CN116316718B

CN116316718B - Energy storage system optimal configuration method considering uncertainty of renewable energy sources

Info

Publication number: CN116316718B
Application number: CN202310114297.3A
Authority: CN
Inventors: 张怀鹏; 刘军福; 殷学农; 马耀东; 周伟昌; 陈雨; 魏鹏; 邹志勇; 齐屹; 陆彦虎; 朱涛; 王超; 闫东丽; 杨占松; 王杰; 王波; 刘波; 褚小娟; 郭虎; 严洁
Original assignee: Zhongwei Power Supply Company State Grid Ningxia Electric Power Co ltd
Current assignee: Zhongwei Power Supply Company State Grid Ningxia Electric Power Co ltd
Priority date: 2023-02-14
Filing date: 2023-02-14
Publication date: 2024-05-28
Anticipated expiration: 2043-02-14
Also published as: CN116316718A

Abstract

The embodiment of the invention provides an energy storage system optimal configuration method considering uncertainty of renewable energy sources, which comprises the following steps: obtaining the output power of renewable energy sources; determining a power grid elasticity index of the power distribution network; according to the output power of renewable energy sources and the power grid elasticity index, an energy storage system optimization configuration model is constructed; optimizing the inertia weight in the original particle swarm optimization method, and improving the original particle swarm optimization method according to Monte Carlo simulation and a global guidance cross search mechanism; and solving an energy storage system optimal configuration model through an improved particle swarm optimization method to obtain the position of the energy storage system connected into the power distribution network and the capacity of the energy storage system. The method provided by the invention can effectively inhibit the capability fluctuation of the power distribution network, reduce the cost of the system and improve the flexibility of the power distribution network, and the improved particle swarm optimization method is used for solving, so that the optimal solution is prevented from falling into a local optimal solution, and the convergence rate is improved.

Description

Energy storage system optimal configuration method considering uncertainty of renewable energy sources

Technical Field

The embodiment of the invention relates to the field of energy storage system configuration, in particular to an energy storage system optimal configuration method considering uncertainty of renewable energy sources.

Background

In recent years, the problems of increasingly serious resource shortage, environmental pollution and the like are attracting attention, and the proportion of renewable energy sources such as wind power and photovoltaic in a power distribution network is continuously increased. Wind power and photovoltaic have the advantages of cleanness, reproducibility and the like, but large-scale renewable energy source access to a power distribution network can cause fluctuation and uncertainty of power output, and meanwhile, power loss generated on a circuit can cause the cost increase of a power system.

Based on this, the technical staff put forward an Energy Storage System (ESS), and the ESS has the characteristics of fast response speed to power fluctuation and bidirectional energy flow, and in a power grid with a relatively high renewable energy source, the ESS can be configured to optimize the output characteristic of renewable energy source power generation, and improve the fluctuation of system voltage and power, so that the reliability and stability of the system are improved. However, since the configuration of the ESS will increase the cost of the system and the investment recovery period will be long, how to configure the ESS position and capacity in the distribution network to reduce the cost of the system is important in the system design.

Disclosure of Invention

Based on the technical problems, the embodiment of the invention provides an energy storage system optimal configuration method considering uncertainty of renewable energy sources so as to reduce system cost.

The embodiment of the invention provides an energy storage system optimal configuration method considering uncertainty of renewable energy sources, which comprises the following steps:

Obtaining the output power of renewable energy sources;

Determining a power grid elasticity index of the power distribution network;

constructing an energy storage system optimization configuration model according to the output power of the renewable energy source and the power grid elasticity index;

optimizing the inertia weight in the original particle swarm optimization method, and improving the original particle swarm optimization method according to Monte Carlo simulation and a global guidance cross search mechanism;

and solving the energy storage system optimal configuration model through an improved particle swarm optimization method to obtain the position of the energy storage system connected into the power distribution network and the capacity of the energy storage system.

In the method of the embodiment of the invention, firstly, the output power of renewable energy sources is obtained; secondly, determining a power grid elasticity index of the power distribution network; then, an energy storage system optimal configuration model is constructed according to the output power of the renewable energy sources and the power grid elasticity index; then, optimizing the inertia weight in the original particle swarm optimization method, and improving the original particle swarm optimization method according to Monte Carlo simulation and a global guidance cross search mechanism; and finally, solving an energy storage system optimal configuration model by an improved particle swarm optimization method to obtain the position of the energy storage system connected into the power distribution network and the capacity of the energy storage system. By the method of the embodiment of the method, uncertainty of renewable energy sources and power grid elasticity indexes are considered when the power distribution network energy storage system is configured, capacity fluctuation of the power distribution network can be effectively restrained, cost of the system is reduced, flexibility of the power distribution network is improved, and the improved particle swarm optimization method is used for solving, so that the optimal solution is prevented from falling into a local optimal solution, and convergence rate is improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments of the present invention will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart illustrating a method for optimizing configuration of an energy storage system that accounts for uncertainty in renewable energy sources according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of an inertial weight curve according to an embodiment of the present invention;

FIG. 3 is a flow chart illustrating a method for solving an energy storage system optimization configuration model by an improved particle swarm optimization method according to an embodiment of the present invention;

FIG. 4 is a schematic diagram of an IEEE-14 system in accordance with an embodiment of the invention;

FIG. 5 is a graph of typical daily loads for an embodiment of the present invention;

FIG. 6 is a graph of exemplary renewable energy output according to an embodiment of the present invention;

FIG. 7 is a graph of node 3 voltage ripple in accordance with an embodiment of the present invention;

FIG. 8 is a comparison of cost function values for a conventional algorithm and a modified algorithm, as shown in one embodiment of the present invention;

FIG. 9 is a graph comparing convergence errors of a conventional algorithm and a modified algorithm according to an embodiment of the present invention.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

Referring to fig. 1, fig. 1 is a flowchart illustrating a method for optimizing configuration of an energy storage system in consideration of uncertainty of renewable energy according to an embodiment of the present invention. As shown in fig. 1, the method comprises the steps of:

step S11: and obtaining the output power of the renewable energy source.

In the embodiment, the uncertainty of renewable energy sources in the power distribution network is considered when the energy storage system is optimally configured, and the output power of the renewable energy sources can be obtained based on the uncertainty of the renewable energy sources.

Step S12: and determining the power grid elasticity index of the power distribution network.

In this embodiment, when the energy storage system is optimally configured, the flexibility of the power distribution network is considered in addition to the uncertainty of the renewable energy source. The flexibility of the distribution network is mainly represented by the power transmission capacity which can be contained in the distribution network, and the power transmission capacity of the distribution network is closely related to the power transmission capacity of each branch in the network. In general, the greater the available transmission capacity per leg, the greater the flexibility of the leg. Therefore, in the optimal configuration of the energy storage system, the embodiment provides the power grid elasticity index based on the flexibility of the power distribution network, wherein the power grid elasticity index characterizes the flexibility of the power distribution network.

Step S13: and constructing an energy storage system optimization configuration model according to the output power of the renewable energy source and the grid elasticity index.

In this embodiment, after determining the output power of the renewable energy source and the grid elasticity index, the energy storage system may be configured in the power distribution network according to the output power of the renewable energy source and the grid elasticity index. Specifically, an energy storage system optimal configuration model can be constructed according to the output power of renewable energy sources and the power grid elasticity index, and the purpose of the energy storage system optimal configuration model is to output the position of an energy storage system connected into a power distribution network and the capacity of the energy storage system so as to reduce the cost of the system and improve the flexibility of the power distribution network.

Step S14: and optimizing the inertia weight in the original particle swarm optimization method, and improving the original particle swarm optimization method according to the Monte Carlo simulation and the global guidance cross search mechanism.

In this embodiment, after the energy storage system optimization configuration model is built, the original particle swarm optimization method may be improved, so as to obtain an improved particle swarm optimization method, and thus the energy storage system optimization configuration model is solved by the improved particle swarm optimization method. The inertial weight in the original particle swarm optimization method (PSO algorithm) is a fixed value, namely the problem of fixed inertial weight exists in the iteration process of the original particle swarm optimization method, and the problem of sinking into a local optimal solution is easy to cause. Therefore, the embodiment provides an improved particle swarm optimization method, optimizes the inertia weight in the original particle swarm optimization method, and introduces a Monte Carlo Simulation (MCS) and a global guidance cross search mechanism to avoid that the optimal point falls into a local optimal solution and a global optimal solution is obtained.

Step S15: and solving the energy storage system optimal configuration model through an improved particle swarm optimization method to obtain the position of the energy storage system connected into the power distribution network and the capacity of the energy storage system.

In this embodiment, after the original particle swarm optimization method is improved, the energy storage system optimization configuration model may be solved by the improved particle swarm optimization method, so as to obtain an output of the energy storage system optimization configuration model: the location where the energy storage system is accessed into the distribution network (i.e., the location where the energy storage system is installed) and the capacity of the energy storage system.

In the embodiment, the uncertainty of renewable energy sources and the power grid elasticity index are considered when the power distribution network energy storage system is configured, so that the system cost is reduced, the capability fluctuation of the power distribution network is effectively restrained, and the flexibility of the power distribution network is improved; on the basis, the inertia weight in the original particle swarm optimization method is optimized, a Monte Carlo simulation and global guidance cross search mechanism is introduced, and the original particle swarm optimization method is improved, so that the improved particle swarm optimization method is used for solving, the situation that the optimal solution is sunk into the local optimal solution to obtain the global optimal solution can be avoided, and the convergence rate of the method is improved.

In combination with the above embodiment, in an implementation manner, the present invention further provides an energy storage system optimization configuration method considering uncertainty of renewable energy, where in the method "optimizing inertial weights in the primary particle swarm optimization method" in step S14 may specifically include step S21 and step S22:

Step S21: and determining the difference value between the particle position and the global optimal particle position corresponding variable of the population.

In this embodiment, the balance between the exploration capability and the development capability of the intelligent optimization algorithm is considered to be the key of successful operation, and the reasonable value of the inertia weight w is an important way for realizing the balance of the two capabilities by the original particle swarm optimization method. In the original particle swarm optimization method, the value of the inertia weight w has different forms, such as fixed weight according to experience value; a time-varying weight that decreases as the number of iterations increases; the fuzzy weights and random weights within a certain range are adjusted based on the dynamics of the fuzzy system. However, none of these inertial weights take into account the characteristics of the particles during the iteration.

The present embodiment considers that the difference between the position of one particle and the global optimal particle corresponding position of the population reflects the difference between the current solution and the optimal solution: when the difference is large, the flying speed can be improved by increasing the value of the inertia weight w, and the global searching capability of particles is improved; when the difference is small, the flying speed can be reduced by reducing the value of the inertia weight w, and the local searching capability of particles is improved. Based on this, in this embodiment, for optimization of the inertia weight, it is first necessary to determine the difference between the particle position and the global optimum particle position corresponding variable of the population.

Step S22: and linearly adjusting the inertia weight of the particles according to the difference value.

In this embodiment, the inertial weight of the particle may be linearly adjusted according to the difference between the particle position and the global optimum particle position corresponding variable of the population. That is, in the improved particle swarm optimization method according to the present embodiment, the magnitude of the inertia weight w is linearly adjusted according to the difference between the variables corresponding to the particle positions and the population optimal particle positions. As shown in fig. 2, fig. 2 is a schematic diagram of an inertial weight curve according to an embodiment of the present invention.

In the present embodiment, in particular, the difference between the position of the ith particle in the kth iteration and the globally optimal position of the populationCan be shown as formula (1); inertial weighting of the position update speed of the ith particle in the kth iterationThis can be defined as a differential inertial weight as shown in equation (2).

Where x _max and x _min are the maximum and minimum values, respectively, of the particle position variable, ω _max and ω _min are the maximum and minimum inertial weights, respectively,Is the d-dimensional component of the optimal position vector in the kth iterative population,/>Is the position of the ith particle in the kth iteration. In this embodiment, the maximum inertia weight ω _max may be set to 1, and ω _min may be set to 0.5.

In this embodiment, the characteristics of the particles in the iterative process are considered, the inertial weights are optimized, and the sizes of the inertial weights are adjusted in real time according to the differences between the particle positions and the corresponding variables of the optimal particle positions of the population, so that the searching capability of the algorithm is improved.

In combination with the above embodiment, in an implementation manner, the present invention further provides an energy storage system optimization configuration method considering uncertainty of renewable energy, in this method, the "improving the primary particle swarm optimization method according to monte carlo simulation and global guidance cross search mechanism" in the step S14 may specifically include step S31 and step S32:

step S31: and determining an initial population of the improved particle swarm optimization method by using Monte Carlo simulation so as to initialize particles according to the initial population.

In this embodiment, since the original particle swarm optimization method is easy to fall into a local optimal solution in the iterative process, after uncertainty modeling, a Monte Carlo Simulation (MCS) is used to determine an initial population of the improved particle swarm optimization method, so as to initialize particles according to the initial population, and solve the problem that the optimal point falls into the local optimal solution and takes a global optimal solution in the traditional original particle swarm optimization method.

Step S32: and updating the particle position through the global guidance cross search mechanism.

In this embodiment, a global guidance cross search mechanism is also introduced, which is used to update the particle positions in the initial population, so as to guide the new solution to the global optimal solution, so as to improve the algorithm convergence speed.

Specifically, the particle position can be updated by the following formula (3):

Wherein, rand is a random value uniformly distributed between 0 and 1, and b is a random value between-1 and 1; is the d-dimensional component of the optimal position vector in the kth iterative population,/> Is the position of the ith particle in the kth iteration; increasing the value of the coefficient cr is advantageous for enhancing the developability of the algorithm, and decreasing the value cr of the coefficient cr is advantageous for the search capability of the algorithm. In this embodiment, cr may be 0.6.

In combination with the above embodiment, in an implementation manner, the present invention further provides an energy storage system optimization configuration method considering uncertainty of renewable energy, where step S15 may specifically include steps 1 to 6:

step 1: and acquiring network data and initial parameters of the improved particle swarm optimization method.

In this embodiment, the improved particle swarm optimization method is used to solve the energy storage system optimization configuration model, network data needs to be acquired first, and parameters of the improved particle swarm optimization method are initialized, specifically, initialization conditions may be created, so as to obtain each initial parameter of the improved particle swarm optimization method. The network parameter may be, among other things, relevant data related to the distribution network. It can be appreciated that the input of the energy storage system optimization configuration model of this embodiment includes: network parameters and initial parameters.

Step 2: and determining an initial population of the improved particle swarm optimization method through the Monte Carlo simulation, and randomly generating particle positions and particle speeds aiming at the initial population.

In this step, the particles need to be initialized. Specifically, a Monte Carlo Simulation (MCS) may be used to determine an initial population of the improved particle swarm optimization method to avoid the optimal points in the traditional algorithm from sinking into the locally optimal solution. After the initial population is obtained, the particle position x _i and the particle velocity v _i may be randomly generated within the set constraints for each particle in the initial population.

Step 3: and determining globally optimal particles from the initial population according to the network data.

In this step, global optimal particles may be determined from the initial population according to the acquired network data, so that the difference between the current solution and the optimal solution may be determined according to the global optimal particles in the following step, thereby optimizing the inertia weight.

Step 4: in each iteration process, according to the initial parameters, the inertia weight of each particle is optimized, and the particle position and the particle speed of each particle are updated according to the global guidance cross variation operation.

In this step, there are multiple iterations in the process of solving by the improved particle swarm optimization method, and the number of iterations may be set according to experience and requirements, which is not limited in this embodiment. In each iteration process, the particle position and the particle speed of each particle can be updated according to the determined initial parameters of the improved particle swarm optimization method and the global guide cross mutation operation, then the inertia weight of each particle is optimized according to the updated particle position and particle speed of each particle, namely the inertia weight of each particle is optimized, and after the optimized inertia weight of each particle is obtained, the optimized inertia weight can be brought into the original particle swarm optimization method, so that the new position and the new speed of each particle in each iteration are determined.

In the original Particle Swarm Optimization (PSO) method, the particle updates its own speed and position by tracking its own historical optimal solution and global optimal solution of the population, and the updating method is as shown in formulas (4) and (5):

Wherein w is an inertial weight (in this step, the optimized inertial weight can be brought into w in formula (4)), c ₁、c₂ is an acceleration factor, and r ₁、r₂ is a random number between 0 and 1; is the d-dimensional component of the ith particle in the kth iteration optimal position vector,/> Is the d-dimensional component of the optimal position vector in the kth iterative population; /(I)Is the position of the ith particle in the kth iteration; /(I)Is the speed of the ith particle in the kth iteration; /(I)Is the position of the ith particle in the k+1th iteration; is the velocity of the ith particle in the k+1th iteration.

Step 5: and determining an objective function value of the energy storage system optimal configuration model corresponding to each particle after the particle position and the particle speed are updated, obtaining an optimal objective function value, and recording an optimal particle corresponding to the optimal objective function value.

In this step, after the particle position and the particle velocity of the particles are updated, the objective function value of the energy storage system optimal configuration model corresponding to each particle after the particle position and the particle velocity are updated may be determined. The energy storage system optimizing configuration model determines that a corresponding objective function exists during configuration.

At this time, according to the network parameters and each particle with updated particle position and particle speed, the objective function value of the energy storage system optimization configuration model corresponding to each updated particle can be determined, so that the optimal objective function value is determined in the objective function values corresponding to all updated particles, the optimal particle corresponding to the optimal objective function value is recorded, and meanwhile, the particle position corresponding to the optimal particle can also be recorded. In this embodiment, the objective function value of the energy storage system optimization configuration model corresponding to each updated particle may be understood as the optimal fitness of each updated particle, so as to calculate and compare the optimal fitness of each updated particle with the global optimal fitness, so as to record the corresponding position of the optimal particle.

Step 6: and judging whether the current iteration number reaches the maximum iteration number according to the maximum iteration number in the initial parameters.

In this step, after the optimal particles are determined, it may be determined whether the current iteration number reaches the maximum iteration number according to the maximum iteration number in the initial parameter, where the maximum iteration number may be freely set.

If not, the steps 4 to 6 are re-executed.

And (3) under the condition that the current iteration number does not reach the maximum iteration number, the steps 4 to 6 are needed to be re-executed, a new round of iteration is carried out, optimization of inertia weight is carried out again, the positions and the speeds of particles are updated, objective function values corresponding to the particles after each update are compared, and the optimal particles are determined.

If so, outputting the position of the energy storage system connected to the power distribution network and the capacity of the energy storage system according to the optimal particles, and ending the flow.

Under the condition that the current iteration number reaches the maximum iteration number, the position of the energy storage system in the power distribution network and the capacity of the energy storage system can be determined and output according to the optimal particles and the corresponding positions of the optimal particles determined by the iteration of the round, and the process is finished.

In the embodiment, in the process of solving an energy storage system optimal configuration model, a Monte Carlo Simulation (MCS) and a global guidance cross search mechanism are introduced for the problem that the traditional particle swarm optimization method has fixed inertia weight values in the iteration process and is easy to fall into a local optimal solution; wherein the MCS determines an initial population of algorithms and globally directs a cross-search mechanism to update the location of particles; the characteristics of different populations are selected by the MCS and the global guidance cross search mechanism avoids the situation that the optimal solution falls into the local optimal solution and the global optimal solution is obtained in the traditional algorithm.

In combination with the above embodiments, in an alternative embodiment, the present invention further proposes a method for optimizing configuration of an energy storage system in consideration of uncertainty of renewable energy, where the network data at least includes: parameters of the power distribution network, grid-connected photovoltaic system positions, load demands at each moment and photovoltaic output data; the step 3 may specifically include steps S51 to S53:

Step S51: and aiming at each particle in the initial population, determining a current objective function value of the energy storage system optimal configuration model, which corresponds to each particle and meets the constraint condition of the energy storage system optimal configuration model, according to the network data.

In this embodiment, for each particle in the initial population, a current objective function value of the energy storage system optimization configuration model, which corresponds to each particle and meets the constraint condition of the energy storage system optimization configuration model, may be determined according to the acquired network data such as the parameters of the power distribution network, the grid-connected photovoltaic system position, the load demands at each moment, and the photovoltaic output data. In short, the current objective function value meeting the constraint condition of the model corresponding to each particle is calculated according to the network configuration.

Step S52: the current objective function value corresponding to each particle is compared according to the size.

In this embodiment, after the current objective function value corresponding to each particle in the initial population is obtained, the current objective function values corresponding to each particle may be compared according to the size, for example, the current objective function values are sorted from small to large.

Step S53: and determining the particle with the smallest current objective function value as the global optimal particle.

In this embodiment, since the objective of the energy storage system optimization configuration model in this embodiment is to reduce the system cost, the objective function of the energy storage system optimization configuration model may be determined by the system cost, and based on this, the particle with the smallest current objective function value in the initial population may be determined as the globally optimal particle in the initial population.

In this embodiment, the speed of the convergence algorithm can be greatly improved, and the flexibility of the optimal configuration method can be improved by determining the global optimal particles in the initial population by adopting the rapid non-dominant sorting technology.

In combination with the above embodiment, in an implementation manner, the present invention further provides a method for optimally configuring an energy storage system in consideration of uncertainty of renewable energy, where the initial parameters at least include: total number of particles, particle dimension, maximum number of iterations; and, the initial parameters may further include: the number of iterations k=1, the maximum inertial weight ω _max, the minimum inertial weight ω _min, the acceleration factor c ₁, the acceleration factor c ₂, the random number r ₁, the random number r ₂, and so on. In addition, in the present method, "optimizing the inertial weight of each particle" in the above-described step 4 may specifically include steps S61 to S63:

Step S61: and determining a new position of each particle according to the current particle position and particle speed of each particle and the global guidance cross mutation operation.

In this embodiment, the new position of each particle is determined according to the current particle position and particle data of each particle and the global pilot cross mutation operation, that is, the particle position is updated once through the global pilot cross mutation operation. Specifically, the new position of each particle can be determined by the above formula (3).

In this embodiment, the particle position and particle data for each particle are currently the particle position and data at the time of entering the present round of iteration. When the iteration is the initial iteration, that is, the iteration number is 1, the current particle position and particle data of each particle at this time are the particle position and particle speed of each particle generated randomly and obtained by initializing the particle.

Step S62: and determining the difference between each particle position and the global optimal particle position of the initial population according to the new position of each particle.

In this embodiment, after determining the new position of each particle according to the global guidance cross mutation operation, the difference between the particle position of each particle and the particle position of the globally optimal particle of the initial population may be determined according to the new position of each particle. Specifically, the difference between each particle position and the particle position of the globally optimal particle of the initial population may be determined by the above formula (1).

Step S63: and determining the updated inertia weight of each particle according to the difference value between the position of each particle and the global optimal particle position of the initial population.

In this embodiment, after determining the difference between each particle position and the global optimal particle position of the initial population, the updated inertial weight of each particle may be determined according to the difference between each particle position and the global optimal particle position of the initial population, so as to implement optimization of the inertial weight. Specifically, the updated inertia weight of each particle may be determined by the above formula (2).

In this embodiment, the position of each particle is updated according to the global guidance cross mutation operation, and then the inertial weight is updated according to the difference between the particle position and the global optimal particle position in the initial population, so that the magnitude of the inertial weight can be adjusted in real time according to the difference between the particle position and the corresponding variable of the population optimal particle position, and the searching capability of the algorithm is improved.

In combination with the above embodiment, in an implementation manner, the present invention further provides an energy storage system optimization configuration method considering uncertainty of renewable energy sources, in which the step S62 may specifically include a step S71 and a step S72, and the step S63 may specifically include a step S73 to a step S75:

In this embodiment, the position of each particle is an optimization variable, and the optimization variable in this embodiment is: and applying the improved particle swarm optimization method to the corresponding optimization variables in the created energy storage system optimization configuration model. The photovoltaic power generation output and the charging and discharging of the energy storage system are both constrained to a certain extent due to the influence of load change. Therefore, in the optimization process, the installation position of the energy storage system and the charge and discharge power of the energy storage system at each moment are used as the optimization variables, that is, the optimization variables in the embodiment include: the installation position variable of the energy storage system and the charging and discharging power variable of the energy storage system at each moment. That is, for the particle position, the particle position vector includes an installation position variable of the energy storage system and a charge-discharge power variable of the energy storage at each time; for particle velocity, the particle velocity vector includes the velocity of the energy storage system position variable and the velocity of the energy storage system power.

The optimized variable codes corresponding to the improved particle swarm optimization method when the improved particle swarm optimization method is applied to the created energy storage system optimizing configuration model are as follows:

x＝(x_l,x_p) (6)

x_l＝(x_l1,x_l2...x_lj...x_lm) (7)

x_p＝(x_p1+1,x_p1+2,...x_p1+T,x_p2+1,x_p2+2,...x_p2+T,...x_pj+t…x_pj+T) (8)

The position x of each particle in the solving process is a variable to be optimized, and as shown in a formula (6), the position x comprises an installation position x _l of an energy storage system in a power distribution network and charging and discharging power x _p at each moment; each particle is an (m+1) x T-dimensional vector. x _lj is the position variable of the jth energy storage system, x _pj+t is the charge and discharge power of the jth energy storage system at the time T, m is the number of energy storage systems in the power distribution network, and T is the total number of times. The determination of the specific inertia weight in the application scenario of the energy storage system configuration can be obtained by bringing the optimization variables into the formulas (1) and (2).

Step S71: and determining a difference value between the installation position variable of the energy storage system and the initial population global optimal particle position of each particle according to the installation position variable of the energy storage system.

In this embodiment, a difference between the installation position variable of the energy storage system and the initial population global optimal particle position of each particle may be determined according to the installation position variable of the energy storage system. Specifically, the difference between the installation position variable of the energy storage system and the initial population global optimal particle position of each particle can be determined by the formula (9):

Wherein, Is the difference between the position variable of the energy storage system and the global optimal position of the population of the ith particle in k iterations; x _lmax and x _lmin are the maximum and minimum values of the energy storage system position variables; m is the number of energy storage systems in the power distribution network,/>Is the d-dimensional component of the optimal position vector in the kth iterative population; /(I)Is the position of the ith particle in the kth iteration.

Step S72: and determining the difference value between the charge and discharge power variable of the energy storage system at each moment and the initial population global optimal particle position of each particle according to the charge and discharge power variable of the energy storage system at each moment.

In this embodiment, the difference between the charge and discharge power variable of each time of the energy storage system and the initial population global optimal particle position of each particle may be determined according to the charge and discharge power variable of each time of the energy storage system. Specifically, the difference between the charge and discharge power variable of the energy storage system at each moment and the initial population global optimal particle position of each particle can be determined by the formula (10):

Wherein, Is the difference between the power variable of the energy storage system and the global optimum position of the population of the ith particle in k iterations; x _pmax and x _pmin are the maximum and minimum values of the energy storage system power variables; m is the number of energy storage systems in the power distribution network, T is the total number of moments,/>Is the d-dimensional component of the optimal position vector in the kth iterative population; /(I)Is the position of the ith particle in the kth iteration.

Step S73: and determining the inertial weight of the update speed of the position variable of the energy storage system corresponding to each particle according to the difference value between the installation position variable of the energy storage system and the initial population global optimal particle position of each particle.

In this embodiment, after determining the difference between the installation position variable of the energy storage system and the initial population global optimal particle position of each particle, the inertial weight of the update speed of the energy storage system position variable corresponding to each particle may be determined according to the difference between the installation position variable of the energy storage system and the initial population global optimal particle position of each particle. Specifically, the inertial weight of the update speed of the position variable of the energy storage system corresponding to each particle can be determined by the formula (11):

Wherein, Inertial weight of the energy storage system position variable update speed in k iterations for the ith particle; omega _max and omega _min maximum inertial weight and minimum inertial weight; /(I)Is the difference between the position variable of the energy storage system and the global optimum position of the population of the ith particle in the k iterations.

Step S74: and determining the inertial weight of the update speed of the charge-discharge power variable of the energy storage system corresponding to each particle according to the difference value between the charge-discharge power variable of the energy storage system at each moment and the initial population global optimal particle position of each particle.

In this embodiment, after determining the difference between the charge and discharge power variable of the energy storage system at each moment and the initial population global optimal particle position of each particle, the inertial weight of the update speed of the charge and discharge power variable of the energy storage system corresponding to each particle may be determined according to the difference between the charge and discharge power variable of the energy storage system at each moment and the initial population global optimal particle position of each particle. Specifically, the inertial weight of the update speed of the charging and discharging power variable of the energy storage system corresponding to each particle can be determined by the formula (12):

Wherein, Inertial weight of the power variable update speed of the energy storage system in k iterations for the ith particle; omega _max and omega _min maximum inertial weight and minimum inertial weight; /(I)Is the difference between the power variable of the energy storage system and the global optimum position of the population of the ith particle in the k iterations.

Step S75: and determining the updated inertia weight of each particle according to the inertia weight of the position variable update speed of the energy storage system corresponding to each particle and the inertia weight of the charge and discharge power variable update speed of the energy storage system corresponding to each particle.

In this embodiment, after determining the inertial weight of the update speed of the position variable of the energy storage system corresponding to each particle and the inertial weight of the update speed of the charge and discharge power variable of the energy storage system corresponding to each particle, the inertial weight of each particle after updating may be determined according to the inertial weight of the update speed of the position variable of the energy storage system corresponding to each particle and the inertial weight of the update speed of the charge and discharge power variable of the energy storage system corresponding to each particle.

Specifically, the inertial weight of the position variable update speed of the energy storage system corresponding to each particle is added with the inertial weight of the charge and discharge power variable update speed of the energy storage system corresponding to each particle to obtain the updated inertial weight of each particle; or the inertial weight of the position variable update speed of the energy storage system corresponding to each particle and the inertial weight of the charge and discharge power variable update speed of the energy storage system corresponding to each particle are weighted and summed to obtain the updated inertial weight of each particle, which is not limited in this embodiment.

In an alternative implementation, please refer to fig. 3, fig. 3 is a flowchart illustrating a method for solving an energy storage system optimization configuration model through an improved particle swarm optimization method according to an embodiment of the present invention.

As shown in fig. 3, after the flow starts, first, network parameters are read; secondly, initializing parameters of the improved particle swarm optimization method to obtain initial parameters; thirdly, initializing particles, specifically, determining an initial population of an improved particle swarm optimization algorithm by using Monte Carlo Simulation (MCS), and randomly generating particle positions and particle speeds in a constraint range; fourthly, calculating the objective function value of the corresponding model for each particle in the initial population, and then sequencing all the objective function values to determine the optimal objective function value, so that the particle with the objective function value equal to the optimal objective function value is determined as the global optimal particle; fifthly, performing inertia weight optimization, calculating new position and speed variables of particles in each iteration, and updating the position and speed variables of the particles according to global guidance cross mutation operation; sixthly, determining the updated optimal fitness (objective function value) of each particle, calculating and comparing the optimal fitness of each particle, and determining the global optimal fitness to record the corresponding position of the particle corresponding to the global optimal fitness; seventh, judging whether the current iteration number reaches the maximum iteration number: and if not, repeatedly executing the fifth step and the sixth step until the set maximum iteration number is reached, and outputting an optimal solution: the location and capacity of the energy storage system access.

In combination with the above embodiment, in an implementation manner, the present invention further provides a method for optimally configuring an energy storage system in consideration of uncertainty of renewable energy, where the method at least includes: photovoltaic energy and wind-electricity energy; the output power of the renewable energy source comprises at least: photovoltaic output power and wind power output power; the step S11 may specifically include steps S81 to S84:

Step S81: modeling the uncertainty of the photovoltaic energy source through a beta function to obtain a photovoltaic uncertainty model.

In this embodiment, the output power of the Photovoltaic (PV) and the wind power (WT) is dependent on the climate conditions, and therefore uncertainty models need to be used to analyze the uncertainty of these renewable energy sources. For the photovoltaic energy source, the uncertainty of the photovoltaic energy source can be modeled through a beta function, and a photovoltaic uncertainty model is obtained. Specifically, modeling of the photovoltaic uncertainty model can be performed by equation (13) -equation (17):

Wherein, beta, alpha is the parameter of beta distribution, mu and sigma are mean value and standard deviation; a is the surface area of the PV panel, η is the efficiency of the panel, γ is the radiation intensity on the PV panel. The photovoltaic power is as follows:

P_PV＝ρVIf(P_PV) (17)

wherein ρ is the power generation efficiency of the photovoltaic module; v, I are the voltage and current of the PV, respectively.

Step S82: and determining the photovoltaic output power according to the photovoltaic uncertainty model.

In this embodiment, the photovoltaic output power may be determined according to the photovoltaic uncertainty model constructed above.

Step S83: and modeling the uncertainty of the wind power energy through a Weibull probability distribution function and a gamma function to obtain a wind power uncertainty model.

In this embodiment, for wind power energy, the uncertainty of the wind power energy can be modeled by using a weibull probability distribution function and a gamma function, so as to obtain a wind power uncertainty model. Specifically, modeling of the wind power uncertainty model may be performed by equation (18) -equation (24):

for wind power output power, wind speed is assumed to be a random variable comprising the Weibull function:

Where f _w (v) is a weibull probability distribution function, k represents a shape parameter, and c represents a scale parameter. The relationship between the weibull distribution parameters and the moment parameters is as follows:

Wherein, the shape parameter and the scale parameter can be calculated by the first moment and the second moment:

wherein Γ () is a gamma function. The power available for wind speed (v) depends on rotor sweep radius (R), air density (ρ), coefficient of performance (C _P) and tip speed ratio (λ), as follows:

where P ^w (v) is the available power of the rotor. In the linear approach, the power of the WT may be approximated as:

where v _ci、v_r and v _co are the lower, rated and upper limit speeds, respectively, associated with WT characteristics. P _r is the power rating of the WT.

Step S84: and determining the wind power output power according to the wind power uncertainty model.

In this embodiment, the wind power output power may be determined according to the wind power uncertainty model constructed above.

In this embodiment, the uncertainty of renewable energy sources is considered in the configuration scenario of the energy storage system, and the uncertainty of renewable energy sources distributed power sources DG such as photovoltaic and wind turbines is modeled through a probability density function, so that the uncertainty of renewable energy sources is used for constructing a subsequent energy storage system optimal configuration model.

In combination with the above embodiment, in an implementation manner, the present invention further provides an energy storage system optimization configuration method considering uncertainty of renewable energy, where step S12 may specifically include step S91 and step S92:

Step S91: and determining a branch load rate weighted average value of the power distribution network according to the branch number of the power distribution network, the load rate of each branch and the elasticity weight coefficient of each branch.

In this embodiment, regarding to consideration of flexibility of the power distribution network, since flexibility is also related to the capability of each branch to withstand power fluctuation, the capability of different branches to withstand power fluctuation is different, and thus flexibility cannot be measured by using available transmission capacity alone. For example, the load rates of the branches may be high, but the power fluctuations on the branches do not cause significant power flow shifts, i.e. the branches have a high resistance to uncertainty factors. Some branches may also cause drastic changes in power flow, even overload, due to the fact that the branches are in special positions in the network topology, and the lower power fluctuation on the branches may cause problems such as overload. Thus, the different situations of different branches in the power distribution network have to be considered.

Based on the analysis, the weighted average of the branch load rates in the power distribution network is used as the power grid elasticity index of the power distribution network, and the weighted average of the branch load rates of the power distribution network can be determined according to the number of branches of the power distribution network, the load rates of all branches and the elasticity weight coefficient of all branches. Specifically, the grid elasticity indicator may be determined according to equation (25) -equation (27):

In the formula (25), F _net (t) is an elasticity index of the power distribution network, namely an elasticity index of the power grid; t is time; m is the selected number of branches; n is the number of branches, the number is determined according to the principle that the load rate of each branch at the moment t is from high to low, and the branch with the highest load rate is numbered 1; s _n (t)% is the load factor of branch n at time t; mu _i is the elastic weight coefficient of branch n.

Wherein, the load factor S _n (t)% is defined as:

in the formula (26), S _n (t) is the power transmission capacity of the branch n obtained by the calculation of the power flow; s _namx is the maximum power transmission capacity of branch n. If the load factor is greater than 1, corresponding measures such as cut-off load need to be taken.

Mu _n is the elastic weight coefficient of the branch n, and the variance of the load rate fluctuation of each branch in the total time is used as the weight, and is defined as:

wherein T is the total time; The average load rate of the branch n in the total time is shown; /(I) Is the variance of the load factor of branch n; mu _n characterizes the ability of each branch to withstand power fluctuation shocks.

When F _net (t) >1, the overload of part of branches in the power distribution network occurs in actual operation. The smaller F _net (t), the more flexible it is to characterize the distribution network.

Step S92: and taking the weighted average value of the branch load rates of the power distribution network as a power grid elasticity index of the power distribution network.

In this embodiment, after obtaining the weighted average value of the branch load rates of the power distribution network, the weighted average value of the branch load rates may be used as a power grid elasticity index of the power distribution network, so as to consider the power grid elasticity index when the energy storage system optimal configuration model is subsequently built.

In combination with the above embodiment, in an implementation manner, the present invention further provides an energy storage system optimization configuration method considering uncertainty of renewable energy, where step S13 may specifically include steps S101 to S103:

The energy storage system ESS can store energy when the electric energy is excessive, and can release energy when the electric energy is insufficient, so that energy transfer in a time dimension is realized, the energy utilization rate is improved, and meanwhile, the operation flexibility and the economy of the power distribution network are also improved. The ESS can be considered as a load when charged and as a power source when discharged, and the mathematical model can be expressed by the state of charge. Therefore, in this embodiment, for the construction of the energy storage system optimal configuration model, the state of charge (SOC) of the energy storage system may be modeled according to the uncertainty of the renewable energy source, and due to the existence of the photovoltaic PV and the wind power WT, the state of charge (SOC) of the energy storage system ESS at time t may be described as follows:

Wherein, SOC is the state of charge of the energy storage system ESS, P _Load (t) is the power demand at time t, V _ESS is the energy storage device voltage, C _ESS is the energy storage capacity, and N _WT and N _PV represent the number of WT and PV, respectively; p _WT (t) is the wind power output power at time t, and P _PV (t) is the photovoltaic output power at time t

And the energy storage system ESS needs to meet the mutual exclusion constraint of charge and discharge, the constraint of charge and discharge power and the constraint of charge state in the operation process:

Where u _e,chr (t) and u _e,dch (t) are the charge and discharge states of the ESS at time t; p _e,min and P _e,max are the minimum and maximum charge and discharge powers, respectively; s _e,min and S _e,max are minimum and maximum states of charge, respectively.

Step S101: and determining an objective function of the energy storage system optimal configuration model according to the first objective function, the second objective function and the third objective function.

In the embodiment, an energy storage system optimal configuration model can be constructed by minimizing the cost of the power system, minimizing the elastic index of the power grid and minimizing the fluctuation of the node voltage. Where it may be that the power system cost is minimized as a first objective function, in particular, in view of the optimal application of the ESS at certain nodes, the system cost optimization must include reducing distributed power DG generation, ESS energy cycling, and system power consumption, and in particular, the first objective function may be expressed by the formula (30):

Where l is bank interest rate, u is return on investment, and d _i is weight for each item in cost minimization; k _DG,i and k _ESS.i are power efficiency factors related to DG and ESS investment, respectively.

The grid elasticity index may be minimized to be the second objective function, specifically, a maximum value of the grid elasticity index in one period may be taken as the second objective function, as shown in formula (31):

F₂＝min{max{F_net(1),F_net(2),…F_net(t)}} (31)

And after the new energy is accessed, the voltage fluctuation of the node can be caused due to the uncertainty of the output of the new energy, so that the network loss is increased. Thus, the node voltage fluctuation may be minimized as a third objective function, and in particular, the third objective function may be as in equation (32):

N is the total number of nodes of the power distribution network; h is the calculated time period; v _i (t) is the per-unit value of the voltage at the moment of the i node t; is the average of the per unit values of the i-node voltage.

In this embodiment, after determining the first objective function, the second objective function, and the third objective function, the objective function of the energy storage system optimization configuration model may be determined according to the first objective function, the second objective function, and the third objective function.

In this embodiment, the multi-objective problem of the energy storage system optimal configuration model may express the importance of each objective through a weight coefficient. The energy storage system ESS configuration of the power distribution network of the embodiment considers the cost of the power system, the flexibility index (the power grid elasticity index) and the node voltage fluctuation. Thus, the weighting function may be used to construct an overall objective function (i.e., an objective function of the energy storage system optimal configuration model), and in particular, the objective function of the energy storage system optimal configuration model may be as shown in equation (33):

F＝min{αF₁+βF₂+γF₃} (33)

In the formula (33), α, β, γ are weight coefficients of system cost, elasticity index, and voltage fluctuation, respectively. In an alternative embodiment, α=β=γ=1/3.

Step S102: and determining constraint conditions of the energy storage system optimal configuration model according to the output power constraint, the energy storage system constraint and the power balance constraint determined according to the output power of the renewable energy source.

In this embodiment, an output power constraint, an energy storage system constraint, and a power balance constraint may be set, where the power balance constraint may be determined according to an uncertainty of the renewable energy source (i.e., an output power of the renewable energy source), so that a constraint condition of the energy storage system optimization configuration model is determined according to the output power constraint, the energy storage system constraint, and the power balance constraint determined according to the output power of the renewable energy source.

Specifically, the power balance constraint may be determined in a system energy balance formula, where the system energy balance formula is shown in equation (34):

|P_PV+P_WT-P_load-P_loss|＝P_ESS (34)

In the formula (34), P _PV is photovoltaic output power; p _WT is wind power output power; p _load is the load power consumption; p _loss is the system loss power; p _ESS is the energy storage system output power.

The output power constraint may be as shown in equation (35):

P_g,i,min≤P_g,i(t)≤P_g,i,max (35)

Where P _g,i,min and P _g,i,max are the minimum and maximum output power of the power supply i.

The energy storage system constraints may be as shown in equations (36) - (39):

C^charge＝C^discharge (38)

E_ESS＝αE_charge+(1-α)E_discharge (39)

Wherein equation (36) is a maximum and minimum of the ESS capacity, equation (37) describes the charge-discharge state limits of the ESS, equation (38) describes the charge-discharge balance of the ESS, and equation (39) indicates that the energy storage of each ESS is a weighted combination of the charge energy and the discharge energy.

Step S103: and constructing the energy storage system optimal configuration model according to the objective function of the energy storage system optimal configuration model and the constraint condition of the energy storage system optimal configuration model.

In this embodiment, after determining the objective function of the energy storage system optimization configuration model and the constraint condition of the energy storage system optimization configuration model, the energy storage system optimization configuration model may be constructed according to the objective function of the energy storage system optimization configuration model and the constraint condition of the energy storage system optimization configuration model.

In the embodiment, the energy storage system optimal configuration model is provided by minimizing the elasticity index, minimizing the cost of the power system, minimizing the voltage fluctuation, and outputting the power constraint, the energy storage system constraint and the power balance constraint, so that an energy storage system configuration scheme capable of outputting and reducing the cost of the system is constructed, the flexibility of the power distribution network is improved, and the cost of the system is reduced.

In an alternative implementation, a case study is performed on the IEEE-14 node system to verify the effectiveness of the energy storage system optimization configuration method that considers the uncertainty of renewable energy sources, which is proposed by the embodiment of the present invention. Referring to fig. 4, 5 and 6, fig. 4 is a schematic diagram of an IEEE-14 system according to an embodiment of the present invention; FIG. 5is a graph of typical daily loads for an embodiment of the present invention; FIG. 6 is a graph of typical renewable energy output according to an embodiment of the present invention. The initial parameters of the improved particle swarm optimization method are shown in Table 1:

Table 1 algorithm initial parameters

The effectiveness of the proposed elasticity index and the optimized energy storage system configuration model is verified by the following different cases: under the condition that the node network models are the same, the energy storage system setting cases are configured at different positions, and the specific cases are as follows: case 1 is a 14-node network standard model, and no energy storage is configured; case 2 is based on a standard model, and energy storage is configured at node 3; case 3 is based on a standard model, and energy storage is configured at node 2; case 4 is based on a standard model, and energy storage is configured at node 5; case 5 is based on a standard model, and energy storage is configured at a node 11; case 6 is based on a standard model with stored energy configured at node 14. According to the model construction method provided by the embodiment of the invention, an energy storage system optimization configuration model is established, and the improved particle swarm optimization algorithm, namely the IPSO algorithm, is used for solving, so that the configuration result of the case is obtained. The main configuration parameters in the network are shown in table 2, and the configuration results of the case are shown in table 3.

Table 2 network main configuration parameters

TABLE 3 configuration results

Comparing the results of case 1 and case 2, it can be seen that case 2 is more costly because case 2 considers the elasticity index of the grid, and the need to configure the energy storage system can increase the flexibility of the grid, but at the same time results in increased cost. The results of the case 1 and other cases show that the power grid elasticity index value is 0.88 when the energy storage system is not added, and the index value is reduced by 6.82% -21.60% after the energy storage system is added, so that the configuration of the energy storage system can effectively improve the flexibility of the power distribution network. Comparing case 2 and case 3, it can be seen that when the total cost is close, if the energy storage system is configured on node No. 3, the elasticity index is better than that on node No. 2, and the index value is reduced by 10.39% although the system cost is increased by only 5.65%. The node 3 has renewable energy sources and variable loads, and the configuration of the energy storage system can effectively inhibit the energy fluctuation of the power grid and improve the flexibility of the power distribution network. From the results of cases 4, 5 and 6, it can be seen that when the energy storage system is configured at these nodes, the flexibility of the distribution network is not significantly improved by configuring the energy storage system at these three locations, because node 5, node 11 and node 14 have only variable loads and the location of these 3 nodes has no significant effect on the power flow of the grid.

As shown in fig. 7, fig. 7 is a graph of node voltage fluctuation No. 3 according to an embodiment of the present invention, in fig. 7, a long dashed line represents node voltage when energy is not stored, and compared with a conventional algorithm and an improved algorithm, it can be found that the configuration of the energy storage system can effectively inhibit node voltage fluctuation, improve power grid flexibility, and the improved algorithm shows better calculation accuracy, and the node voltage is closer to a node voltage average value.

FIG. 8 is a comparison of cost function values for a conventional algorithm and a modified algorithm, as shown in one embodiment of the present invention; FIG. 9 is a graph comparing convergence errors of a conventional algorithm and a modified algorithm according to an embodiment of the present invention. As shown in fig. 8 and 9, it can be found that the cost function value of the improved algorithm (i.e., the improved particle swarm optimization scheme) proposed in the present embodiment is lower than that of the conventional algorithm, and the convergence error is slightly lower than that of the conventional algorithm.

In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described by differences from other embodiments, and identical and similar parts between the embodiments are all enough to be referred to each other.

It should be noted that, for simplicity of description, the method embodiments are shown as a series of acts, but it should be understood by those skilled in the art that the embodiments are not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the embodiments. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred embodiments, and that the acts are not necessarily required by the embodiments of the invention.

While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the scope of the embodiments of the invention.

Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or terminal device that comprises the element.

The above description is made in detail on the energy storage system optimizing configuration method taking the uncertainty of renewable energy sources into consideration, and specific examples are applied to explain the principle and implementation mode of the invention, and the description of the above examples is only used for helping to understand the method and core idea of the invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.

Claims

1. An energy storage system optimal configuration method considering uncertainty of renewable energy sources, which is characterized by comprising the following steps:

Obtaining the output power of renewable energy sources;

Determining a power grid elasticity index of the power distribution network;

solving the energy storage system optimal configuration model through an improved particle swarm optimization method to obtain the position of an energy storage system connected into the power distribution network and the capacity of the energy storage system;

Solving the energy storage system optimizing configuration model through the improved particle swarm optimization method to obtain the position of the energy storage system connected into the power distribution network and the capacity of the energy storage system, wherein the method comprises the following steps:

step 1: acquiring network data and initial parameters of the improved particle swarm optimization method;

Step 2: determining an initial population of the improved particle swarm optimization method through the Monte Carlo simulation, and randomly generating particle positions and particle speeds for the initial population;

step 3: determining globally optimal particles from the initial population according to the network data;

step 4: in each iteration process, optimizing the inertia weight of each particle according to the initial parameters, and updating the particle position and the particle speed of each particle according to the global guidance cross variation operation;

Step 5: determining an objective function value of the energy storage system optimal configuration model corresponding to each particle after the particle position and the particle speed are updated, obtaining an optimal objective function value, and recording an optimal particle corresponding to the optimal objective function value;

step 6: judging whether the current iteration number reaches the maximum iteration number according to the maximum iteration number in the initial parameters;

If not, re-executing the steps 4 to 6;

if so, outputting the position of the energy storage system connected to the power distribution network and the capacity of the energy storage system according to the optimal particles, and ending the flow;

Wherein the initial parameters include at least: total number of particles, particle dimension, maximum number of iterations; the optimizing the inertial weight of each particle includes:

Determining a new position of each particle according to the current particle position and particle speed of each particle and the global guidance cross mutation operation;

Determining a difference value between each particle position and a global optimal particle position of the initial population according to the new position of each particle;

Determining the updated inertia weight of each particle according to the difference value between the position of each particle and the global optimal particle position of the initial population;

The position of each particle is an optimization variable, and the optimization variable comprises: the energy storage system comprises an installation position variable of the energy storage system and a charging and discharging power variable of the energy storage system at each moment;

The determining a difference between each particle position and a global optimal particle position of the initial population according to the new position of each particle comprises:

Determining a difference value between the installation position variable of the energy storage system and the initial population global optimal particle position of each particle according to the installation position variable of the energy storage system;

according to the charge and discharge power variable of each moment of the energy storage system, determining the difference between the charge and discharge power variable of each moment of the energy storage system and the initial population global optimal particle position of each particle;

the determining the updated inertia weight of each particle according to the difference value between the position of each particle and the position of the globally optimal particle of the initial population comprises the following steps:

determining the inertial weight of the update speed of the position variable of the energy storage system corresponding to each particle according to the difference value between the installation position variable of the energy storage system and the initial population global optimal particle position of each particle;

determining the inertial weight of the update speed of the charge-discharge power variable of the energy storage system corresponding to each particle according to the difference value between the charge-discharge power variable of the energy storage system at each moment and the initial population global optimal particle position of each particle;

And determining the updated inertia weight of each particle according to the inertia weight of the position variable update speed of the energy storage system corresponding to each particle and the inertia weight of the charge and discharge power variable update speed of the energy storage system corresponding to each particle.

2. The energy storage system optimizing configuration method considering uncertainty of renewable energy according to claim 1, wherein the optimizing the inertial weight in the primary particle swarm optimization method includes:

determining the difference value of the corresponding variable between the particle position and the global optimal particle position of the population;

And linearly adjusting the inertia weight of the particles according to the difference value.

3. The energy storage system optimizing configuration method considering uncertainty of renewable energy according to claim 1, wherein the improvement of the primary particle swarm optimization method according to monte carlo simulation and global guidance cross search mechanism comprises:

Determining an initial population of the improved particle swarm optimization method by using Monte Carlo simulation so as to initialize particles according to the initial population;

And updating the particle position through the global guidance cross search mechanism.

4. The energy storage system optimal configuration method considering renewable energy uncertainty as claimed in claim 1, wherein the network data at least comprises: parameters of the power distribution network, grid-connected photovoltaic system positions, load demands at each moment and photovoltaic output data; the determining global optimal particles from the initial population according to the network data comprises the following steps:

For each particle in the initial population, determining a current objective function value of the energy storage system optimal configuration model, which corresponds to each particle and meets the constraint condition of the energy storage system optimal configuration model, according to the network data;

comparing the current objective function value corresponding to each particle according to the size;

and determining the particle with the smallest current objective function value as the global optimal particle.

5. The energy storage system optimizing configuration method considering uncertainty of renewable energy according to claim 1, wherein the renewable energy source at least comprises: photovoltaic energy and wind-electricity energy; the obtaining the output power of the renewable energy source comprises the following steps:

modeling the uncertainty of the photovoltaic energy source through a beta function to obtain a photovoltaic uncertainty model;

Determining photovoltaic output power according to the photovoltaic uncertainty model;

modeling the uncertainty of the wind power energy source through a Weibull probability distribution function and a gamma function to obtain a wind power uncertainty model;

And determining the wind power output power according to the wind power uncertainty model.

6. The method for optimizing configuration of an energy storage system taking into account uncertainty of renewable energy according to claim 1, wherein determining a grid elasticity index of a power distribution network comprises:

Determining a branch load rate weighted average value of the power distribution network according to the number of branches of the power distribution network, the load rate of each branch and the elastic weight coefficient of each branch;

and taking the weighted average value of the branch load rates of the power distribution network as a power grid elasticity index of the power distribution network.

7. The energy storage system optimizing configuration method considering uncertainty of renewable energy according to claim 1, wherein the constructing an energy storage system optimizing configuration model according to the output power of the renewable energy and the grid elasticity index comprises:

the method comprises the steps of minimizing cost of an electric power system to be a first objective function, minimizing an elastic index of a power grid to be a second objective function, minimizing fluctuation of node voltage to be a third objective function, and determining an objective function of an energy storage system optimizing configuration model according to the first objective function, the second objective function and the third objective function;

Determining constraint conditions of the energy storage system optimal configuration model according to the output power constraint, the energy storage system constraint and the power balance constraint determined according to the output power of the renewable energy source;

and constructing the energy storage system optimal configuration model according to the objective function of the energy storage system optimal configuration model and the constraint condition of the energy storage system optimal configuration model.