Urban power grid wind and light storage multi-target planning method and device

Abstract

The invention relates to the technical field of new energy and power system optimization planning, in particular to an urban power grid wind and light storage multi-target planning method and device, and the method comprises the steps: constructing an urban micro-grid cluster system model, and configuring a micro-grid operation mode; generating a wind and light output scene by adopting Vine Copula based on historical wind and light output data; a multi-target optimization model with the minimum annual total cost and the minimum source load power mismatching degree as the target is established based on the wind and light output scene; and solving the multi-objective optimization model by adopting an improved multi-objective ant lion optimization algorithm to obtain a Pareto optimal wind and light storage capacity planning scheme. Compared with the prior art, the method can effectively improve the authenticity of a wind and light output scene and the robustness of system planning, achieves the energy coordination and complementation between urban microgrids, reduces the system operation cost, and improves the renewable energy consumption rate.

Description

A Multi-Objective Planning Method and Device for Urban Power Grid Wind-Solar-Storage Programming Technical Field

[0001] This invention relates to the field of power system optimization planning technology, and in particular to a multi-objective planning method and apparatus for urban power grid wind-solar-storage based on Vine Copula and Antlion optimization. Background Technology

[0002] With the low-carbon transformation of urban energy structures and the widespread application of distributed energy, renewable energy sources such as wind power and photovoltaics are gradually becoming an important part of urban power supply. Microgrids, as an important carrier connecting distributed energy and user-side loads, have advantages such as flexible regulation and regional autonomy. However, in the complex urban environment, wind and solar energy resources exhibit significant randomness and spatial variability, making it difficult to simultaneously achieve both economic efficiency and reliability in the coordinated planning and operation of multiple microgrids.

[0003] Traditional microgrid planning is mostly based on deterministic or single-objective optimization models, which fail to fully consider spatiotemporal correlation and multi-energy complementarity characteristics, resulting in the following problems: 1. Insufficient accuracy of scenario modeling. Traditional microgrid planning often adopts simplified probabilistic assumptions (such as Gaussian distribution and linear correlation), which cannot accurately capture the non-Gaussian and high-dimensional spatiotemporal dependence characteristics of wind and solar energy, resulting in large deviations between the generated scenarios and actual operation, and poor robustness of the planning scheme.

[0004] 2. Poor coordination among multiple objectives: Existing methods often focus on a single objective (such as minimizing cost) and ignore the trade-off between economy and reliability, making it difficult to meet the dual requirements of low energy consumption and high reliability of microgrid clusters.

[0005] 3. Weak interconnection and coordination capabilities: isolated microgrid planning cannot utilize the energy complementarity characteristics within the cluster, and over-reliance on the main grid for power supply leads to low renewable energy consumption rate and high operating costs.

[0006] 4. The optimization algorithm is inefficient. Traditional multi-objective optimization algorithms have problems such as slow convergence and insufficient solution diversity, making it difficult to quickly find the globally optimal capacity configuration scheme under complex constraints. Therefore, there is an urgent need for a microgrid cluster planning method that can accurately model the uncertainty of wind and solar power, efficiently balance multiple objectives, and give full play to the advantages of interconnection and collaboration. Summary of the Invention

[0007] The purpose of this invention is to address the problems of poor multi-objective coordination, weak interconnection and coordination capabilities, and low optimization algorithm efficiency in current technical solutions, and to provide a multi-objective planning method for urban power grid wind, solar and energy storage based on Vine Copula and Antlion optimization.

[0008] The objective of this invention can be achieved through the following technical solution: As a first aspect of this invention, a multi-objective planning method for wind, solar, and energy storage in urban power grids is provided, comprising the following steps: constructing a model of an urban microgrid cluster system and configuring the microgrid operation mode; generating wind and solar power output scenarios using Vine Copula based on historical wind and solar power output data; establishing a multi-objective optimization model based on the wind and solar power output scenarios with the objectives of minimizing the annual total cost and minimizing the source-load power mismatch; and solving the multi-objective optimization model using an improved multi-objective antlion optimization algorithm to obtain a Pareto optimal wind, solar, and energy storage capacity planning scheme.

[0009] As a preferred technical solution, the urban microgrid cluster system model comprises a cluster system of multiple microgrids, each microgrid including wind turbines, photovoltaics, energy storage, and loads; bidirectional power transmission is achieved between microgrids through interconnection lines, and the microgrids are connected to the main grid through the distribution network; the microgrid operation modes include independent and coordinated operation; in the independent operation mode, the microgrid only interacts with the main grid, and the source-load difference is preferentially compensated by energy storage, and when energy storage is insufficient / surplus, it purchases / sells electricity to the main grid; in the coordinated operation mode, the microgrid preferentially interacts with other microgrids within the cluster, and the remaining difference is then interacted with the main grid.

[0010] As a preferred technical solution, the generation of wind and solar power output scenarios using Vine Copula is as follows: Based on historical wind and solar power output data, the marginal distributions of wind turbine output and photovoltaic output are respectively fitted; the pairwise dependency relationship of wind and solar power output is calculated using Kendall tau coefficients; the high-dimensional wind and solar power output distribution is decomposed into nested bivariate Copula trees using regularized Vine, with each tree edge corresponding to a conditional bivariate Copula distribution; uniform samples that follow the joint distribution of Vine Copula are generated, and the actual wind and solar power output scenarios are obtained through inverse marginal distribution transformation.

[0011] As a preferred technical solution, the marginal distribution of wind turbine output is fitted with a Weibull distribution, and the marginal distribution of photovoltaic output is fitted with a Beta distribution.

[0012] As a preferred technical solution, the total annual cost in the multi-objective optimization model includes investment cost, operation and maintenance cost, interaction cost with the main grid, and interaction cost between microgrids; the investment cost The specific expression is determined by the investment situation in wind power, photovoltaics, and energy storage: in, Indicates the discount rate; Indicates the lifespan of the equipment; This is expressed as equipment capacity; Expressed as unit investment cost; operation and maintenance cost of a single microgrid. Represented as: in, The operation and maintenance coefficient of the microgrid; the interaction cost with the main grid. Calculated by year, the specific expression is: in, and These are the main grid purchase price and the electricity sales price, respectively. and These represent the power purchased / sold; and the interaction cost between the microgrids. Formed by transactions between micro-networks, the specific expression is: in, and These refer to the electricity purchase / sale prices between microgrids; and They represent microgrids To microgrids The power purchased / sold.

[0013] As a preferred technical solution, in the multi-objective optimization model, the charging and discharging power of energy storage under the corresponding scenario is used as the optimization variable, and the objective function for minimizing the source-load power imbalance is expressed as: in, Represented as scene weights; Let i be the output power of fan i in scenario s; Let i be the output power of photovoltaic i in scenario s; For energy storage in scenario s The charging and discharging power; For microgrids The load power.

[0014] As a preferred technical solution, the constraints of the multi-objective optimization model include: capacity constraint, the capacity of each device cannot exceed the maximum capacity; power balance constraint: in, Indicates from microgrid To microgrids Loss coefficient; Islanding operation guarantee constraints: in, Indicates the capacity of energy storage facilities deployed; Indicates the safety factor; Indicates the percentage of non-adjustable load; microgrid Maximum load power; This represents the minimum islanding time that a microgrid must satisfy.

[0015] As a preferred technical solution, the improved multi-objective antlion optimization algorithm is used to solve the multi-objective optimization model. The specific steps are as follows: Set the population size and iteration count; initialize particles, with each particle corresponding to a set of wind and solar storage capacities; perform non-dominated sorting, and stratify the particles according to Pareto dominance: if the particles... Both target values ​​are not inferior to And at least one objective is better, then Dominate Non-dominated particles are assigned to layer 1, and the remaining particles are sorted repeatedly; the crowding degree of particles in each layer is calculated, and particles in the same layer are sorted according to the objective function value, based on the first layer. The maximum and minimum values ​​of the target and the target values ​​of neighboring particles are calculated to determine the first target. Crowding of individual particles The particle position is updated based on the antlion hunting behavior, and the update calculation process is shown in the following formula: in, and The first In the nth iteration The optimal and worst particle values ​​for each variable; and The value is a random number in the range of 0-1; after reaching the maximum number of iterations, the Pareto optimal solution set is output, and the final planning scheme is selected according to the actual operation requirements of the distribution network.

[0016] As a second aspect of the present invention, a multi-objective planning device for wind, solar and energy storage in urban power grids is provided, including a memory, a processor, and a program stored in the memory, wherein the processor executes the program to implement the multi-objective planning method for wind, solar and energy storage in urban power grids as described above.

[0017] As a third aspect of the present invention, a storage medium is provided having a program stored thereon, which, when executed, implements the urban power grid wind-solar-storage multi-objective planning method as described above.

[0018] Compared with the prior art, the present invention has the following beneficial effects: 1) The multi-objective planning method for wind, solar and energy storage in urban power grid proposed in the present invention, and the wind, solar and energy storage system capacity optimization model proposed with the goal of minimizing the annual total cost and minimizing the source-load power mismatch, can achieve synergistic optimization of annual cost and source-load deviation, effectively improve the realism of wind and solar power output scenarios and the robustness of system planning, realize energy coordination and complementarity among urban microgrids, reduce system operating costs and improve the renewable energy consumption rate.

[0019] 2) This invention proposes a multi-objective programming method based on antlion optimization for the constructed multi-objective optimization model. This method simplifies the crowding calculation of the two objectives, reducing the computational cost of each iteration. Furthermore, it optimizes the particle update strategy, where particles no longer wander randomly but update around the current optimal solution direction, enhancing convergence guidance. This method can optimize and obtain a Pareto front that balances the two objectives, and its algorithm efficiency is significantly higher than conventional optimization algorithms and classic intelligent algorithms.

[0020] 3) Based on the non-Gaussian and spatiotemporally dependent characteristics of Vine Copula in capturing wind and solar energy, this invention has small mean, standard deviation and skewness errors between the scene and historical data, resulting in a highly realistic planning scene.

[0021] 4) This invention proposes an operation mode for energy exchange between microgrids, which reduces the dependence of microgrids on the main grid, and ensures that they have a long-term islanding capability by introducing islanding operation constraints. Attached Figure Description

[0022] Figure 1 is a flowchart of the multi-objective planning method for urban power grid wind-solar-storage based on Vine Copula and Antlion optimization according to the present invention. Detailed Implementation

[0023] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0024] Example 1: This invention proposes a multi-objective planning method for urban power grid wind, solar, and energy storage based on Vine Copula and Antlion optimization to improve the realism of wind and solar power output scenarios and the robustness of system planning, realize energy coordination and complementarity among urban microgrids, reduce system operating costs, and improve the renewable energy consumption rate. As shown in Figure 1, the specific steps of this method include: S1: Constructing an urban microgrid cluster system model, defining the output models of wind turbines, photovoltaics, and energy storage, as well as the independent and coordinated operation strategies of the microgrids.

[0025] A cluster system comprising N microgrids is constructed. Each microgrid includes wind turbines (WT), photovoltaic (PV), energy storage (ES), and loads. The microgrids are interconnected via bidirectional power transmission lines, and connected to the main grid through the distribution network. Wind power output is affected by wind speed. Based on Weibull distribution modeling of wind speed characteristics, the corresponding WT output formula is: in, , , These are the fan cut-in, rated, and cut-out wind velocities, respectively. This refers to the rated output of the fan; This indicates the actual output of the fan.

[0026] Similarly, photovoltaic (PV) output is also affected by solar irradiance. Based on the Beta distribution model of solar irradiance, the corresponding PV output formula is: in, , These are the actual and rated irradiance, respectively. , These are the actual and rated temperatures, respectively. Temperature coefficient; This indicates the rated output of the photovoltaic system. This indicates the actual output of photovoltaic power.

[0027] Energy storage operation constraints are represented by charge and discharge behavior, and can be expressed as: in, Indicates energy storage Capacity of time, and They represent energy storage The charging power and discharging power at any given time. and They represent energy storage The charging and discharging indicator variables at any given time; and These represent charging efficiency and discharging efficiency, respectively. and They represent energy storage The minimum and maximum charging power; and They represent energy storage The minimum and maximum discharge power; and They represent energy storage The minimum and maximum capacity.

[0028] Microgrid operation modes can be divided into two categories: independent and coordinated. In the independent operation mode, the microgrid only interacts with the main grid, and the source-load difference is first compensated by energy storage. When energy storage is insufficient or surplus, the microgrid purchases / sells electricity to the main grid. In the coordinated operation mode, the microgrid prioritizes interaction with other microgrids within the cluster, and only interacts with the main grid when there is a remaining difference, thus reducing dependence on the main grid.

[0029] S2: Generate spatiotemporal scenes of wind and solar energy based on Vine Copula, including marginal distribution fitting, dependency structure estimation, Vine Copula construction and scene inverse transformation.

[0030] Based on historical wind power output data, the marginal distribution of wind turbine output was fitted using a Weibull distribution. Beta distribution fits the marginal distribution of photovoltaic power output .

[0031] The pairwise dependency of wind and solar power output is calculated using the Kendall tau coefficient, as shown in the formula: in, , Random variables contributing to the landscape , For its own independent copy, As expected.

[0032] The high-dimensional wind and solar power output distribution is decomposed into nested bivariate Copula trees using regularized Vine, with each tree edge corresponding to a conditional bivariate Copula distribution. Their joint density can be expressed as: Generate uniform samples that follow a Vine Copula joint distribution. The actual wind and solar power output scenario is obtained through inverse marginal distribution transformation: S3: Establish a multi-objective optimization model with the goal of minimizing the total annual cost and the source-load power mismatch, and embed constraints such as capacity, power balance, and energy storage SOC.

[0033] The first objective is to minimize the total annual cost, which is expressed as including investment costs, operation and maintenance costs, and mainnet interaction costs, specifically as follows: in, Expressed as investment cost; Represented as operation and maintenance costs; This is expressed as the cost of interacting with the mainnet; This is expressed as the interaction cost between microgrids.

[0034] Investment costs The specific expression is determined by the investment situation in wind power, photovoltaics, and energy storage: in, Indicates the discount rate; Indicates the lifespan of the equipment; This is expressed as equipment capacity, while It is expressed as unit investment cost.

[0035] Operation and maintenance costs of a single microgrid It can be represented as: in, This represents the operation and maintenance coefficient of the microgrid.

[0036] Mainnet interaction cost The specific expression for calculation by year is as follows: in, and These are the main grid purchase price and the electricity sales price, respectively. and These represent the power purchased / sold, respectively; 8760 represents the number of hours per year, meaning that this embodiment simulates a year of operation.

[0037] Inter-micronet interaction cost Formed by transactions between micro-networks, the specific expression is: in, and These refer to the electricity purchase / sale prices between microgrids; and They represent microgrids To microgrids The power purchased / sold.

[0038] The second objective is to minimize the power imbalance between the source and load, and its objective function is expressed as: in, Represented as scene weights; For energy storage in scenario s The charging and discharging power is the variable to be optimized, and the optimization is performed in step 4. For microgrids The load power.

[0039] After considering multiple microgrids, additional constraints were introduced to ensure coordination with WT, PV, and energy storage.

[0040] During the planning process, the capacity of each device must not exceed its maximum capacity, as shown in the following formula: in, This represents the maximum capacity of the equipment.

[0041] Meanwhile, within each microgrid, power balance needs to be satisfied, and its balance expression is as follows: In addition, considering the power loss on the line, the loss is expressed as: in, Indicates from microgrid To microgrids Loss coefficient.

[0042] Finally, the short-term islanding requirements in the microgrid need to be met, therefore islanding operation guarantee constraints are added: in, Indicates the capacity of energy storage facilities deployed; Indicates the safety factor; Indicates the percentage of non-adjustable load; microgrid Maximum load power; This indicates the minimum islanding time that the microgrid must meet, which is generally guaranteed to be more than 2 hours.

[0043] S4: Develop and improve the multi-objective antlion optimization algorithm solution model, and obtain the Pareto optimal wind-solar-storage capacity planning scheme through non-dominated sorting, crowding degree calculation and particle update.

[0044] First, set the population size. Number of iterations Initialize particles (each particle corresponds to a set of wind and solar storage capacities) Then, a non-dominated sort is performed, stratifying the particles according to Pareto dominance: if the particles Both target values ​​are not inferior to And at least one objective is better, then Dominate Non-dominated particles are assigned to layer 1, and the remaining particles are sorted repeatedly. Then, the crowding degree of each layer is calculated. For particles in the same layer, after sorting by objective function value, the crowding degree of the next layer is calculated using the following formula. Crowding of individual particles : in, and The first The maximum and minimum values ​​of each target and These represent the target values ​​of adjacent particles. Traditional multi-objective optimization algorithms, such as NSGA-II, require sorting particles for each target dimension, calculating the distance between adjacent particles, and handling special cases of boundary particles (e.g., setting the boundary particle crowding to infinity), making the process cumbersome. This invention optimizes the crowding calculation logic for dual-objective scenarios, sorting only non-dominated particles in the same layer according to their two target values, eliminating the need for cross-calculations across multiple target dimensions, significantly reducing the computational cost of each iteration. Finally, particle positions are updated. The particle positions are updated based on antlion hunting behavior, and the update calculation process is shown in the following formula: in, and The first In the nth iteration The optimal and worst particle values ​​for each variable; and The value is a random number ranging from 0 to 1. Traditional antlion optimization algorithms rely on random search for particle updates, which can easily lead to particles getting trapped in local optima or wandering aimlessly, resulting in slow convergence. This invention reconstructs the particle position update formula and introduces an optimal-worst particle guidance mechanism. Particles no longer wander randomly but update around the current optimal solution, reducing invalid search steps and accelerating convergence to the Pareto front.

[0045] After reaching the maximum number of iterations, the Pareto optimal solution set is output, and the final planning scheme is selected based on the actual operation requirements of the power distribution network.

[0046] Example 2, as a second aspect of the present invention, also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the multi-objective planning method for urban power grid wind-solar-storage optimization based on Vine Copula and Antlion as described above. In addition to the processors, memory, and interfaces described above, any data processing device in the embodiment may also include other hardware depending on the actual function of the data processing device, which will not be elaborated further.

[0047] As a third aspect of the present invention, this application also provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the aforementioned multi-objective planning method for urban power grid wind-solar-storage optimization based on Vine Copula and Antlion. The computer-readable storage medium can be an internal storage unit of any data-processing device as described in any of the foregoing embodiments, such as a hard disk or memory. The computer-readable storage medium can also be an external storage device, such as a plug-in hard disk, smart media card (SMC), SD card, flash card, etc., equipped on the device. Furthermore, the computer-readable storage medium can include both internal storage units of any data-processing device and external storage devices. The computer-readable storage medium is used to store the computer program and other programs and data required by the data-processing device, and can also be used to temporarily store data that has been output or will be output.

[0048] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A multi-objective planning method for wind, solar, and energy storage in urban power grids, characterized by the following steps: include: A model of an urban microgrid cluster system was constructed, and the microgrid operation mode was configured; based on historical wind and solar power output data, wind and solar power output scenarios were generated using Vine Copula. A multi-objective optimization model is established based on the wind and solar power output scenario, with the goal of minimizing the total annual cost and the source-load power mismatch. An improved multi-objective antlion optimization algorithm is used to solve the multi-objective optimization model, and the Pareto optimal wind-solar-storage capacity planning scheme is obtained.

2. The multi-objective planning method for wind, solar, and energy storage in urban power grids according to claim 1, characterized in that, The urban microgrid cluster system model comprises a cluster system of multiple microgrids, each microgrid including wind turbines, photovoltaics, energy storage, and loads. Microgrids are interconnected via bidirectional power transmission lines, and are connected to the main grid through the distribution network. The microgrid operation modes include independent and coordinated operation. In independent operation mode, the microgrid interacts only with the main grid, with the source-load difference primarily compensated by energy storage; when energy storage is insufficient or surplus, it purchases / sells electricity to the main grid. In coordinated operation mode, the microgrid prioritizes interaction with other microgrids within the cluster, and only interacts with the main grid for any remaining difference.

3. The multi-objective planning method for wind, solar, and energy storage in urban power grids according to claim 1, characterized in that, The specific steps for generating wind and solar power output scenarios using Vine Copula are as follows: Based on historical wind and solar power output data, the marginal distributions of wind turbine output and photovoltaic output are fitted to obtain them respectively; the pairwise dependency relationship of wind and solar power output is calculated using Kendall tau coefficients; the high-dimensional wind and solar power output distribution is decomposed into nested bivariate Copula trees using regularized Vine, with each tree edge corresponding to a conditional bivariate Copula distribution; uniform samples that follow the joint distribution of Vine Copula are generated, and the actual wind and solar power output scenarios are obtained through inverse marginal distribution transformation.

4. The multi-objective planning method for wind, solar, and energy storage in urban power grids according to claim 3, characterized in that, The marginal distribution of wind turbine output is fitted using a Weibull distribution, and the marginal distribution of photovoltaic output is fitted using a Beta distribution.

5. The multi-objective planning method for wind, solar, and energy storage in urban power grids according to claim 1, characterized in that, The total annual cost in the multi-objective optimization model includes investment cost, operation and maintenance cost, interaction cost with the main grid, and interaction cost between microgrids; the investment cost The specific expression is determined by the investment situation in wind power, photovoltaics, and energy storage: in, Indicates the discount rate; Indicates the lifespan of the equipment; This is expressed as equipment capacity; Expressed as unit investment cost; operation and maintenance cost of a single microgrid. Represented as: in, The operation and maintenance coefficient of the microgrid; the interaction cost with the main grid. Calculated by year, the specific expression is: in, and These are the main grid purchase price and the electricity sales price, respectively. and These represent the power purchased / sold; and the interaction cost between the microgrids. Formed by transactions between micro-networks, the specific expression is: in, and These refer to the electricity purchase / sale prices between microgrids; and They represent microgrids To microgrids The power purchased / sold.

6. The multi-objective planning method for wind, solar, and energy storage in urban power grids according to claim 1, characterized in that, In the multi-objective optimization model, the charging and discharging power of energy storage under the corresponding scenario is taken as the optimization variable, and the objective function for minimizing the source-load power imbalance is expressed as: in, Represented as scene weights; Let i be the output power of fan i in scenario s; Let i be the output power of photovoltaic i in scenario s; For energy storage in scenario s The charging and discharging power; For microgrids The load power.

7. The multi-objective planning method for wind, solar, and energy storage in urban power grids according to claim 1, characterized in that, The constraints of the multi-objective optimization model include: capacity constraint, the capacity of each device cannot exceed the maximum capacity; power balance constraint: in, Indicates from microgrid To microgrids Loss coefficient; Islanding operation guarantee constraints: in, Indicates the capacity of energy storage facilities deployed; Indicates the safety factor; Indicates the percentage of non-adjustable load; microgrid Maximum load power; This represents the minimum islanding time that a microgrid must satisfy.

8. The multi-objective planning method for wind, solar, and energy storage in urban power grids according to claim 1, characterized in that, The improved multi-objective antlion optimization algorithm is used to solve the multi-objective optimization model. The specific steps are as follows: Set the population size and number of iterations; initialize particles, with each particle corresponding to a set of wind and solar storage capacity; perform non-dominated sorting, and stratify the particles according to Pareto dominance: if the particles Both target values ​​are not inferior to And at least one objective is better, then Domination Non-dominated particles are assigned to layer 1, and the remaining particles are sorted repeatedly; the crowding degree of particles in each layer is calculated, and particles in the same layer are sorted according to the objective function value, based on the first layer. The maximum and minimum values ​​of the target and the target values ​​of neighboring particles are calculated to determine the first target. Crowding of individual particles The particle position is updated based on the antlion hunting behavior, and the update calculation process is shown in the following formula: in, and The first In the nth iteration The optimal and worst particle values ​​for each variable; and The value is a random number in the range of 0-1; after reaching the maximum number of iterations, the Pareto optimal solution set is output, and the final planning scheme is selected according to the actual operation requirements of the distribution network.

9. A multi-objective planning device for urban power grid wind-solar-storage systems, comprising a memory, a processor, and a program stored in the memory, characterized in that, When the processor executes the program, it implements the urban power grid wind-solar-storage multi-objective planning method as described in any one of claims 1-8.

10. A storage medium having a program stored thereon, characterized in that, When the program is executed, it implements the urban power grid wind-solar-storage multi-objective planning method as described in any one of claims 1-8.

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