Two-stage wind storage coordinated planning method and device considering source load uncertainty

Abstract

The invention relates to the technical field of power systems, in particular to a two-stage wind storage coordinated planning method and device considering source load uncertainty, and the method comprises the steps: obtaining distributed wind power planning data and distributed energy storage planning data; based on the distributed wind power planning data and the distributed energy storage planning data, establishing a two-stage wind storage coordinated planning scheme considering the source load uncertainty; the two-stage wind storage coordinated planning scheme is input into a target model, the target model outputs respective target access positions and target capacities of the wind power plant and the energy storage equipment, the target model is established based on a target ant lion algorithm, and an adaptive Levy flight mechanism and a golden sine algorithm are introduced into position updating of the target ant lion algorithm. Therefore, the problems of output uncertainty, voltage out-of-limit, power grid loss increase, operation cost increase and the like of the distributed power supply in related technologies are solved.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to a two-stage wind-storage coordination planning method and device that considers source-load uncertainty. Background Art

[0002] As the world pays more and more attention to clean energy and efficient energy utilization, distributed power sources have received extensive attention and application worldwide due to their significant advantages such as clean and environmental protection, low power generation cost and flexible power supply mode. Distributed power sources mainly include trigeneration, internal combustion engine power generation, small hydropower generation, photovoltaic power generation, wind power generation and fuel cells. Under the concept of active distribution network, these distributed power sources are incorporated into the comprehensive control system, and the power grid is supported and adjusted through a reasonable regulatory environment and access criteria, thereby improving the overall operation efficiency and reliability of the power grid.

[0003] However, the access of distributed power sources to the distribution network, especially the access of wind storage systems, has greatly changed the structure and operating characteristics of the traditional power grid. The transformation of the traditional single power supply radial topology to a multi-power supply system has brought new challenges to the operation and dispatch of the power grid. The randomness of the output of the wind storage system not only increases the difficulty of planning, but also aggravates the fluctuation of the grid load and voltage, which has an adverse impact on the power quality.

[0004] In the study of distributed power planning models, although cost analysis is the mainstream method, most studies still use simple deterministic models, ignoring the impact of source-load uncertainty and distributed energy storage. This results in the planning scheme being unable to effectively cope with the complex and changing power grid environment in practical applications. Therefore, comprehensively considering the output uncertainty of distributed power sources and energy storage access in the planning model has become the key to improving the safe, reliable and economical operation of the power system.

[0005] In terms of optimization algorithms, although traditional numerical optimization solutions and heuristic methods have their own advantages, they both have limitations. Although numerical optimization methods are rigorous and accurate, they are difficult to cope with the complexity and uncertainty of actual power grids; although heuristic methods are flexible and simple, they can often only obtain local optimal solutions. In contrast, intelligent optimization algorithms such as the Ant Lion Algorithm have attracted much attention due to their strong adaptability and high computational efficiency. However, when applying the Ant Lion Algorithm to wind-storage coordination planning, it is still necessary to pay attention to challenges such as algorithm parameter adjustment, convergence analysis, adaptability, multi-objective optimization, energy storage collaborative optimization, and algorithm parallelization to ensure the effectiveness and reliability of the algorithm in practical applications. Summary of the invention

[0006] The present application provides a two-stage wind-storage coordination planning method and device that takes into account source-load uncertainty, so as to solve the problems of distributed power source output uncertainty, voltage over-limit, increased grid losses and increased operating costs in related technologies.

[0007] The first aspect of the present application provides a two-stage wind-storage coordination planning method considering source-load uncertainty, including the following steps: acquiring distributed wind power planning data and distributed energy storage planning data; establishing a two-stage wind-storage coordination planning scheme considering source-load uncertainty based on the distributed wind power planning data and the distributed energy storage planning data; inputting the two-stage wind-storage coordination planning scheme into a target model, and the target model outputs the target access position and target capacity of the wind farm and the energy storage device respectively, wherein the target model is established based on a target ant lion algorithm, and an adaptive Levy flight mechanism and a golden sine algorithm are introduced in the position update of the target ant lion algorithm.

[0008] Optionally, the target model is:

[0009]

[0010] v~N(0,1)

[0011]

[0012] in, represents the position of the i-th ant in the t-th iteration; represents the position of the i-th ant in the t+1th iteration; a represents the random step length; Levy(θ) represents the random search path that follows the Levy distribution, θ represents the Levy distribution parameter, u and ν represent random numbers that follow the standard normal distribution, and R 1 and R 2 is a random number, x 1 and x 2 is the coefficient obtained by the golden section, is the position of the i-th particle at the T-th iteration in n-dimensional space, is the historical best position of the ith particle in the Tth iteration, is the standard deviation of a normal distribution.

[0013] Optionally, based on distributed wind power planning data and distributed energy storage planning data, a two-stage wind-storage coordination planning scheme that takes into account source-load uncertainty is established, including: establishing first-stage distributed wind power planning targets based on distributed wind power planning data; and establishing second-stage distributed energy storage planning targets based on distributed energy storage planning data.

[0014] Optionally, the distributed wind power planning data includes a system active power loss index, a voltage deviation index and a system comprehensive cost index, and the distributed energy storage planning data includes an energy storage operation and maintenance cost index and an energy storage investment cost index.

[0015] Optionally, the calculation formula for the first stage distributed wind power planning target is:

[0016]

[0017]

[0018] Among them, f loss is the system active power loss, G i,j is the conductance value between node i and node j, L represents the number of system lines, U i is the voltage value of node i, U j is the voltage value of node j, cosδ ij is the voltage phase angle difference between nodes i and j, f ΔU is the system voltage offset value, U n is the system rated voltage; N is the number of system nodes;

[0019] f cb is the system comprehensive cost, P L、W (s) is the probability of occurrence of typical wind load scenarios, f s yx is the operating cost in a typical scenario; f s tz is the investment cost in a typical scenario, f s loss is the network loss cost in a typical scenario, f s ec is the environmental benefit cost in a typical scenario, f s gd is the cost of purchasing electricity from the large power grid in a typical scenario, f s bt is the operating cost of a typical scenario, f yx is the wind power operation cost, h f is the unit cost coefficient of wind power operation, P f,i is the power generated by the i-th wind turbine, T f is the annual wind power generation duration, N f is the set of wind power accessible nodes, f tz is the wind power investment cost, c f is the unit capacity investment cost of wind power, d is the annual interest rate, T f is the whole life cycle of wind power, c s is the line loss cost per unit length, P jl is the maximum network loss of line j, T max is the annual maximum load loss time, n is the number of environmental pollutants, c em is the unit cost of the mth pollutant, e m is the emission index of the mth pollutant under the traditional power generation mode, P load is the total line load, P loss is the network loss, c gd is the unit cost of electricity purchased, Ploaadmax is the maximum load of the system, T 1 is the annual maximum load utilization time, c bt is the unit government subsidy fee, f ec is the environmental benefit cost of wind power, f gd is the wind power purchase cost, f bt The operating cost of wind power;

[0020] f lmax is the maximum active power loss of the system, f cbmax is the maximum comprehensive cost of the system, α is the inertia weight coefficient of the system active power loss, β is the inertia weight coefficient of the system voltage offset value, and γ is the inertia weight coefficient of the system comprehensive cost.

[0021] Optionally, the calculation formula for the second-stage distributed energy storage planning target is:

[0022] f ye =h e (∫η 1 s c P ec -P ef s f / η 2 dt)

[0023]

[0024] minf 2 =f ye +f te

[0025] Among them, f ye is the operating cost of the energy storage device, h e is the unit cost coefficient of energy storage operation, η 1 is the charging efficiency of the energy storage device, η 2 is the discharge efficiency of the energy storage device, P ec is the energy storage charging power, P ef is the energy storage discharge power, s c is the charging state, s f is the discharge state, f te is the investment cost of energy storage device, c ep is the unit power of energy storage, c es is the energy storage capacity cost, P e is the actual power of energy storage; S e is the energy storage capacity; d is the annual interest rate, T e For the entire life cycle of the energy storage device.

[0026] The second aspect of the present application provides a two-stage wind-storage coordination planning device that takes into account source-load uncertainty, including: an acquisition module for acquiring distributed wind power planning data and distributed energy storage planning data; an establishment module for establishing a two-stage wind-storage coordination planning scheme that takes into account source-load uncertainty based on the distributed wind power planning data and the distributed energy storage planning data; an output module for inputting the two-stage wind-storage coordination planning scheme into a target model, and the target model outputs the target access position and target capacity of the wind farm and the energy storage device respectively, wherein the target model is established based on a target ant lion algorithm, and an adaptive Levy flight mechanism and a golden sine algorithm are introduced in the position update of the target ant lion algorithm.

[0027] The third aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a two-stage wind-storage coordination planning method that considers source-load uncertainty as in the above-mentioned embodiment.

[0028] The fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement a two-stage wind-storage coordination planning method considering source-load uncertainty as in the above-mentioned embodiment.

[0029] The fifth aspect of the present application provides a computer program product, including a computer program or instructions, which stores a computer program or instructions that, when executed, are used to implement a two-stage wind-storage coordination planning method that takes into account source-load uncertainty as in the above-mentioned embodiment.

[0030] Therefore, this application has at least the following beneficial effects:

[0031] The embodiment of the present application comprehensively considers the planning data of distributed wind power and energy storage, and establishes a two-stage wind storage coordination planning scheme based on the uncertainty of source and load, which can more comprehensively consider the complexity and variability in actual operation, and use the target ant lion algorithm combined with the adaptive Levy flight mechanism and the golden sine algorithm to optimize the location and capacity configuration of wind farms and energy storage equipment to achieve the optimal configuration of resources, improve the economy and reliability of the system, and at the same time, enhance the adaptability to changes in the external environment, which helps to reduce operating costs and environmental impacts. As a result, technical problems such as uncertainty in the output of distributed power sources, voltage over-limit, increased grid losses, and increased operating costs in related technologies are solved.

[0032] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0034] Figure 1 A flow chart of a two-stage wind-storage coordination planning method considering source-load uncertainty provided according to an embodiment of the present application;

[0035] Figure 2 A flowchart of a two-stage wind-storage coordination planning method considering source-load uncertainty provided according to an embodiment of the present application;

[0036] Figure 3 A flowchart of a two-stage wind-storage coordination planning method provided according to another embodiment of the present application;

[0037] Figure 4 A flow chart of a target ant lion algorithm based on adaptive Levy flight and golden sine algorithm provided according to an embodiment of the present application;

[0038] Figure 5 A graph showing changes in fitness values ​​of three intelligent optimization algorithms according to an embodiment of the present application as a function of the number of iterations;

[0039] Figure 6 A probability diagram of a typical output scenario of an industrial load provided according to an embodiment of the present application;

[0040] Figure 7 A probability diagram of a typical output scenario of a residential load provided according to an embodiment of the present application;

[0041] Figure 8 A probability diagram of a typical wind load output scenario provided according to an embodiment of the present application;

[0042] Fig. 9 A comparison diagram of planning locations of different solutions provided according to an embodiment of the present application;

[0043] Fig.10 A distribution diagram of voltages of different schemes provided according to an embodiment of the present application;

[0044] Fig.11 A distribution diagram of system costs of different solutions provided according to an embodiment of the present application;

[0045] Fig.12 A schematic diagram of a wind-storage coordination optimization model provided according to an embodiment of the present application;

[0046] Fig.13 A schematic diagram of the energy storage charging and discharging state provided according to an embodiment of the present application;

[0047] Fig.14 A schematic diagram of energy storage charging and discharging power provided according to an embodiment of the present application;

[0048] Fig.15 A schematic diagram of wind load output on a typical day provided according to an embodiment of the present application;

[0049] Fig.16 A diagram comparing system power before and after adding energy storage provided in an embodiment of the present application;

[0050] Fig.17 A schematic diagram of a two-stage wind-storage coordination planning device considering source-load uncertainty provided according to an embodiment of the present application;

[0051] Fig.18 Schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0052] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0053] The following describes the two-stage wind-storage coordination planning method and device considering the uncertainty of source and load in the embodiment of the present application with reference to the accompanying drawings. In response to the problem of uncertainty in the output of distributed power sources mentioned in the above background technology, the present application provides a two-stage wind-storage coordination planning method considering the uncertainty of source and load. In this method, by comprehensively considering the planning data of distributed wind power and energy storage, and establishing a two-stage wind-storage coordination planning scheme based on the uncertainty of source and load, it is possible to more comprehensively consider the complexity and variability in actual operation, and use the target ant lion algorithm combined with the adaptive Levy flight mechanism and the golden sine algorithm to optimize the location and capacity configuration of wind farms and energy storage equipment to achieve the optimal configuration of resources, improve the economy and reliability of the system, and at the same time, enhance the adaptability to changes in the external environment, which helps to reduce operating costs and environmental impacts. As a result, the problems of uncertainty in the output of distributed power sources, voltage over-limit, increased grid losses and increased operating costs in the related technologies are solved.

[0054] Specifically, Figure 1 A flow chart of a two-stage wind-storage coordination planning method considering source-load uncertainty provided in an embodiment of the present application.

[0055] like Figure 1 As shown in FIG, the two-stage wind-storage coordination planning method considering source-load uncertainty includes the following steps:

[0056] In step S101, distributed wind power planning data and distributed energy storage planning data are obtained.

[0057] Among them, distributed wind power planning data may include system active power loss indicators, voltage deviation indicators and system comprehensive cost indicators, and distributed energy storage planning data may include energy storage operation and maintenance cost indicators and energy storage investment cost indicators.

[0058] It can be understood that the embodiments of the present application facilitate subsequent planning of wind power and energy storage systems by acquiring distributed wind power planning data and distributed energy storage planning data.

[0059] In step S102, based on the distributed wind power planning data and the distributed energy storage planning data, a two-stage wind power storage coordination planning scheme is established that takes into account the uncertainty of source and load.

[0060] It can be understood that the embodiment of the present application establishes a two-stage wind-storage coordination planning scheme by comprehensively considering the source-load uncertainty in the distributed wind power and energy storage planning data, thereby improving the operating efficiency and safety of the power grid.

[0061] In an embodiment of the present application, based on distributed wind power planning data and distributed energy storage planning data, a two-stage wind-storage coordination planning scheme that takes into account source-load uncertainty is established, including: establishing a first-stage distributed wind power planning target based on the distributed wind power planning data; and establishing a second-stage distributed energy storage planning target based on the distributed energy storage planning data.

[0062] It can be understood that the embodiment of the present application divides the planning process into two stages, wind power and energy storage, and each stage sets goals based on its specific planning data, so that the planning work is more organized and the goals are clearer. Among them, the distributed wind power planning goals in the first stage can accurately locate the construction location and scale of the wind farm based on actual data such as wind power resource distribution, wind speed characteristics, topography, etc., to ensure the maximum utilization of wind power resources; the second stage determines the configuration plan of the energy storage system based on data such as the characteristics of energy storage technology and energy storage demand forecasts to meet the system's demand for energy storage capacity.

[0063] In the embodiment of the present application, the calculation formula for the first stage distributed wind power planning target is:

[0064]

[0065]

[0066] Among them, f loss is the system active power loss index, G i,j is the conductance value between node i and node j, L represents the number of system lines, U i is the voltage value of node i, U j is the voltage value of node j, cosδ ij is the voltage phase angle difference between nodes i and j, fΔU is the system voltage offset value, U n is the system rated voltage; N is the number of system nodes;

[0067] f cb is the comprehensive cost index of the system, P L、W (s) is the probability of occurrence of typical wind load scenarios, f s yx is the operating cost in a typical scenario; f s tz is the investment cost in a typical scenario, f s loss is the network loss cost in a typical scenario, f s ec is the environmental benefit cost under typical scenarios, f s gd is the cost of purchasing electricity from the large power grid in a typical scenario, f s bt is the operating cost of a typical scenario, f yx is the wind power operation cost, h f is the unit cost coefficient of wind power operation, P f,i is the power generated by the i-th wind turbine, T f is the annual wind power generation duration, N f is the set of wind power accessible nodes, f tz is the wind power investment cost, c f is the unit capacity investment cost of wind power, d is the annual interest rate, T f is the whole life cycle of wind power, c s is the line loss cost per unit length, P jl is the maximum network loss of line j, T max is the annual maximum load loss time, n is the number of environmental pollutants, c em is the unit cost of the mth pollutant, e m is the emission index of the mth pollutant under the traditional power generation mode, P load is the total line load, P loss is the network loss, c gd is the unit cost of electricity purchased, P loaadmax is the maximum load of the system, T 1 is the annual maximum load utilization time, c bt is the unit government subsidy fee, f ec is the environmental benefit cost of wind power, f gd is the wind power purchase cost, f bt The operating cost of wind power;

[0068] f lmax is the maximum active power loss of the system, f cbmaxis the maximum comprehensive cost of the system, α is the inertia weight coefficient of the system active power loss, β is the inertia weight coefficient of the system voltage offset value, and γ is the inertia weight coefficient of the system comprehensive cost.

[0069] In the embodiment of the present application, the calculation formula for the second stage distributed energy storage planning target is:

[0070] f ye =h e (∫η 1 s c P ec -P ef s f / η 2 dt)

[0071]

[0072] minf 2 =f ye +f te

[0073] Among them, f ye is the operating cost of the energy storage device, h e is the unit cost coefficient of energy storage operation, η 1 is the charging efficiency of the energy storage device, η 2 is the discharge efficiency of the energy storage device, P ec is the energy storage charging power, P ef is the energy storage discharge power, s c is the charging state, s f is the discharge state, f te is the investment cost of energy storage device, c ep is the unit power of energy storage, c es is the energy storage capacity cost, P e is the actual power of energy storage; S e is the energy storage capacity; d is the annual interest rate, T e For the entire life cycle of the energy storage device.

[0074] In step S103, the two-stage wind-storage coordination planning scheme is input into the target model, and the target model outputs the target access position and target capacity of the wind farm and the energy storage device respectively. The target model is established based on the target ant lion algorithm, and the adaptive Levy flight mechanism and golden sine algorithm are introduced in the position update of the target ant lion algorithm.

[0075] Among them, Levy flight can be a random walk model that dynamically adjusts flight parameters according to the information in the search process; the golden sine algorithm can be an optimization algorithm based on the golden ratio and the sine function.

[0076] It can be understood that the embodiment of the present application combines the adaptive Levy flight mechanism and the target ant lion algorithm of the golden sine algorithm to perform two-stage coordinated planning of wind farms and energy storage equipment, accurately determine the location and capacity of wind farms and energy storage equipment, reduce investment costs and improve system flexibility and reliability.

[0077] In the embodiment of the present application, the target model is:

[0078]

[0079] v~N(0,1)

[0080]

[0081] in, represents the position of the i-th ant in the t-th iteration; represents the position of the i-th ant in the t+1th iteration; a represents the random step length; Levy(θ) represents the random search path that follows the Levy distribution, θ represents the Levy distribution parameter, u and ν represent random numbers that follow the standard normal distribution, and R 1 and R 2 is a random number, x 1 and x 2 is the coefficient obtained by the golden section, is the position of the i-th particle at the T-th iteration in n-dimensional space, is the historical best position of the ith particle in the Tth iteration, is the standard deviation of a normal distribution.

[0082] According to the two-stage wind-storage coordination planning method considering the uncertainty of source and load proposed in the embodiment of the present application, by comprehensively considering the planning data of distributed wind power and energy storage, and establishing a two-stage wind-storage coordination planning scheme based on the uncertainty of source and load, it is possible to more comprehensively consider the complexity and variability in actual operation, and use the target ant lion algorithm combined with the adaptive Levy flight mechanism and the golden sine algorithm to optimize the location and capacity configuration of wind farms and energy storage equipment to achieve the optimal configuration of resources, improve the economy and reliability of the system, and at the same time, enhance the adaptability to changes in the external environment, which helps to reduce operating costs and environmental impacts. As a result, the problems of uncertainty in the output of distributed power sources, voltage over-limit, increased grid losses, and increased operating costs in related technologies are solved.

[0083] The following will be combined Figure 2 The two-stage wind-storage coordination planning method considering source-load uncertainty is specifically described as follows:

[0084] S1. Taking into account the power flow constraints, node voltage constraints, branch power constraints and wind turbine capacity constraints, the system active loss index, voltage deviation index and system comprehensive cost index are constructed respectively, and the first-stage distributed wind power planning objectives are proposed;

[0085] Specifically, Figure 3 As shown in the figure, the system active power loss index, voltage deviation index and system comprehensive cost index are constructed respectively. Among them, the calculation formula of the first stage distributed wind power planning target is:

[0086]

[0087] Among them, f loss is the system active power loss, G i,j is the conductance value between node i and node j, L represents the number of system lines, U i is the voltage value of node i, U j is the voltage value of node j, cosδ ij is the voltage phase angle difference between nodes i and j, f ΔU is the system voltage offset value, U n is the system rated voltage; N is the number of system nodes;

[0088] f cb is the system comprehensive cost, P L、W (s) is the probability of occurrence of typical wind load scenarios, f s yx is the operating cost in a typical scenario; f s tz is the investment cost in a typical scenario, f s loss is the network loss cost in a typical scenario, f s ec is the environmental benefit cost under typical scenarios, f s gd is the cost of purchasing electricity from the large power grid in a typical scenario, f s bt is the operating cost of a typical scenario, f yx is the wind power operation cost, h f is the unit cost coefficient of wind power operation, P f,i is the power generated by the i-th wind turbine, T f is the annual wind power generation duration, N f is the set of wind power accessible nodes, f tz is the wind power investment cost, c f is the unit capacity investment cost of wind power, d is the annual interest rate, T f is the whole life cycle of wind power, c s is the line loss cost per unit length, P jlis the maximum network loss of line j, T max is the annual maximum load loss time, n is the number of environmental pollutants, c em is the unit cost of the mth pollutant, e m is the emission index of the mth pollutant under the traditional power generation mode, P load is the total line load, P loss is the network loss, c gd is the unit cost of electricity purchased, P loaadmax is the maximum load of the system, T 1 is the annual maximum load utilization time, c bt is the unit government subsidy fee, f ec is the environmental benefit cost of wind power, f gd is the wind power purchase cost, f bt The operating cost of wind power;

[0089] f lmax is the maximum active power loss of the system, f cbmax is the maximum comprehensive cost of the system, α is the inertia weight coefficient of the system active power loss, β is the inertia weight coefficient of the system voltage offset value, and γ is the inertia weight coefficient of the system comprehensive cost.

[0090] It should be noted that the power flow constraint is:

[0091]

[0092] Among them, P i represents the active power of node i, Q i represents the reactive power of node i; U i represents the actual voltage at node i, U j represents the actual voltage at node j; G ij represents the branch conductance between nodes i and j, B ij represents the branch susceptance between nodes i and j; δ ij represents the voltage phase angle difference between nodes i and j; N is the number of system nodes;

[0093] The node voltage constraints are:

[0094] U imin ≤U i ≤U imax

[0095] Among them, U imin represents the lower voltage limit of node i, U imax Represents the voltage upper limit of node i.

[0096] The branch power constraint is:

[0097] P ijmin ≤P ij≤P ijmax

[0098] Among them, P ij represents the active power of the line between nodes i and j; P ijmin represents the lower limit of the active power of the line connecting nodes i and j, P ijmax Indicates the upper limit of the active power of the line connecting nodes i and j.

[0099] The wind turbine capacity constraint is:

[0100] P fmin ≤P f ≤P fmax

[0101] Among them, P f Indicates the maximum capacity of the wind turbine; P fmax Respectively represent the maximum capacity of wind power; P fmin They represent the minimum capacity of wind power respectively.

[0102] S2. Taking into account the energy storage charging and discharging power constraints, energy storage capacity constraints, energy storage charging and discharging state constraints and system power balance constraints, respectively construct energy storage operation and maintenance cost indicators and energy storage investment cost indicators, and propose the second-stage distributed energy storage planning goals;

[0103] Specifically, Figure 3 As shown in the figure, the energy storage operation and maintenance cost index and the energy storage investment cost index are constructed respectively. The calculation formula of the distributed energy storage planning target in the second stage is:

[0104] f ye =h e (∫η 1 s c P ec -P ef s f / η 2 dt)

[0105]

[0106] minf 2 =f ye +f te

[0107] Among them, f ye is the operating cost of the energy storage device, h e is the unit cost coefficient of energy storage operation, η 1 is the charging efficiency of the energy storage device, η 2 is the discharge efficiency of the energy storage device, P ec is the energy storage charging power, P ef is the energy storage discharge power, s c is the charging state, sf is the discharge state, f te is the investment cost of energy storage device, c ep is the unit power of energy storage, c es is the energy storage capacity cost, P e is the actual power of energy storage; S e is the energy storage capacity; d is the annual interest rate, T e For the entire life cycle of the energy storage device.

[0108] It should be noted that the energy storage charging and discharging power constraints are:

[0109]

[0110] Among them, P ecmax Indicates the maximum charging power of energy storage, P efmax Indicates the maximum discharge power of energy storage, P ecmin Represents the minimum charging power of energy storage, P efmin Indicates the minimum discharge power of energy storage.

[0111] The energy storage capacity constraint is:

[0112] 0≤S e ≤S emax

[0113] Among them, S emax Indicates the maximum capacity of the energy storage device.

[0114] The energy storage charging and discharging state constraints are:

[0115] s c +s f =1

[0116] The system power balance constraint is:

[0117] P L +P ec s c =P f +P ef s f

[0118] Among them, P L Indicates the power of the load.

[0119] S3. Use the target ant lion algorithm with adaptive Levy flight and golden sine theory as improvement factors to solve the first-stage distributed wind power planning objectives and the second-stage distributed energy storage planning objectives to obtain the access location and capacity of the wind farm and energy storage.

[0120] Specifically, Figure 3As shown in the figure, the target ant lion algorithm is used to solve the first-stage distributed wind power planning target and the second-stage distributed energy storage planning target. The specific steps are as follows:

[0121] S3.1. Parameter initialization and construction of ant random walk model:

[0122] X i,j,t =[0,cumsum[2r(1)-1],...,cumsum[2r(t)-1],...,cumsum[2r(T)-1]}

[0123] Among them, X i,j,t represents the random walk step length of the i-th ant in the t-th iteration of the j-th variable; cumsum represents the cumulative sum of positions; t represents the current number of iterations; T represents the maximum number of iterations; r(i) represents a random number uniformly randomly distributed on [0,1] during the i-th iteration:

[0124]

[0125] Among them, rand represents a randomly generated random number.

[0126] In order to maintain the randomness of the ant's movement route and the ant lion's search space is limited, the ant's random walk model is normalized:

[0127]

[0128] Among them, X i,j,t ' represents the normalized route of random walk of ants; X i,j,t Indicates the unnormalized route of ant random walk; min(X i,j,t ) represents the minimum value of the random walk of the i-th ant in the j-dimensional variable; max(X i,j,t ) represents the maximum value of the random walk of the i-th ant in the j-dimensional variable; min(X i,j,t ) t represents the minimum value of the random walk of the i-th ant after the i-th iteration of the j-dimensional variable; max(X i,j,t ) t It represents the maximum value of the random walk of the i-th ant after the t-th iteration of the j-dimensional variable.

[0129] S3.2. Randomly initialize the positions of antlions and ants, and construct an antlion trap model. The trap constructed by the antlion is affected by the random walk mechanism of ants. The roulette wheel selection mechanism is used to simulate the process:

[0130]

[0131] Among them, AL j trepresents the position of the jth antlion at the tth iteration; min(X) t represents the minimum value of all ants after the tth iteration; max(X) t Represents the maximum value of all ants after the tth iteration.

[0132] S3.3. Optimize the two-stage wind-storage coordination planning scheme considering source-load uncertainty. When ants fall into the trap constructed by antlions, they will randomly walk around the trap, and antlions will continue to dig out sand to prevent the ants from escaping.

[0133] The trap range is:

[0134]

[0135] Where I represents the size of the trap range; w represents the adjustment coefficient; and T represents the maximum number of iterations.

[0136] Constructing an elite antlion model is an important part of the intelligent evolutionary algorithm, which can make the entire population converge to the optimal solution. In the tth iteration, the position of the i-th ant is:

[0137]

[0138] in, represents the position of the i-th ant in the t-th iteration; represents the step length of the ant's random walk around the antlion selected by the roulette mechanism in the tth iteration; It represents the step length of the ant’s random walk around the elite antlion in the tth iteration.

[0139] S3.4. Output the optimal two-stage wind-storage coordination planning solution considering the uncertainty of source and load. When the fitness of the ant is higher than that of the ant lion, the ant is captured. At this time, the ant lion sets the capture point as the place to reconstruct the trap. The model formula is:

[0140]

[0141] in, represents the position of the i-th ant in the t-th iteration.

[0142] It should be noted that the traditional antlion algorithm has relatively poor local search capabilities and is prone to falling into local optimal solutions and being unable to jump out. At the same time, the traditional antlion algorithm also has the problem of insufficient precision, and it still needs to be improved in high-precision application scenarios to obtain better optimization effects. Therefore, adaptive Levy flight and golden sine theory are introduced to improve the antlion algorithm.

[0143] An adaptive Levy flight mechanism is introduced into the position update of ants to mutate them. The random walk mode of Levy flight obeys the Levy distribution:

[0144]

[0145] Among them, a represents the peak height of the Levy distribution diagram, where 0 < a ≤ 2. When a = 1, P a,b (x) is a Cauchy distribution. When a = 2, P a,b (x) is a Gaussian distribution; m represents the width-to-length ratio of the Levy distribution diagram, where m > 0.

[0146] When a is a non-integer positive real number, the position update formula is as follows:

[0147]

[0148] Among them, represents the position of the i-th ant in the (t + 1)-th iteration; a represents the random step size; Levy(θ) represents the random search path that follows the Levy distribution.

[0149] Levy(θ) conforms to the constraint definition as follows:

[0150]

[0151] Among them, θ represents the Levy distribution parameter; u and ν represent random numbers that follow the standard normal distribution; is the standard deviation that follows the normal distribution.

[0152] The expression of

[0153]

[0154] Introduce the golden sine algorithm in the position update of the elite antlion. The position update process of the golden sine algorithm:

[0155]

[0156] Among them, R 1 and R 2 are random numbers, x 1 and x 2 are coefficients obtained by the golden section, is the position of the i-th particle in the n-dimensional space at the T-th iteration, is the historical best position of the i-th particle at the T-th iteration.

[0157] During the position update process, first randomly generate s individual positions, and use to represent the position of the i-th particle in the n-dimensional space at the T-th iteration, where i = 1, 2,..., s, and use To represent the historical best position of the i-th particle in the T-th iteration. 1 Determines the moving distance of the individual in the next iteration, R 1 ∈[0,2π], R 2 Determines the position update direction for the next iteration, R 2 ∈[0,π]. 1 and x 2 It can narrow the search space and lead individuals to the optimal value, the golden ratio x 1 = -π+2π(1-τ), x 2 =-π+2πτ.

[0158] In order to verify the effectiveness of the target ant lion algorithm in the embodiment of the present application, the ant lion algorithm, the target ant lion algorithm, and the particle swarm algorithm are respectively used for optimization and solution. The number of iterations of each algorithm is set to 100 times, the number of populations is 40, and the running results are shown in Table 1 below.

[0159] Table 1 Comparison of results of three intelligent optimization algorithms

[0160]

[0161] From Table 1 and Figure 5 It can be seen that compared with the other two algorithms, the target ant lion algorithm has a longer operation time due to the addition of adaptive Levy flight and golden sine algorithm modules. The initial iteration curves of the three algorithms are approximately linearly declining, which also shows that the initial optimization speed of the three is relatively fast. However, after multiple iterations, the convergence speed of the particle swarm algorithm and the ant lion algorithm is significantly lower than that of the target ant lion algorithm. The target ant lion algorithm obtains the optimal solution at the 18th iteration, the ant lion algorithm obtains the optimal solution at the 44th iteration, and the particle swarm algorithm obtains the optimal solution at the 56th iteration. Therefore, it can be seen that the introduction of the target ant lion algorithm improvement factor makes its convergence speed faster, the optimization ability is stronger, and the convergence accuracy is correspondingly increased. Therefore, it can be verified that the target ant lion algorithm in the embodiment of the present application has the advantages of strong search ability, fast convergence speed and high accuracy in dealing with distributed power planning problems.

[0162] In view of the effectiveness of using the target ant lion algorithm to solve the two-stage wind storage coordination planning scheme considering the uncertainty of source and load, the embodiment of the present application refers to the IEEE33-node standard distribution network model and calculates the power flow of the system through the forward and backward method. The system contains 33 nodes, 32 branches, the reference voltage level is 12.66KV, the three-phase reference power value is 10MVA, and 1 is a balancing node without access to distributed power sources. The total load size of the system is 3715+j2300KVA, of which the industrial load selects nodes 1-8, 17-26, and the total load size is 2150+j1045KVA. The probability distribution is as follows Figure 6As shown; the residential load is nodes 9-16, 27-33, the total load size is 1565+j1255KVA, and the probability distribution is as follows Figure 7 The typical wind load scenario is taken as 10, and the probability distribution is as follows Figure 8 shown.

[0163] For the solution of the distributed wind power planning target in the first phase, 40 antlion individuals were selected through the target antlion algorithm, and the maximum number of iterations was 100 for optimization calculation. In order to verify the effectiveness of the proposed method, three planning schemes were introduced in the embodiment of the present application for comparative analysis. The solution results of the four schemes are shown in Table 2 below.

[0164] Table 2 Power supply planning schemes under different methods

[0165]

[0166] From Table 2 and Fig. 9 It can be seen that the planning scheme solved by the target ant lion algorithm has the largest power access capacity and the highest penetration rate, which lays a good foundation for the economic and reliable operation of the system; the wind power access location obtained by the target ant lion algorithm is at nodes 12 and 31, both of which are in the middle and end of the distribution network line. In the results solved by the ant lion algorithm and the particle swarm algorithm, node 24 is at the end of the line, node 14 is at the middle and end of the line, and nodes 8 and 26 are at the front end of the line. In the solution results without considering the uncertainty of source and load, nodes 4 and 19 are both at the front end of the line. The connection of distributed power sources to the middle and back ends of the system has a significant improvement on the system voltage distribution and network loss. Therefore, the planning scheme based on the target ant lion algorithm can more effectively improve the system operation performance than other schemes.

[0167] The embodiment of the present application can more carefully verify the effectiveness of the two-stage wind-storage coordination planning method that takes into account source-load uncertainty, and will analyze four aspects: voltage distribution, active power loss, voltage deviation, and system comprehensive cost.

[0168] System voltage distribution, such as Fig.10 As shown in the figure, the voltage improvement effect of the scheme obtained by the target ant lion algorithm is the most obvious, which further verifies the superiority of the planning scheme based on the target ant lion algorithm.

[0169] System active power loss and voltage deviation are shown in Table 3 below:

[0170] Table 3 Active power loss and voltage deviation

[0171]

[0172] It can be seen from Table 3 that the active power loss and voltage deviation solved by the target ant lion algorithm are the smallest compared with the other three schemes, and can more effectively improve the economy and stability of power system operation than other schemes.

[0173] System comprehensive cost, the solution results are shown in Table 4 below:

[0174] Table 4 Comprehensive costs under different schemes

[0175]

[0176] From Table 4 and Fig.11 It can be seen that the target ant lion algorithm planning scheme has the largest access power capacity and the highest penetration rate, so its power investment and operating costs are higher than other schemes. However, due to the reasonable planning of the scheme, the costs of system line loss, environmental pollution and electricity purchase costs have been greatly reduced, effectively offsetting the high investment and operation costs; the line loss cost of the target ant lion algorithm planning scheme is 187,100 yuan, which is the smallest compared with the other three schemes, indicating that the target ant lion algorithm can well assist distributed power sources in reducing line network losses and improving the economy of system operation; in terms of electricity purchase costs, due to the access of distributed power sources, the system's self-power supply capacity increases, and the demand for purchasing electricity from the large power grid is correspondingly reduced, thereby reducing the cost of purchasing electricity.

[0177] Compared with the other three schemes, the introduction of the target ant lion algorithm further reduces the system's electricity purchase cost, verifying the superiority of the algorithm; compared with the above four schemes, the target ant lion algorithm, ant lion algorithm and particle swarm algorithm solutions that take into account the uncertainty of source and load output, due to the introduction of the probability of typical scenarios, the system's comprehensive cost is greatly reduced compared with before the introduction. This is because the source and load output are closely related to the comprehensive cost. The output scenario will cause changes in indicators such as system network loss and environmental costs. Subdividing the output scenario will also optimize the cost indicators. Therefore, reasonable consideration of source and load uncertainty can effectively improve the reliability and economy of system operation.

[0178] The distributed energy storage planning objectives for the second phase are solved and the energy storage configuration results are shown in Table 5 below.

[0179] Table 5 Distributed energy storage configuration schemes under different methods

[0180]

[0181] From Table 5 and Fig.12 It can be seen that the energy storage installation capacity based on the particle swarm algorithm and the planning scheme that does not consider the uncertainty of source and load is small. Although the energy storage access capacity based on the ant lion algorithm is large, the 4 nodes are close to the front end of the line, and the larger the energy storage capacity, the higher the economic cost. Therefore, the solution of the ant lion algorithm is not suitable as the optimal processing method compared with the target ant lion algorithm. The introduction of the target ant lion algorithm makes the energy storage installation capacity moderate, the economic benefits are good, and the installation location is close to the distributed power source, which is in line with the original intention of energy storage installation, that is, to suppress the real-time power fluctuations caused by the access of distributed power sources.

[0182] like Fig.13 and Fig.14 As shown, the energy storage is not always working, but is indirectly charged and discharged according to the real-time operating status of the system. In order to verify the effect of energy storage access, the embodiment of this application selects Fig.15 The validity is verified on a typical day shown.

[0183] By Fig.16 It can be seen that with the addition of distributed energy storage devices, the load curve fluctuations of the system on a typical day have a significant easing trend compared to before the addition of energy storage devices, and the peak-to-valley difference in load output is significantly reduced, and power fluctuations are suppressed; during peak load operation, the energy storage device acts as a power source to assist distributed wind power in providing electricity to the system, while during low load periods, the energy storage device acts as a load and plays a charging role; the access of distributed energy storage devices cooperates with distributed wind power generation. When wind power output is low, the energy storage acts as a power source to supply power to the load, thereby achieving reliable operation of the system.

[0184] Next, a two-stage wind-storage coordination planning device considering source-load uncertainty proposed in an embodiment of the present application is described with reference to the accompanying drawings.

[0185] Fig.17 It is a block diagram of a two-stage wind-storage coordination planning device considering source-load uncertainty according to an embodiment of the present application.

[0186] like Fig.17 As shown, the two-stage wind-storage coordination planning device 10 considering source-load uncertainty includes: an acquisition module 100 , an establishment module 200 and an output module 300 .

[0187] Among them, the acquisition module 100 is used to obtain distributed wind power planning data and distributed energy storage planning data; the establishment module 200 is used to establish a two-stage wind-storage coordination planning scheme taking into account source-load uncertainty based on the distributed wind power planning data and the distributed energy storage planning data; the output module 300 is used to input the two-stage wind-storage coordination planning scheme into the target model, and the target model outputs the target access position and target capacity of the wind farm and the energy storage equipment respectively, wherein the target model is established based on the target ant lion algorithm, and the adaptive Levy flight mechanism and the golden sine algorithm are introduced in the position update of the target ant lion algorithm.

[0188] It should be noted that the above explanation of the embodiment of the two-stage wind-storage coordination planning method considering source-load uncertainty is also applicable to the two-stage wind-storage coordination planning device considering source-load uncertainty of this embodiment, and will not be repeated here.

[0189] According to the two-stage wind-storage coordination planning device that considers the uncertainty of source and load proposed in the embodiment of the present application, by comprehensively considering the planning data of distributed wind power and energy storage, and establishing a two-stage wind-storage coordination planning scheme based on the uncertainty of source and load, it is possible to more comprehensively consider the complexity and variability in actual operation, and use the target ant lion algorithm combined with the adaptive Levy flight mechanism and the golden sine algorithm to optimize the location and capacity configuration of wind farms and energy storage equipment to achieve the optimal configuration of resources, improve the economy and reliability of the system, and at the same time, enhance the adaptability to changes in the external environment, which helps to reduce operating costs and environmental impacts. As a result, the problems of uncertainty in the output of distributed power sources, voltage over-limit, increased grid losses, and increased operating costs in related technologies are solved.

[0190] Fig.18 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0191] A memory 1801 , a processor 1802 , and a computer program stored in the memory 1801 and executable on the processor 1802 .

[0192] When the processor 1802 executes the program, the two-stage wind-storage coordination planning method considering source-load uncertainty provided in the above embodiment is implemented.

[0193] Furthermore, the electronic device further comprises:

[0194] The communication interface 1803 is used for communication between the memory 1801 and the processor 1802 .

[0195] The memory 1801 is used to store computer programs that can be executed on the processor 1802 .

[0196] The memory 1801 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0197] If the memory 1801, the processor 1802 and the communication interface 1803 are implemented independently, the communication interface 1803, the memory 1801 and the processor 1802 can be connected to each other through a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig.18 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0198] Optionally, in a specific implementation, if the memory 1801, the processor 1802 and the communication interface 1803 are integrated on a chip, the memory 1801, the processor 1802 and the communication interface 1803 can communicate with each other through an internal interface.

[0199] The processor 1802 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0200] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned two-stage wind-storage coordination planning method considering source-load uncertainty.

[0201] An embodiment of the present application also provides a computer program product, including a computer program or instructions, which, when executed, implements the above-mentioned two-stage wind-storage coordination planning method considering source-load uncertainty.

[0202] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" 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 application. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0203] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0204] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0205] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array, a field programmable gate array, etc.

[0206] A person skilled in the art may understand that all or part of the steps in the above-mentioned embodiment method may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0207] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A two-stage wind-storage coordination planning method considering source-load uncertainty, characterized in that: The following steps are involved: Obtain distributed wind power planning data and distributed energy storage planning data; Based on the distributed wind power planning data and the distributed energy storage planning data, a two-stage wind-storage coordination planning scheme considering source-load uncertainty is established; The two-stage wind-storage coordination planning scheme is input into a target model, and the target model outputs the target access position and target capacity of the wind farm and the energy storage device respectively. The target model is established based on a target ant lion algorithm, and an adaptive Levy flight mechanism and a golden sine algorithm are introduced in the position update of the target ant lion algorithm.

2. The two-stage wind-storage coordination planning method considering source-load uncertainty according to claim 1 is characterized in that: The target model is: v~N(0,1) in, represents the position of the i-th ant in the t-th iteration; represents the position of the i-th ant in the t+1th iteration; a represents the random step length; Levy(θ) represents the random search path that obeys the Levy distribution, θ represents the Levy distribution parameter, u and ν represent random numbers that obey the standard normal distribution, R1 and R2 are random numbers, and x1 and x2 are coefficients obtained by the golden section. is the position of the ith particle in the Tth iteration in n-dimensional space, P i T is the historical best position of the ith particle in the Tth iteration, is the standard deviation of a normal distribution.

3. The two-stage wind-storage coordination planning method considering source-load uncertainty according to claim 1 is characterized in that: The two-stage wind power storage coordination planning scheme considering source-load uncertainty is established based on the distributed wind power planning data and the distributed energy storage planning data, including: Establishing the first phase distributed wind power planning target according to the distributed wind power planning data; A second-stage distributed energy storage planning target is established based on the distributed energy storage planning data.

4. The two-stage wind-storage coordination planning method considering source-load uncertainty according to claim 3 is characterized in that: The distributed wind power planning data includes a system active power loss index, a voltage deviation index and a system comprehensive cost index, and the distributed energy storage planning data includes an energy storage operation and maintenance cost index and an energy storage investment cost index.

5. The two-stage wind-storage coordination planning method considering source-load uncertainty according to claim 3 is characterized in that: The calculation formula for the first phase distributed wind power planning target is: Among them, f loss is the system active power loss, G i,j is the conductance value between node i and node j, L represents the number of system lines, U i is the voltage value of node i, U j is the voltage value of node j, cosδ ij is the voltage phase angle difference between nodes i and j, f ΔU is the system voltage offset value, U n is the system rated voltage; N is the number of system nodes; f cb is the system comprehensive cost, P L、W (s) is the probability of occurrence of typical wind load scenarios, f s yx is the operating cost in a typical scenario; f s tz is the investment cost in a typical scenario, f s loss is the network loss cost in a typical scenario, f s ec is the environmental benefit cost under typical scenarios, f s gd is the cost of purchasing electricity from the large power grid in a typical scenario, f s bt is the typical scenario operating cost, f yx is the wind power operation cost, h f is the unit cost coefficient of wind power operation, P f,i is the power generated by the i-th wind turbine, T f is the annual wind power generation duration, N f is the set of wind power accessible nodes, f tz is the wind power investment cost, c f is the unit capacity investment cost of wind power, d is the annual interest rate, T f is the whole life cycle of wind power, c s is the line loss cost per unit length, P jl is the maximum network loss of line j, T max is the annual maximum load loss time, n is the number of environmental pollutants, c em is the unit cost of the mth pollutant, e m is the emission index of the mth pollutant under the traditional power generation mode, P load is the total line load, P loss is the network loss, c gd is the unit cost of electricity purchased, P loaadmax is the maximum load of the system, T1 is the annual maximum load utilization time, c bt is the unit government subsidy fee, f ec is the environmental benefit cost of wind power, f gd is the wind power purchase cost, f bt The operating cost of wind power; f lmax is the maximum active power loss of the system, f cbmax is the maximum comprehensive cost of the system, α is the inertia weight coefficient of the system active power loss, β is the inertia weight coefficient of the system voltage offset value, and γ is the inertia weight coefficient of the system comprehensive cost.

6. The two-stage wind-storage coordination planning method considering source-load uncertainty according to claim 3 is characterized in that: The calculation formula for the second stage distributed energy storage planning target is: f ye =h e (∫η1s c P ec -P ef s f / η2dt) minf2=f ye +f te Among them, f ye is the operating cost of the energy storage device, h e is the unit cost coefficient of energy storage operation, η1 is the charging efficiency of the energy storage device, η2 is the discharging efficiency of the energy storage device, P ec is the energy storage charging power, P ef is the energy storage discharge power, s c is the charging state, s f is the discharge state, f te is the investment cost of energy storage device, c ep is the unit power of energy storage, c es is the energy storage capacity cost, P e is the actual power of energy storage; S e is the energy storage capacity; d is the annual interest rate, T e The entire life cycle of the energy storage device.

7. A two-stage wind-storage coordination planning device considering source-load uncertainty, characterized in that: include: An acquisition module, used to acquire distributed wind power planning data and distributed energy storage planning data; Establishing a module for establishing a two-stage wind power storage coordination planning scheme taking into account source-load uncertainty based on the distributed wind power planning data and the distributed energy storage planning data; The output module is used to input the two-stage wind-storage coordination planning scheme into a target model, and the target model outputs the target access position and target capacity of the wind farm and the energy storage device respectively, wherein the target model is established based on the target ant lion algorithm, and the adaptive Levy flight mechanism and the golden sine algorithm are introduced in the position update of the target ant lion algorithm.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the two-stage wind-storage coordination planning method considering source-load uncertainty as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed, the two-stage wind-storage coordination planning method considering source-load uncertainty as described in any one of claims 1-6 is implemented.

10. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed, the two-stage wind-storage coordination planning method considering source-load uncertainty as described in any one of claims 1-6 is implemented.

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