Wind-light-storage collaborative planning method based on improved bat algorithm

Abstract

A wind-light-storage collaborative planning method based on an improved bat algorithm comprises the steps that a double-layer optimization model is constructed, the upper layer is a planning model, and site selection and capacity configuration of a wind power system, a photovoltaic system and an energy storage system are optimized with the full-life-cycle net income maximization as the target; the lower layer is an operation model, and on the basis of receiving the upper layer planning result, the operation scheduling between the renewable energy source and the energy storage system is coordinated by taking wind abandoning, light abandoning and network loss minimization as targets so as to guarantee the economical efficiency and the stability of the system operation; secondly, using an improved bat algorithm to carry out iterative solution on the bilayer programming model; according to the method, the consumption capability of the power distribution network on the distributed renewable energy sources can be effectively improved, and the operation reliability and safety of the power grid under the high-proportion renewable energy source access condition are further improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power system planning and renewable energy grid-connected dispatching, in particular to a wind-solar-storage collaborative planning method based on an improved bat algorithm, which is suitable for joint site selection and capacity configuration of distributed wind power, photovoltaic and energy storage systems in distribution systems, to improve renewable energy consumption capacity, optimize system operation efficiency and enhance the economy and flexibility of the power grid. BACKGROUND

[0002] With the promotion of the "double carbon" goal and the large-scale grid connection of renewable energy, the proportion of distributed energy such as wind power and photovoltaic power in the power system is increasing, which brings unprecedented challenges to the planning and operation of distribution networks. Wind power and photovoltaic power generation have the characteristics of strong fluctuation and difficulty in prediction, which can easily cause voltage fluctuation, power flow reversal and wind and light abandonment, affecting the stability and operation efficiency of the power grid. In order to enhance the control ability of the power grid, energy storage systems are widely introduced as adjustment resources and are coordinated with wind power and photovoltaic power.

[0003] However, the optimal configuration of wind-solar-storage resources has complex nonlinear coupling relationships, involving site selection, capacity determination, operation scheduling and other multi-dimensional decision variables. Traditional planning methods mostly use linear programming, single-layer optimization or heuristic methods, which cannot balance the long-term economy and operation coordination of the system, and mostly only consider a single objective or static scenario, making it difficult to adapt to the dynamic characteristics and multi-scenario uncertainty of wind and light output. In terms of algorithms, traditional heuristic algorithms such as standard particle swarm and genetic algorithm are prone to local optimization when solving complex multi-variable problems, have slow convergence speed, are sensitive to model dimension, and are difficult to guarantee the globality and stability of the solution.

[0004] Therefore, it is urgent to build a collaborative optimization method that can balance the planning and operation layers, has a dynamic feedback mechanism, and can adapt to the uncertainty of renewable energy output. At the same time, it is also necessary to improve existing intelligent optimization algorithms to improve the global search ability and convergence efficiency of the algorithm in complex high-dimensional planning models. Therefore, a wind-solar-storage collaborative planning method based on an improved bat algorithm is proposed, which coordinates resource configuration and operation scheduling by building a double-layer optimization model, and introduces mechanisms such as chaotic search, sinusoidal disturbance and Levy flight to optimize algorithm performance, improve the renewable energy consumption capacity and operation economy of the system, and provide efficient and intelligent technical support for the development of distribution networks. SUMMARY

[0005] The purpose of the present application is to solve the technical problems of the existing technology that the planning and operation coordination is difficult to be realized due to the volatility and uncertainty in the process of renewable energy access to the power distribution network, and the technical defects of slow convergence speed, low search precision, weak local search ability and easy to fall into local optimum when the existing bat algorithm is used to solve the double-layer optimization model, thereby resulting in the technical problems of low calculation efficiency, unstable global optimal solution and insufficient accuracy of information transmission between upper and lower layers of the double-layer optimization model, and the present application is proposed.

[0006] In order to solve the above technical problems, the technical scheme adopted by the present application is as follows: A wind-solar-storage collaborative planning method based on an improved bat algorithm, comprising the following steps: Step 1: constructing a planning and operation coordinated power distribution network double-layer optimization model; The upper layer of the power distribution network double-layer optimization model is a planning model, which optimizes the site selection and capacity configuration of wind power, photovoltaic and energy storage devices with the maximum net benefit in the whole life cycle as the target; the lower layer is an operation model, which coordinates the operation scheduling among wind power, photovoltaic and energy storage devices with the minimum power loss and the minimum power loss as the target on the basis of receiving the planning results of the upper layer; Step 2: solving the planning and operation coordinated power distribution network double-layer optimization model constructed in step 1 to finally obtain an optimal planning scheme.

[0007] In step 1, the upper layer planning model comprehensively considers the power selling revenue and the device investment and operation and maintenance cost, and optimizes the site selection and capacity configuration scheme of wind power, photovoltaic and energy storage system with the investment budget, capacity ratio, stable operation of power grid as the constraints, so as to realize the maximum net benefit in the whole life cycle.

[0008] The objective function of the upper layer planning model is to maximize the net benefit in the whole life cycle, including the power selling revenue in the planning period and the device life cycle cost, and the objective function of the specific planning model is as follows: (1); Wherein, represents the power selling revenue in the time period t of the planning period, is the annualized net present value of the device investment cost, T is the planning period, is the upper optimization target, i.e. the net benefit; The power selling revenue is calculated as follows: (2); In the formula, is the power generation power of the wind turbine i in the time period t ; For photovoltaic units j In the time period t The power generation; For energy storage system in the time period t Charging and discharging power, positive for discharge, negative for charge; For the electricity price in the time period t ; N And M The total number of wind turbine units and photovoltaic units, respectively; Equipment investment cost The calculation is as follows: (3); Equipment investment cost The total life cycle cost of wind power, photovoltaic and energy storage, including initial investment cost and operation and maintenance cost, and using annual discount rate To be unified discounted; i, j, e Represent wind turbine, photovoltaic and energy storage equipment, respectively; , , 0-1 decision variable, respectively, indicating the construction state of wind turbine, photovoltaic and energy storage, taking 1 indicates installation; , The unit investment cost of wind turbine i And photovoltaic unit j ; , , The operating life of wind turbine, photovoltaic and energy storage equipment, respectively; , , The annual operation and maintenance cost of wind turbine, photovoltaic and energy storage equipment, respectively; , , The rated power of wind turbine, photovoltaic and energy storage equipment, respectively; Indicates the set of newly planned energy storage equipment; Indicates the investment cost of the k The unit power capacity of the The unit energy capacity of the k Energy storage equipment energy capacity Energy storage equipment other initial investment cost .

[0009] The constraint conditions of the upper planning model are expressed as follows: The total investment cost of equipment in the planning period shall not exceed the upper limit of the budget, and the constraint is expressed as follows: (4); In the formula, is the total investment cost of the equipment, i.e., the sum of the construction costs of the wind power, photovoltaic and energy storage equipment; is the maximum total investment limit allowed; The total installed capacity of the distributed power generation must be within a certain proportion of the system load to ensure stable operation of the power grid, and the constraint is expressed as follows: (5); In the formula, P t is the total load power of the system in the time period t is the ratio of the distributed power supply power to the total load power of the system.

[0010] In step 1, the lower layer operation model determines the optimal coordination strategy between renewable energy and energy storage based on the site selection and capacity planning scheme provided by the planning layer, considering power balance of the power grid, equipment output characteristics, node voltage constraints and dynamic operation characteristics of the energy storage. The objective function of the lower layer operation model is to minimize the total operation cost, including wind and light abandonment cost and network loss cost. The objective function of the specific operation model is as follows: (6); In the formula, , are the unit wind and light abandonment costs, respectively; , are the abandoned power of the t th wind turbine and the i th photovoltaic module, respectively; j is the electricity price, is the annual maximum load hours; is the total number of branches; L is the conductance of branch , l are the node voltage amplitudes of branch and branch , respectively, i is the phase angle difference between nodes j , is the ij of the wind turbine and the i of the photovoltaic module, j is the equipment index number, and i , j are the physical node numbers of the power grid.

[0011] The constraint conditions of the lower layer operation model include node power balance constraint, distributed power supply output constraint, node voltage constraint and energy storage system operation constraint, which are expressed as follows: Power grid power balance constraint: the real-time power balance of the power grid must be maintained, and the constraint is expressed as follows:​ (7); In the formula, For wind turbine i Rated power; For photovoltaic j Rated power; This refers to the discharge power of the energy storage device. The charging power for energy storage devices; P L This refers to the system load power. Distributed power generation output constraints: The real-time output of wind and solar power must meet their rated capacity limits and follow the output curve. The constraints are expressed as follows: (8); (9); In the formula, , for t Actual output of wind turbines and solar power during specific time periods; Node voltage constraints: i , j The physical nodes of the power grid are numbered, and the node voltage amplitude must meet the dynamic safe operating range. The constraints are as follows: (10); In the formula, for t Time period nodes i voltage amplitude, , for t Time period nodes i The upper and lower limits of the voltage amplitude.

[0012] Energy storage system operating constraints: The energy storage system must simultaneously satisfy the State of Charge (SOC) dynamic equation, capacity limits, and charge / discharge power limits, as expressed below: (11); (12); (13); The operation of energy storage systems must comply with strict dynamic constraints: among which For energy storage systems t State of charge during a period of time This represents the state of charge at the previous moment. for t Time-based charge and discharge power, and For charging and discharging efficiency; The time interval for updating the energy storage system status; With SoCmax and SoCmin are the maximum and minimum state of charge allowed by the system, which are the safe operating boundaries of state of charge to prevent device life attenuation caused by overcharging or overdischarging; in the energy storage capacity parameter, SoCmax and SoCmin are the maximum and minimum state of charge allowed by the system, which are the safe operating boundaries of state of charge to prevent device life attenuation caused by overcharging or overdischarging; in the energy storage capacity parameter, SoCmax and SoCmin are the maximum and minimum state of charge allowed by the system, which are the safe operating boundaries of state of charge to prevent device life attenuation caused by overcharging or overdischarging; in the energy storage capacity parameter,

[0013] In step 2, the upper model takes the maximum net benefit of the whole life cycle as the objective function, and takes the site selection and capacity configuration of distributed wind power, photovoltaic and energy storage system as the decision variable. This problem belongs to the integer linear programming class, so the commercial solver CPLEX is used for solving; the upper planning model outputs the selected wind power station, photovoltaic station, energy storage location node number and the optimal capacity corresponding to each node, and serves as the input of the lower operation model; the lower model takes the minimum of the abandoned electricity and the network loss as the objective function, and coordinates the operation scheduling between wind power, photovoltaic and energy storage equipment. This problem involves nonlinear and coupled variables, so the improved bat algorithm is used for solving; the upper and lower layers are optimized through "feedback iteration" mode, until convergence, and finally the optimal planning scheme is obtained.

[0014] The step 2 specifically includes the following steps: Step 2.1 initializes the basic parameters; the basic parameters of the whole system are initialized and set, the grid structure parameters are input, including node number, branch topology, node voltage range, conductance; load parameters, such as load time series data of each node, annual maximum load hours, etc.; wind and photovoltaic resource data; wind turbine, photovoltaic generator and energy storage device parameters; electricity price parameters; Step 2.2 uses the commercial solver CPLEX to solve the upper planning model, the objective function, decision variable and constraint condition are shown in formula (1) to formula (5) in step 1; the solver outputs the planning scheme solved in this round, including: the selected wind turbine, photovoltaic generator, energy storage device configuration node number, the optimal capacity corresponding to each node, and economic allocation strategy; Step 2.3 passes the site selection and capacity configuration planning scheme solved by the upper planning model to the lower operation model as input data; Step 2.4, the lower operation model needs to optimize the operation control strategy according to the input current planning scheme, so as to achieve the minimum of wind light abandoned electricity and network loss, therefore, the improved bat algorithm is used for solving.

[0015] In step 2.4, the improved bat algorithm is used for solving, including the following steps: Step 2.4.1 initializes the basic parameters of the improved bat algorithm; input the grid structure parameters and the objective function of the lower operation model FThe corresponding formula (6) is used to calculate the fitness value of the objective function; the initialization settings for the bat population and algorithm parameters are specifically included: randomly generating N The initial position of the bat and speed ( i =1,2,…, N ), the location of each bat It is a D-dimensional vector representing a feasible planning strategy based on upper-level capacity configuration, and also a solution to the two-level optimization model of the distribution network that coordinates planning and operation; algorithm parameters, loudness , emit sound wave frequency Sound wave frequency range and Used to generate random factors Chaotic mapping control parameters Random walk parameters of Levy flight Pulse emission rate Chaotic inertial weights Range of inertia weight and and the maximum number of iterations ; Step 2.4.2 Based on the objective function of the lower-level running model F Calculate the fitness value of each individual bat and mark the optimal location of the bats in the current population. The speed and position of the bat are iteratively updated according to the rules defined by the following formula; For each bat i In the t iterations (t=1,2,…) Perform the following operations when: (1) Update the sound wave frequency The original formula is as follows: (14); For the first i The frequency of sound waves emitted by an individual bat during this iteration; and The lower and upper limits of the set sound wave frequency; For interval Random numbers.

[0016] The present application considers the sine chaotic mapping technology, and improves the sound wave frequency updating equation. The sine perturbation has the characteristics of limited amplitude and smooth change, and can enhance the search jumping ability without introducing violent fluctuations, thereby improving the global convergence ability of the bat algorithm. By superimposing the sine perturbation term in each iteration process, the sound wave frequency has a certain volatility, which can control the search jumping amplitude and increase the population diversity. Therefore, the non-repetitive, ergodic and mixed characteristics of chaotic mapping are used to optimize the sound wave frequency of the bat by using the sine chaotic mapping. The updating rule of the sound wave frequency of each bat is as follows: (15); In the formula, represents the sound wave frequency of the bat in the i th iteration; t i is a chaotic mapping control coefficient.

[0017] (2) Update the speed v i of the i th bat, and the original formula is as follows: (16); is the speed vector of the i th bat in the i th iteration; i is the speed vector in the last iteration; t is the position of the i th bat in the last iteration; is the current local optimal solution (position) in the group. i of

[0018] The present application considers the chaotic inertia weight, and improves the speed updating equation. In the iteration process, the bat algorithm is easy to fall into local optimum, which often hinders its accurate identification of the global optimal solution. In order to solve this problem, a chaotic inertia weight strategy is proposed to enhance the original speed updating mechanism. By introducing the chaotic inertia weight, the ability of the algorithm to avoid local optimum is improved, thereby enhancing the global search performance. The chaotic inertia weight generated in each iteration is represented as follows: (17); In the formula: , respectively represent the upper limit and the lower limit of the inertia weight parameter; is the total maximum iteration number; is the current iteration number, is a chaotic disturbance factor randomly generated in the interval .

[0019] ​​​​​​The chaotic inertia weight strategy is introduced into the velocity update equation. In each iteration, the chaotic inertia weight is calculated and the dynamic adjustment of the bat speed is enhanced. The improved velocity update equation containing the chaotic inertia weight is expressed as follows: (18); wherein, is the chaotic inertia weight, which dynamically changes with iterations and dynamically adjusts the flight speed of the bat.

[0020] (3) update the position . For each bat i The position update is performed at the t th iteration (t = 1, 2, …, ), and a new solution of the planning and operation coordinated distribution network bi-level optimization model is generated. The original position update formula is as follows: (19); is the position vector of the i th bat at the t th iteration.

[0021] The present application considers the Lévy flight strategy and improves the position update equation. Lévy flight introduces large-scale and occasional jumps in the search process, the direction changes frequently and abruptly, which helps the bat individuals to avoid falling into local optimum and expand the search space. Therefore, the optimization performance in high-dimensional space is significantly improved, thereby enhancing the overall effect of the algorithm. The present application improves the bat algorithm by introducing the random walk characteristics of Lévy flight. After applying the Lévy flight strategy, the update formula of the position of each bat individual is as follows: (20); wherein is the Lévy random walk path; is the gamma function; is the scaling parameter, which determines the probability of large jumps in the step length, and is usually taken as , The closer to 1, the larger the jump amplitude during the search; The closer to 2, the jump amplitude tends to be stable.

[0022] Step 2.4.3 performs local search and update. In order to enhance the local search ability of the algorithm in the solution space, set the local disturbance, and carry out small range search on part of the bat individuals, and moderately guide the solution to converge near the optimal solution.

[0023] Specifically, a random number is generated, and if it satisfies ,, an optimal individual is selected from the current optimal bats, and a local solution is generated around the selected optimal individual by the following local disturbance formula (21), otherwise, a new solution satisfying the objective function is updated according to the position update formula (20), and the local disturbance formula is as follows: (21); Wherein, is a random number in the interval [0, 1], is the average sound loudness of the whole bat colony at this round of iteration.

[0024] Step 2.4.4 performs global search and update. A random number is generated, if , and the fitness value of the objective function is better than the new solution in step 2.4.3, the solution is accepted, and the sound loudness and the pulse emission rate are reduced according to the rules of formula (23-24).

[0025] (1) The original sound loudness update formula is as follows: (22); Wherein, is the sound loudness of bat i at the tth iteration; is the sound loudness of bat i at the next iteration; is the sound loudness attenuation coefficient.

[0026] and the improved sound wave frequency update equation, the present application considers the sine chaotic mapping technology, and improves the sound loudness update equation: (23); Wherein, is a chaotic mapping control coefficient.

[0027] (2) The pulse emission rate update equation is as follows: (24); Wherein, is a pulse emission rate enhancement factor.

[0028] Step 2.4.5 selects the best solution with the highest fitness as the global optimal and updates its position. The fitness values of all bat individuals in the population are sorted, and the bat position with the highest fitness value is found, that is, the current best position, which is the optimal solution , in the iteration process, the solution is the optimization result of the lower layer running model.

[0029] ​Step 2.4.6 judges whether the maximum iteration number is reached, and outputs the optimal solution. If the maximum iteration number is reached, or the global optimal solution has no significant improvement in continuous generations, the iteration is terminated, and the current wind-light-storage configuration and its operation result, i.e. the solution of the planning and operation coordinated distribution network bi-level optimization model, is output. Otherwise, return to step 2.4.2-step 2.4.5 for iterative calculation. The solution of the current iteration, i.e. the lower-level optimization result, is input into the upper-level planning model, and is fed back to the planning layer to update the planning configuration node, power and capacity, for a new round of iteration, and jump to step 2.2 to solve the upper-level planning model by using the commercial solver CPLEX solver to complete the optimization calculation.

[0030] Compared with the prior art, the present application has the following technical effects: 1) By constructing a bi-level optimization model, the rationality of wind-light-storage resource configuration and the economy of system operation are improved; by constructing a bi-level optimization model coordinated by upper and lower layers, the economic configuration of station site and capacity is realized in the upper layer, and the operation scheduling and energy coordination control are realized in the lower layer, so that the overall investment return rate and renewable energy consumption efficiency of the system are effectively improved; 2) The traditional bat algorithm is improved to improve the convergence speed and global optimization ability of the solving algorithm. The traditional bat algorithm is improved by introducing chaos mapping, chaos inertia weight and Lévy flight strategy, which effectively avoids the algorithm from falling into local optimum, improves the globality and convergence speed of the solution, and is suitable for high-dimensional complex optimization problems.

[0031] 3) The optimization strategy proposed in the present application can significantly reduce the wind-light curtailment rate, reduce the network loss, and improve the node voltage stability, thereby enhancing the operation reliability and flexibility of the distribution network under high proportion of distributed energy access. BRIEF DESCRIPTION OF DRAWINGS

[0032] The present application will be further described below in combination with the drawings and examples: Figure 1 The flow chart of the present application based on the improved bat algorithm for solving the planning and operation coordinated distribution network bi-level optimization model; Figure 2 The topological structure diagram of the IEEE 33-node test system for simulation verification; Figure 3 The electricity price change curve; Figure 4 And Figure 5 The typical daily output curve of the wind power and photovoltaic system in different seasons; Figure 6 The convergence characteristic curve comparison diagram of different algorithms; Figure 7 The node voltage distribution diagram of different planning scenarios. DETAILED DESCRIPTION

[0033] A wind-solar-storage collaborative planning method based on an improved bat algorithm, comprising the following steps: Step 1: Construct a distribution network double-layer optimization model for planning and operation collaboration; The upper layer of the distribution network double-layer optimization model is a planning model, which optimizes the site selection and capacity configuration of wind power, photovoltaic and energy storage equipment with the goal of maximizing the net income in the whole life cycle; the lower layer is an operation model, which coordinates the operation scheduling among wind power, photovoltaic and energy storage equipment with the goal of minimizing the power loss and the amount of abandoned electricity on the basis of receiving the planning results of the upper layer; In step 1, the upper layer planning model considers the electricity sales revenue and the equipment investment and operation cost comprehensively, optimizes the site selection and capacity configuration scheme of wind power, photovoltaic and energy storage system under the constraints of investment budget, capacity ratio, stable operation of power grid, etc., and realizes the maximization of net income in the whole life cycle.

[0034] The objective function of the upper layer planning model is to maximize the net income in the whole life cycle, including the electricity sales revenue in the planning period and the equipment life cycle cost, and the objective function of the specific planning model is as follows: (1); Wherein, represents the electricity sales revenue in period t in the planning period, is the annualized net present value of equipment investment cost, T is the planning period, is the upper optimization target, i.e. net income.

[0035] The electricity sales revenue is calculated as follows: (2); In the formula, is the power generation of wind turbine i in period t ; is the power generation of photovoltaic unit j in period t ; is the charge and discharge power of energy storage system in period t , positive value for discharge and negative value for charge; is the electricity price in period t ; N and M are the total number of wind turbine and photovoltaic unit respectively.

[0036] The equipment investment cost is calculated as follows: (3); The equipment investment cost The sum of the total life cycle cost of wind power, photovoltaic and energy storage, including initial investment cost and operation and maintenance cost, and using annual discount rate Unify the discount; i, j, e Respectively represent the wind turbine, photovoltaic and energy storage equipment; , , 0-1 decision variable, respectively, indicates the construction state of wind turbine, photovoltaic and energy storage, take 1 indicates installation; , Respectively, the unit investment cost of wind turbine i And photovoltaic unit j ; , , Respectively, the operating life of wind turbine, photovoltaic and energy storage equipment; , , Respectively, the annual operation and maintenance cost of wind turbine, photovoltaic and energy storage equipment; , , Respectively, the rated power of wind turbine, photovoltaic and energy storage equipment; Indicates the set of newly planned energy storage equipment; Indicates the investment cost of the k Unit power capacity of energy storage equipment, Indicates the investment cost of the k Unit energy capacity of energy storage equipment, The energy capacity of energy storage equipment, The other initial investment cost of energy storage equipment.

[0037] The constraint conditions of the upper planning model are expressed as follows: The total investment cost of equipment in the planning period shall not exceed the upper limit of the budget, and the constraint is expressed as follows: (4); In the formula, The total investment cost of equipment, that is, the construction cost of wind power, photovoltaic and energy storage equipment; The maximum total investment limit allowed; The total installed capacity of distributed power generation needs to be within a certain proportion of the system load to ensure stable operation of the power grid, and the constraint is expressed as follows: (5); In the formula, P t The total load power of the system in time period t , The ratio of distributed power supply power to the total load power of the system.

[0038] The lower-level operation model is based on the site selection and capacity determination scheme provided by the planning layer. It comprehensively considers grid power balance, equipment output characteristics, node voltage constraints, and the dynamic operation characteristics of energy storage to determine the optimal coordination strategy between renewable energy and energy storage. The objective function of the lower-level operation model is to minimize the total operating cost, including wind and solar curtailment costs and grid loss costs. The specific objective function of the operation model is as follows: (6); In the formula, , These represent the unit costs of wind and solar power curtailment, respectively. , They are respectively t Time period i The wind turbine unit and the first j The amount of electricity wasted by each photovoltaic unit; For electricity price, This refers to the annual maximum load hours. L The total number of branch roads; branch road l electrical conductivity, , Branch roads i and branch roads j The node voltage amplitude, For nodes ij Phase angle difference between wind turbine units i and photovoltaic units j For the device index number, and in the node voltage i , j This refers to the physical node number of the power grid.

[0039] The constraints of the lower-level operational model include node power balance constraints, distributed generation output constraints, node voltage constraints, and energy storage system operational constraints, which are specifically expressed as follows: Power balance constraint: The real-time power flow balance of the power grid must be maintained. The constraint is expressed as follows: (7); In the formula, For wind turbine i Rated power; For photovoltaic j Rated power; This refers to the discharge power of the energy storage device. The charging power for energy storage devices; P L This refers to the system load power. Distributed power generation output constraints: The real-time output of wind and solar power must meet their rated capacity limits and follow the output curve. The constraints are expressed as follows: (8); (9); In the formula, , for t Actual output of wind turbines and solar power during specific time periods; Node voltage constraints: i , j The physical nodes of the power grid are numbered, and the node voltage amplitude must meet the dynamic safe operating range. The constraints are as follows: (10); In the formula, for t Time period nodes i voltage amplitude, , for t Time period nodes i The upper and lower limits of the voltage amplitude.

[0040] Energy storage system operating constraints: The energy storage system must simultaneously satisfy the State of Charge (SOC) dynamic equation, capacity limits, and charge / discharge power limits, as expressed below: (11); (12); (13); The operation of energy storage systems must comply with strict dynamic constraints: among which For energy storage systems t State of charge during a period of time This represents the state of charge at the previous moment. for t Time-based charge and discharge power, and For charging and discharging efficiency; The time interval for updating the energy storage system status; and The maximum and minimum states of charge allowed by the system are the safe operating boundaries of the state of charge, preventing equipment lifespan degradation caused by overcharging or over-discharging; among the energy storage capacity parameters, For the planned energy storage system's rated capacity, Configure the actual capacity of the energy storage system to ensure that the system has the necessary redundancy to cope with load fluctuations.

[0041] Step 2: Solve the planning and operation coordinated distribution network bi-level optimization model constructed in step 1. The solution of the upper planning model belongs to the integer linear programming class, so the commercial solver CPLEX is used for solving. The upper planning model outputs the selected wind power station, photovoltaic station, energy storage location node number and the optimal capacity corresponding to each node, and serves as the input of the lower operation model. The solution of the lower operation model involves nonlinear and coupled variables, so the improved bat algorithm is used for solving. The upper and lower layers are optimized through "feedback iteration" interaction until convergence, and the optimal planning scheme is finally obtained.

[0042] The upper model takes the maximum net income in the whole life cycle as the objective function, and takes the site selection and capacity configuration of distributed wind power, photovoltaic and energy storage system as the decision variable. This problem belongs to the integer linear programming class, so the commercial solver CPLEX is used for solving. The upper planning model outputs the selected wind power station, photovoltaic station, energy storage location node number and the optimal capacity corresponding to each node, and serves as the input of the lower operation model. The lower model takes the minimum of power loss and curtailment as the objective function, and coordinates the operation scheduling among wind power, photovoltaic and energy storage equipment. This problem involves nonlinear and coupled variables, so the improved bat algorithm is used for solving. The upper and lower layers are optimized through "feedback iteration" interaction until convergence, and the optimal planning scheme is finally obtained. The specific methods include: Step 2.1 Initialize basic parameters; initialize and set the basic parameters of the whole system, input the grid structure parameters such as node number, branch topology, node voltage range, conductance, etc.; load parameters such as load time series data of each node, annual maximum load hours, etc.; wind and photovoltaic resource data; wind turbine, photovoltaic generator and energy storage device parameters; electricity price parameters.

[0043] Step 2.2 Use the commercial solver CPLEX to solve the upper planning model, and the objective function, decision variable and constraint condition are shown in formula (1-5) in step 1. The solver outputs the planning scheme solved in this round, including: the selected wind turbine, photovoltaic generator, energy storage device configuration node number, optimal capacity corresponding to each node, and economic allocation strategy.

[0044] Step 2.3 The site selection and capacity configuration planning scheme solved by the upper planning model is input data, which is passed to the lower operation model.

[0045] Step 2.4 The lower operation model needs to optimize the operation control strategy according to the input current planning scheme to achieve the minimum wind light curtailment and network loss, so the improved bat algorithm is used for solving.

[0046] Step 2.4.1 Initialize the basic parameters of the improved bat algorithm. Input the grid structure parameters and the objective function of the lower operation model FThe corresponding formula (6) is used to calculate the fitness value of the objective function; the initialization settings for the bat population and algorithm parameters are specifically included: randomly generating N The initial position of the bat and speed ( i =1,2,…, N ), the location of each bat It is a D-dimensional vector representing a feasible planning strategy based on upper-level capacity configuration, and also a solution to the two-level optimization model of the distribution network that coordinates planning and operation; algorithm parameters, loudness , emit sound wave frequency Sound wave frequency range and Used to generate random factors Chaotic mapping control parameters Random walk parameters of Levy flight Pulse emission rate Chaotic inertial weights Range of inertia weight and and maximum number of iterations .

[0047] Step 2.4.2 Based on the objective function of the lower-level running model F Calculate the fitness value of each individual bat and mark the optimal location of the bats in the current population. The speed and position of the bat are iteratively updated according to the rules defined by the following formula.

[0048] For each bat i In the t iterations (t=1,2,…) Perform the following operations when: (1) Update the sound wave frequency The original formula is as follows: (14); For the first i The frequency of sound waves emitted by an individual bat during this iteration; and The lower and upper limits of the set sound wave frequency; For interval Random numbers.

[0049] This invention considers sinusoidal chaotic mapping technology to improve the acoustic frequency update equation. Sinusoidal perturbations, with their finite amplitude and smooth changes, can enhance the search jump capability without introducing drastic fluctuations, thereby improving the global convergence of the bat algorithm. By superimposing sinusoidal perturbation terms in each iteration, the acoustic frequency acquires a certain degree of volatility, which can control the search jump amplitude and increase population diversity. Therefore, utilizing the non-repetitive, ergodic, and hybrid properties of chaotic mapping, sinusoidal chaotic mapping is used to optimize the bat's acoustic frequency. The update rule for each bat's acoustic frequency is as follows: (15); In the formula, Indicates the first t In the next iteration, the bat i The frequency of the sound wave; These are the control coefficients for the chaotic mapping.

[0050] (2) Update speed The original formula is as follows: (16); For the first i The bat in the t The velocity vector during round iteration; This is the velocity vector from the previous iteration; For the bat in the previous iteration i of Location; This represents the current local optimum (position) within the population.

[0051] This invention considers chaotic inertia weights to improve the velocity update equation. During iteration, the Bat Algorithm is prone to getting trapped in local optima, often hindering its accurate identification of the global optimum. To address this issue, a chaotic inertia weight strategy is proposed to enhance the original velocity update mechanism. By introducing chaotic inertia weights, the algorithm's ability to avoid local optima is improved, thereby enhancing global search performance. The chaotic inertia weights generated in each iteration are represented as follows: (17); In the formula: , These represent the upper and lower limits of the inertia weight parameter, respectively. This represents the maximum total number of iterations. This represents the current iteration number. For in the interval The internally randomly generated chaotic perturbation factor.

[0052] The chaotic inertia weight strategy is introduced into the velocity update equation. In each iteration, the chaotic inertia weight is calculated and the dynamic adjustment of the bat speed is enhanced. The improved velocity update equation containing the chaotic inertia weight is shown as follows: (18); wherein, is the chaotic inertia weight, which dynamically changes with iterations and dynamically adjusts the flight speed of the bat.

[0053] (3) update the position . For each bat i , the position update is performed at the t th iteration (t = 1, 2, …, ), a new solution of the planning and operation coordinated distribution network bi-level optimization model is generated, and the position update formula is shown as follows: (19); is the position vector of the i th bat at the t th iteration.

[0054] The present application considers the Lévy flight strategy and improves the position update equation. Lévy flight introduces large-scale and occasional jumps in the search process, the direction changes frequently and abruptly, which helps the bat individuals to avoid falling into local optimum and expand the search space. Therefore, the optimization performance in high-dimensional space is significantly improved, thereby enhancing the overall effect of the algorithm. The present application improves the bat algorithm by introducing the random walk characteristics of Lévy flight. After applying the Lévy flight strategy, the update formula of the position of each bat individual is as follows: (20); wherein is the Lévy random walk path; is the gamma function; is the scaling parameter, which determines the probability of large jumps in the step length, and is usually taken as , the closer to 1, the larger the jump amplitude during the search; the closer to 2, the jump amplitude tends to be stable.

[0055] Step 2.4.3 performs local search and update. In order to enhance the local search ability of the algorithm in the solution space, local disturbance is set, and small-range search is carried out for part of the bat individuals, and the solution is moderately guided to converge near the optimal solution.

[0056] Specifically, a random number is generated, and if it satisfies , the local search is performed., an optimal individual is selected in the current optimal bat, and a local solution is generated in the vicinity of the selected optimal individual by the following local disturbance formula (21), otherwise a new solution satisfying the objective function is updated according to the position update formula (20), and the local disturbance formula is as follows: (21); Wherein, is a random number in the interval [0, 1], is a random number in the interval [0, 1], is the average sound loudness of the entire bat colony at this round of iteration.

[0057] Step 2.4.4 performs global search and update. A random number is generated, if , and the fitness value of the objective function is better than the new solution in step 2.4.3, the solution is accepted, and the sound loudness and the pulse emission rate are reduced according to the rules of formula (23-24).

[0058] (1) The original sound loudness update formula is as follows: (22); Wherein, is the sound loudness of bat i at the tth iteration; is the sound loudness of bat i at the next iteration; is the sound loudness decay coefficient.

[0059] The improved sound wave frequency update equation is similar, the present application considers the sine chaotic mapping technology, and improves the sound loudness update equation: (23); Wherein, is a chaotic mapping control coefficient.

[0060] (2) The pulse emission rate update equation is as follows: (24); Wherein, is a pulse emission rate enhancement factor.

[0061] Step 2.4.5 compares the fitness values of all bat individuals, selects the best solution with the highest fitness value as the global optimal and updates its position. The fitness values of all bat individuals in the population are sorted, and the bat position with the highest fitness value is found, that is, the current best position, and the optimal solution is found, and in the iteration process, the solution is the optimization result of the lower layer running model.

[0062] Step 2.4.6 judges whether the maximum number of iterations is reached, and outputs the optimum. If the maximum number of iterations is reached; or when the relative error of the upper target function between two consecutive iterations is less than 0.5% through simulation test on various energy storage configuration schemes, the iteration process is terminated. If yes, the iteration is terminated, and the current wind-solar-storage configuration and its operation result, i.e. the solution of the distribution network bi-level optimization model of planning and operation coordination sought, are output. If no, return to step 2.4.2-step 2.4.5 for iterative calculation. The solution of the current iteration, i.e. the lower optimization result, is input into the upper planning model, and is fed back to the planning layer to update the planning configuration node, power and capacity, and a new round of iteration is performed, and jump to step 2.2 to use the commercial solver CPLEX solver to solve the upper planning model to complete the optimization calculation.

[0063] The economic performance and system operation under different planning schemes are further analyzed. To evaluate the effectiveness of the method, the following three groups of comparisons are designed: Scenario 1: only consider distributed generation, without configuring energy storage system; Scenario 2: consider the configuration of distributed generation and energy storage system at the same time, and solve by using standard bat algorithm; Scenario 3: consider the configuration of distributed generation and energy storage system at the same time, and solve by using improved bat algorithm; Embodiment: As shown in Figure 1 , the method proposed in the application is simulated and verified based on IEEE 33-node test system, and the topological structure is as shown in Figure 2 . The related parameters of the energy storage system and the distributed power supply are listed in Table 1 and Table 2, Figure 3 , which is the electricity price change curve. Figure 4 , and Figure 5 are the typical daily output curves of wind power and photovoltaic system in different seasons. Figure 6 is the convergence characteristic curve comparison of different algorithms. Figure 7 is the node voltage distribution of different planning scenarios.

[0064] Table 1 Energy storage system parameters

[0065] Table 2 Distributed power generation system parameters

[0066] Under the typical operation scenario in spring and autumn, the convergence of the target function indicates that the model parameters have reached the optimal state. Figure 6For the iterative process of different algorithms, the particle swarm algorithm converges at the 39th iteration, reaching the optimal solution, while the standard bat algorithm needs 28 iterations and the improved bat algorithm only needs 17 iterations to converge, and the convergence accuracy is higher. Analysis shows that the performance of the bat algorithm is better than that of the particle swarm algorithm, the main reason is to perform dynamic frequency adjustment and local disturbance, which effectively avoids the premature convergence phenomenon in high-dimensional non-convex scheduling problems by enhancing the jumping ability of the solution space, verifying the excellent adaptability of the bat algorithm in the optimization of complex energy systems. The improved bat algorithm uses the chaos mapping technology to optimize the pulse frequency, amplitude and inertia weight of the bat individual, enhances the global search ability of the algorithm, and avoids the iteration process from falling into local optimum and affecting the global result. In addition, the Levy Flight strategy is introduced to expand the search space, thereby improving the convergence speed and solution accuracy of the algorithm. Table 3 compares the configuration results of wind power, photovoltaic and energy storage under different scenarios.

[0067] Table 3 Wind-light-storage resource configuration scheme under different scenarios

[0068] As shown in Table 3, in scenario 1 without configuring energy storage, wind turbines with a capacity of 635 kW and 350 kW are installed at nodes 15 and 17, respectively, and photovoltaic units with a capacity of 180 kW each are configured at the same nodes. Comparison shows that the installed capacity of wind power and photovoltaic in scenarios 2 and 3 is higher, indicating that the configuration of energy storage system effectively improves the system's ability to accommodate distributed energy. Therefore, reasonable configuration of energy storage can help improve the utilization rate and accommodation level of distributed renewable energy.

[0069] In scenarios 2 and 3, wind power and photovoltaic are installed at nodes 9, 15 and 24, but the planned configuration capacity is different because the methods used to solve the lower-level model are different. Different algorithms produce different optimal operation strategies, which are then fed back to the upper-level model, affecting the planned capacity configuration results. The economic comparison and analysis results of each planning scenario are shown in Table 4.

[0070] Table 4 Economic comparison and analysis under different planning scenarios

[0071] As shown in Table 4, the total cost of Scenario 1 is the lowest, mainly because the energy storage system is not configured, and the installed capacity of wind power and photovoltaic is relatively low, so that the initial investment cost is the lowest. Correspondingly, its operating cost and annual electricity sales revenue are also less. In comparison, Scenarios 2 and 3 are configured with energy storage systems, and the network loss cost is significantly reduced by 25.7% and 32.9% respectively compared with Scenario 1. Analysis shows that the coordinated operation of wind-light-storage can effectively reduce the network loss of the system. Although the configuration of energy storage increases the initial investment cost, the operating benefits brought by reducing network loss and reducing power curtailment can gradually offset the additional cost in the long-term operation, thereby reducing the total cost of the system. At the same time, although the investment cost of Scenario 3 is 2.2% higher than that of Scenario 2, the operating cost of Scenario 3 is lower, and ultimately the total cost of the system is reduced by 4.1%. Analysis shows that in Scenario 3, higher capacity of wind power, photovoltaic and energy storage is configured, although the investment cost is increased, but the optimized operation strategy effectively reduces the operating cost. At the same time, the increase of installed capacity improves the power generation, so that the annual electricity sales revenue of Scenario 3 is increased by 4.9% compared with Scenario 2. Therefore, from the long-term planning point of view, the economy of Scenario 3 is better. The application of the improved bat algorithm can effectively improve the system's ability to consume distributed energy, and the effectiveness of the proposed bi-level planning model and its solving method is proved. The utilization rates of wind energy and solar energy under the three planning scenarios are shown in Table 5.

[0072] Table 5 Renewable energy utilization efficiency under different planning scenarios

[0073] As shown in Table 5, in Scenario 1, the utilization rates of photovoltaic and wind power are 83.67% and 87.23% respectively; in Scenario 2, by configuring the energy storage system, the utilization rates of photovoltaic and wind power are increased to 85.32% and 89.45% respectively; in Scenario 3, after using the improved bat algorithm, the utilization rate of photovoltaic is further increased to 90.27%, and the utilization rate of wind power reaches 92.18%. The data shows that the reasonable configuration of energy storage system can significantly reduce the phenomenon of wind and light curtailment, and the improved bat algorithm can coordinate the operation strategy of source-storage, thereby improving the efficiency of renewable energy grid connection.

[0074] The connection of distributed power supply usually increases the voltage of each node in the distribution network, and the configuration of energy storage system can further improve the voltage distribution characteristics. The node voltage distribution under the three planning scenarios is shown in Table 6. Figure 7 The results show that the voltage regulation performance of the system is the best after using the improved bat algorithm. In Scenario 1 without configuring the energy storage system, the lowest node voltage is 0.908 p.u; in Scenario 2 using the standard bat algorithm, the lowest node voltage is increased to 0.936 p.u; in Scenario 3 using the improved bat algorithm, the value is further increased to 0.937 p.u.

[0075] The above results show that the improved bat algorithm can effectively improve the system voltage stability and power quality, especially suitable for distribution network with large-scale distributed power access, and improve the reliability of power grid operation.

[0076] Based on the planning scheme of scenario 3, the influence of producer-consumer behavior on system performance is quantitatively analyzed. Three trading modes are set for comparative study: mode 1 is a scenario without producer-consumer participation, and all power demand is supplied by the main grid; mode 2 is a standard scenario, there are producers and consumers, but their participation is limited, and only through the grid to purchase electricity to meet the demand; mode 3 is an optimized producer-consumer mode, the producer-consumer not only can consume local photovoltaic power generation, but also can adjust the load through the energy storage system, and participate in point-to-point (P2P) power trading based on the planning scheme. The income comparison results under different power generation trading modes are shown in Table 6.

[0077] Table 6: Income comparison under different power generation trading modes

[0078] As shown in Table 6, in the trading mode 1 without producer-consumer participation, the power distribution network has the highest electricity sales revenue, but the operation income of distributed power supply and energy storage system is the lowest, and the electricity purchase cost of users is the highest. Compared with mode 1, the daily net income of the power distribution network of mode 2 and mode 3 decreases by 61.3% and 33.4% respectively, while the operation income of distributed power supply and energy storage system increases by 76.7% and 58.7% respectively. The electricity purchase cost of users of mode 2 and mode 3 is reduced by 6.7% and 6.9% respectively. Analysis shows that the electricity sales revenue of the power distribution network of mode 2 is the lowest, but the operation income of distributed power supply and energy storage system is significantly increased, and the electricity purchase cost of users is reduced. In contrast, although the electricity sales revenue of the power distribution network of mode 3 is increased, the operation income of distributed power supply and energy storage system is reduced. Analysis shows that distributed energy trading can effectively promote renewable energy consumption, improve the operation efficiency of distributed power supply and energy storage system, and protect the income of users. Mode 3 optimizes the electricity price structure through collaborative optimization, improves the income of the power distribution network, reduces the electricity purchase cost of users, and realizes the win-win of operation subjects and power consumers.

[0079] The present application proposes a wind-light-storage collaborative planning method based on improved bat algorithm, which realizes efficient operation of distributed power supply and energy storage through upper and lower model collaborative optimization. The simulation results show that: the configuration of energy storage system can significantly reduce the wind and light abandoned power, thereby effectively reducing the system operation cost; the collaborative configuration of wind energy, photovoltaic and energy storage effectively reduces the power loss of distribution system. At the same time, the improved bat algorithm has obvious advantages in improving the solution performance of the double-layer model. Compared with the traditional heuristic algorithm, the improved bat algorithm can more efficiently solve the complex source-storage planning problem, effectively improve the global search ability, speed up the convergence speed, and avoid local optimum.

Claims

1. A wind-solar-storage collaborative planning method based on an improved bat algorithm, characterized in that, Comprising the following steps: Step 1: Constructing a planning and operation coordinated distribution network bi-level optimization model; The upper layer of the distribution network bi-level optimization model is a planning model, which optimizes the site selection and capacity configuration of wind power, photovoltaic and energy storage equipment with the goal of maximizing the net benefit in the whole life cycle; The lower layer is an operation model, which coordinates the operation scheduling between wind power, photovoltaic and energy storage equipment based on the planning results of the upper layer with the goal of minimizing the power loss and the amount of abandoned electricity; Step 2: Solving the planning and operation coordinated distribution network bi-level optimization model constructed in step 1 to obtain the optimal planning scheme.

2. The method of claim 1, wherein, In step 1, the upper layer planning model comprehensively considers the electricity sales revenue and equipment investment and operation and maintenance cost, optimizes the site selection and capacity configuration of wind power, photovoltaic and energy storage system under the constraints of investment budget, capacity ratio, and stable operation of power grid, and realizes the maximization of net benefit in the whole life cycle.

3. The method of claim 2, wherein, The objective function of the upper layer planning model is to maximize the net benefit in the whole life cycle, including the electricity sales revenue and the whole life cycle cost of equipment in the planning period, and the objective function of the specific planning model is as follows: （1）； wherein, represents the electricity sale revenue of the time period within the planning period, t is the annualized net present value of the equipment investment cost, T is the planning period, is the upper optimization objective, i.e., the net revenue;​ Electricity sales revenue This is calculated as follows: （2）； wherein: is the wind turbine i is the power generation of the wind turbine in the time period t ; is the photovoltaic turbine j is the power generation of the photovoltaic turbine in the time period t ; is the charge-discharge power of the energy storage system in the time period t , positive for discharging and negative for charging; is the electricity price in the time period t ; N and M are the total number of wind turbines and photovoltaic turbines, respectively. Equipment investment cost The calculation is as follows: （3）； Equipment investment cost The total cost of wind power, photovoltaic and energy storage in the whole life cycle, including initial investment cost and operation and maintenance cost, and using annual discount rate Unify the discount; i, j, e Respectively represent the wind turbine, photovoltaic and energy storage equipment; , , 0-1 decision variable, respectively indicates the construction state of wind turbine, photovoltaic and energy storage, takes 1 when installed; , Respectively for the unit investment cost of wind turbine i And photovoltaic unit j ; , , Respectively for the operation life of wind turbine, photovoltaic and energy storage equipment; , , Respectively for the annual operation and maintenance cost of wind turbine, photovoltaic and energy storage equipment; , , Respectively for the rated power of wind turbine, photovoltaic and energy storage equipment; Indicates the set of newly planned energy storage equipment; Indicates the investment cost of the unit power capacity of the k Energy storage equipment, Indicates the investment cost of the unit energy capacity of the k Energy storage equipment, The energy capacity of the energy storage equipment, The other initial investment cost of the energy storage equipment.

4. The method according to claim 2 or 3, characterized in that, The constraint conditions of the upper layer planning model are expressed as follows: The total investment cost of equipment in the planning period should not exceed the upper limit of the budget, and the constraint is expressed as follows: （4）； In the formula, is the total investment cost of the equipment, i.e. the sum of the construction costs of the wind power, photovoltaic and energy storage equipment; is the maximum total investment limit allowed; The total installed capacity of distributed generation should be within a certain proportion of the system load to ensure the stable operation of the power grid, and the constraint is expressed as follows: （5）； In the formula, P t The total load power of the system in the time period t The total load power of the system in the time period The ratio of the distributed power to the total load power of the system.

5. The method of claim 1, wherein, In step 1, the lower layer operation model determines the optimal coordination strategy between renewable energy and energy storage based on the site selection and capacity configuration scheme provided by the planning layer, considering the power balance of the power grid, the output characteristics of the equipment, the node voltage constraint and the dynamic operation characteristics of the energy storage; The objective function of the lower layer operation model is to minimize the total operation cost, including the cost of abandoned wind and light and the cost of power loss, and the objective function of the specific operation model is as follows: （6）； In the formula, , These represent the unit costs of wind and solar power curtailment, respectively. , They are respectively t Time period i The wind turbine unit and the first j The amount of electricity wasted by each photovoltaic unit; For electricity price, This refers to the annual maximum load hours. L The total number of branch roads; branch road l electrical conductivity, , Branch roads i and branch roads j The node voltage amplitude, For nodes ij Phase angle difference between wind turbine units i and photovoltaic units j For the device index number, and in the node voltage i , j This refers to the physical node numbering of the power grid.

6. The method of claim 5, wherein, The constraint conditions of the lower layer operation model include the node power balance constraint, the distributed power output constraint, the node voltage constraint, and the energy storage system operation constraint, and the specific constraint expression is as follows: The power grid power balance constraint: the real-time power balance of the power grid must be maintained, and the constraint is expressed as follows: （7）； wherein is the rated power of the fan; i is the rated power of the photovoltaic; j is the discharge power of the energy storage device, is the charge power of the energy storage device; P L is the system load power;​​ The distributed power output constraint: the real-time output of wind power and photovoltaic power must meet the rated capacity limit and follow the output curve, and the constraint is expressed as follows: （8）； （9）； In the formula, , is t Period fan and photovoltaic actual output; Node voltage constraint: i , j is the physical node number of the power grid, the node voltage amplitude needs to meet the dynamic safe operation range, and its constraint condition is: （10）； wherein is t a period node i a voltage amplitude at the period node, , is t a period node i upper and lower limits of the voltage amplitude at the period node; The energy storage system operation constraint: the energy storage system must meet the state of charge (SOC) dynamic equation, capacity limit and charge and discharge power limit at the same time, and the constraint is expressed as follows: （11）； （12）； （13）； The operation of the energy storage system needs to follow strict dynamic constraints: where SoC is the state of charge of the energy storage system t SoC is the state of charge of the energy storage system SoC is the state of charge of the energy storage system SoC is the state of charge of the energy storage system t P is the charge and discharge power of the energy storage system P is the charge and discharge power of the energy storage system P is the charge and discharge power of the energy storage system T is the time interval of the energy storage system state update T is the time interval of the energy storage system state update SoC is the maximum and minimum state of charge allowed by the system, which is the safe operating boundary of the state of charge to prevent device life attenuation caused by overcharging or over-discharging; in the energy storage capacity parameters, SoC is the rated capacity of the planned energy storage system SoC is the actual configuration capacity of the energy storage system, ensuring that the system has the necessary redundancy to respond to load fluctuations.

7. The method according to claim 1 or 2 or 3 or 5 or 6, characterized in that, In step 2, the upper layer model takes the maximization of net benefit in the whole life cycle as the objective function, and takes the site selection and capacity configuration of distributed wind power, photovoltaic and energy storage system as the decision variables; This problem belongs to the integer linear programming class, so the solver CPLEX is used for solving; The upper layer planning model outputs the selected wind power station, photovoltaic station, energy storage location node number and the optimal capacity corresponding to each node, and serves as the input of the lower layer operation model.

8. The method of claim 7, wherein, The lower layer model takes the minimization of abandoned electricity and power loss as the objective function, and coordinates the operation scheduling between wind power, photovoltaic and energy storage equipment; This problem involves nonlinear and coupled variables, so the improved bat algorithm is used for solving; The upper and lower layers are optimized through "feedback iteration" mode, until convergence, and finally the optimal planning scheme is obtained.

9. The method of claim 8, wherein, The step 2 is specifically implemented as follows: Step 2.1 initializing basic parameters; the basic parameters of the whole system are initialized and set, the input power grid structure parameters are included, such as the number of nodes, branch topology, node voltage range, conductance; load parameters, such as the load time series data of each node, annual maximum load hours and the like; wind energy and photovoltaic resource data; wind turbine, photovoltaic generator and energy storage device parameters; electricity price parameters; Step 2.2, the upper planning model is solved by using the solver CPLEX, the objective function, decision variable and constraint condition are shown in formula (1) to formula (5) in step 1; the planning scheme solved by the solver in this round is output, including the selected wind turbine, photovoltaic generator, energy storage device configuration node number, the optimal capacity corresponding to each node and economic allocation strategy; Step 2.3, the site selection and capacity planning scheme solved by the upper planning model is taken as input data and transmitted to the lower operation model; Step 2.4, the lower operation model needs to optimize the operation control strategy according to the input current planning scheme to achieve the wind-light-rejecting-electricity and minimum network loss, therefore, the improved bat algorithm is used for solving.

10. The method of claim 9, wherein, In step 2.4, the improved bat algorithm is used for solving, including the following steps: Step 2.4.1 initializes the basic parameters of the improved bat algorithm; inputs the grid structure parameters and the objective function of the lower layer operation model F , corresponding to formula (6), for calculating the fitness value of the objective function; the bat population and algorithm parameters are initialized and set, including: randomly generating N the initial positions and speeds of the bats, where i =1, 2, …, N ; the position of each bat is a D-dimensional vector, representing a feasible planning strategy based on the upper layer capacity configuration, and is also the solution of the planning and operation coordinated distribution network bi-level optimization model; the algorithm parameters, loudness , emission sound frequency , sound frequency range and , chaotic mapping control parameters for generating random factors , random walk parameters of Levy flight , pulse emission rate , chaotic inertia weight , range of inertia weight and , and the maximum number of iterations ; Step 2.4.2 Objective function according to lower layer running model F Calculate the fitness value of each bat individual, mark the optimal position of the bat in the current population And iteratively update the speed and position of the bat according to the rules defined by the following formula; For each bat i At the t next iteration, the following is performed: (1) updating the sound wave frequency The original formula is shown below: （14）； the frequency of the sound wave emitted by the i-th bat individual at the current iteration; i and the lower and upper limits of the set of sound wave frequencies; is a random number from the interval is a random number from the interval is a random number from the interval Optimizing the sound wave frequency of the bat; the updating rule of the sound wave frequency of each bat is as follows: （15）； In the formula, Indicates the first t In the next iteration, the bat i The frequency of the sound wave; These are the control coefficients for the chaotic mapping; (2) update speed The original formula is as follows: （16）； velocity vector of the i-th bat at the j-th iteration; i velocity vector of the i-th bat at the j-th iteration; t velocity vector of the i-th bat at the j-th iteration; velocity vector of the i-th bat at the j-th iteration; position of the i-th bat at the j-th iteration; The chaotic inertia weight is introduced to improve the ability of the algorithm to avoid local optimum, thereby enhancing the global search performance; the chaotic inertia weight generated in each iteration is represented as follows: position of the i-th bat at the j-th iteration; current local optimum solution i.e. position in the population; The chaotic inertia weight strategy is introduced into the speed updating equation; in each iteration, the chaotic inertia weight is calculated and the dynamic adjustment of the bat speed is enhanced; the improved speed updating equation containing the chaotic inertia weight is represented as follows: （17）； wherein: , respectively represent the upper and lower limits of the inertia weight parameter; is the total maximum number of iterations; is the current iteration number, is a chaotic disturbance factor randomly generated within the interval . The bat algorithm is improved by introducing the random walk characteristics of Lévy flight; after applying the Lévy flight strategy, the updating formula of the position of each bat individual is as follows: （18）； wherein, is the chaotic inertia weight, which dynamically changes with iterations, dynamically adjusting the flight speed of the bats; (3) Update the position ; for each bat i At the first t iteration, the position update is performed to generate a new solution of the planning and operation coordinated distribution network bi-level optimization model. The original position update formula is shown as follows: （19）； for the first i only bat for the t position vector at the wheel iteration; Step 2.4.3, performing local search and updating; in order to enhance the local search ability of the algorithm in the solution space, local disturbance is set, partial bat individuals are searched in a small range, and the solution is moderately guided to converge near the optimal solution; （20）； wherein is a Lévy random walk path; is a gamma function; is a scaling parameter, determining the probability of large jumps in the step size; (2) the pulse emission rate updating equation is shown as follows: In particular, a random number is generated , if the condition is satisfied, an optimal individual is selected from the current optimal bats, and a local solution is generated in the vicinity of the selected optimal individual by the following local perturbation formula (21), otherwise a new solution satisfying the objective function is updated by the position update formula (20), and the local perturbation formula is as follows: （21）； wherein, is a random number from the interval, is the average echolocation loudness of the entire bat population at the current iteration. Step 2.4.4 performs global search and update; generates random number , if , and the fitness value of the objective function is better than the new solution in step 2.4.3, then accept the solution, and decrease the sound loudness and increase the pulse emission rate according to the rules of equations (23-24) (1) Original sound wave loudness The update formula is as follows: （22）； wherein, is the sound loudness of the bat i at the tth iteration; is the sound loudness of the bat i at the next iteration; is the sound loudness decay coefficient; With improved acoustic wave frequency Update equation similar to the improved acoustic wave loudness Update equation: （23）； wherein is a chaotic map control coefficient; Step 2.4.6, judging whether the maximum iteration number is reached, and outputting the optimal; if the maximum iteration number is reached, or the global optimal solution has no significant improvement in continuous generations, the iteration is terminated, and the current wind-light-storage configuration and its operation result are output, that is, the solution of the planning and operation coordinated distribution network double-layer optimization model is obtained; otherwise, return to step 2.4.2-step 2.4.5 for iterative calculation; the solution of this round of iteration, that is, the lower optimization result is input into the upper planning model, and is fed back to the planning layer to update the planning configuration node, power and capacity, and a new round of iteration is performed, and the step 2.2 is jumped to use the CPLEX solver to complete the optimization calculation of the upper planning model. （24）； wherein, is the pulse emission enhancement factor; Step 2.4.5 Select the best solution with the highest fitness as the global optimum and update its position; sort the fitness values of all bat individuals in the population, find the bat position with the highest fitness value, that is, the current best position, and thus find the optimal solution In the iteration process, this solution is the optimization result of the lower-level running model; ​

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