Elastic power distribution network planning method considering active support strategy of new factor and new business state

Abstract

The invention relates to an elastic power distribution network planning method considering a new element new industry state active support strategy, and the method comprises the steps: carrying out the layering of a power distribution network, constructing a multi-level elastic index, and combining a typhoon disaster time-space probability model, and quantifying the multi-stage dynamic influence of a typhoon disaster on the fault probability of a power distribution network line; formulating a new element new industry state multi-stage active support strategy of the power distribution network; an elastic power distribution network planning model is constructed, the elastic power distribution network planning model is a planning-operation double-layer model, and an upper-layer planning model takes maximization of elastic indexes of all levels and minimization of power distribution network planning cost as optimization targets; the lower-layer planning model performs operation optimization of each stage in a serial propulsion mode based on the new element new industry state multi-stage active support strategy; and solving the elastic power distribution network planning model to obtain an elastic power distribution network planning scheme. Compared with the prior art, the flexibility of the power distribution network can be effectively improved, and the loss of the power distribution network under typhoon disasters is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network planning, and in particular to a flexible distribution network planning method that takes into account active support strategies for new elements and new business formats. Background Art

[0002] At present, in order to achieve a safe and stable power supply and a clean and low-carbon transformation, the distribution network needs to be further upgraded to create a new distribution system that is safe, efficient, clean, low-carbon, flexible, and intelligently integrated. In recent years, the rapid development of new elements and new business formats such as distributed power sources, electric vehicles, new energy storage, and microgrids has put forward higher requirements for the planning and construction of distribution networks. At the same time, extreme disasters such as typhoons have occurred frequently, seriously threatening the safety and stability of distribution networks. For example, the invention with publication number CN115062833A discloses a mobile energy storage planning method that considers the reliability and resilience of distribution networks under extreme weather conditions. By simulating the spatiotemporal characteristics of extreme weather and constructing an evaluation index system, it optimizes the mobile energy storage configuration of the distribution network, solving the problem of improving the reliability and resilience of the distribution network under extreme weather conditions.

[0003] However, existing planning methods fail to fully consider the proactive support role of new elements and new business models in typhoon disasters, making it difficult to effectively reduce the impact of typhoon disasters on distribution networks. Therefore, it is urgent to propose a distribution network resilience planning method that adapts to typhoon disasters and improves distribution network resilience while taking into account planning costs. Summary of the Invention

[0004] The purpose of the present invention is to overcome the defects of the above-mentioned existing technologies and provide a flexible distribution network planning method that takes into account the active support strategy of new elements and new business formats, effectively improve the flexibility of the distribution network, and reduce the distribution network losses under typhoon disasters.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] A flexible distribution network planning method considering active support strategies for new elements and new business formats includes the following steps:

[0007] The distribution network is layered and multi-level resilience indicators are constructed. Combined with a pre-built spatiotemporal probability model of typhoon disasters, the multi-stage dynamic impact of typhoon disasters on the probability of distribution network line failures is quantified.

[0008] Formulate a multi-stage active support strategy for new elements and new business forms in the distribution network. This strategy uses different strategies for defense and recovery of the distribution network according to different stages of typhoon disasters. The strategy takes into account new elements and new business forms, including distributed power sources, energy storage, electric vehicles, and microgrids.

[0009] Construct a flexible distribution network planning model. This model is a two-layer planning-operation model, including an upper-level planning model and a lower-level planning model. The upper-level planning model optimizes the flexibility indicators of each level and minimizes the planning cost of the distribution network. The lower-level planning model optimizes the operation in a serial manner based on a multi-stage active support strategy for new elements and new business formats.

[0010] The flexible distribution network planning model is solved to obtain a flexible distribution network planning solution.

[0011] Furthermore, the stratification of the distribution network is specifically as follows:

[0012] The distribution network is divided into a module layer, a unit layer and a grid layer. The module layer is a distribution network layer including several lines and switches, the unit layer is a distribution network layer including multiple feeders and substations, and the grid layer is a distribution network layer including several unit layers.

[0013] The multi-level elasticity indicators include:

[0014] The weighted average failure probability of the line is used as the core resilience indicator of the module layer;

[0015] Emergency resource adequacy, used as the core resilience indicator at the unit level;

[0016] Elastic performance surface, used as the core elasticity indicator of the grid layer.

[0017] Furthermore, the calculation expression of the weighted average failure probability of the line is:

[0018]

[0019] Where A1 is the weighted average failure probability of the line, k is a constant term, X is the total number of lines in the distribution network, α x is the number of edges passing through line x in the shortest path between any two nodes in the network, N is the number of nodes, and λ line,x is the failure rate of line x;

[0020] The calculation expression of the emergency resource adequacy is:

[0021]

[0022] Where A2 is the emergency resource adequacy, N UNI.G is the number of nodes in the distribution network unit layer, is the emergency power supply capacity of node i in the distribution network unit layer, is the power capacity of the distribution network node j;

[0023] The calculation expression of the elastic efficiency surface is:

[0024]

[0025] Where A3 is the elastic efficiency surface, t f is the time when the typhoon disaster ends, F0(t) is the system fault-free state curve, and F1(t) is the system fault state curve.

[0026] Furthermore, the processing process of the typhoon disaster spatiotemporal probability model is specifically as follows:

[0027] Generate a typhoon path set based on historical typhoon data and extract the spatial distribution of typhoon wind speed field;

[0028] Establish a mapping relationship between wind speed and line fault probability and define a line fault probability function;

[0029] The typhoon passage process is divided into three periods: landing period, duration period, and subsidence period. The line failure probability dynamic change curve in each period is calculated based on the line failure probability function.

[0030] Furthermore, the line failure probability function is expressed as:

[0031]

[0032] Where, P failure,ij (t) is the failure probability of line ij in period t, k is the fitting coefficient, v ij (t) is the wind speed at the line during the typhoon passing period, v threshold is the wind resistance strength threshold of the line tower;

[0033] The calculation expression of the dynamic change curve of line fault probability in each time period is:

[0034]

[0035] Where, is the dynamic change curve of the total line failure probability of line ij, T land is the landing period, T pers is the duration period, T reces is the recession period, and ω(t) is the disaster impact weight of period t.

[0036] Furthermore, the multi-stage active support strategy for new elements and new business formats includes:

[0037] In the pre-disaster prevention phase, corresponding to the aforementioned landfall period, the impact of the disaster is limited to a local area through active islanding of the microgrid;

[0038] During the disaster resistance phase, corresponding to the duration, dynamic adjustment of the microgrid island range and energy storage scheduling are used to ensure critical loads on the distribution network.

[0039] During the post-disaster recovery phase, corresponding to the aforementioned subsidence period, peak load shaving and valley filling can be achieved through the coordinated regulation of distributed power sources and energy storage, as well as the orderly charging and discharging of electric vehicles, thereby reducing the pressure on the distribution network to ensure supply, while ensuring the system recovery process.

[0040] Furthermore, the decision variables of the upper-level planning model include the construction of new lines, new interconnection lines, line reinforcement, and the location, capacity, and number of substations, distributed power sources, energy storage, and electric vehicle charging stations.

[0041] The lower-level planning model is divided into a pre-disaster prevention stage, a disaster resistance stage, and a post-disaster recovery stage;

[0042] In the pre-disaster prevention phase, the goal is to minimize the total operation and maintenance cost, and the decision variables include the line switch status under the microgrid island division;

[0043] In the disaster resistance phase, the goal is to minimize the operation and maintenance and load shedding penalty costs. The decision variables include the line switch status under island range adjustment and the energy storage charging and discharging power.

[0044] In the post-disaster recovery phase, the goal is to minimize the operation and maintenance and electric vehicle control costs. The decision variables include distributed power generation, energy storage output and electric vehicle scheduling.

[0045] Furthermore, the objective function expression for maximizing the elasticity index of each level in the upper-level planning model is:

[0046] F=max(w MOD F MOD +w UNI F UNI +w GRI F GRI )

[0047] Where F is the objective function for maximizing the elasticity index of each level, max is the maximum value, and F MOD is the module level indicator, F UNI is the unit level index, F GRI is the grid layer index, w MOD 、w UNI and w GRI are the weights of the core elasticity index of the distribution network module layer, the core elasticity index of the unit layer, and the core elasticity index of the grid layer;

[0048] The objective function expression for minimizing the distribution network planning cost is:

[0049]

[0050] Where C I is the objective function for minimizing the cost of distribution network planning, Ω sub ,Ω se ,Ω line ,Ω TL ,Ω B They are new substation construction, substation expansion, new line construction, new interconnection line construction, and line reinforcement; Ω PVG ,Ω WTG ,Ω EV ,Ω ESS are the candidate installation node sets for PVG, WTG, EV and ESS respectively; c sub 、c se 、c PVG 、c WTG 、c EV 、c ESS and They are the unit capacity investment costs of new substation construction, substation expansion, PVG, WTG, EV, ESS, and the cost of reinforcement measures for a single line (i, j); is the investment cost per unit length of the M-type line; represents the investment decision variables for substation construction, substation expansion, line (i, j) construction, interconnection line (i, j) construction, and line (i, j) reinforcement, all of which are 0-1 variables; are the decision variables for the construction quantity of PVG, WTG, EV and ESS respectively; P i PVG 、P i WTG 、P i EV 、P i ESS are the installed capacities of PVG, WTG, EV, and ESS at node i; l ij represents the length of line (i, j); b is the discount rate; y is the useful life of the equipment;

[0051] The constraints of the upper-level planning model include the state constraints of substation construction and expansion, the state constraints of radial network line construction, the DG installation capacity constraints, the EV charging facility CS installation power and capacity constraints, and the ESS installation power and capacity constraints.

[0052] Furthermore, the objective function of the pre-disaster prevention stage is:

[0053]

[0054] Where f1 is the objective function of the pre-disaster prevention stage, Time-of-use electricity price for the upper power grid; are the unit cost coefficients of DG and ESS respectively; Purchase power from higher levels for the distribution network; is the charge and discharge power of ESS, Ω b is the set of distribution network nodes, Ω DG The set of nodes that can be installed as DG alternatively, Ω ESS A set of candidate installation nodes for ESS. and are the installed capacities of PVG and WTG at node i at time t, is the amount of ESS investment at node i;

[0055] The objective function of the disaster resistance stage is:

[0056]

[0057] Where f2 is the objective function of the disaster resistance stage, S′ is the set of disaster scenarios s′; Ns is the number of disaster scenarios; Z s′ is the number of days the disaster scenario s′ lasts; ρ s′ is the probability of occurrence of disaster scenario s′; ns is the number of hours that disaster scenario s′ lasts per day; Q(y′,z s′ ) is the minimum load shedding cost after implementing response and recovery measures under disaster scenario s′, T2 is the system load reduction operation time, T3 is the end time of the typhoon disaster, is the unit cost coefficient of ESS, is the installed capacity of ESS at node i;

[0058] The objective function of the post-disaster recovery phase is:

[0059]

[0060] Where f3 is the objective function of the post-disaster recovery phase, T5 is the time when the distribution network returns to normal state, The dispatch fee for each electric vehicle; is the dispatch volume of electric vehicles; is the unit cost coefficient of DG; It is the dispatch output of distributed power generation;

[0061] The constraints of the lower-level planning model include operation constraints and topological constraints;

[0062] The operation constraints include system flow constraints, node voltage constraints, line flow constraints, substation output constraints, distributed power supply operation and regulation constraints, energy storage operation constraints, electric vehicle operation scheduling constraints and load abandonment constraints established by the DistFlow branch flow method;

[0063] The topology constraints include line state constraints, root node constraints, fault line endpoint constraints, and radial and connectivity constraints.

[0064] Furthermore, the solution method of the flexible distribution network planning model is:

[0065] The non-dominated sorting genetic algorithm and mixed integer second-order cone programming are used to solve the elastic distribution network planning model, specifically including:

[0066] For the upper-level planning model, a non-dominated sorting genetic algorithm is used to solve it. The solution process includes: initializing a set of planning schemes and evaluating each scheme based on planning cost and elasticity level; then, through non-dominated sorting, crossover and mutation operations, new schemes are generated and the optimal solution is selected; finally, the optimal solution is fed back to the lower-level planning model for further optimization until the upper-level planning model converges and forms a Pareto solution set of planning cost and elasticity level.

[0067] For the lower-level planning model, a mixed-integer second-order cone programming is used for solution. The solution process includes: first, the original problem is gradually transformed into a mixed-integer second-order cone programming problem through second-order cone relaxation and the big-M method; then, the mixed-integer second-order cone programming problem is solved based on the branch and bound method built into Gurobi, gradually approaching the global optimal solution; finally, the calculated operating cost and elasticity index are fed back to the upper-level planning model to evaluate the advantages and disadvantages of different planning schemes and provide a basis for the non-dominated sorting genetic algorithm optimization of the upper-level planning model.

[0068] Compared with the prior art, the present invention has the following advantages:

[0069] (1) The multi-level distribution network resilience index established by the present invention can evaluate the resilience of the distribution network at multiple levels, including the module level, unit level, and grid level, accurately characterizing the adaptability of different levels to typhoon disasters, and providing a scientific basis for optimizing distribution network planning and formulating efficient emergency recovery strategies;

[0070] The multi-stage active support strategy for new elements and new business formats can not only improve the distribution network's adaptability to typhoon disasters, but also tap the active support capabilities of multiple types of resources to reduce the economic losses of the distribution network;

[0071] Through the planning-operation two-layer model, the multi-level elasticity indicators of the distribution network and the multi-stage active support strategy of new elements and new business formats are integrated to comprehensively solve the elastic distribution network planning scheme. The multi-stage support role of new elements and new business formats under typhoon disasters is taken into account, which can effectively improve the elasticity of the distribution network and reduce the distribution network losses under typhoon disasters.

[0072] (2) The spatiotemporal probability model of typhoon disasters constructed by the present invention fits the time-varying failure probability of distribution network lines under typhoon disasters based on the changes in typhoon wind speed, and quantifies the multi-stage dynamic impact of typhoon disasters on distribution network lines.

[0073] (3) The present invention effectively reduces the computational complexity in the multi-objective optimization solution process, making the planning scheme more feasible and practical, while providing a series of planning scheme sets for decision makers to choose from to balance economic and flexibility goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 A flow chart of a flexible distribution network planning method that considers active support strategies for new elements and new business formats, provided in an embodiment of the present invention;

[0075] Figure 2 A state curve diagram of a distribution network during an extreme disaster provided in an embodiment of the present invention;

[0076] Figure 3 A schematic diagram of a Portugal 54-node system to be planned provided in an embodiment of the present invention;

[0077] Figure 4 This is a topology diagram of a Portugal 54-node new element and new business model to be planned, provided in an embodiment of the present invention;

[0078] Figure 5 This is a frontier graph of NSGA-IIPareto under solution 4 provided in an embodiment of the present invention;

[0079] Figure 6 The following are radar charts of indicators under the four solutions provided in the embodiments of the present invention. DETAILED DESCRIPTION

[0080] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0081] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort shall fall within the scope of protection of the present invention.

[0082] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0083] Example 1

[0084] like Figure 1 As shown, this embodiment provides a flexible distribution network planning method that considers active support strategies for new elements and new business formats, including the following steps:

[0085] S1: The distribution network is layered and multi-level resilience indicators are constructed. Combined with the pre-built spatiotemporal probability model of typhoon disasters, the multi-stage dynamic impact of typhoon disasters on the probability of distribution network line failures is quantified;

[0086] Specifically, multi-level elasticity indicators at the module, unit, and grid levels of the distribution network were established. Combined with the spatiotemporal probability model of typhoon disasters, the dynamic impact of typhoon disasters on the distribution network from the equipment to the system level was quantified.

[0087] S2: Develop a multi-stage active support strategy for new elements and new business forms in the distribution network. This strategy adopts different strategies for defense and recovery of the distribution network according to different stages of typhoon disasters. The strategy takes into account new elements and new business forms, including distributed power generation, energy storage, electric vehicles, and microgrids.

[0088] S3: Determine the objective function, constraints, and decision variables, and establish a flexible distribution network planning model that considers a multi-stage active support strategy for new elements and new business formats. This flexible distribution network planning model is a two-layer planning-operation model, including an upper-level planning model and a lower-level planning model. The upper-level planning model optimizes the flexibility indicators at each level and minimizes the planning cost of the distribution network. The lower-level planning model performs operational optimization in a serial manner based on the multi-stage active support strategy for new elements and new business formats.

[0089] The above-mentioned serial advancement method is to serially solve each stage in the multi-stage active support strategy for new elements and new business formats in turn to obtain the overall result of the lower-level planning model.

[0090] S4: Solve the elastic distribution network planning model to obtain the elastic distribution network planning scheme. Specifically, the elastic distribution network planning model proposed by non-dominated sorting genetic algorithm (NSGA-II) and mixed integer second-order cone programming is used to solve the proposed scheme.

[0091] Specifically, in step S1, the multi-level elasticity indicators of the distribution network module layer, unit layer, and grid layer are as follows:

[0092] First, the distribution network module layer refers to the distribution network level that includes several lines and switches. Its adaptability to typhoon disasters can be quantified by the line's survivability. Therefore, this paper selects the line weighted average failure probability as the core resilience indicator of the module layer, which can be expressed as:

[0093]

[0094] Then, the distribution network unit layer refers to the distribution network level that includes multiple feeders and substations, and is often equipped with distributed power supplies, energy storage, and other equipment. Its adaptability to typhoon disasters can be quantified by the support capacity of emergency resources. Therefore, this paper selects the emergency resource abundance as the core resilience indicator of the unit layer, which can be expressed as:

[0095]

[0096] Finally, the distribution network grid layer refers to the distribution network hierarchy consisting of several unit layers. Its adaptability to typhoon disasters can be quantified by its resilience performance at each disaster stage. Therefore, this paper selects the resilience performance surface as the core resilience indicator of the grid layer, which can be expressed as:

[0097]

[0098] In step S1, a spatiotemporal probability model of typhoon disasters is established to quantify the multi-period dynamic impact of typhoon paths and wind speeds on the probability of distribution network line failures. The steps are as follows:

[0099] First, a typhoon path set is generated based on historical typhoon data, and the spatial distribution of typhoon wind speed field is extracted;

[0100] Then, a mapping relationship between wind speed and line fault probability is established, and the line fault probability function is defined:

[0101]

[0102] Where: v ij (t) is the wind speed at the line during the typhoon passing period, v threshold is the wind resistance strength threshold of the line tower, k is the fitting coefficient;

[0103] Finally, the typhoon passage process is divided into three periods: landing period, duration period, and subsidence period, and the dynamic change curve of line failure probability in each period is calculated:

[0104]

[0105] Where: ω(t) is the disaster impact weight in time period t, which is determined by the typhoon intensity attenuation model.

[0106] In step S2, the multi-stage active support strategy for new elements and new business formats is as follows:

[0107] like Figure 2 As shown in Figure 2, the distribution network status curve under typhoon disasters can be divided into three stages according to the system defense and recovery process: pre-disaster prevention, disaster resistance, and post-disaster recovery.

[0108] Before a disaster, the microgrid's active islanding can limit the impact of the disaster to a local area, reducing the number of affected users.

[0109] During disasters, dynamic adjustment of the microgrid island range and flexible scheduling of energy storage can ensure the critical loads of the distribution network;

[0110] During the post-disaster recovery phase, peak load shaving and valley filling can be achieved through coordinated regulation of distributed power sources and energy storage, and orderly charging and discharging of electric vehicles, reducing the pressure on the distribution network to ensure supply while ensuring the system recovery process.

[0111] In step S3, the objective function of the upper-level planning model is as follows:

[0112] First, the objective function that describes the elasticity level is considered in the model at the module level, the unit level, and the grid level.

[0113] The resilience performance of the three distribution network layers is quantified. The module layer's adaptability to typhoon disasters can be quantified by the line weighted average failure probability index, the unit layer's adaptability to typhoon disasters can be quantified by the emergency resource sufficiency index, and the grid layer's adaptability to typhoon disasters can be quantified by the resilience performance surface index. The goal of the proposed model is to maximize the resilience performance of each layer of the distribution network against disasters. Therefore, the objective function of the proposed model is defined as:

[0114] F=max(w MOD F MOD +w UNI F UNI +w GRI F GRI ) (6)

[0115] Where: F is the objective function of the model proposed in this paper; w MOD 、w UNI and w GRI are the weights of distribution network module layer indicators, unit layer indicators, and grid layer indicators respectively.

[0116] The second is the objective function that describes the cost of distribution network planning:

[0117]

[0118] Where: Ω sub ,Ω se ,Ω line ,Ω TL ,ΩB They are new substation construction, substation expansion, new line construction, new interconnection line construction, and line reinforcement; Ω PVG ,Ω WTG ,Ω EV ,Ω ESS are the candidate installation node sets for PVG, WTG, EV and ESS respectively; c sub 、c se 、c PVG 、c WTG 、c EV 、c ESS and They are the unit capacity investment costs of new substation construction, substation expansion, PVG, WTG, EV, ESS, and the cost of reinforcement measures for a single line (i, j); is the investment cost per unit length of the M-type line; represents the investment decision variables for substation construction, substation expansion, line (i, j) construction, interconnection line (i, j) construction, and line (i, j) reinforcement, all of which are 0-1 variables; are the decision variables for the construction quantity of PVG, WTG, EV and ESS respectively; P i PVG 、P i WTG 、P i EV 、P i ESS are the installed capacities of PVG, WTG, EV, and ESS at node i; l ij represents the length of line (i, j); b is the discount rate; y is the useful life of the equipment.

[0119] Furthermore, the constraints of the planning model specifically include the status constraints of substation construction and expansion, the status constraints of radial network line construction, DG installation capacity constraints, EV charging facility CS installation power and capacity constraints, and ESS installation power and capacity constraints.

[0120] The state constraints for substation construction and expansion are:

[0121]

[0122] The construction status constraints of the radial network lines are:

[0123]

[0124] The DG installation capacity constraint is:

[0125]

[0126] The CS installation power and capacity constraints of EV charging facilities are:

[0127]

[0128] The ESS installation power and capacity constraints are:

[0129]

[0130] Aiming at the fault state scenarios in the underlying operation model, a multi-stage active support strategy for new elements and new business forms is formulated. The objective function corresponding to each stage is as follows:

[0131] (1) Pre-disaster prevention stage

[0132] The goal of the pre-disaster prevention phase is to minimize the total operation and maintenance cost, and the decision variable is the line switch status under the microgrid island division. The objective function of the pre-disaster prevention phase is:

[0133]

[0134] Where: Time-of-use electricity price for the upper power grid; are the unit cost coefficients of DG and ESS respectively; Purchase power from higher levels for the distribution network; is the charging and discharging power of ESS.

[0135] (2) Disaster resistance stage

[0136] The goal of the disaster resistance phase is to minimize the operation and maintenance and load shedding penalty costs. The decision variables include the line switch status under island range adjustment, energy storage charging and discharging power, etc. The objective function of the disaster resistance phase is:

[0137]

[0138] Where: S′ is the set of disaster scenarios s′; Ns is the number of disaster scenarios; Z s′ is the number of days the disaster scenario s′ lasts; ρ s′ is the probability of occurrence of disaster scenario s′; ns is the number of hours that disaster scenario s′ lasts per day; Q(y′,z s′ ) is the minimum load shedding cost after implementing response and recovery measures under disaster scenario s′.

[0139] (3) Post-disaster recovery phase

[0140] The goal of the post-disaster recovery phase is to minimize the operation and maintenance and electric vehicle control costs. The decision variables include distributed power generation, energy storage output, and electric vehicle scheduling. The objective function of the post-disaster recovery phase is:

[0141]

[0142] Where: The dispatch fee for each electric vehicle; is the dispatch volume of electric vehicles; It is the dispatching output of distributed power sources.

[0143] The constraints in the lower-level operation problem mainly include operation constraints and topological constraints.

[0144] Operational constraints include system power flow constraints established by the DistFlow branch power flow method, node voltage constraints, line power flow constraints, substation output constraints, distributed power generation operation and regulation constraints, energy storage operation constraints, electric vehicle operation scheduling constraints, and load abandonment constraints, which can be represented as follows:

[0145] The DistFlow system power flow constraints are:

[0146]

[0147]

[0148] The node voltage constraints are:

[0149]

[0150] The branch current constraint is:

[0151]

[0152] The line power flow constraint is:

[0153]

[0154] The substation output constraint is:

[0155]

[0156] The distributed generation operation and regulation constraints for the pre-disaster and post-disaster stages are:

[0157]

[0158] The energy storage operation constraints for the pre-disaster, disaster, and post-disaster stages are:

[0159] 1) Energy storage battery charging and discharging status and power constraints:

[0160]

[0161] 2) Constraints on remaining capacity of energy storage batteries:

[0162]

[0163] The operation and dispatch constraints of electric vehicles in the post-disaster period are:

[0164] 1) The first charging mode (not participating in distribution network regulation):

[0165]

[0166] 2) The second charging mode (only participating in peak shaving of the distribution network):

[0167]

[0168] 3) The third charging mode (participating in peak load shaving and valley filling of distribution network):

[0169]

[0170] P EV =P t EV,1 +P t EV,2 +P t EV,3 (33)

[0171] The load shedding constraint during the disaster period is:

[0172]

[0173] Topological constraints specifically include line state constraints, root node constraints, fault line endpoint constraints, and radial and connectivity constraints, which can be represented as follows:

[0174] The line state constraints are:

[0175]

[0176] The root node constraints under the island constraint are:

[0177]

[0178] The fault line endpoint constraints are:

[0179]

[0180] The radial and connectivity constraints are:

[0181]

[0182] Furthermore, in step S4, the upper-level planning problem is solved using a non-dominated sorting genetic algorithm (NSGA-II). The specific steps are as follows: first, a set of planning alternatives is initialized and each is evaluated based on the planning cost and elasticity level; then, through non-dominated sorting, crossover, and mutation operations, new alternatives are generated and the optimal solution is selected; finally, the optimal solution is fed back to the lower level for further optimization until the model converges and a Pareto solution set of planning cost and elasticity level is formed.

[0183] The lower-level operation problem is solved using a mixed-integer second-order cone programming (MISOCP). The specific steps are as follows: First, the original problem is gradually transformed into a mixed-integer second-order cone programming problem through second-order cone relaxation and the large-M method. Then, the problem is solved using Gurobi's built-in branch-and-bound method, gradually approaching the global optimal solution. Finally, the calculated operation cost and elasticity index are fed back to the upper level to evaluate the advantages and disadvantages of different planning solutions and provide a basis for optimizing the upper-level NSGA-II algorithm.

[0184] Preferably, the above method further comprises converting each constraint condition into a second-order cone constraint.

[0185] Furthermore, each constraint condition is converted into a second-order cone constraint. Specifically, the power flow relaxation method is used to replace the network loss with the quadratic terms in each electrical constraint condition according to the network structure constraint. The replacement process is as follows:

[0186]

[0187] Where: u i,t 、l ij,t is the introduced intermediate variable.

[0188] Furthermore, after second-order cone relaxation and large-M method transformation, we can obtain:

[0189] The DistFlow system power flow constraint can be converted into the following second-order cone form:

[0190]

[0191] The voltage and current constraints are converted into:

[0192]

[0193] The energy storage constraint is converted into:

[0194]

[0195] After the relevant transformations, the feasible domain of the original problem is relaxed to a convex second-order cone feasible domain. Considering that some variables in the model are 0-1, the original problem is converted into a mixed-integer second-order cone programming problem. This problem can then be solved using Gurobi's built-in branch-and-bound method, gradually approaching its global optimal solution. Ultimately, the calculated operating cost and elasticity indicators are fed back to the upper layer to evaluate the pros and cons of different planning schemes and provide a basis for optimizing the upper-layer NSGA-II algorithm.

[0196] An optional implementation manner of the present invention is described in detail below.

[0197] As an optional embodiment, in order to verify the rationality of the distribution network elasticity planning method proposed in the present invention that considers the multi-stage active support strategy for new elements and new business formats, the following example is set:

[0198] The improved Portugal 54-node distribution network example is used for verification. The total load is 81.73MW. The network topology to be planned is as follows: Figure 3 The nodes to be planned and the upper limit of installation capacity of each new element and new business form of the distribution network are shown in Table 1. The network topology diagram containing the nodes to be planned of the new elements and new business forms is shown in Figure 4 As shown in Figure 2, considering the impact of wind power, photovoltaic power, energy storage and electric vehicle charging stations on the distribution network planning scheme, four planning scenarios are set, namely:

[0199] Case 1: Considering the site selection and sizing of distributed generation without considering line reinforcement measures;

[0200] Case 2: Considering the site selection and sizing of distributed power generation, charging stations, and line reinforcement measures simultaneously;

[0201] Case 3: Considering the site selection and sizing of distributed generation, energy storage, and line reinforcement measures simultaneously;

[0202] Case 4: Consider the site selection and sizing of distributed power sources, charging stations, energy storage, and line reinforcement measures simultaneously.

[0203] Table 1 Nodes to be planned and upper limit of installation capacity

[0204]

[0205]

[0206] Figure 5 The NSGA-IIPareto frontier under Scheme 4 is the planned operating cost of the determined scheme, which is 37.8×10 6This solution not only ensures a high level of flexibility but also avoids excessive costs. Compared with the lower-cost solution, this solution has a better level of flexibility and can effectively improve the adaptability of the distribution network to typhoon disasters. In addition, based on this solution, further increasing the cost (such as 38.2×10 6 The elasticity improvement brought by 37.8×10 6 Yuan's solution achieves a reasonable balance between cost and flexibility, and has better overall benefits.

[0207] Table 2 shows the results of distribution network resilience planning. As can be seen, Scheme 4 has the lowest overall cost, at only 37.80×106 yuan, a 15.7% reduction compared to Scheme 1, which does not consider reinforcement. Furthermore, the load loss penalty cost decreases from 3.16×106 yuan to 0.32×106 yuan, demonstrating that combining reinforcement measures with the support capabilities of new elements significantly improves the resilience and economic efficiency of the distribution network. Furthermore, the investment cost of distributed generation (DGs) decreases with scheme optimization, particularly with photovoltaic investment decreasing from 5.83×106 yuan to 4.40×106 yuan. This demonstrates that the introduction of energy storage and charging stations helps optimize local power consumption and improves resource utilization efficiency. Furthermore, the introduction of electric vehicles and energy storage significantly reduces total operating costs. Furthermore, Schemes 2 and 3 have similar operating costs. While Scheme 4 slightly increases investment costs, it reduces operating costs to only 26.03×106 yuan, demonstrating that both schemes offer comparable results in balancing supply and demand and enhancing distribution network resilience. Overall, the multi-stage active support strategy can effectively reduce the economic losses caused by typhoon disasters and improve the flexibility of the distribution network while taking into account planning costs.

[0208] Table 2 Elastic planning results

[0209] <![CDATA[Annual cost (10 6 yuan)]]> Solution 1 Option 2 Option 3 Option 4 Annual comprehensive expenses 44.82 40.56 40.30 37.80 PVG investment fees 5.83 5.35 4.80 4.40 WTG investment fees 5.37 5.42 5.42 5.38 EV investment costs 0 0 0.49 0.60 ESS investment fees 0 0.172 0 0.19 Total operating costs 32.85 28.30 28.25 26.03 Load loss penalty cost 3.16 0.49 0.40 0.32

[0210] Figure 6 This is a radar chart of elasticity indicators under four schemes. The selected module-level indicator is the line anti-destruction capability indicator, the unit-level indicator is the emergency resource abundance indicator, and the grid-level indicator is the elasticity performance surface indicator. In addition, analysis of voltage offset, annual comprehensive cost and load loss cost is added.

[0211] Among them, in terms of the four indicators of annual comprehensive expenses, load loss costs, elastic efficiency surface, and emergency resource abundance, Plan 4 is better than Plans 1, 2, and 3, indicating that comprehensive consideration of new factors and new business models can improve the elasticity of the distribution network while taking into account planning costs.

[0212] The above describes in detail the preferred embodiments of the present invention. It should be understood that those skilled in the art can make numerous modifications and variations based on the concepts of the present invention without inventive effort. Therefore, any technical solutions that can be derived by those skilled in the art through logical analysis, reasoning, or limited experimentation based on the concepts of the present invention and the prior art should be within the scope of protection defined by the claims.

Claims

1. A flexible distribution network planning method that considers active support strategies for new elements and new business formats, characterized by: The following steps are involved: The distribution network is layered and multi-level resilience indicators are constructed. Combined with a pre-built spatiotemporal probability model of typhoon disasters, the multi-stage dynamic impact of typhoon disasters on the probability of distribution network line failures is quantified. Formulate a multi-stage active support strategy for new elements and new business forms in the distribution network. This strategy uses different strategies for defense and recovery of the distribution network according to different stages of typhoon disasters. The strategy takes into account new elements and new business forms, including distributed power sources, energy storage, electric vehicles, and microgrids. Constructing a flexible distribution network planning model, which is a two-layer planning-operation model, including an upper-layer planning model and a lower-layer planning model. The upper-layer planning model optimizes the flexibility indicators of each layer and minimizes the distribution network planning cost. The lower-level planning model optimizes operations in a serial advancement manner based on a multi-stage active support strategy for new elements and new business formats; The flexible distribution network planning model is solved to obtain a flexible distribution network planning solution.

2. A flexible distribution network planning method considering active support strategies for new elements and new business formats according to claim 1, characterized in that: The distribution network is specifically layered as follows: The distribution network is divided into a module layer, a unit layer and a grid layer. The module layer is a distribution network layer including several lines and switches, the unit layer is a distribution network layer including multiple feeders and substations, and the grid layer is a distribution network layer including several unit layers. The multi-level elasticity indicators include: The weighted average failure probability of the line is used as the core resilience indicator of the module layer; Emergency resource adequacy, used as the core resilience indicator at the unit level; Elastic performance surface, used as the core elasticity indicator of the grid layer.

3. A flexible distribution network planning method considering active support strategies for new elements and new business formats according to claim 2, characterized in that: The calculation expression of the weighted average failure probability of the line is: Where A1 is the weighted average failure probability of the line, k is a constant term, X is the total number of lines in the distribution network, α x is the number of edges passing through line x in the shortest path between any two nodes in the network, N is the number of nodes, and λ line,x is the failure rate of line x; The calculation expression of the emergency resource adequacy is: Where A2 is the emergency resource adequacy, N UNI.G is the number of nodes in the distribution network unit layer, is the emergency power supply capacity of node i in the distribution network unit layer, is the power capacity of node j in the distribution network; The calculation expression of the elastic efficiency surface is: Where A3 is the elastic efficiency surface, t f is the time when the typhoon disaster ends, F0(t) is the system fault-free state curve, and F1(t) is the system fault state curve.

4. The flexible distribution network planning method considering the active support strategy of new elements and new business formats according to claim 1 is characterized in that: The processing process of the typhoon disaster spatiotemporal probability model is specifically as follows: Generate a typhoon path set based on historical typhoon data and extract the spatial distribution of typhoon wind speed field; Establish a mapping relationship between wind speed and line fault probability and define a line fault probability function; The typhoon passage process is divided into three periods: landing period, duration period, and subsidence period. The line failure probability dynamic change curve in each period is calculated based on the line failure probability function.

5. A flexible distribution network planning method considering active support strategies for new elements and new business formats according to claim 4, characterized in that: The expression of the line fault probability function is: Where, P failure,ij (t) is the failure probability of line ij in period t, k is the fitting coefficient, v ij (t) is the wind speed at the line during the typhoon passing period, v threshold is the wind resistance strength threshold of the line tower; The calculation expression of the dynamic change curve of line fault probability in each time period is: Where, is the dynamic change curve of the total line failure probability of line ij, T land is the landing period, T pers is the duration period, T reces is the recession period, and ω(t) is the disaster impact weight of period t.

6. A flexible distribution network planning method considering active support strategies for new elements and new business formats according to claim 4, characterized in that: The multi-stage active support strategy for new elements and new business formats includes: In the pre-disaster prevention phase, corresponding to the aforementioned landfall period, the impact of the disaster is limited to a local area through active islanding of the microgrid; During the disaster resistance phase, corresponding to the duration, dynamic adjustment of the microgrid island range and energy storage scheduling are used to ensure critical loads on the distribution network. During the post-disaster recovery phase, corresponding to the aforementioned subsidence period, peak load shaving and valley filling can be achieved through the coordinated regulation of distributed power sources and energy storage, as well as the orderly charging and discharging of electric vehicles, thereby reducing the pressure on the distribution network to ensure supply, while ensuring the system recovery process.

7. A flexible distribution network planning method considering active support strategies for new elements and new business formats according to claim 6, characterized in that: The decision variables of the upper-level planning model include the construction of new lines, the construction of new interconnection lines, line reinforcement, and the location, capacity, and number of substations, distributed power sources, energy storage, and electric vehicle charging stations. The lower-level planning model is divided into a pre-disaster prevention stage, a disaster resistance stage, and a post-disaster recovery stage; In the pre-disaster prevention phase, the goal is to minimize the total operation and maintenance cost, and the decision variables include the line switch status under the microgrid island division; In the disaster resistance phase, the goal is to minimize the operation and maintenance and load shedding penalty costs. The decision variables include the line switch status under island range adjustment and the energy storage charging and discharging power. In the post-disaster recovery phase, the goal is to minimize the operation and maintenance and electric vehicle control costs. The decision variables include distributed power generation, energy storage output and electric vehicle scheduling.

8. The flexible distribution network planning method considering the active support strategy of new elements and new business formats according to claim 2 is characterized in that: The objective function expression for maximizing the elasticity index of each level in the upper-level planning model is: F=max(w MOD F MOD +w UNI F UNI +w GRI F GRI ) Where F is the objective function for maximizing the elasticity index of each level, max is the maximum value, and F MOD is the module level indicator, F UNI is the unit level index, F GRI is the grid layer index, w MOD 、w UNI and w GRI are the weights of the core elasticity index of the distribution network module layer, the core elasticity index of the unit layer, and the core elasticity index of the grid layer; The objective function expression for minimizing the distribution network planning cost is: Where C I is the objective function for minimizing the cost of distribution network planning, Ω sub ,Ω se ,Ω line ,Ω TL ,Ω B They are new substation construction, substation expansion, new line construction, new interconnection line construction, and line reinforcement; Ω PVG ,Ω WTG ,Ω EV ,Ω ESS are the candidate installation node sets for PVG, WTG, EV and ESS respectively; c sub 、c se 、c PVG 、c WTG 、c EV 、c ESS and They are the unit capacity investment costs of new substation construction, substation expansion, PVG, WTG, EV, ESS, and the cost of reinforcement measures for a single line (i, j); is the investment cost per unit length of the M-type line; represents the investment decision variables for substation construction, substation expansion, line (i, j) construction, interconnection line (i, j) construction, and line (i, j) reinforcement, all of which are 0-1 variables; are the decision variables for the construction quantity of PVG, WTG, EV and ESS respectively; P i PVG 、P i WTG 、P i EV 、P i ESS are the installed capacities of PVG, WTG, EV, and ESS at node i; l ij represents the length of line (i, j); b is the discount rate; y is the useful life of the equipment; The constraints of the upper-level planning model include the state constraints of substation construction and expansion, the state constraints of radial network line construction, the DG installation capacity constraints, the EV charging facility CS installation power and capacity constraints, and the ESS installation power and capacity constraints.

9. The flexible distribution network planning method considering the active support strategy of new elements and new business formats according to claim 7 is characterized in that: The objective function of the pre-disaster prevention stage is: Where f1 is the objective function of the pre-disaster prevention stage, Time-of-use electricity price for the upper power grid; are the unit cost coefficients of DG and ESS respectively; Purchase power from higher levels for the distribution network; is the charge and discharge power of ESS, Ω b is the set of distribution network nodes, Ω DG The set of candidate installation nodes for DG, Ω ESS A set of candidate installation nodes for ESS. and are the installed capacities of PVG and WTG at node i at time t, is the amount of ESS investment at node i; The objective function of the disaster resistance stage is: Where f2 is the objective function of the disaster resistance stage, S′ is the set of disaster scenarios s′; Ns is the number of disaster scenarios; Z s′ is the number of days the disaster scenario s′ lasts; ρ s′ is the probability of occurrence of disaster scenario s′; ns is the number of hours that disaster scenario s′ lasts per day; Q(y′,z s′ ) is the minimum load shedding cost after implementing response and recovery measures under disaster scenario s′, T2 is the system load reduction operation time, T3 is the end time of the typhoon disaster, is the unit cost coefficient of ESS, is the installed capacity of ESS at node i; The objective function of the post-disaster recovery phase is: Where f3 is the objective function of the post-disaster recovery phase, T5 is the time when the distribution network returns to normal state, The dispatch fee for each electric vehicle; is the dispatch volume of electric vehicles; is the unit cost coefficient of DG; It is the dispatch output of distributed power generation; The constraints of the lower-level planning model include operation constraints and topological constraints; The operation constraints include system flow constraints, node voltage constraints, line flow constraints, substation output constraints, distributed power supply operation and regulation constraints, energy storage operation constraints, electric vehicle operation scheduling constraints and load abandonment constraints established by the DistFlow branch flow method; The topology constraints include line state constraints, root node constraints, fault line endpoint constraints, and radial and connectivity constraints.

10. The flexible distribution network planning method considering the active support strategy of new elements and new business formats according to claim 1 is characterized in that: The solution method of the flexible distribution network planning model is: The non-dominated sorting genetic algorithm and mixed integer second-order cone programming are used to solve the elastic distribution network planning model, specifically including: For the upper-level planning model, a non-dominated sorting genetic algorithm is used to solve it. The solution process includes: initializing a set of planning schemes and evaluating each scheme based on planning cost and elasticity level; then, through non-dominated sorting, crossover and mutation operations, new schemes are generated and the optimal solution is selected; finally, the optimal solution is fed back to the lower-level planning model for further optimization until the upper-level planning model converges and forms a Pareto solution set of planning cost and elasticity level. For the lower-level planning model, a mixed-integer second-order cone programming is used for solution. The solution process includes: first, the original problem is gradually transformed into a mixed-integer second-order cone programming problem through second-order cone relaxation and the big-M method; then, the mixed-integer second-order cone programming problem is solved based on the branch and bound method built into Gurobi, gradually approaching the global optimal solution; finally, the calculated operating cost and elasticity index are fed back to the upper-level planning model to evaluate the advantages and disadvantages of different planning schemes and provide a basis for the non-dominated sorting genetic algorithm optimization of the upper-level planning model.

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