, and distributes the cooperative surplus, which helps to improve the medium- and long-term distributed renewable energy absorption capacity of the distribution network and improve the return on investment of intelligent soft switches.

[0137] Based on the above embodiment, the above S901 includes the following steps:

[0138] Based on the master-slave game and asymmetric Nash bargaining theory, with the distribution network as the lower follower and the operator as the upper leader, a hybrid game dynamic programming model is constructed according to the first profit model and the second profit model; the hybrid game dynamic programming model includes a lower follower model, an upper leader model and a cooperative game model.

[0139] For example, the upper-level leader model can be expressed as:

[0140]

[0141] The lower follower model can be expressed as:

[0142]

[0143] The cooperative game model can be expressed as follows:

[0144]

[0145] Reference Figure 10 , Figure 10 This is a flow chart of another method for determining a target intelligent soft switch planning strategy provided by an embodiment of the present application. This embodiment involves a possible implementation method for determining the target intelligent soft switch planning strategy for each planning period based on a hybrid game dynamic programming model. Based on the above embodiment, S902 includes the following steps:

[0146] S1001, solving the lower follower model to obtain the first investment planning strategy of the distribution network in each planning period.

[0147] S1002: Solve the upper-level leader model based on the first investment planning strategy to obtain the operator's second investment planning strategy in each planning cycle.

[0148] S1003 , with the goal of maximizing revenue, solving a cooperative game model based on the first investment planning strategy and the second investment planning strategy to obtain a target intelligent soft switching planning strategy for each planning period.

[0149] For example, the lower-level follower model can be solved first to obtain the first investment planning strategy of the distribution network in each planning cycle, and then the first investment planning strategy can be substituted into the upper-level leader model, and the upper-level leader model can be solved to obtain the second investment planning strategy of the operator in each planning cycle. Finally, with the goal of maximizing profits, the cooperative game model is solved based on the first investment planning strategy and the second investment planning strategy to obtain the target intelligent soft switching planning strategy for each planning cycle.

[0150] Reference Figure 11 , Figure 11This is a flow chart of a method for mid- to long-term dynamic game planning of an intelligent soft switch provided by an embodiment of the present application. The method includes the following steps:

[0151] S1101: Obtain distribution network parameters for each planning period of the distribution network.

[0152] S1102 : Generate a smart soft switch planning strategy set based on distribution network parameters, candidate locations of each smart soft switch in the distribution network, and unit capacity cost of each planning period.

[0153] S1103: Construct a first revenue model for the operator and a second revenue model for the distribution network based on the distribution network parameters and the intelligent soft switch planning strategy set.

[0154] S1104, based on the master-slave game and asymmetric Nash bargaining theory, takes the distribution network as the lower follower and the operator as the upper leader, and constructs a hybrid game dynamic programming model according to the first and second profit models.

[0155] S1105 , solving a hybrid game dynamic programming model to obtain a target intelligent soft switching planning strategy for each planning period.

[0156] In order to more clearly introduce the beneficial effects of the embodiments of the present application, the following three solutions are compared and analyzed in conjunction with Tables 3-6. Table 4 shows the planned capacity (MVA) of the intelligent soft-switching converter in each cycle, Table 5 shows the cost analysis of the third-party operator under Solution II (millions of RMB), and Table 6 shows the distribution network cost analysis in each cycle under Solution I and Solution II (millions of RMB):

[0157] Solution I: No intelligent soft switch is planned. Each regional distribution network only trades with the main grid and operates independently. The initial operating status of each regional distribution network in all cycles is obtained.

[0158] Solution II: Adopt the medium- and long-term dynamic game planning method for intelligent soft switches in distribution networks under source-load growth provided by the embodiments of this application, consider periodic joint planning, and introduce end-to-end transactions;

[0159] Option III: Without considering mid- to long-term dynamic planning, only the final source-load carrying demand is considered for advanced one-time deployment of SOP, and the investment cost is calculated based on the price of the initial planning period;

[0160] The planned capacity of intelligent soft switches in different cycles is shown in Table 4. The comprehensive cost comparison of Schemes I, II, and III is shown in Table 3. To further illustrate the rationality of the total comprehensive cost under Scheme II, Tables 5 and 6 are used to analyze the annual costs of each item of third-party operators and regional distribution networks under Schemes I and II.

[0161] The computer hardware environment for performing the optimization calculation is Intel(R) Core(TM) CPU I7-10700 with a main frequency of 3.20GHz and a memory of 16GB; the software environment is the Windows 11 operating system.

[0162] Compared with Scheme I, Schemes II and III improve operational economics for all stakeholders through the rational configuration of intelligent soft switches. In contrast, Scheme III, which only considers the ultimate source-load carrying requirements for advanced planning of intelligent soft switches, results in high investment costs for each regional distribution network throughout all cycles, resulting in lower overall economic efficiency than Scheme II. By adopting the proposed periodic dynamic game planning method, not only can the operating costs of the active distribution network be effectively reduced, but the economic benefits of intelligent soft switches can also be fully utilized while saving investment.

[0163]

[0164] Table 4

[0165]

[0166] Table 5

[0167]

[0168] Table 6

[0169] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0170] Based on the same inventive concept, embodiments of the present application also provide a distribution network intelligent soft switch planning device for implementing the aforementioned distribution network intelligent soft switch planning method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more distribution network intelligent soft switch planning device embodiments provided below can be found in the aforementioned limitations of the distribution network intelligent soft switch planning method, and will not be further elaborated here.

[0171] In one embodiment, Figure 12 As shown, Figure 12 1 is a block diagram of a smart soft switch planning device for a distribution network provided in an embodiment of the present application. The device 1200 includes:

[0172] The acquisition module 1201 is used to acquire the distribution network parameters of each planning period of the distribution network.

[0173] The generating module 1202 is configured to generate a smart soft switch planning strategy set based on the distribution network parameters, the candidate locations of each smart soft switch in the distribution network, and the unit capacity cost of each planning period.

[0174] The construction module 1203 is used to construct a first revenue model of the operator and a second revenue model of the distribution network according to the distribution network parameters and the intelligent soft switch planning strategy set.

[0175] The determination module 1204 is configured to determine a target intelligent soft switch planning strategy for each planning period based on the first profit model and the second profit model.

[0176] In one embodiment, the determining module 1204 includes:

[0177] The construction unit is used to construct a hybrid game dynamic programming model based on the master-slave game and the asymmetric Nash bargaining theory according to the first profit model and the second profit model.

[0178] The determination unit is used to determine the target intelligent soft switch planning strategy of each planning cycle according to the hybrid game dynamic programming model.

[0179] In one embodiment, the construction unit is specifically used to construct a hybrid game dynamic programming model based on the master-slave game and asymmetric Nash bargaining theory, with the distribution network as the lower follower and the operator as the upper leader, according to the first profit model and the second profit model; the hybrid game dynamic programming model includes a lower follower model, an upper leader model and a cooperative game model.

[0180] In one embodiment, the determining unit includes:

[0181] The first solving subunit is used to solve the lower follower model to obtain the first investment planning strategy of the distribution network in each planning period.

[0182] The second solving sub-unit is used to solve the upper-level leader model based on the first investment planning strategy to obtain the second investment planning strategy of the operator in each planning cycle.

[0183] The third solving sub-unit is used to solve the cooperative game model based on the first investment planning strategy and the second investment planning strategy with the goal of maximizing benefits, so as to obtain the target intelligent soft switching planning strategy for each planning period.

[0184] In one embodiment, the first revenue objective function of the first revenue model is determined based on the operator's end-to-end transaction revenue, the first investment cost, the first operation and maintenance cost, the first civil engineering cost, the first interconnection line reconstruction cost, and the profit sharing cost;

[0185] The first constraint condition of the first benefit model includes: intelligent soft switch investment constraint and intelligent soft switch operation constraint in each planning period.

[0186] In one embodiment, the second profit objective function of the second profit model is determined based on the profit share of the distribution network, the second investment cost, the second operation and maintenance cost, the second civil engineering cost, the second tie line transformation cost, the power purchase cost, the end-to-end transaction cost, and the voltage deviation penalty cost;

[0187] The second constraint conditions of the second benefit model include: smart soft switch investment constraints and distribution network operation constraints within each planning period.

[0188] Each module in the intelligent soft switching planning device for a distribution network can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0189] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0190] Obtaining distribution network parameters for each planning period of the distribution network;

[0191] Generate a set of smart soft switch planning strategies based on distribution network parameters, candidate locations of each smart soft switch in the distribution network, and unit capacity costs of each planning period;

[0192] Based on the distribution network parameters and the intelligent soft switch planning strategy set, the operator's first profit model and the distribution network's second profit model are constructed;

[0193] A target intelligent soft switch planning strategy for each planning period is determined according to the first profit model and the second profit model.

[0194] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0195] Based on the master-slave game and asymmetric Nash bargaining theory, a hybrid game dynamic programming model is constructed according to the first and second profit models;

[0196] The target intelligent soft switching planning strategy for each planning period is determined based on the hybrid game dynamic programming model.

[0197] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0198] Based on the master-slave game and asymmetric Nash bargaining theory, with the distribution network as the lower follower and the operator as the upper leader, a hybrid game dynamic programming model is constructed according to the first profit model and the second profit model; the hybrid game dynamic programming model includes a lower follower model, an upper leader model and a cooperative game model.

[0199] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0200] Solve the lower follower model to obtain the first investment planning strategy of the distribution network in each planning period;

[0201] Solve the upper-level leader model based on the first investment planning strategy to obtain the operator's second investment planning strategy in each planning cycle;

[0202] With the goal of maximizing revenue, a cooperative game model is solved based on the first investment planning strategy and the second investment planning strategy to obtain the target intelligent soft switching planning strategy for each planning period.

[0203] In one embodiment, the first revenue objective function of the first revenue model is determined based on the operator's end-to-end transaction revenue, the first investment cost, the first operation and maintenance cost, the first civil engineering cost, the first interconnection line reconstruction cost, and the profit sharing cost;

[0204] The first constraint condition of the first benefit model includes: intelligent soft switch investment constraint and intelligent soft switch operation constraint in each planning period.

[0205] In one embodiment, the second profit objective function of the second profit model is determined based on the profit share of the distribution network, the second investment cost, the second operation and maintenance cost, the second civil construction cost, the second tie line transformation cost, the power purchase cost, the end-to-end transaction cost, and the voltage deviation penalty cost;

[0206] The second constraint conditions of the second benefit model include: smart soft switch investment constraints and distribution network operation constraints within each planning period.

[0207] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0208] Obtaining distribution network parameters for each planning period of the distribution network;

[0209] Generate a set of smart soft switch planning strategies based on distribution network parameters, candidate locations of each smart soft switch in the distribution network, and unit capacity costs of each planning period;

[0210] Based on the distribution network parameters and the intelligent soft switch planning strategy set, the operator's first profit model and the distribution network's second profit model are constructed;

[0211] A target intelligent soft switch planning strategy for each planning period is determined according to the first profit model and the second profit model.

[0212] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0213] Based on the master-slave game and asymmetric Nash bargaining theory, a hybrid game dynamic programming model is constructed according to the first and second profit models;

[0214] The target intelligent soft switching planning strategy for each planning period is determined based on the hybrid game dynamic programming model.

[0215] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0216] Based on the master-slave game and asymmetric Nash bargaining theory, with the distribution network as the lower follower and the operator as the upper leader, a hybrid game dynamic programming model is constructed according to the first profit model and the second profit model; the hybrid game dynamic programming model includes a lower follower model, an upper leader model and a cooperative game model.

[0217] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0218] Solve the lower follower model to obtain the first investment planning strategy of the distribution network in each planning period;

[0219] Solve the upper-level leader model based on the first investment planning strategy to obtain the operator's second investment planning strategy in each planning cycle;

[0220] With the goal of maximizing revenue, a cooperative game model is solved based on the first investment planning strategy and the second investment planning strategy to obtain the target intelligent soft switching planning strategy for each planning period.

[0221] In one embodiment, the first revenue objective function of the first revenue model is determined based on the operator's end-to-end transaction revenue, the first investment cost, the first operation and maintenance cost, the first civil engineering cost, the first interconnection line reconstruction cost, and the profit sharing cost;

[0222] The first constraint condition of the first benefit model includes: intelligent soft switch investment constraint and intelligent soft switch operation constraint in each planning period.

[0223] In one embodiment, the second profit objective function of the second profit model is determined based on the profit share of the distribution network, the second investment cost, the second operation and maintenance cost, the second civil construction cost, the second tie line transformation cost, the power purchase cost, the end-to-end transaction cost, and the voltage deviation penalty cost;

[0224] The second constraint conditions of the second benefit model include: smart soft switch investment constraints and distribution network operation constraints within each planning period.

[0225] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0226] Obtaining distribution network parameters for each planning period of the distribution network;

[0227] Generate a set of smart soft switch planning strategies based on distribution network parameters, candidate locations of each smart soft switch in the distribution network, and unit capacity costs of each planning period;

[0228] Based on the distribution network parameters and the intelligent soft switch planning strategy set, the operator's first profit model and the distribution network's second profit model are constructed;

[0229] A target intelligent soft switch planning strategy for each planning period is determined according to the first profit model and the second profit model.

[0230] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0231] Based on the master-slave game and asymmetric Nash bargaining theory, a hybrid game dynamic programming model is constructed according to the first and second profit models;

[0232] The target intelligent soft switching planning strategy for each planning period is determined based on the hybrid game dynamic programming model.

[0233] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0234] Based on the master-slave game and asymmetric Nash bargaining theory, with the distribution network as the lower follower and the operator as the upper leader, a hybrid game dynamic programming model is constructed according to the first profit model and the second profit model; the hybrid game dynamic programming model includes a lower follower model, an upper leader model and a cooperative game model.

[0235] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0236] Solve the lower follower model to obtain the first investment planning strategy of the distribution network in each planning period;

[0237] Solve the upper-level leader model based on the first investment planning strategy to obtain the operator's second investment planning strategy in each planning cycle;

[0238] With the goal of maximizing revenue, a cooperative game model is solved based on the first investment planning strategy and the second investment planning strategy to obtain the target intelligent soft switching planning strategy for each planning period.

[0239] In one embodiment, the first revenue objective function of the first revenue model is determined based on the operator's end-to-end transaction revenue, the first investment cost, the first operation and maintenance cost, the first civil engineering cost, the first interconnection line reconstruction cost, and the profit sharing cost;

[0240] The first constraint condition of the first benefit model includes: intelligent soft switch investment constraint and intelligent soft switch operation constraint in each planning period.

[0241] In one embodiment, the second profit objective function of the second profit model is determined based on the profit share of the distribution network, the second investment cost, the second operation and maintenance cost, the second civil construction cost, the second tie line transformation cost, the power purchase cost, the end-to-end transaction cost, and the voltage deviation penalty cost;

[0242] The second constraint conditions of the second benefit model include: smart soft switch investment constraints and distribution network operation constraints within each planning period.

[0243] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented 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 memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may 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 may include random access memory (RAM) or external cache memory, etc. By way of 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 the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.

[0244] The technical features of the above embodiments can 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 specification.

[0245] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for intelligent soft switching planning of a distribution network, characterized in that: The method comprises: Obtaining distribution network parameters for each planning period of the distribution network; generating a smart soft switch planning strategy set according to the distribution network parameters, the candidate locations of each smart soft switch in the distribution network, and the unit capacity cost of each planning period; Constructing a first revenue model for the operator and a second revenue model for the distribution network based on the distribution network parameters and the intelligent soft switch planning strategy set; the first revenue model is used to represent the investment revenue of the operator, and the second revenue model is used to represent the investment revenue of the distribution network; A target intelligent soft switch planning strategy for each planning period is determined according to the first profit model and the second profit model.

2. The method according to claim 1, characterized in that Determining the target intelligent soft switch planning strategy for each planning period according to the first profit model and the second profit model includes: Based on the master-slave game and asymmetric Nash bargaining theory, a hybrid game dynamic programming model is constructed according to the first profit model and the second profit model; the hybrid game dynamic programming model is used to characterize the collaborative cooperation mechanism between the operator and the distribution network; The target intelligent soft switch planning strategy of each planning period is determined according to the hybrid game dynamic programming model.

3. The method according to claim 2, characterized in that The hybrid game dynamic programming model is constructed based on the master-slave game and asymmetric Nash bargaining theory according to the first profit model and the second profit model, including: Based on the master-slave game and asymmetric Nash bargaining theory, with the distribution network as the lower-level follower and the operator as the upper-level leader, a hybrid game dynamic programming model is constructed according to the first profit model and the second profit model; the hybrid game dynamic programming model includes a lower-level follower model, an upper-level leader model and a cooperative game model; the lower-level follower model is used to characterize the response behavior of the distribution network as a lower-level follower, the upper-level leader model is used to characterize the dominant decision-making behavior of the operator as an upper-level leader, and the cooperative game model is used to characterize the cooperation mechanism between the distribution network and the operator.

4. The method according to claim 3, characterized in that Determining the target intelligent soft switch planning strategy for each planning period according to the hybrid game dynamic programming model includes: Solving the lower follower model to obtain a first investment planning strategy for the distribution network in each planning period; Solving the upper-level leader model based on the first investment planning strategy to obtain a second investment planning strategy of the operator in each planning cycle; With the goal of maximizing revenue, the cooperative game model is solved based on the first investment planning strategy and the second investment planning strategy to obtain a target intelligent soft switching planning strategy for each planning period.

5. The method according to any one of claims 1 to 4, characterized in that The first revenue objective function of the first revenue model is determined based on the operator's end-to-end transaction revenue, first investment cost, first operation and maintenance cost, first civil engineering cost, first interconnection line reconstruction cost, and profit sharing cost; The first constraint condition of the first revenue model includes: an intelligent soft switch investment constraint and an intelligent soft switch operation constraint within each planning period.

6. The method according to any one of claims 1 to 4, characterized in that The second profit objective function of the second profit model is determined based on the profit share of the distribution network, the second investment cost, the second operation and maintenance cost, the second civil construction cost, the second tie line transformation cost, the power purchase cost, the end-to-end transaction cost and the voltage deviation penalty cost; The second constraint conditions of the second profit model include: smart soft switch investment constraints and distribution network operation constraints within each planning period.

7. An intelligent soft switch planning device for a distribution network, characterized in that: The device comprises: An acquisition module is used to obtain distribution network parameters of each planning period of the distribution network; A generating module, configured to generate a smart soft switch planning strategy set according to the distribution network parameters, the candidate locations of each smart soft switch in the distribution network, and the unit capacity cost of each planning period; A construction module, configured to construct a first revenue model for the operator and a second revenue model for the distribution network based on the distribution network parameters and the intelligent soft switch planning strategy set; A determination module is configured to determine a target intelligent soft switch planning strategy for each planning period according to the first profit model and the second profit model.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images