Flexible power distribution network planning method based on wind-solar-storage combined output

Abstract

The invention belongs to the technical field of power distribution network planning, and particularly relates to an elastic power distribution network planning method based on wind-solar-storage combined output. Comprising the steps of S1, establishing a power distribution network fault rate model; s2, constructing and selecting a typical disaster scene; establishing and screening typical disaster scenes by combining a Monte Carlo simulation method and information entropy; s3, establishing an elastic power distribution network planning model; on the basis of a disaster scene, an elastic power distribution network planning configuration model is established, and a power distribution network planning model in the disaster scene is established in combination with wind power generation, photovoltaic power generation, a micro gas turbine, energy storage and other elements; s4, solving the planning model; solving the planning model by adopting an NSGA-II algorithm to obtain an optimal configuration result of the power distribution network; renewable energy and an energy storage technology are integrated, various energy sources are reasonably distributed, dependence on a traditional power source is reduced, meanwhile, the overall toughness and disaster resistance of a power grid are enhanced, and therefore the reliability and stability of the power grid are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network planning, and in particular relates to a flexible distribution network planning method based on the combined output of wind, solar and energy storage. Background Art

[0002] In recent years, the frequent occurrence of extreme disasters has had an extremely adverse impact on the safe and stable operation of power systems. Taking extreme typhoon disasters as an example, the fierce wind force often causes the collapse of towers and the breakage of distribution lines in traditional power systems, which in turn causes large-scale load blackouts, which seriously affects people's normal production and life order. In order to meet this challenge, the concept of elastic distribution network provides a new idea for the emergency response of power systems. As a flexible and intelligent power system form, the elastic distribution network can improve the system's disaster resistance and disaster response capabilities by integrating distributed energy (DG), energy storage technology and intelligent dispatching control.

[0003] At present, domestic and foreign scholars have conducted a lot of research on improving the resilience of distribution networks under extreme disasters. For example, the two-stage power supply restoration strategy for elastic distribution networks based on the coordination of multiple distributed resources under typhoon scenarios [J] uses the minimum diameter spanning tree method to achieve the optimal scheduling of various energy sources. The post-disaster application framework considering emergency power support and rapid load recovery is designed in the paper Coordinating multi-energy to improve urban integrated energy system resilience against extreme weather events [J], and verifies that the coordination of different types of distributed resources helps to achieve rapid recovery of the power grid. The paper Co-optimization approach to post-storm recovery for interdependent power and transportation systems [J] provides a post-disaster recovery scheduling method to help cities restore power and transportation systems faster. The paper Distribution network resilience improvement strategy considering the spatiotemporal characteristics of typhoons and the coordinated optimization of flexibility resources [J / OL] establishes a two-stage three-layer defense-attack-defense model that combines pre-disaster defense and post-disaster recovery to effectively improve the resilience of the distribution network. In contrast, in pre-disaster research, most scholars focus on how to strengthen the resistance of lines and towers to reduce the degree of system damage when disasters occur. Reference: Method for identifying weak links in overhead distribution lines in distribution networks based on typhoon scenario simulation [J]. The power automation equipment is rationally distributed to reinforce windproof guy wires and concrete poles before the disaster, effectively improving the grid's ability to resist disasters. Reference: Method for improving resilience through differentiated reinforcement planning of distribution networks taking into account the simulation of the entire typhoon disaster process [J]. Taking into account information such as line failure rate, distribution network structure and load importance, differentiated reinforcement is carried out on lines of different importance levels to achieve the effect of improving the grid's ability to resist disasters. Reference: Hardening planning of overhead distribution lines in typhoon-prone areas by considering the typhoon motion paths and the line load reliability [J] The practicality of the proposed distribution line reinforcement planning scheme under the typhoon uniform linear motion model. Since energy storage has the characteristics of fast response and high flexibility in resisting disasters (a resilient distribution network planning method considering energy storage configuration under extreme natural disasters [D]), some scholars have also used energy storage in pre-disaster planning. The literature Energy storage planning strategy to improve the resilience of distribution networks under typhoon weather [J] constructs an overhead line failure rate model based on PHM theory, which effectively improves system resilience and reduces load losses during disasters.Literature Optimization method for site selection and sizing of energy storage systems considering distribution network resilience[J] Based on the conditional value at risk theory, an optimization model for site selection and sizing of energy storage systems considering distribution network resilience is constructed to address the problem of increased power outage losses after disasters. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and to provide a flexible distribution network planning method based on the combined output of wind, solar and storage, integrate renewable energy and energy storage technology, rationally allocate various energy sources, reduce dependence on traditional power sources, and at the same time enhance the overall resilience and disaster resistance of the power grid, thereby improving the reliability and stability of the power grid.

[0005] The object of the present invention is achieved by: a method for planning a flexible distribution network based on the combined output of wind, solar and energy storage, which comprises the following steps:

[0006] S1. Establish a distribution network failure rate model; comprehensively consider the impact of disasters on the probability of failure of overhead lines in the distribution network, and establish a failure rate model for the conductors and towers;

[0007] S2. Construction and selection of typical disaster scenarios: Based on historical data and considering the impact of disasters on wind and solar power output, the Monte Carlo simulation method is combined with information entropy to establish and screen typical disaster scenarios;

[0008] S3. Establishment of a flexible distribution network planning model: Based on the disaster scenario, a flexible distribution network planning configuration model is established, and combined with wind power generation, photovoltaic power generation, micro gas turbines, energy storage and other elements, a distribution network planning model under the disaster scenario is established to improve the system's flexibility and emergency response capabilities, ensuring that the distribution network can quickly resume normal operation when a disaster occurs;

[0009] S4. Solve the planning model; use the NSGA-II algorithm to solve the planning model and obtain the optimal configuration result of the distribution network.

[0010] Furthermore, the step S1 of establishing a distribution network failure rate model includes:

[0011] The impact of typhoon disasters on the failure probability of overhead lines in distribution networks can usually be determined by the stress σ of the conductors and towers. g and M T To describe, it is expressed as follows,

[0012]

[0013] In the formula, p f1 is the conductor failure rate of line i, p fp is the failure rate of the electric pole of line i; μ1 and μ p are the mean strengths of the conductor and the pole respectively; δ1 and Mp are the variances of the strength that the conductor and the pole can withstand when facing typhoon disasters;

[0014] The necessary and sufficient condition for the normal operation of line i is that the wires and poles on the line are operating normally. Therefore, the wires and poles can be understood as a series relationship.

[0015]

[0016] In the formula, p f,i is the failure rate of line i, m1 is the number of poles erected on line i, p fl,i is the failure rate of line i, p fp,k,i is the failure rate of the kth pole of line i, p fl,i (v) and p fp,k,i (v) are all functions of the time-varying wind speed on the conductor.

[0017] Furthermore, the construction and selection of typical disaster scenarios in step S2 includes:

[0018] 1) Determine the various variables and parameters that affect disaster scenarios, such as geological conditions, climate data, population density, building structure, etc.;

[0019] 2) Based on historical data, the Monte Carlo simulation method is used to generate a set of uncertain wind and solar power output scenarios Y = {pv1·wt1, pv2·wt2, …, pv s ·wt s}, where pv·wt is the set of wind-solar joint output scenarios, and s is the number of representative wind-solar joint output scenarios;

[0020] 3) Select appropriate information entropy indicators to measure the complexity or uncertainty of different disaster scenarios;

[0021] 4) Run Monte Carlo simulations to generate a large number of possible disaster scenario data, each scenario is based on randomly sampled variable values, simulating possible disaster occurrences and impacts;

[0022] 5) For each generated disaster scenario, calculate its information entropy; the calculation of information entropy can evaluate its complexity and uncertainty based on the probability distribution of the scenario;

[0023] 6) Based on the evaluation results of information entropy, select representative and typical disaster scenarios;

[0024] When selecting a reasonable elasticity analysis scenario, the system entropy value W corresponding to each scenario is s It should be within a reasonable range, that is,

[0025]

[0026] Where W min , W max are the minimum and maximum values ​​of the system information entropy respectively.

[0027] Furthermore, the step S3 of establishing a flexible distribution network planning model includes:

[0028] Taking the minimum comprehensive cost and the maximum distribution network elasticity index as the objective function, the formula is as follows:

[0029]

[0030] Where f1 is the annual comprehensive cost of the distribution network; C inv is the investment cost of the distribution network; C e is the operating cost of the distribution network; C sub is the government subsidy fee; f2 is the comprehensive performance loss area of ​​the distribution network;

[0031] Where S area The calculation method of is as follows:

[0032]

[0033] Where H0 is the performance of the system under normal conditions; H s (t) is the performance of the system under disaster scenario s at time t; w(ψ) is the number of disaster scenarios, and a quantitative index of resilience is established.

[0034]

[0035] In the formula, α1 represents the elasticity quantitative index coefficient, and the index is fixed between 0 and 1; when the load loss value of the distribution network is smaller, the performance loss area is smaller, and the R1 value is higher, indicating that the elasticity of the distribution network is higher, and vice versa;

[0036] Investment cost inv Including investment costs of DG (wind turbine, photovoltaic, gas turbine) and ESS (energy storage system); operating costs C e Including the operation and maintenance costs of wind turbines, photovoltaics, energy storage power stations and gas turbines, the main grid power purchase costs, grid loss costs, load loss costs, wind and solar abandonment costs, voltage deviation penalty costs, etc.

[0037] 1) DG and ESS investment cost C inv

[0038]

[0039] Where N DG、ESS is the investment equivalent coefficient; r is the discount rate; y is the full life cycle of various equipment; c DG 、cESS are the unit investment prices of various types of DG and ESS respectively; are the total installed capacity of DG and ESS at node i respectively; Ω DG ,Ω ESS They are the node sets to be installed of DG and ESS respectively;

[0040] 2) DG operating costs

[0041]

[0042] Where, ψ is the set of typical typhoon disaster scenarios; p s is the probability of occurrence of scenario s; Z is the number of operating days in a year, which is 365 days; ρ DG are the unit electricity operation costs of each type of DG; are the active power injected into node i by each type of DG at time t in scenario s; DG are the access node sets of each type of DG;

[0043] 3) ESS operating costs

[0044]

[0045] In the formula, ρ ESS They are the unit charge and discharge operation costs of ESS respectively; are the charging and discharging power at node i at time t in scenario s; ESS are the access node sets of each type of ESS;

[0046] 4) Cost of curtailing wind and solar power

[0047]

[0048] In the formula, c ab,PV 、c ab,WT are the PV curtailment penalty price and the WT curtailment penalty price respectively; ΔP ab,PV , ΔP ab,WT They are the total amount of wind and solar power abandoned respectively;

[0049] 5) Cost of purchasing electricity from the upper grid

[0050]

[0051] In the formula, is the electricity purchase price at time t; is the electricity purchase amount at time t in scenario s;

[0052] 6) Network loss costs

[0053]

[0054] In the formula, Ω line is the set of lines; loss is the unit network loss cost; I ij,s,t is the current of line ij at time t in scenario s; r ij is the resistance of line ij;

[0055] 7) Load loss cost

[0056]

[0057] Where n z is the total number of nodes; ρ lose is the unit load loss cost; i,s is the load importance weight of node i in scenario s; is the active load lost by node i at time t under scenario s;

[0058] 8) Voltage offset penalty cost

[0059]

[0060] In the formula, ρ vol Penalty cost per unit voltage offset; is the voltage offset of node i at time t in scenario s;

[0061] 9) Government subsidy costs C sub

[0062] Considering the impact of government subsidies on the optimal configuration of the distribution network and taking into account the government subsidy costs, as shown in the following formula:

[0063]

[0064] In the formula, is the total power generation of the distribution network system at time t under scenario s; ρ sub Compensation for unit government expenses;

[0065] The constraints are as follows;

[0066] 1) Constraints on the number of DG nodes to be installed

[0067]

[0068] Where n DG The number of single node installations for each type of DG equipment; The maximum number of single node installations for each type of DG equipment; Ω DG It is the set of nodes to be installed in DG;

[0069] 2) DG installation capacity constraints

[0070]

[0071] In the formula, The maximum capacity of various DG devices installed at node i;

[0072] 3) Constraints on the number of ESS nodes to be installed

[0073]

[0074] Where n ESS The number of single node installations for each type of ESS equipment; The maximum number of single node installations for each type of ESS equipment;

[0075] 4) ESS installation capacity constraints

[0076]

[0077] In the formula, The maximum capacity of the ESS installed on node i.

[0078] Furthermore, the step S4 of solving the planning model includes:

[0079] S41, unify the multi-objective optimization problems of minimizing the comprehensive cost of the distribution network and minimizing the comprehensive performance loss area;

[0080]

[0081] Where f(x) is the target vector, (f1(x), f2(x)) is the decision variable vector, and Ω is the feasible solution space;

[0082] S42, Initial population Generate a random initial population P0, whose size is N, and each individual is represented by x i , its objective function value is f(x i );

[0083] S43, sort the population by non-dominance and determine the dominance level (frontier) of each individual;

[0084] Dominance relationship: Solution x i dominatex j , if and only if: f k (x i )≤f k (x j) and there exists at least one k such that f k (x i ) > f k (x j ). According to the domination relationship, it is divided into multiple Pareto fronts {F1, F2, …, F l}, where F1 is the highest level (i.e., the front that is not dominated by any other individual);

[0085] S44. For each front, calculate the crowding distance of individuals to ensure population diversity; for each objective k, sort the individuals according to the objective values; define the crowding degree of boundary individuals as infinity: For the sorted individual i, the crowding distance d i is

[0086]

[0087] The total crowding distance d i is the sum of all objective crowding distances,

[0088]

[0089] S45. Use the crowding comparison operator for tournament selection. For two individuals p and q, the selection rule is: if r(p) < r(q) or r(p) = r(q) and d(p) > d(q), select p, where r(p) and r(q) are the levels of non-dominated sorting, and d(p) and d(q) are the crowding distances;

[0090] S46. Simulated binary crossover (SBX). For parental individuals x1 and x2, generate offspring c1 and c2,

[0091]

[0092] c1 = 0.5((1 + β q )x1 + (1 - β q )x2)

[0093] c2 = 0.5((1 - β q )x1 + (1 + β q )x2)

[0094] where u is a uniformly distributed random number, and n c is the crossover distribution index;

[0095] S47. Combine the parental population P t and the offspring population Q t ; form the combined population R t = P t ∪Q t ; for R tPerform non-dominated sorting; select the first N individuals with better non-dominated sorting to form a new population P t+1 ;

[0096] S48. Repeat steps S43 to S47 until the termination condition is reached (such as reaching the maximum number of generations G or the population converges).

[0097] Beneficial effects of the present invention: The present invention provides a flexible distribution network planning method based on the combined output of wind, solar and storage, which comprehensively considers the impact of disasters on the probability of failure of overhead lines in the distribution network, and establishes failure rate models for the two components of conductors and towers. Based on historical data, the impact of disasters on wind and solar output is considered, and the Monte Carlo simulation method is combined with information entropy to establish and screen typical disaster scenarios. Then, a distribution network planning and configuration model is established with the minimum comprehensive cost and the maximum distribution network elasticity index as the objective function. Finally, the NSGA-II optimization algorithm is used to solve the model, and the feasibility and effectiveness of the proposed scheme are analyzed through simulation verification. The constructed flexible distribution network planning scheme is of great significance for the power grid to resist natural disasters and improve the overall elasticity of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0098] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0099] Figure 1 This is the distribution network elasticity performance curve.

[0100] Figure 2 This is the topological structure diagram of the distribution network.

[0101] Figure 3 It is the time-varying failure rate diagram of the component.

[0102] Figure 4 is the probability distribution of information entropy value and fault multiplicity in different disaster scenarios.

[0103] Figure 5 This is the power distribution network output diagram under normal circumstances.

[0104] Figure 6 This is the load loss diagram of the flexible distribution network.

[0105] Figure 7 This is the network loss diagram of the elastic distribution network. DETAILED DESCRIPTION

[0106] The present invention will be further described below in conjunction with the accompanying drawings.

[0107] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0108] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0109] like Figure 1-7 As shown, a flexible distribution network planning method based on wind, solar and storage combined output of the present invention includes S1, establishing a distribution network failure rate model; comprehensively considering the impact of disasters on the probability of failure of overhead lines in the distribution network, and establishing failure rate models for two components, the conductor and the tower;

[0110] S2. Construction and selection of typical disaster scenarios: Based on historical data and considering the impact of disasters on wind and solar power output, the Monte Carlo simulation method is combined with information entropy to establish and screen typical disaster scenarios;

[0111] S3. Establishment of a flexible distribution network planning model: Based on the disaster scenario, a flexible distribution network planning configuration model is established, and combined with wind power generation, photovoltaic power generation, micro gas turbines, energy storage and other elements, a distribution network planning model under the disaster scenario is established to improve the system's flexibility and emergency response capabilities, ensuring that the distribution network can quickly resume normal operation when a disaster occurs;

[0112] S4. Solve the planning model; use the NSGA-II algorithm to solve the planning model and obtain the optimal configuration result of the distribution network.

[0113] Furthermore, the step S1 of establishing a distribution network failure rate model includes:

[0114] According to the Unified Standard for Reliability Design of Building Structures, the probability distribution of equipment disaster resistance usually adopts normal distribution. The impact of typhoon disasters on the probability of failure of overhead lines in distribution networks can usually be determined by the stress σ of the two components, conductors and towers. g and M T To describe, it is expressed as follows,

[0115]

[0116] In the formula, p f1 is the conductor failure rate of line i, p fp is the failure rate of the electric pole of line i; μ1 and μ p are the mean strengths of the conductor and the pole respectively; δ1 and Mp are the variances of the strength that the conductor and the pole can withstand when facing typhoon disasters;

[0117] The necessary and sufficient condition for the normal operation of line i is that the wires and poles on the line are operating normally. Therefore, the wires and poles can be understood as a series relationship.

[0118]

[0119] In the formula, p f,i is the failure rate of line i, m1 is the number of poles erected on line i, p fl,i is the failure rate of line i, p fp,k,i is the failure rate of the kth pole of line i, p fl,i (v) and p fp,k,i (v) are all functions of the time-varying wind speed on the conductor.

[0120] Furthermore, the construction and selection of typical disaster scenarios in step S2 includes:

[0121] 5) Determine the various variables and parameters that affect disaster scenarios, such as geological conditions, climate data, population density, building structure, etc. These variables can affect the probability and impact of disasters;

[0122] 6) Based on historical data, the Monte Carlo simulation method is used to generate a set of uncertain wind and solar power output scenarios Y = {pv1·wt1, pv2·wt2, …, pv s ·wt s}, where pv·wt is the set of wind-solar joint output scenarios, and s is the number of representative wind-solar joint output scenarios;

[0123] 7) Select appropriate information entropy indicators to measure the complexity or uncertainty of different disaster scenarios. Information entropy can help evaluate the diversity of disaster scenarios and the balance of probability distribution;

[0124] 8) Run Monte Carlo simulations to generate a large number of possible disaster scenario data, each scenario is based on randomly sampled variable values, simulating possible disaster occurrences and impacts;

[0125] 5) For each generated disaster scenario, calculate its information entropy; the calculation of information entropy can evaluate its complexity and uncertainty based on the probability distribution of the scenario;

[0126] 6) Based on the evaluation results of information entropy, select representative and typical disaster scenarios, such as Figure 4 As shown;

[0127] In each elastic analysis scenario, there is a corresponding variable z i,tTo determine whether all components of the system fail at a certain moment; considering the uncertainty of the occurrence of failure scenarios, the variable z i,t The value of should be consistent with the distribution of failure rate. This entropy value reflects the amount of information and degree of uncertainty of the system in this scenario. When the failure rate of a line is high, the uncertainty event of failure of the line is more likely to occur, so the corresponding z i,t There will be more scenarios with a value of 1. Therefore, according to the probability of the actual scenario, the variable W s The value of needs to be moderate. In short, when selecting a reasonable elastic analysis scenario, the system entropy value W corresponding to each scenario s It should be within a reasonable range, that is,

[0128]

[0129] Where W min , W max are the minimum and maximum values ​​of the system information entropy respectively; selecting fault scenarios based on entropy constraints usually has a higher probability of occurrence and serious fault consequences. Such scenarios have become typical fault cases for distribution network resilience analysis. By analyzing these typical fault cases, we can clarify the scale of faults that may be caused by extreme weather and the corresponding probability distribution.

[0130] Furthermore, the step S3 of establishing a flexible distribution network planning model includes:

[0131] Taking the minimum comprehensive cost and the maximum distribution network elasticity index as the objective function, the formula is as follows:

[0132]

[0133] Where f1 is the annual comprehensive cost of the distribution network; C inv is the investment cost of the distribution network; C e is the operating cost of the distribution network; C sub is the government subsidy fee; f2 is the comprehensive performance loss area of ​​the distribution network;

[0134] Where S area The calculation method of is as follows:

[0135]

[0136] Where H0 is the performance of the system under normal conditions; H s (t) is the performance of the system under disaster scenario s at time t; w(ψ) is the number of disaster scenarios. Combining the above formula and Figure 1 As shown, establish elasticity quantitative indicators,

[0137]

[0138] In the formula, α1 represents the elasticity quantitative index coefficient, and the index is fixed between 0 and 1; when the load loss value of the distribution network is smaller, the performance loss area is smaller, and the R1 value is higher, indicating that the elasticity of the distribution network is higher, and vice versa;

[0139] Investment cost inv Including investment costs of DG (wind turbine, photovoltaic, gas turbine) and ESS (energy storage system); operating costs C e Including the operation and maintenance costs of wind turbines, photovoltaics, energy storage power stations and gas turbines, the main grid power purchase costs, grid loss costs, load loss costs, wind and solar abandonment costs, voltage deviation penalty costs, etc.

[0140] 1) DG and ESS investment cost C inv

[0141]

[0142] Where N DG、ESS is the investment equivalent coefficient; r is the discount rate; y is the full life cycle of various equipment; c DG 、c ESS are the unit investment prices of various types of DG and ESS respectively; are the total installed capacity of DG and ESS at node i respectively; Ω DG ,Ω ESS They are the node sets to be installed of DG and ESS respectively;

[0143] 2) DG operating costs

[0144]

[0145] Where, ψ is the set of typical typhoon disaster scenarios; p s is the probability of occurrence of scenario s; Z is the number of operating days in a year, which is 365 days; ρ DG are the unit electricity operation costs of each type of DG; are the active power injected into node i by each type of DG at time t in scenario s; DG are the access node sets of each type of DG;

[0146] 3) ESS operating costs

[0147]

[0148] In the formula, ρ ESS They are the unit charge and discharge operation costs of ESS respectively; are the charging and discharging power at node i at time t in scenario s; ESSare the access node sets of each type of ESS;

[0149] 4) Cost of curtailing wind and solar power

[0150]

[0151] In the formula, c ab,PV 、c ab,WT are the PV curtailment penalty price and the WT curtailment penalty price respectively; ΔP ab,PV , ΔP ab,WT They are the total amount of wind and solar power abandoned respectively;

[0152] 5) Cost of purchasing electricity from the upper grid

[0153]

[0154] In the formula, is the electricity purchase price at time t; is the amount of electricity purchased at time t in scenario s;

[0155] 6) Network loss costs

[0156]

[0157] In the formula, Ω line is the set of lines; loss is the unit network loss cost; I ij,s,t is the current of line ij at time t in scenario s; r ij is the resistance of line ij;

[0158] 7) Load loss cost

[0159]

[0160] Where n z is the total number of nodes; ρ lose is the unit load loss cost; i,s is the load importance weight of node i in scenario s; is the active load lost by node i at time t under scenario s;

[0161] 8) Voltage offset penalty cost

[0162]

[0163] In the formula, ρ vol Penalty cost per unit voltage offset; is the voltage offset of node i at time t in scenario s;

[0164] 9) Government subsidy costs C sub

[0165] Considering the impact of government subsidies on the optimal configuration of the distribution network and taking into account the government subsidy costs, as shown in the following formula:

[0166]

[0167] In the formula, is the total power generation of the distribution network system at time t under scenario s; ρ sub Compensation for unit government expenses;

[0168] The constraints are as follows;

[0169] 1) Constraints on the number of DG nodes to be installed

[0170]

[0171] Where n DG The number of single node installations for each type of DG equipment; The maximum number of single node installations for each type of DG equipment; Ω DG It is the set of nodes to be installed in DG;

[0172] 2) DG installation capacity constraints

[0173]

[0174] In the formula, The maximum capacity of various DG devices installed at node i;

[0175] 3) Constraints on the number of ESS nodes to be installed

[0176]

[0177] Where n ESS The number of single node installations for each type of ESS equipment; The maximum number of single node installations for each type of ESS equipment;

[0178] 4) ESS installation capacity constraints

[0179]

[0180] In the formula, The maximum capacity of the ESS installed on node i.

[0181] Furthermore, the step S4 of solving the planning model includes:

[0182] The present invention uses the NSGA-II algorithm to solve the multi-objective optimization problem of minimizing the comprehensive cost and minimizing the comprehensive performance loss area of ​​the distribution network. First, based on the decision variables and constraints of the two objectives, the two objective functions are analyzed; then, by initializing the population, evaluating the fitness of the individuals, non-dominated sorting, congestion calculation and selection operations, new individuals are generated and the population is updated by applying crossover and mutation operations; finally, according to the set termination conditions, the optimal Pareto frontier set of the two objective functions is output, and then the optimal trade-off solution between the comprehensive cost and the distribution network elasticity index is solved; the specific steps are as follows;

[0183] S41, unify the multi-objective optimization problems of minimizing the comprehensive cost of the distribution network and minimizing the comprehensive performance loss area;

[0184]

[0185] Where f(x) is the target vector, (f1(x), f2(x)) is the decision variable vector, and Ω is the feasible solution space;

[0186] S42, Initial population Generate a random initial population P0, whose size is N, and each individual is represented by x i , its objective function value is f(x i );

[0187] S43, sort the population by non-dominance and determine the dominance level (frontier) of each individual;

[0188] Dominance relationship: Solution x i dominatex j , if and only if: f k (x i )≤f k (x j ), and there exists at least one k such that f k (x i )>f k (x j ), divided into multiple Pareto frontiers {F1, F2, …, F l}, where F1 is the highest level (i.e., the frontier that is not dominated by any other individual);

[0189] S44. For each frontier, calculate the crowding distance of individuals to ensure population diversity; for each target k, sort the individuals according to the target value; define the crowding degree of the boundary individuals as infinite: For the sorted individual i, the crowding distance d i for,

[0190]

[0191] Total crowding distance d i is the sum of all objective crowding distances.

[0192]

[0193] S45. Conduct tournament selection using the crowding comparison operator. For two individuals p and q, the selection rule is: select p if r(p) < r(q) or r(p) = r(q) and d(p) > d(q), where r(p) and r(q) are the levels of non-dominated sorting, and d(p) and d(q) are the crowding distances.

[0194] S46. Simulated binary crossover (SBX). For parent individuals x1 and x2, generate offspring c1 and c2.

[0195]

[0196] c1 = 0.5((1 + β q )x1 + (1 - β q )x2)

[0197] c2 = 0.5((1 - β q )x1 + (1 + β q )x2)

[0198] where u is a uniformly distributed random number, and n c is the crossover distribution index.

[0199] S47. Combine the parent population P t and the offspring population Q t ; form the combined population R t = P t ∪Q t ; perform non-dominated sorting on R t ; select the top N non-dominated and relatively better individuals to form a new population P t+1 ;

[0200] S48. Repeat steps S43 to S47 until the termination condition is reached (such as reaching the maximum number of generations G or population convergence).

[0201] To verify the feasibility of the flexible distribution network planning model proposed in this invention, the following 4 schemes are set for comparative analysis:

[0202] 1) Strategy 1: A planning scheme that does not consider the reasonable configuration of wind, light, and energy storage.

[0203] 2) Strategy 2: A planning scheme that only considers the energy storage configuration.

[0204] 3) Strategy 3: A planning scheme that only considers the wind and light configuration.

[0205] 4) Strategy 4: the method mentioned in the present invention;

[0206] Through research and analysis, Figure 6 , Figure 7 It can be seen that compared with the planning results of strategies 1, 2, 3, and 4, the method proposed in this paper can better reduce network losses and load losses when facing typhoon disasters, and effectively enhance the overall resilience of the distribution network.

[0207] In summary, the present invention provides a flexible distribution network planning method based on the combined output of wind, solar and storage. It comprehensively considers the impact of disasters on the probability of failure of overhead lines in the distribution network, and establishes failure rate models for the two components of conductors and towers. Based on historical data, the impact of disasters on wind and solar output is considered, and the Monte Carlo simulation method is combined with information entropy to establish and screen typical disaster scenarios. Then, a distribution network planning and configuration model is established with the minimum comprehensive cost and the maximum distribution network elasticity index as the objective function. Finally, the NSGA-II optimization algorithm is used to solve the model, and the feasibility and effectiveness of the proposed scheme are analyzed through simulation verification. The constructed flexible distribution network planning scheme is of great significance for the power grid to resist natural disasters and improve the overall elasticity of the distribution network.

[0208] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0209] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A flexible distribution network planning method based on wind, solar and storage combined output, characterized in that: It includes the following steps: S1. Establish a failure rate model for the distribution network; comprehensively consider the impact of disasters on the failure probability of overhead lines in the distribution network, and establish a failure rate model for two components, namely conductors and poles; S2. Construction and selection of typical disaster scenarios; based on historical data, considering the impact of disasters on the output of wind and light, use the Monte Carlo simulation method combined with information entropy to establish and screen typical disaster scenarios; S3. Establish an elastic distribution network planning model; on the basis of disaster scenarios, establish an elastic distribution network planning configuration model, and combine elements such as wind power generation, photovoltaic power generation, micro gas turbines, and energy storage to establish a distribution network planning model under disaster scenarios; S4. Solve the planning model; use the NSGA-II algorithm to solve the planning model and obtain the optimal configuration result of the distribution network.

2. A flexible distribution network planning method based on wind, solar and storage combined output as claimed in claim 1, characterized in that: The step S1 of establishing a failure rate model for the distribution network includes: The impact of typhoon disasters on the failure probability of overhead lines in distribution networks can usually be determined by the stress σ of the conductors and towers. g and M T To describe, it is expressed as follows, In the formula, p f1 is the conductor failure rate of line i, p fp is the failure rate of the electric pole of line i; μ1 and μ p are the mean strengths of the conductor and the pole respectively; δ1 and M p are the variances of the strength that the conductor and the pole can withstand when facing typhoon disasters; The conductor and the electric pole can be understood as a series relationship, In the formula, p f,i is the failure rate of line i, m1 is the number of poles erected on line i, p fl,i is the failure rate of line i, p fp,k,i is the failure rate of the kth pole of line i, p fl,i (v) and p fp,k,i (v) are all functions of the time-varying wind speed on the conductor.

3. A flexible distribution network planning method based on wind, solar and storage combined output as claimed in claim 1, characterized in that: The step S2 of constructing and selecting typical disaster scenarios includes: 1) Determine various variables and parameters affecting the disaster scenario; 2) Based on historical data, the Monte Carlo simulation method is used to generate a set of uncertain scenarios for wind and solar power output, Y = {pv1·wt1, pv2·wt2, …, pv s ·wt s }, where pv·wt is the set of wind-solar joint output scenarios, and s is the number of representative wind-solar joint output scenarios; 3) Select appropriate information entropy indicators to measure the complexity or uncertainty of different disaster scenarios; 4) Run the Monte Carlo simulation to generate a large number of possible disaster scenario data, and each scenario is based on randomly sampled variable values to simulate possible disaster occurrences and impacts; 5) Calculate the information entropy for each generated disaster scenario; 6) According to the evaluation results of the information entropy, screen out representative and typical disaster scenarios; When selecting a reasonable elasticity analysis scenario, the system entropy value W corresponding to each scenario is s It should be within a reasonable range, that is, Where W min , W max are the minimum and maximum values ​​of the system information entropy respectively.

4. A flexible distribution network planning method based on wind, solar and storage combined output as claimed in claim 1, characterized in that: The step S3 of establishing an elastic distribution network planning model includes: Taking the minimum comprehensive cost and the maximum distribution network elasticity index as the objective function, and its formula is as follows, Where f1 is the annual comprehensive cost of the distribution network; C inv is the investment cost of the distribution network; C e is the operating cost of the distribution network; C sub is the government subsidy fee; f2 is the comprehensive performance loss area of ​​the distribution network; Where S area The calculation method of is as follows: Where H0 is the performance of the system under normal conditions; H s (t) is the performance of the system under disaster scenario s at time t; w(ψ) is the number of disaster scenarios, and a quantitative index of resilience is established. In the formula, α1 represents the elastic quantization index coefficient, and the index is fixed between 0 and 1; when the load loss value of the distribution network is smaller, the performance loss area is smaller, and the R1 value is higher, indicating that the elasticity of the distribution network is higher, and vice versa, the elasticity is lower; Investment cost inv Including investment cost of DG and ESS; operation cost C e Including the operation and maintenance costs of wind turbines, photovoltaics, energy storage power stations and gas turbines, the main grid power purchase costs, grid loss costs, load loss costs, wind and solar abandonment costs, voltage deviation penalty costs, etc. 1) DG and ESS investment cost C inv Where N DG、ESS is the investment equivalent coefficient; r is the discount rate; y is the full life cycle of various equipment; c DG 、c ESS are the unit investment prices of various types of DG and ESS respectively; are the total installed capacity of DG and ESS at node i respectively; Ω DG ,Ω ESS They are the node sets to be installed of DG and ESS respectively; 2) DG operating costs Where, ψ is the set of typical typhoon disaster scenarios; p s is the probability of occurrence of scenario s; Z is the number of operating days in a year, which is 365 days; ρ DG are the unit electricity operation costs of each type of DG; are the active power injected into node i by each type of DG at time t in scenario s; DG are the access node sets of each type of DG; 3) ESS operating costs In the formula, ρ ESS They are the unit charge and discharge operation costs of ESS respectively; are the charging and discharging power at node i at time t in scenario s; ESS are the access node sets of each type of ESS; 4) Cost of curtailing wind and solar power In the formula, c ab,PV 、c ab,WT are the PV curtailment penalty price and the WT curtailment penalty price respectively; ΔP ab,PV , ΔP ab,WT They are the total wind curtailment and solar curtailment respectively; 5) Cost of purchasing electricity from the upper grid In the formula, is the electricity purchase price at time t; is the amount of electricity purchased at time t in scenario s; 6) Network loss costs In the formula, Ω line is the set of lines; loss is the unit network loss cost; I ij,s,t is the current of line ij at time t in scenario s; r ij is the resistance of line ij; 7) Load loss cost Where n z is the total number of nodes; ρ lose is the unit load loss cost; σ i,s is the load importance weight of node i in scenario s; is the active load lost by node i at time t under scenario s; 8) Voltage offset penalty cost In the formula, ρ vol Penalty cost per unit voltage offset; is the voltage offset of node i at time t in scenario s; 9) Government subsidy costs C sub Consider the impact of government subsidies on the optimal configuration of the distribution network, and take into account the subsidy costs of the government, as shown in the following formula, In the formula, is the total power generation of the distribution network system at time t under scenario s; ρ sub Compensation for unit government expenses; The constraint conditions are as follows; 1) Constraint on the installation quantity of DG to be installed at the node Where n DG The number of single node installations for each type of DG equipment; The maximum number of single node installations for each type of DG equipment; Ω DG It is the set of nodes to be installed in DG; 2) Constraint on the installation capacity of DG In the formula, The maximum capacity of various DG devices installed at node i; 3) Constraint on the installation quantity of ESS to be installed at the node Where n ESS The number of single node installations for each type of ESS equipment; The maximum number of single node installations for each type of ESS equipment; 4) Constraint on the installation capacity of ESS In the formula, The maximum capacity of the ESS installed on node i.

5. The method for planning a flexible distribution network based on wind, solar and storage combined output as claimed in claim 1, characterized in that: The step S4 of solving the planning model includes: S41. Uniformly handle the multi-objective optimization problem of minimizing the comprehensive cost of the distribution network and minimizing the comprehensive performance loss area; In the formula, f(x) is the objective vector, (f1(x), f2(x)) is the decision variable vector, and Ω is the feasible solution space; S42, Initial population Generate a random initial population P0, whose size is N, and each individual is represented by x i , its objective function value is f(x i ); S43. Perform non-dominated sorting on the population to determine the dominance level of each individual; Dominance relationship: Solution x i dominatex j , if and only if: f k (x i )≤f k (x j ), and there exists at least one k such that f k (x i )>f k (x j ), divided into multiple Pareto frontiers {F1, F2, …, F l }, where F1 is the highest level; S44. For each frontier, calculate the crowding distance of individuals to ensure population diversity; for each target k, sort the individuals according to the target value; define the crowding degree of the boundary individuals as infinite: For the sorted individual i, the crowding distance d i for, Total crowding distance d i is the sum of all target crowding distances, S45. Use the crowding comparison operator for tournament selection. For two individuals p and q, the selection rule is: if r(p) < r(q) or r(p) = r(q) and d(p) > d(q), select p, where r(p) and r(q) are the levels of non-dominated sorting, and d(p) and d(q) are the crowding distances; S46. Simulate binary crossover. For parental individuals x1 and x2, generate offspring c1 and c2, c1=0.5((1+β q )x1+(1-β q )x2) c2=0.5((1-β q )x1+(1+β q )x2) Where u is a uniformly distributed random number, n c is the cross-distribution index; S47, merge parent population P t and the offspring population Q t ; Form a joint population R t =P t ∪Q t ; for R t Perform non-dominated sorting; select the first N individuals with better non-dominated sorting to form a new population P t+1 ; S48. Repeat steps S43 to S47 until the termination condition is reached.

Images