CN115912343A - A planning method, device, equipment and medium for a wind-solar-storage micro-grid system - Google Patents

Abstract

The application provides a method, a device, equipment and a medium for planning a wind-solar storage micro-grid system, wherein the method comprises the following steps: constructing a target function of the wind-light storage micro-grid system based on demand response, prediction error and cost of the wind-light storage micro-grid system, wherein the target function takes the annual net income of the wind-light storage micro-grid system as the maximum target; inputting a cost parameter of the wind-solar storage micro-grid system, a cost parameter of a grid tie line and a user side load parameter into a target function; and determining a coordination strategy between the wind-solar-energy storage micro-grid system and each user by utilizing a gradient projection algorithm, and solving an objective function by combining a mixed integer linear programming algorithm according to the coordination strategy and constraint conditions to obtain a planning scheme of the wind-solar-energy storage micro-grid system. In the application, the objective function is established by considering relevant influence factors of the wind-light storage micro-grid system, so that the economy of the wind-light storage micro-grid planning is developed comprehensively, and the economy and the feasibility of the planning scheme of the wind-light storage micro-grid system are improved.

Description

A wind-solar-storage microgrid system planning method, device, equipment and medium

Technical Field

The present application relates to the technical field of wind, solar and storage system planning, and in particular to a wind, solar and storage microgrid system planning method, device, equipment and medium.

Background Art

Against the backdrop of the strategic goal of "carbon peak and carbon neutrality", the installed capacity of new energy continues to rise. A lot of research has been conducted on wind, solar and storage microgrids from the aspects of planning, operation, and full life cycle scheduling. At present, the cost of building a wind, solar and storage microgrid system is relatively mature, and most studies focus on the impact of wind and solar power output forecast deviations and load demand side response characteristics on the planning of wind, solar and storage microgrid systems. However, wind and solar power output is random, volatile, and intermittent, which leads to extremely high uncertainty in the planning stage of wind and solar microgrid systems.

In the related technologies, in terms of the uncertainty of wind and solar power output, assessment indicators for the utilization and consumption of wind and solar microgrids are established for independent microgrids, and the impact of wind and solar power output deviation on wind and solar microgrid planning investment is considered; for grid-connected microgrids, economic assessment indicators are established. When the wind and solar microgrid power output is higher than the predicted result and higher than the demand on the load side, the wind and solar microgrid will send the remaining capacity to the upper grid, but the error of the wind and solar microgrid is still a result that affects the economic assessment indicators of the wind and solar microgrid. In addition, only considering the deviation of wind and solar power output is not enough to fully explore the economic efficiency of microgrid planning.

Therefore, how to plan the wind-solar-storage microgrid based on the relevant influencing factors of the wind-solar-storage microgrid system in the planning stage (system cost, wind-solar power output prediction deviation, demand-side response characteristics, etc.) is an urgent problem to be solved.

Summary of the invention

In view of the above problems, the embodiments of the present application provide a wind-solar-storage microgrid system planning method, device, equipment and storage medium to overcome the above problems or at least partially solve the above problems.

In a first aspect of an embodiment of the present application, a wind-solar-storage microgrid system planning method is disclosed, the method comprising:

Based on demand response, prediction error and wind-solar-storage microgrid system cost, an objective function of the wind-solar-storage microgrid system is constructed, wherein the objective function aims to maximize the annual net profit of the wind-solar-storage microgrid system;

Inputting wind-solar-storage microgrid system cost parameters, grid tie line cost parameters, and user-side load parameters into the objective function;

The gradient projection algorithm is used to determine the coordination strategy between the wind-solar-storage microgrid system and each user;

According to the coordination strategy and the constraints of the objective function, the objective function is solved by combining a mixed integer linear programming algorithm to obtain a planning scheme for a wind-solar-storage microgrid system, which includes: wind farm rated power, photovoltaic rated power, energy storage rated capacity and energy storage rated power.

Optionally, constructing the wind-solar-storage microgrid system objective function includes:

Constructing a cost function for a wind-solar-storage microgrid system, the cost function comprising: the annual equivalent cost of a wind farm, the annual equivalent cost of a photovoltaic power station, the cost of an energy storage device, the annual equivalent investment cost of connecting the wind-solar-storage microgrid system to the grid, and the cost of purchasing electricity from a higher-level power grid for the wind-solar-storage microgrid system;

Construct wind and solar power output functions to determine the actual output power of wind farms and photovoltaic power stations;

Constructing a revenue function of a wind-solar-storage microgrid system, the revenue function comprising: revenue from electricity sales of the wind-solar-storage microgrid system, revenue from residual value recovery of the wind-solar-storage microgrid system, and environmental revenue from carbon emission conversion;

The objective function of the wind-solar-storage microgrid system is constructed according to the cost function, wind-solar power output function and revenue function.

Optionally, the objective function S of the wind-solar-storage microgrid system is expressed as:

Among them, S _w&pv&b represents the annual income from electricity sales of the wind-solar-storage microgrid system, S _w&pv&b,re represents the income from the residual value recovery of the wind-solar-storage microgrid system, _Sen represents the environmental benefit converted from carbon emissions, C _w,a and C _wc,a represent the annual equivalent cost of the wind farm and the annual equivalent cost of the converter connected to the wind farm, respectively, C _pv,a and C _pc,a represent the annual equivalent cost of the photovoltaic power station and the annual equivalent cost of the converter connected to the photovoltaic power station, respectively, C _b,a and C _bc,a represent the annual equivalent cost of the battery energy storage system and the annual equivalent cost of the converter connected to the battery energy storage system, respectively, C _mg,a represents the annual equivalent cost of grid connection of the wind-solar-storage microgrid system, C _g represents the annual equivalent cost of the wind-solar-storage microgrid system purchasing electricity from the superior power grid, and C _ls represents the cost of load shedding from users when the wind-solar-storage microgrid is short of power.

Optionally, the constraints of the objective function include:

A first constraint condition, wherein the first constraint condition is used to constrain the charge and discharge power of the energy storage device and the state of charge of the energy storage device to calculate the rated power and rated capacity of the energy storage device;

A second constraint condition, wherein the second constraint condition is used to calculate the rated power of the wind farm and the rated power of the photovoltaic power station;

A third constraint condition, wherein the third constraint condition is used to constrain the power of the wind-solar-micro-storage grid system so as to keep the power of the wind-solar-micro-storage grid system balanced;

The fourth constraint condition is used to constrain the transmission power of the connecting line between the wind-solar micro-storage grid and the upper-level grid.

Optionally, the method of using a gradient projection algorithm to determine a coordination strategy between the wind-solar-storage microgrid system and each user includes:

Constructing a user cost objective function, wherein the user cost objective function aims to minimize the user's daily energy consumption cost and discomfort cost;

Determine a demand constraint condition, where the demand constraint condition is used to constrain the power of the user-side elastic load and the inelastic load to determine the user's total load power demand;

The price range of the wind-solar-storage microgrid system electricity sales price is determined based on the cost parameters of the wind-solar-storage microgrid system, the revenue of the wind-solar-storage microgrid system, the cost of purchasing electricity from the superior power grid, and the price constraints of the power market;

According to the demand constraints and the price range of the electricity sales price, the user cost objective function is solved using the gradient projection algorithm to obtain a coordination strategy between the wind, solar, and storage microgrid system and each user. The coordination strategy includes: the price at which the wind, solar, and storage microgrid system sells electricity to users and the amount of electricity sold by users.

表示为：Optionally, the user cost objective function includes: user inappropriate cost and energy consumption cost, and the user cost objective function

It is expressed as:

Among them, ζ(u _i,t ) represents the discomfort cost of user i, χ(u _i,t ) represents the energy consumption cost of user i, and u _i,t represents the total electricity consumption of user i.

Optionally, the method further comprises:

The wind-solar-storage microgrid system is planned according to the planning scheme, and the net income of the wind-solar-storage microgrid system over its entire life cycle is calculated while taking into account the uncertainty factors of wind and solar power output.

In a second aspect of the embodiments of the present application, a wind-solar-storage microgrid system planning device is disclosed, the device comprising:

A function construction module is used to construct an objective function of the wind-solar-storage microgrid system based on demand response, prediction error and wind-solar-storage microgrid system cost, wherein the objective function aims to maximize the annual net profit of the wind-solar-storage microgrid system;

A parameter input module, used to input wind, solar and storage cost parameters, grid tie line cost parameters, and user-side load parameters into the wind, solar and storage microgrid system objective function;

A strategy determination module is used to determine the coordination strategy between the wind-solar-storage microgrid system and each user using a gradient projection algorithm;

The function solving module is used to solve the objective function according to the coordination strategy and the constraints of the objective function in combination with a mixed integer linear programming algorithm to obtain a planning scheme for the wind-solar-storage microgrid system. The planning scheme includes: wind farm rated power, photovoltaic rated power, energy storage rated capacity and energy storage rated power.

According to a third aspect of an embodiment of the present application, an electronic device is disclosed, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When executed, the processor implements the wind, solar, and storage microgrid system planning method as described in the first aspect of the present application.

In a fourth aspect of an embodiment of the present application, a computer-readable storage medium is disclosed, on which a computer program/instruction is stored. When the computer program/instruction is executed by a processor, the wind-solar-storage microgrid system planning method described in the first aspect of the present embodiment is implemented.

The embodiments of the present application include the following advantages:

In the embodiment of the present application, the relevant influencing factors of the wind, solar and storage microgrid system are comprehensively considered, and based on the demand response, prediction error and the cost of the wind, solar and storage microgrid system, an objective function with the goal of maximizing the annual net profit of the wind, solar and storage microgrid system is constructed. Then, in order to reduce the energy consumption cost of users, a coordination strategy between the wind, solar and storage microgrid system and each user is formulated, and then according to the constraints and coordination strategy of the objective function, a mixed integer linear programming algorithm is combined to solve the problem to obtain the planning scheme of the wind, solar and storage microgrid system. Since the objective function is established by considering the relevant influencing factors of the wind, solar and storage microgrid system, it is possible to explore the economic efficiency of the wind, solar and storage microgrid planning from all aspects, thereby improving the economy and feasibility of the planning scheme of the wind, solar and storage microgrid system.

BRIEF DESCRIPTION OF THE DRAWINGS

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

FIG1 is a flow chart of the steps of a wind-solar-storage microgrid system planning method provided in an embodiment of the present application;

FIG2 is an application flow chart of a wind-solar-storage microgrid system planning method provided in an embodiment of the present application;

FIG3 is a schematic diagram of the structure of a wind-solar-storage microgrid system planning device provided in an embodiment of the present application.

DETAILED DESCRIPTION

In order to make the above-mentioned purposes, features and advantages of the present application more obvious and understandable, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

The embodiment of the present application provides a wind-solar-storage microgrid system planning method, as shown in FIG1 , which is a flow chart of steps of a wind-solar-storage microgrid system planning method provided by the embodiment of the present application, including steps S101 to S103:

Step S101: Based on demand response, prediction error and wind-solar-storage microgrid system cost, construct a wind-solar-storage microgrid system objective function, wherein the objective function aims to maximize the annual net profit of the wind-solar-storage microgrid system.

In this embodiment, the wind-solar-storage microgrid system refers to a microgrid system composed of a wind farm, a photovoltaic power station and an energy storage device, wherein the wind farm and the photovoltaic power station are used to transmit electricity to users and the upper-level power grid, and the energy storage device is used to purchase electricity from the upper-level power grid for storage when the electricity price is low, and transmit the electricity in the energy storage device to users or the upper-level power grid when the electricity price is high, so as to achieve arbitrage.

Demand response refers to the load demand on the user side, and the load demand on the user side includes: elastic load demand (for example, air conditioners, electric boilers, electric vehicles, etc.) and inelastic load demand. The prediction error refers to the fact that the actual output power of wind farms and photovoltaic power stations is random, so there is a certain deviation between the predicted output power of wind farms and photovoltaic power stations and the actual output power. The cost of the wind, solar, and storage microgrid system refers to the annual equivalent investment and construction cost of the wind, solar, and storage microgrid system (the investment and construction cost within one year). In this embodiment, the demand response, prediction error, and wind, solar, and storage microgrid system cost are comprehensively considered, and then the objective function of the wind, solar, and storage microgrid system is constructed to realize the economic feasibility of the wind, solar, and storage microgrid planning from all aspects, thereby improving the economy and feasibility of the wind, solar, and storage microgrid system planning scheme.

In an optional embodiment, the construction of the wind-solar-storage microgrid system objective function includes steps A1 to A4:

Step A1: Construct a cost function for the wind, solar, and storage microgrid system, the cost function including: the annual equivalent cost of the wind farm, the annual equivalent cost of the photovoltaic power station, the cost of the energy storage device, the annual equivalent investment cost of the wind, solar, and storage microgrid system connected to the grid, and the cost of purchasing electricity from the upper power grid for the wind, solar, and storage microgrid system.

(1) Annual equivalent cost of wind farm.

The annual equivalent cost model of a wind farm includes: the annual average investment cost of the wind farm, the annual maintenance cost and the annual equivalent cost of the converter, which can be specifically expressed as:

Among them, C _w,a and C _wc,a represent the annual equivalent cost of the wind farm and the annual equivalent cost of the converter connected to the wind farm respectively, C _w,p and C _w,ct represent the unit price of the wind farm power cost and the unit price of the power cost of the converter connected to the wind farm respectively, C _w,m and C _w,mc represent the unit price of the wind farm power maintenance cost and the unit price of the power maintenance cost of the converter connected to the wind farm respectively, N _w,y and N _w,ct represent the service life of the wind farm and the service life of the converter connected to the wind farm respectively, P _w,r represents the rated power of the wind farm, and r represents the discount rate.

(2) Annual equivalent cost of photovoltaic power station.

The annual equivalent cost of a photovoltaic power station includes the annual equivalent cost of a photovoltaic power station and the annual equivalent cost of a converter, which can be expressed as follows:

Among them, C _pv,a and C _pc,a represent the annual equivalent cost of the PV power station and the annual equivalent cost of the converter connected to the PV power station respectively, C _pv,p and C _pv,ct represent the power cost unit price of the PV power station and the power cost unit price of the converter connected to the PV power station respectively, C _pv,m and C _pv,mc represent the power maintenance cost unit price of the PV power station and the power maintenance cost unit price of the converter connected to the PV power station respectively, N _pv,y and N _pv,ct represent the service life of the PV power station and the service life of the converter connected to the PV power station respectively, and P _pv,r represents the rated power of the PV power station.

(3) Cost of energy storage device.

The cost of the energy storage device includes: the cost of the energy storage device and the converter, which is specifically expressed as:

Among them, C _b,a and C _bc,a represent the annual equivalent cost of the battery energy storage system and the annual equivalent cost of the converter connected to the battery energy storage system, respectively; C _b,p and C _b,ct represent the unit cost of the battery energy storage system power and the unit cost of the converter connected to the energy storage system, respectively; C _b,e represents the unit cost of the battery energy storage system capacity; C _b,m and C _b,mc represent the unit cost of the battery energy storage system power maintenance and the unit cost of the converter connected to the energy storage system, respectively; N _b,y and N _b,ct represent the service life of the energy storage system and the converter connected to the energy storage system, respectively; E _b,r and P _b,r represent the rated capacity and power of the battery energy storage, respectively.

(4) Annual equivalent investment cost of wind, solar and energy storage microgrids connected to the grid.

The annual equivalent investment cost of wind, solar and storage microgrid grid connection refers to the cost of power transmission and transformation equipment, which is specifically expressed as:

Among them, C _mg,a represents the annual equivalent cost of microgrid grid connection, C _mg,p represents the unit power cost of microgrid, C _mg,m represents the unit price of microgrid power maintenance cost, N _mg,y represents the service life of substation, and P _mg,r represents the rated power transmitted by the interconnection line between microgrid and upper grid.

In this step, the cost functions of each part of the wind-solar-storage microgrid system are established, so as to construct the objective function of the wind-solar-storage microgrid system based on the cost function of the wind-solar-storage microgrid system in the subsequent steps.

Step A2: Construct a wind-solar power output function to determine the actual output power of the wind farm and photovoltaic power station. The wind-solar power output power refers to the actual power output function of the wind farm and photovoltaic power station. In practical applications, there is a certain deviation between the predicted output power and the actual output power of the wind farm and photovoltaic power station. Therefore, the wind-solar power output function can be expressed as:

和

分别表示光伏电站和风电场在时段t预测功率出力，P_pv,j,t和P_w,j,t分别表示光伏电站和风电场在时段t的实际功率出力，ε_pv,j(t)表示光伏电站在时段t实际功率出力与预测功率出力的误差值，ε_w,j(t)表示风电场在时段t实际功率出力与预测功率出力的误差值。in,

and

They represent the predicted power outputs of the photovoltaic power station and wind farm in time period t respectively, P _pv,j,t and P _w,j,t represent the actual power outputs of the photovoltaic power station and wind farm in time period t respectively, ε _pv,j (t) represents the error value between the actual power output and the predicted power output of the photovoltaic power station in time period t, and ε _w,j (t) represents the error value between the actual power output and the predicted power output of the wind farm in time period t.

Specifically, the constraint model of ε _pv,j (t) and ε _w,j (t) can be expressed as:

Among them, ε _pv,j,min and ε _pv,j,max represent the minimum error value and maximum error value of the power output of the photovoltaic power station respectively, and ε _w,j,min and ε _w,j,max represent the minimum error value and maximum error value of the power output of the wind farm.

In this step, based on the predicted power output and error constraint model of the wind farm and photovoltaic power station, a wind-solar power output function is constructed, and then the actual output power of the wind farm and photovoltaic power station can be determined according to the wind-solar power output function.

Step A3: Construct a revenue function of the wind, solar, and storage microgrid system, which includes: revenue from electricity sales of the wind, solar, and storage microgrid system, revenue from residual value recovery of the wind, solar, and storage microgrid system, and environmental revenue from carbon emission conversion.

(1) Revenue from electricity sales of wind, solar, and energy storage microgrid systems.

The revenue from electricity sales of the wind, solar, and storage microgrid system includes: the revenue from electricity sales to users and the revenue from electricity transmission to the superior power grid. Specifically, it can be expressed as:

Among them, S _w&pv&b represents the annual income from electricity sales of the wind-solar-storage microgrid system, P _b,d,t represents the discharge power of the energy storage device in time period t, P _gs,t represents the power sold by the wind-solar-storage microgrid system to the superior power grid in time period t, T _a represents the set of electricity demand periods of users within a year, λ _su,t represents the unit price of electricity sold by the wind-solar-storage microgrid system to user i, λ _su,t represents the unit price of energy subsidy obtained by the wind-solar-storage microgrid system when selling electricity to user i in time period t, and λ _g,t represents the electricity price of the superior power grid in time period t.

(2) The benefits of residual value recovery of wind-solar-storage microgrid system are specifically expressed as:

Among them, S _w&pv&b,re represents the annual equivalent residual value recovery of the wind-solar-storage microgrid system, λ _w,re , λ _pv,re , and λ _b,re represent the annual equivalent residual value recovery unit prices of wind power stations, photovoltaic power stations, and energy storage devices, respectively, and J represents the total number of wind-solar-storage microgrid systems.

(3) Environmental benefits converted from carbon emissions.

In this embodiment, the electric energy provided by the wind-solar-storage microgrid system is a new energy source and will not have any impact on the environment. Therefore, the electric energy provided by the wind-solar-storage microgrid system is converted into environmental benefits of carbon emissions, which can be specifically expressed as follows:

表示碳排放价格系数。in,

Represents the carbon emission price coefficient.

Step A4: construct the objective function of the wind-solar-storage microgrid system according to the cost function, the wind-solar power output function and the revenue function.

The objective function of the wind-solar-storage microgrid system is the net income of the wind-solar-storage microgrid system in one year. In this embodiment, the cost function, wind-solar power output function and income function of the wind-solar-storage microgrid system are considered to construct the objective function of the wind-solar-storage microgrid system. At the same time, in the case of power shortage, the cost of the wind-solar-storage microgrid system shedding loads for low-level users and the cost of the wind-solar-storage microgrid system purchasing electricity from the superior power grid are considered. For example, the objective function S of the wind-solar-storage microgrid system is expressed as:

Among them, S _w&pv&b represents the annual income from electricity sales of the wind-solar-storage microgrid system, S _w&pv&b,re represents the income from residual value recovery of the wind-solar-storage microgrid system, _Sen represents the environmental benefit converted from carbon emissions, C _w,a and C _wc,a represent the annual equivalent cost of the wind farm and the annual equivalent cost of the converter connected to the wind farm, respectively, C _pv,a and C _pc,a represent the annual equivalent cost of the photovoltaic power station and the annual equivalent cost of the converter connected to the photovoltaic power station, respectively, C _b,a and C _bc,a represent the annual equivalent cost of the battery energy storage system and the annual equivalent cost of the converter connected to the battery energy storage system, respectively, C _mg,a represents the annual equivalent cost of grid connection of the wind-solar-storage microgrid system, C _g represents the annual equivalent cost of the wind-solar-storage microgrid system purchasing electricity from the superior power grid, and C _ls represents the cost of load shedding from users when the wind-solar-storage microgrid is short of power.

Specifically, the cost _Cg of wind-solar-storage microgrid system purchasing electricity from the upper grid is expressed as:

Among them, P _gb,t represents the power purchased by the wind, solar and storage system from the upper power grid in time period t.

The cost C _ls generated by the wind-solar-storage microgrid system shedding loads for low-level users is expressed as:

表示常数，通过风光储微电网企业与用户i协商确定的常数，

表示风光储微电网企业与用户i中断协议的补偿系数，

表示用户i在时段t的切负荷功率。in,

represents a constant, which is determined through negotiation between the wind-solar-storage microgrid enterprise and user i.

represents the compensation coefficient of the wind-solar-storage microgrid enterprise and user i’s interruption agreement,

Represents the load shedding power of user i in time period t.

In this embodiment, the relevant influencing factors of the wind, solar, and storage microgrid system are comprehensively considered to construct the objective function of the wind, solar, and storage microgrid system, so as to fully explore the economic feasibility of the wind, solar, and storage microgrid planning, thereby improving the economy and feasibility of the wind, solar, and storage microgrid system planning scheme.

Step S102: inputting the wind-solar-storage microgrid system cost parameters, the grid tie line cost parameters, and the user-side load parameters into the wind-solar-storage microgrid system objective function.

In this embodiment, the cost parameters of the wind, solar and storage microgrid system include: wind farm power cost unit price, photovoltaic power station power cost unit price, energy storage power cost unit price, energy storage capacity unit price and wind, solar and storage cost maintenance unit price. The grid interconnection line cost parameters include: power cost unit price and microgrid interconnection line maintenance cost unit price. The user side load parameters include: elastic load and inelastic load.

Step S103: using a gradient projection algorithm to determine the coordination strategy between the wind-solar-storage microgrid system and each user.

In this embodiment, the coordination strategy between the wind-solar-storage microgrid system and each user refers to the price of electricity sold by the wind-solar-storage microgrid system to each user and the amount of electricity sold to the user, that is, the actual electricity consumption and electricity price after the user participates in the demand-side response. Specifically, the gradient projection algorithm is used to determine the coordination strategy between the wind-solar-storage microgrid system and each user, including steps B1 to B4:

表示为：Step B1: Construct a user cost objective function, wherein the user cost objective function aims to minimize the user's daily energy consumption cost and discomfort cost. Specifically, the user cost objective function includes: user discomfort cost and energy consumption cost.

It is expressed as:

Among them, ζ(u _i,t ) represents the discomfort cost of user i, χ(u _i,t ) represents the energy consumption cost of user i, and u _i,t represents the total electricity consumption of user i.

In this embodiment, the discomfort cost refers to the inappropriate cost of user i participating in the demand response of the wind, solar and storage microgrid system. For example, the temperature of the air conditioner used by user i is 26 degrees (the most comfortable temperature), but user i sets the temperature to 28 degrees in order to participate in the demand response of the wind, solar and storage microgrid system. Then user i sets the air conditioner temperature from 26 degrees to 28 degrees, which will incur a certain discomfort cost.

For example, the inappropriate cost ζ(u _i,t ) of user i can be expressed as:

Wherein, yi _,t represents the load power in time period t when user i does not participate in user-side demand-side response, _{and λdc} represents the unit price of user-side discomfort cost caused by user participation in demand-side response.

In this embodiment, the user's daily energy consumption cost refers to the cost of purchasing electricity for the user. For example, the energy consumption cost of user i χ(u _i,t ) can be expressed as:

In this embodiment, the user side formulates its own daily electricity consumption curve and acceptable wind, solar and storage microgrid system electricity price with the goal of minimizing its own daily energy consumption cost and discomfort cost. When users participate in the energy dispatch of the wind, solar and storage microgrid system, they need to meet the demand response constraint conditions in step B2, that is, the user cost objective function must meet the demand response constraint conditions in step B2.

Step B2: Determine demand response constraint conditions, where the demand response constraint conditions are used to constrain the power of user-side elastic loads and inelastic loads to determine the user's total load power demand.

In this embodiment, the demand response constraints include: elastic load demand (for example, air conditioner, electric boiler, electric vehicle, etc.) and inelastic load demand. Specifically, the total load power demand of the user can be expressed as:

和

分别表示用户i在时段t的功率增加和减少。Wherein, ui _,t represents the total load power demand of user i in time period t, _Pac,i,t represents the adjustment power of air conditioner i in time period t, _Peb,i,t represents the adjustment power of electric boiler i in time period t, _Li represents the inelastic load of user i,

and

They represent the power increase and decrease of user i in time period t respectively.

Specifically, the load power of each part can be expressed as:

(1) Air conditioning load can be expressed as:

和

分别表示空调在温度T₁和T₂的功率，P_ac,i,t空调i在时段t的调节功率，Q_ac,i表示空调i的调节容量，M表示空调集合，T_ac表示用户使用空调时段集合。in,

and

They represent the power of the air conditioner at temperatures T ₁ and T ₂ respectively, P _ac,i,t represents the adjustment power of air conditioner i in time period t, Q _ac,i represents the adjustment capacity of air conditioner i, M represents the set of air conditioners, and T _ac represents the set of time periods when users use air conditioners.

(2) The load of electric boiler can be expressed as:

和

分别表示电锅炉在温度T₁和T₂的功率，P_eb,i,t表示电锅炉i在时段t的调节功率，Q_eb,i表示电锅炉i的调节容量，T_eb表示用户使用电锅炉时段集合。in,

and

represent the power of the electric boiler at temperatures _T1 and _T2 respectively, _Peb,i,t represents the regulated power of electric boiler i in time period t, _Qeb,i represents the regulated capacity of electric boiler i, and _Teb represents the time period set when the user uses the electric boiler.

(3) The electric vehicle load is expressed as:

和

分别表示电动汽车i接入微电网充电时刻的荷电状态和期望荷电状态值，E_ev,r,i表示电动汽车i电池的额定容量，η_c,i表示电动汽车i电池的充电效率。Where P _ev,i represents the battery charging power of electric vehicle i, t _in,i and t _end,i represent the charging time and charging end time of electric vehicle i connected to the microgrid respectively, t _Δ,i represents the charging time of electric vehicle i,

and

They represent the state of charge and the expected state of charge when electric vehicle i is connected to the microgrid for charging, E _ev,r,i represents the rated capacity of the battery of electric vehicle i, and η _c,i represents the charging efficiency of the battery of electric vehicle i.

(4) Other inelastic and elastic loads are expressed as:

和

分别表示用户i在时段t的功率增加和减少，α表示弹性负荷占非弹性负荷的最大比例，L_i表示非弹性负荷，T表示用户一天内用电时段集合。in,

and

They represent the power increase and decrease of user i in time period t, α represents the maximum proportion of elastic load to inelastic load, _Li represents inelastic load, and T represents the set of electricity consumption periods of the user in a day.

In this step, demand response constraints are created. When users participate in the energy scheduling of the wind, solar and storage microgrid system (i.e., purchase electricity from the wind, solar and storage microgrid system), they need to meet the demand response constraints.

Step B3: Determine the price range of the wind-solar-storage microgrid system's electricity sales price based on the system's cost parameters, revenue, cost of purchasing electricity from the superior power grid, and price constraints of the power market.

In this embodiment, the wind, solar, and storage microgrid system needs to consider its own electricity purchase cost from the upper power grid, the annual equivalent cost required by the wind, solar, and storage microgrid system, that is, the annual equivalent cost of the wind farm, the annual equivalent cost of the photovoltaic power station, the cost of the energy storage device, and the annual equivalent investment cost of the wind, solar, and storage microgrid system to formulate the lower limit of the electricity sales price. At the same time, the price constraints of the power market are considered, that is, in order to ensure the rationality of the power market, the relevant departments set an upper limit for the wind, solar, and storage microgrid system to sell electricity, and then obtain the electricity sales price range, which is specifically expressed as:

λ _pr,min,t ≤λ _pr,t ≤λ _pr,max,t

Among them, λ _pr,max,t and λ _pr,min,t represent the upper and lower limits of the electricity sales price of the wind-solar-storage microgrid system in time period t, respectively.

Step B4: According to the demand constraints and the price range of the electricity sales price, the user cost objective function is solved using the gradient projection algorithm to obtain the coordination strategy between the wind, solar, and storage microgrid system and each user. The coordination strategy includes: the price at which the wind, solar, and storage microgrid system sells electricity to users and the amount of electricity sold by users.

In this embodiment, the demand constraint in step B2 and the price range of the electricity price in step B3 are used as constraints, and the user cost objective function is solved using the gradient projection algorithm. Specifically, steps C1 to C7 are included:

并且满足

Step C1: Use decreasing step size

And satisfy

步长l₀>0，u_i,t,0＝y_i,t。Step C2: Set initial value: iterative index, allowable electricity price error

The step size l ₀ >0, u _i,t,0 = y _i,t .

Step C3: The wind, solar and energy storage microgrid system collects the energy consumption of each user i, summarizes the total energy consumption of all users, and then sets the kth electricity selling price λ _pr,t,k according to the electricity selling price range determined in step B3: λ _pr,min,t ≤λ _pr,t ≤λ _pr,max,t .

Step C4: Each user updates its own power consumption u _i,t,k+1 according to the electricity price λ _pr,t,k, that is:

约束条件为满足步骤B2中的需求约束条件。Step C5: The feasible solution of demand-side response should satisfy the formula:

The constraint condition is to satisfy the demand constraint condition in step B2.

执行步骤C7。Step C6: Continue iterating k=k+1, and when the iteration end condition is met:

Execute step C7.

Step C7: Output the price λ _pr,t,k at which the wind-solar-storage microgrid system sells electricity to users and the amount of electricity sold by users u _i,t,k .

In this embodiment, in order to reduce the user's energy consumption cost and improve the economic benefits of the entire system, the cost of the wind, solar and storage microgrid system, the user's electricity energy consumption, the user's comfort and other factors are comprehensively considered to construct a user cost objective function and a wind, solar and storage microgrid system electricity selling price range. Then, the coordination strategy between the wind, solar and storage system and each user is determined through the gradient projection algorithm, that is, the actual electricity consumption and electricity price after the user participates in the demand-side response are determined.

Step B104: According to the coordination strategy and the constraints of the objective function, the objective function is solved in combination with a mixed integer linear programming algorithm to obtain a planning scheme for the wind-solar-storage microgrid system, wherein the planning scheme includes: wind farm rated power, photovoltaic rated power, energy storage rated capacity and energy storage rated power.

In this embodiment, the constraint conditions of the objective function refer to the constraint conditions on the wind-solar-storage microgrid system planning, microgrid system interconnection lines, power balance, etc. Specifically, in this step, according to the unit price of electricity sold to each user and the amount of electricity sold to the user by the wind-solar-storage microgrid system, the constraint conditions of the objective function are used as constraints, and the nonlinear constraint conditions in the objective function are converted into a mixed integer linear programming problem by using a mixed integer linear programming algorithm for solution, thereby obtaining a planning scheme for the wind-solar-storage microgrid system.

Among them, the rated power of a wind farm refers to the rated power (maximum power) output by the wind farm. During operation, the actual output power of the wind farm will not exceed the rated power. The rated power of photovoltaic power refers to the rated power (maximum power) output by the photovoltaic power station. During operation, the actual output power of the photovoltaic power station will not exceed the rated power. The rated capacity of energy storage refers to the rated capacity of the energy storage device. The rated power of energy storage refers to the rated power of the energy storage device.

In an optional embodiment, the constraints of the objective function include: a first constraint condition, a second constraint condition, a third constraint condition and a fourth constraint condition. Specifically:

The first constraint condition is used to constrain the charge and discharge power of the energy storage device and the state of charge of the energy storage device to calculate the rated power and rated capacity of the energy storage device. For example, the first constraint condition is expressed as:

Charge and discharge power constraints:

State of charge and capacity constraints:

Wherein, P _b,c,t and P _b,d,t represent the charging power and discharging power of the energy storage device in time period t, respectively; SOC _min and SOC _max represent the minimum and maximum state of charge of the energy storage device, respectively; η _c and η _d represent the conversion efficiency of charging and discharging of the energy storage device, respectively; SOC ₀ and SOC _T+1 represent the initial and initial state of charge values of the energy storage device in the next scheduling cycle, respectively; ψ _b,c,t and ψ _b,d,t represent the charging and discharging states of the energy storage device in time period t, respectively; when ψ _b,c,t = 1, it means that the energy storage device is in the charging state in time period t, and at this time ψ _b,d,t = 0; and E _b,r and E _b,r represent the rated capacity and power of the energy storage device, respectively.

The second constraint condition is used to calculate the rated power of the wind farm and the rated power of the photovoltaic power station. For example, the first constraint condition is expressed as:

Wherein, P _pv,j,t and P _w,j,t represent the actual power outputs of the PV power station and the wind farm respectively in time period t, P _w,r represents the rated power of the wind farm, and P _pv,r represents the rated power of the PV power station.

In this embodiment, the actual output power of the wind farm does not exceed the rated power configured for the wind farm, and the actual output power of the photovoltaic power station does not exceed the rated power configured for the photovoltaic power station. Therefore, the rated power that needs to be configured for the wind farm and the photovoltaic power station can be determined based on the actual output power parameters of the wind farm and the photovoltaic power station.

The third constraint condition is used to constrain the power of the wind-solar-micro-storage grid system so that the power of the wind-solar-micro-storage grid system remains balanced. For example, the third constraint condition is expressed as:

P _pv,j,t +P _w,j,t +P _gb,t -P _gs,t +P _b,d,t -P _b,c,t -u _i,t =0

Among them, P _g,i,t represents the power purchased by the wind, solar and energy storage system from the superior power grid in time period t, P _pv,j,t and P _w,j,t represent the actual power output of the photovoltaic power station and the wind farm in time period t respectively, P _gb,t represents the power purchased by the wind, solar and energy storage microgrid system from the superior power grid in time period t, P _b,d,t represents the discharge power of the energy storage device in time period t, P _gs,t represents the power sold by the wind, solar and energy storage system to the superior power grid in time period t, _{and ui,t} represents the total load of user i in time period t.

In this embodiment, the output power of the wind, solar and storage microgrid system is equal to the demand power, that is, the sum of the output power of the wind farm, the output power of the photovoltaic power station, the power purchased by the wind, solar and storage microgrid system from the superior power grid and the power discharged and output by the energy storage device, which is equal to the power transmitted by the wind, solar and storage microgrid system to the superior power grid, the charging power of the energy storage device and the power demanded by the user.

The fourth constraint condition is used to constrain the transmission power of the tie line between the wind-solar micro-storage grid system and the upper grid. For example, the fourth constraint condition is expressed as:

-P _mg,r ≤max(P _gb,t ,P _gs,t )≤P _mg,r

Among them, P _mg,r represents the rated power transmitted by the interconnection line between the wind-solar-storage microgrid system and the upper-level power grid.

In this embodiment, in order to ensure system safety, the power of electricity purchased by the wind-solar-storage microgrid system from the superior power grid and the power transmitted to the superior power grid cannot exceed the rated power transmission of the interconnection line between the wind-solar-storage microgrid system and the superior power grid.

In an optional embodiment, after obtaining a planning scheme for a wind, solar, and storage microgrid system, the wind, solar, and storage microgrid system is planned according to the planning scheme, and the net benefit of the wind, solar, and storage microgrid system over its entire life cycle is calculated while taking into account the uncertainty of wind and solar power output.

Specifically, the net present value of the wind-solar-storage microgrid system over its entire life cycle is expressed as:

Among them, S _NPV is the net present value of the wind-solar-storage microgrid system over its entire life cycle, S is the objective function of the wind-solar-storage microgrid system, N _project is the project life, g _inf is the inflation rate, and r is the discount rate.

In this embodiment, the full life cycle refers to the time of the wind, solar, and storage microgrid system during the entire project life. The overall benefit during the full life cycle is analyzed based on the net present value, inflation rate, and discount rate of the wind, solar, and storage microgrid system.

In this embodiment, the relevant influencing factors of the wind, solar and storage microgrid system are fully considered. Based on demand response, prediction error and wind, solar and storage microgrid system cost, an objective function with the maximum annual net profit of the wind, solar and storage microgrid system as the goal is constructed. Then, in order to reduce the user's energy consumption cost, a coordination strategy between the wind, solar and storage microgrid system and each user is formulated. Then, according to the constraints and coordination strategy of the objective function, a mixed integer linear programming algorithm is combined to solve the problem to obtain the planning scheme of the wind, solar and storage microgrid system. Since the objective function is established by considering the relevant influencing factors of the wind, solar and storage microgrid system, the economic efficiency of the wind, solar and storage microgrid planning is fully explored, thereby improving the economic efficiency and feasibility of the wind, solar and storage microgrid system planning scheme.

For example, as shown in Figure 2, Figure 2 is an application flow chart of a wind, solar, and storage microgrid system planning method provided by an embodiment of the present application. In an actual application scenario, the planning method of a wind, solar, and storage microgrid system includes: first, inputting wind, solar, and storage microgrid related data, microgrid interconnection line related data, and user related data, wherein the wind, solar, and storage microgrid related data include: wind farm power cost unit price, photovoltaic power station power cost unit price, energy storage power cost unit price, energy storage capacity unit price, wind, solar, and storage cost maintenance unit price, grid interconnection line related data include: microgrid interconnection line power cost unit price and microgrid interconnection line maintenance cost unit price, and user related data include: elastic and inelastic load data on the user side.

Then, an objective function with the goal of maximizing the annual net income of the wind-solar-storage microgrid system is established. The cost in this objective function comes from the annual equivalent investment cost of wind farms, photovoltaic power stations, energy storage devices, and grid-connected interconnection lines, and the cost of purchasing electricity from the upper power grid for the wind-solar-storage microgrid system; the income comes from the wind-solar-storage microgrid system supplying electricity to users and the upper power grid, the wind-solar-storage microgrid system recovering residual value, and the environmental benefits of carbon emissions conversion. In order to reduce the user's energy consumption cost and improve the economic benefits of the wind-solar-storage microgrid system, the cost of the wind-solar-storage microgrid system, the user's electricity consumption, user comfort and other factors are comprehensively considered to construct the user cost objective function and the price range of the wind-solar-storage microgrid system's electricity sales price. Then, the gradient projection algorithm is used to determine the coordination strategy between the wind-solar-storage system and each user, that is, the price of electricity sold by the wind-solar-storage microgrid system to users and the amount of electricity sold by users.

After that, the relevant constraints for wind-solar-storage microgrid planning are established, including battery energy storage power constraints, energy storage capacity constraints, wind-solar power constraints, tie line constraints, and wind-solar-storage microgrid constraints. The nonlinear constraints are converted into mixed integer linear programming problems using mixed integer linear programming algorithms to solve them, and the rated power of the wind farm, the rated power of the photovoltaic power station, the rated capacity of the energy storage device, and the rated power of the energy storage device are calculated. Finally, considering the uncertainty of wind-solar power output, the net income of the wind-solar-storage microgrid system over its entire life cycle is calculated.

The present application also provides a wind-solar-storage microgrid system planning device, as shown in FIG3 , which is a schematic diagram of the structure of a wind-solar-storage microgrid system planning device provided in the present application embodiment, the device comprising:

A function construction module 31 is used to construct an objective function of the wind-solar-storage microgrid system based on demand response, prediction error and wind-solar-storage microgrid system cost, wherein the objective function aims to maximize the annual net income of the wind-solar-storage microgrid system;

A parameter input module 32, used to input the wind-solar-storage microgrid system cost parameters, the grid tie line cost parameters, and the user-side load parameters into the objective function;

A strategy determination module 33 is used to determine the coordination strategy between the wind-solar-storage microgrid system and each user by using a gradient projection algorithm;

The function solving module 34 is used to solve the objective function according to the coordination strategy and the constraints of the objective function in combination with a mixed integer linear programming algorithm to obtain a planning scheme for the wind-solar-storage microgrid system, wherein the planning scheme includes: wind farm rated power, photovoltaic rated power, energy storage rated capacity and energy storage rated power.

In an optional embodiment, the function building module includes:

The first function construction module is used to construct a cost function of the wind-solar-storage microgrid system, wherein the cost function includes: the annual equivalent cost of the wind farm, the annual equivalent cost of the photovoltaic power station, the cost of the energy storage device, the annual equivalent investment cost of the wind-solar-storage microgrid system connected to the grid, and the cost of the wind-solar-storage microgrid system purchasing electricity from the upper power grid;

The second function building module is used to build a wind and solar power output function to determine the actual output power of the wind farm and the photovoltaic power station;

The third function construction module is used to construct the revenue function of the wind-solar-storage microgrid system, wherein the revenue function includes: the revenue from electricity sales of the wind-solar-storage microgrid system, the revenue from residual value recovery of the wind-solar-storage microgrid system, and the environmental revenue from carbon emission conversion;

The fourth function construction module is used to construct the objective function of the wind-solar-storage microgrid system according to the cost function, wind-solar power output function and revenue function.

In an optional embodiment, the function solving module includes:

A first constraint building module, used to determine a first constraint condition, wherein the first constraint condition is used to constrain the charge and discharge power of the energy storage device and the state of charge of the energy storage device to calculate the rated power and rated capacity of the energy storage device;

A second constraint building module, used for determining a second constraint condition, wherein the second constraint condition is used for calculating the rated power of the wind farm and the rated power of the photovoltaic power station;

A third constraint building module is used to determine a third constraint condition, where the third constraint condition is used to constrain the power of the wind-solar-micro-storage grid system so that the power of the wind-solar-micro-storage grid system remains balanced;

The fourth constraint construction module is used to determine the fourth constraint condition, and the fourth constraint condition is used to constrain the transmission power of the connecting line between the wind-solar micro-storage grid and the upper-level grid.

In an optional embodiment, the function solving module includes:

A cost function building module, used to build a user cost objective function, wherein the user cost objective function aims to minimize the user's daily energy consumption cost and discomfort cost;

A demand constraint building module, used to determine demand constraint conditions, wherein the demand constraint conditions are used to constrain the power of elastic load and inelastic load at the user side to determine the total load power demand of the user;

The price constraint module is used to determine the price range of the wind-solar-storage microgrid system's electricity sales price based on the system's cost parameters, revenue, cost of purchasing electricity from the superior power grid, and price constraints in the power market;

The strategy solving module is used to solve the user cost objective function according to the demand constraints and the price range of the electricity selling price by using the gradient projection algorithm to obtain the coordination strategy between the wind, solar, and storage microgrid system and each user. The coordination strategy includes: the price at which the wind, solar, and storage microgrid system sells electricity to the user and the amount of electricity sold by the user.

In an optional embodiment, the device further includes:

The profit calculation module is used to plan the wind-solar-storage microgrid system according to the planning scheme, and calculate the net profit of the wind-solar-storage microgrid system over its entire life cycle while taking into account the uncertainty factors of wind and solar power output.

An embodiment of the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes, the wind-solar-storage microgrid system planning method described in the embodiment of the present application is implemented.

The embodiment of the present application also provides a computer-readable storage medium on which a computer program/instruction is stored. When the computer program/instruction is executed by a processor, the wind-solar-storage microgrid system planning method described in the embodiment of the present application is implemented.

The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present application.

Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

The above is a detailed introduction to a wind, solar and storage microgrid system planning method, device, equipment and medium provided by the present application. This article uses specific examples to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for general technical personnel in this field, according to the idea of the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims (10)

1. A wind-solar-storage microgrid system planning method, characterized in that the method comprises: Based on demand response, prediction error and wind-solar-storage microgrid system cost, an objective function of the wind-solar-storage microgrid system is constructed, wherein the objective function aims to maximize the annual net profit of the wind-solar-storage microgrid system; Inputting wind-solar-storage microgrid system cost parameters, grid tie line cost parameters, and user-side load parameters into the objective function; The gradient projection algorithm is used to determine the coordination strategy between the wind-solar-storage microgrid system and each user; According to the coordination strategy and the constraints of the objective function, the objective function is solved by combining a mixed integer linear programming algorithm to obtain a planning scheme for a wind-solar-storage microgrid system, which includes: wind farm rated power, photovoltaic rated power, energy storage rated capacity and energy storage rated power. 2. The method according to claim 1 is characterized in that the objective function of the wind-solar-storage microgrid system is constructed, comprising: Constructing a cost function for a wind-solar-storage microgrid system, the cost function comprising: the annual equivalent cost of a wind farm, the annual equivalent cost of a photovoltaic power station, the cost of an energy storage device, the annual equivalent investment cost of connecting the wind-solar-storage microgrid system to the grid, and the cost of purchasing electricity from a higher-level power grid for the wind-solar-storage microgrid system; Construct wind and solar power output functions to determine the actual output power of wind farms and photovoltaic power stations; Constructing a revenue function of a wind-solar-storage microgrid system, the revenue function comprising: revenue from electricity sales of the wind-solar-storage microgrid system, revenue from residual value recovery of the wind-solar-storage microgrid system, and environmental revenue from carbon emission conversion; The objective function of the wind-solar-storage microgrid system is constructed according to the cost function, wind-solar power output function and revenue function. 3. The method according to claim 2 is characterized in that the objective function S of the wind-solar-storage microgrid system is expressed as:

Among them, S _w&pv&b represents the annual income from electricity sales of the wind-solar-storage microgrid system, S _w&pv&b,re represents the income from residual value recovery of the wind-solar-storage microgrid system, _Sen represents the environmental benefit converted from carbon emissions, C _w,a and C _wc,a represent the annual equivalent cost of the wind farm and the annual equivalent cost of the converter connected to the wind farm, respectively, C _pv,a and C _pc,a represent the annual equivalent cost of the photovoltaic power station and the annual equivalent cost of the converter connected to the photovoltaic power station, respectively, C _b,a and C _bc,a represent the annual equivalent cost of the battery energy storage system and the annual equivalent cost of the converter connected to the battery energy storage system, respectively, C _mg,a represents the annual equivalent cost of grid connection of the wind-solar-storage microgrid system, C _g represents the annual equivalent cost of the wind-solar-storage microgrid system purchasing electricity from the superior power grid, and C _ls represents the cost of load shedding from users when the wind-solar-storage microgrid is short of power. 4. The method according to claim 1, characterized in that the constraints of the objective function include: A first constraint condition, wherein the first constraint condition is used to constrain the charge and discharge power of the energy storage device and the state of charge of the energy storage device to calculate the rated power and rated capacity of the energy storage device; A second constraint condition, wherein the second constraint condition is used to calculate the rated power of the wind farm and the rated power of the photovoltaic power station; A third constraint condition, wherein the third constraint condition is used to constrain the power of the wind-solar-micro-storage grid system so as to keep the power of the wind-solar-micro-storage grid system balanced; The fourth constraint condition is used to constrain the transmission power of the connecting line between the wind-solar micro-storage grid and the upper-level grid. 5. The method according to claim 1 is characterized in that the use of a gradient projection algorithm to determine the coordination strategy between the wind-solar-storage microgrid system and each user comprises: Constructing a user cost objective function, wherein the user cost objective function aims to minimize the user's daily energy consumption cost and discomfort cost; Determine a demand response constraint condition, wherein the demand response constraint condition is used to constrain the power of the user-side elastic load and the inelastic load to determine the user's total load power demand; The price range of the wind-solar-storage microgrid system electricity sales price is determined according to the cost parameters of the wind-solar-storage microgrid system, the revenue of the wind-solar-storage microgrid system, the cost of purchasing electricity from the upper power grid, and the price constraints of the power market; According to the demand constraints and the price range of electricity sales, the user cost objective function is solved using a gradient projection algorithm to obtain a coordination strategy between the wind, solar, and storage microgrid system and each user. The coordination strategy includes: the price at which the wind, solar, and storage microgrid system sells electricity to users and the amount of electricity sold by users. 6. The method according to claim 5, characterized in that the user cost objective function includes: user inappropriate cost and energy consumption cost, and the user cost objective function

It is expressed as:

Among them, ζ(u _i,t ) represents the discomfort cost of user i, χ(u _i,t ) represents the energy consumption cost of user i, and u _i,t represents the total electricity consumption of user i. 7. The method according to claim 1, characterized in that the method further comprises: The wind-solar-storage microgrid system is planned according to the planning scheme, and the net income of the wind-solar-storage microgrid system over its entire life cycle is calculated while taking into account the uncertainty factors of wind and solar power output. 8. A wind, solar and energy storage microgrid system planning device, characterized in that the device comprises: A function construction module is used to construct an objective function of the wind-solar-storage microgrid system based on demand response, prediction error and wind-solar-storage microgrid system cost, wherein the objective function aims to maximize the annual net profit of the wind-solar-storage microgrid system; A parameter input module, used to input wind, solar and energy storage cost parameters, grid tie line cost parameters, and user-side load parameters into the objective function; A strategy determination module is used to determine the coordination strategy between the wind-solar-storage microgrid system and each user using a gradient projection algorithm; The function solving module is used to solve the objective function according to the coordination strategy and the constraints of the objective function in combination with a mixed integer linear programming algorithm to obtain a planning scheme for the wind-solar-storage microgrid system. The planning scheme includes: wind farm rated power, photovoltaic rated power, energy storage rated capacity and energy storage rated power. 9. An electronic device, characterized in that it includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes, the wind-solar-storage microgrid system planning method according to any one of claims 1 to 7 is implemented. 10. A computer-readable storage medium having a computer program/instruction stored thereon, characterized in that when the computer program/instruction is executed by a processor, the wind-solar-storage microgrid system planning method as described in any one of claims 1 to 7 is implemented.

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