Wind and light storage micro-grid dispatching method based on ant colony algorithm

Abstract

The invention provides a wind and light storage micro-grid dispatching method based on an ant colony algorithm, and relates to the technical field of micro-grid dispatching, and the method comprises the following steps: S1, building a wind and light storage micro-grid system, and building a model of each device in a micro-grid; s2, establishing a comprehensive cost model, and forming a target function considering both the economic cost and the environmental cost of the system; s3, various constraint conditions of the system are established; s4, establishing a mathematical model of microgrid optimization scheduling based on S1-S3, and obtaining various basic parameters; s5, initializing related parameters and a pheromone matrix of the ant colony algorithm; according to the method, the comprehensive energy system micro-grid model is constructed, the economic cost and the environmental cost are taken as indexes, the output condition of each device in the system is reasonably configured, so that the cost is reasonably dispatched and reduced, the micro-grid is taken as a small system capable of realizing power generation and distribution, the utilization rate of clean energy can be increased, and the energy utilization rate is increased. The method plays an important role in energy conservation and environmental protection.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid scheduling, and particularly relates to a scheduling method for a wind-solar-storage microgrid based on an ant colony algorithm. Background Art

[0002] With the increasing energy demand and energy types, as well as the development of energy conversion technologies, developing an integrated energy system characterized by multi-energy coupling has gradually become a low-carbon, green, and efficient means. However, renewable energy sources such as wind and solar have characteristics such as volatility and intermittency. The access of a high proportion of renewable energy will bring great challenges to the safe and stable operation of the power grid and the realization of the supply-demand balance of the integrated energy system. A microgrid is a small power generation and distribution system composed of distributed power sources, energy storage devices, energy conversion devices, loads, etc. Under the background of the low-carbon transformation of the energy structure, as the core carrier for the consumption of distributed energy, the microgrid realizes seamless switching between grid-connected and island modes through power electronic converters (such as dual-mode inverters, solid-state transformers, etc.). The microgrid is an autonomous system that can achieve self-control, protection, and management, and can operate either grid-connected with the external power grid or independently. There are two typical operating modes for the microgrid: under normal circumstances, the microgrid operates grid-connected with the distribution network, which is called the grid-connected mode; when a grid fault or power quality does not meet the requirements is detected, the microgrid will promptly disconnect from the distribution network and operate independently, which is called the island mode. The switching between the two must be smooth and fast. The microgrid appears as a single controlled unit relative to the external large power grid and can simultaneously meet the requirements of users for power quality, power supply safety, etc. The power sources inside the microgrid are mainly responsible for energy conversion by power electronic devices and provide necessary control. As a new type of power supply system, the microgrid is characterized by high efficiency, greenness, recycling, low carbon, and intelligence, and can effectively achieve the supply-demand balance of green energy in a local area, which is of great significance for promoting the green and low-carbon transformation of energy, improving the acceptance, configuration, and regulation capabilities of the power grid for clean energy, and enhancing the supply capacity of clean energy;

[0003] The optimal dispatching of microgrids is of great significance for improving energy supply efficiency, enhancing the reliability and stability of power grids, and improving the economy and sustainable development ability of power grids. However, existing optimal dispatching schemes generally adopt a mixed-integer linear programming (MILP) model with the core goal of minimizing operating costs. Although this model can reduce costs to a certain extent, it exposes significant limitations in practical applications: on the one hand, existing models overly focus on economic indicators and do not incorporate user satisfaction quantification parameters (such as power supply interruption duration, voltage fluctuation qualification rate) into the optimization system; on the other hand, the dispatching system adopts a hierarchical decoupled architecture of day-ahead planning and real-time control, resulting in spatio-temporal mismatch between the state of charge (SOC) of energy storage and demand response. This single-dimensional optimization logic can no longer meet the technical requirements of the new power system for the coordinated optimization of "source-grid-load-storage". Therefore, the present invention proposes a dispatching method for a wind-solar-storage microgrid based on the ant colony algorithm to solve the problems existing in the prior art. Summary of the Invention

[0004] In view of the above problems, the present invention proposes a dispatching method for a wind-solar-storage microgrid based on the ant colony algorithm. The dispatching method for a wind-solar-storage microgrid based on the ant colony algorithm constructs a microgrid model of an integrated energy system, and takes economic cost and environmental cost as indicators to reasonably allocate the output of each device in the system, thereby reasonably dispatching to reduce costs.

[0005] To achieve the object of the present invention, the present invention is realized through the following technical solutions: A dispatching method for a wind-solar-storage microgrid based on the ant colony algorithm, comprising the following steps:

[0006] S1: Build a wind-solar-storage microgrid system and establish models of each device in the microgrid;

[0007] S2: Establish an integrated cost model to form an objective function that takes into account both the economic cost and environmental cost of the system;

[0008] S3: Establish various constraint conditions of the system;

[0009] S4: Based on S1-S3, establish a mathematical model for the optimal dispatching of the microgrid and obtain various basic parameters;

[0010] S5: Initialize the relevant parameters of the ant colony algorithm and the pheromone matrix;

[0011] S6: Initialize the output of each device, that is, the decision variable;

[0012] S7: Calculate the total power consumption cost according to the output of each power source and the unit power consumption cost, as well as the total amount of unmet load, calculate the objective function f, and determine the local optimal solution and the global optimal solution F;

[0013] S8: Use the ant colony algorithm to update the pheromone and position of each ant in the ant colony;

[0014] S9: Return to S7, update the local optimal solution, and compare it with the global optimal solution obtained in the previous cycle. The smaller one is updated as the global optimal solution F until the number of iterations is reached;

[0015] S10: Solve the model and obtain the optimized scheduling result.

[0016] A further improvement is that in S1, each device model includes a wind power generation model, a photovoltaic power generation model, a gas turbine power generation model and an energy storage device model, and the wind power generation model is:

[0017]

[0018] Among them, v t is the instantaneous wind speed, v in , v r and v out are cut-in wind speed, rated wind speed and cut-out wind speed respectively, P r is the rated power of the fan.

[0019] Further improvement is: the photovoltaic power generation model is:

[0020]

[0021] Among them, P PV is the output power of the photovoltaic panel, G t is the solar irradiance, G STC is the irradiance under standard test conditions, The temperature coefficient is -0.0047, T STC The surface temperature of the photovoltaic panel under standard test conditions is 25 ℃, P STC The output power of photovoltaic panels measured by standard.

[0022] Further improvement is that the gas turbine power generation model is:

[0023]

[0024] Among them, V NG is the instantaneous consumption volume of natural gas, ŋ GT is the gas turbine output efficiency, K NG The power generation capacity of natural gas per cubic meter is 9.7 kWh / m 3 .

[0025] Further improvement is that the energy storage device model is:

[0026]

[0027] Among them, SOC (t+1) and SOC (t) are the state of charge of the battery at time t+1 and t, and are the charging and discharging powers of the battery, and are the charging and discharging efficiencies of the battery, respectively.

[0028] The further improvement lies in that in the step S2, the objective function is:

[0029]

[0030] In the formula, is the initial investment cost, is the operation and maintenance cost, is the energy cost, is the grid interaction cost and is the carbon emission cost;

[0031] Among them, the investment cost model of the system is:

[0032]

[0033] Among them, and are the total amounts of equipment optimized according to the number and capacity of equipment respectively; is the investment cost per unit equipment or per unit power; is the actual rate of return, is the expected service life of equipment k, is the capacity of equipment k;

[0034] The operation and maintenance cost model of the system is:

[0035]

[0036] Among them, Γ d is the total number of days of designed operation; k is the total number of equipment; ξ k is the maintenance cost per kWh of equipment k, is the output power of equipment k;

[0037] The energy cost model of the system is:

[0038]

[0039] Among them, is the unit price of natural gas;

[0040] The grid interaction cost model of the system is:

[0041]

[0042] Among them, is the unit power purchase price;

[0043] The carbon emission cost model of the system is as follows:

[0044]

[0045] Among them, κ is the treatment price per kg of carbon dioxide;

[0046]

[0047] Among them, ε ng and ε ng are the carbon emission coefficients of natural gas and purchased electricity.

[0048] The further improvement lies in that in S3, the constraint conditions are: the operation of the microgrid system needs to meet the overall electric power balance constraint; the operation of the microgrid system needs to meet the upper and lower limit constraints of each device; the operation of the microgrid system needs to meet the operation constraints of each energy storage device; the operation of the microgrid system needs to meet the ramping constraints and penalty constraints of each energy storage device; specifically including:

[0049] The operation of the integrated energy system needs to meet the overall electric balance, heat balance and cold balance constraints:

[0050]

[0051] The operation of the integrated energy system needs to meet the upper and lower limit constraints of each device:

[0052]

[0053] The operation of the integrated energy system needs to meet the operation constraints of each energy storage device:

[0054]

[0055] The operation of the integrated energy system needs to meet the ramping constraints and penalty constraints of each energy storage device.

[0056] The further improvement lies in that in S4, obtaining the basic parameters includes: initial electric load data, annual meteorological historical data, time-of-use electricity price data, equipment operation efficiency, rated power, start-stop time.

[0057] A further improvement lies in that in S5, the parameters related to the ant colony algorithm and the pheromone matrix include: the pheromone weight coefficient α, the heuristic function weight coefficient β, the ant colony size m, the distance heuristic function, the pheromone concentration function, the maximum number of iterations, and the maximum allowable exchanged power between the microgrid and the distribution network; after an ant completes a path search, under the action of the evaporation coefficient ρ, the residual pheromone concentration of the previous iteration on this path evaporates, and the ants in the current iteration leave a new pheromone increment. A group of ants is randomly generated, and each ant represents a possible solution. The transition probability p ij (t) of the ant k moving from the current node i to the next node j at time t is calculated as follows:

[0058]

[0059] where ;

[0060] In the formula: α is the pheromone weight coefficient; β is the heuristic function weight coefficient; s is the number of visited cities; τ ij (t) is the pheromone concentration function; allowed(k) is the set of the next feasible nodes; η ij (t) is the distance heuristic function; (x i , y i ) are the coordinates of the current node i; (x j , y j ) are the coordinates of the next node j; d ij is the distance between the current node i and the next node j;

[0061] Ants release pheromone on the paths they pass through, but the pheromone left before also gradually volatilizes over time. Let ρ (0 < ρ < 1) represent the volatilization degree of the pheromone. When the ants have traversed all the nodes, the pheromone remaining in the environment will be updated. The update mechanism of the pheromone (ant position) is as follows:

[0062]

[0063] where ,

[0064] In the formula: m is the total number of ants in each round; Δτ ij k (t) is the pheromone generated by the ant k on the path (i, j) in this round of iteration; Δτ ij (t) is the total pheromone generated by all the ants (ant colony) on the path (i, j) in this iteration. Q is the total amount of pheromone, and L k is the total length of the path passed by the ant k in this loop.

[0065] A further improvement lies in that in S6, the output conditions of each device include the SOC curve of the energy storage power supply and the output conditions of other power supplies.

[0066] The beneficial effects of the present invention are as follows:

[0067] 1. The present invention constructs a microgrid model of an integrated energy system. Taking economic cost and environmental cost as indicators, it rationally allocates the output conditions of each device in the system, thereby rationally dispatching to reduce costs. As a small system that can realize power generation and distribution, the microgrid can be rationally allocated to improve the utilization rate of clean energy, which plays an important role in energy conservation and environmental protection.

[0068] 2. The present invention enhances the economy and environmental performance of the system, improves the multi-energy complementarity and conversion ability of the system, can achieve effective convergence in a short time, and reaches the optimal balance of economic and environmental performance. In addition, the cost generated by the microgrid optimal scheduling method using the ant colony algorithm is lower than that of the conventional scheduling method. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 is the structural block diagram of the wind-solar-storage microgrid system of the present invention;

[0070] Figure 2 is the flowchart of the scheduling method of the present invention;

[0071] Figure 3 is the annual meteorological change trend chart of Region A disclosed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0072] In order to deepen the understanding of the present invention, the following will further elaborate on the present invention in combination with embodiments. These embodiments are only used to explain the present invention and do not constitute a limitation on the protection scope of the present invention.

[0073] Embodiment 1

[0074] According to Figure 1 shown, this embodiment proposes a wind-solar-storage microgrid scheduling method based on the ant colony algorithm. Among them, the wind-solar-storage microgrid system based on the ant colony algorithm consists of two renewable energy sources, namely photovoltaic and wind power, a gas turbine, an external power grid, and a storage battery.

[0075] The power system includes a wind turbine generator for converting wind energy into electrical energy; a photovoltaic unit for converting light energy into electrical energy, and an external power grid;

[0076] An energy storage system, including a storage battery.

[0077] The mathematical model of the gas turbine is:

[0078]

[0079] Among them, P GT is the output electric power of the gas turbine, V NG is the instantaneous consumption volume of natural gas, ŋ GT is the output efficiency of the gas turbine, K NG is the power generation per cubic meter of natural gas, 9.7 kWh / m 3 .

[0080] This microgrid system has two operating modes: islanding and grid-connected. In the grid-connected operating state, the microgrid is directly connected to the main grid (large grid), and electric energy flows bidirectionally, and it can send electricity to the main grid or purchase electricity from the main grid; when the main grid suddenly loses power, the microgrid needs to detect and switch to the islanding mode within milliseconds, disconnect from the main grid, and operate independently, relying only on its own distributed power sources (such as photovoltaic, wind turbines, energy storage, etc.) for power supply.

[0081] Example Two

[0082] According to Figure 2 as shown, this embodiment proposes a scheduling method for a wind-solar-storage microgrid based on the ant colony algorithm. When the ant colony algorithm starts, first initialize the pheromone matrix and parameters, and randomly generate the initial positions of the ants. Then, calculate the objective function value (such as the path length) of each ant and record its walking path. After constructing the solution space by initializing the decision variables, the ants select the next route according to the transition probability formula, simulating the behavior of ants in nature to find the optimal path through pheromones. After each iteration is completed, the pheromone is updated according to the path performance of the ants (such as enhancing the pheromone concentration of high-quality paths). Repeat this process until the maximum number of iterations is reached, and finally output the optimal solution (such as the shortest path or the optimal objective function value). This algorithm gradually approaches the optimal solution of complex optimization problems by simulating the cooperation and pheromone feedback mechanism of the ant colony.

[0083] The objective function considering both the economic cost and environmental cost of the integrated energy system is established as:

[0084]

[0085] In the formula, is the initial investment cost, is the operation and maintenance cost, is the energy cost, is the grid interaction cost and is the carbon emission cost.

[0086] Among them, the investment cost model of the energy storage system is:

[0087]

[0088] Among them, and The total amount of equipment optimized according to the number and capacity of equipment respectively; is the investment cost per unit equipment or unit power; is the actual rate of return, is the expected service life of equipment k, is the capacity of equipment k.

[0089] The operation and maintenance cost model of the energy storage system is:

[0090]

[0091] where Γ d is the total number of days of designed operation; k is the total number of equipment; ξ k is the maintenance cost per kWh of equipment k, is the output power of equipment k.

[0092] The energy cost model of the energy storage system is:

[0093]

[0094] where, is the unit price of natural gas.

[0095] The grid interaction cost model of the energy storage system is:

[0096]

[0097] where, is the unit power purchase price.

[0098] The carbon emission cost model of the energy storage system is:

[0099]

[0100] where κ is the treatment price per kg of carbon dioxide.

[0101]

[0102] where, ε ng and ε ng are the carbon emission coefficients of natural gas and power purchase.

[0103] Example 3

[0104] According to Figure 3 shown, this example proposes a wind-solar-storage microgrid scheduling method based on the ant colony algorithm, which acts on Area A. The local hourly wind speed, global irradiance and environmental temperature are as Figure 3As shown. The date of meteorological data starts from January 1st of a certain year and lasts for a whole year. Based on the annual meteorological data and the models of wind turbines and photovoltaics, the power outputs of wind power and photovoltaics throughout the year can be calculated. The region adopts a peak-valley electricity price model, and its time-of-use electricity price is shown in Table 1. It is set that the peak-hour electricity price for the system to purchase electricity from the external power grid is 1.1662 yuan / kWh, the normal-hour electricity price is 0.8724 yuan / kWh, and the valley-hour electricity price is 0.6072 yuan / kWh. The economic and technical parameters of the equipment are necessary data in the system optimization process. Table 2 lists the economic parameters of each subsystem.

[0105] Table 1 Local Time-of-Use Electricity Price

[0106] Time period Time (h) Electricity price (yuan / kWh) Peak period 10:00-13:00,17:00-22:00 1.1662 Flat period 7:00-10:00,13:00-17:00,22:00-23:00 0.8724 Valley period 0:00-7:00, 23:00-24:00 0.6072

[0107] Table 2 Economic Parameters of Each Equipment

[0108] Type Rated capacity Unit investment cost Unit operation and maintenance cost Lifespan / year Photovoltaic panel 50 kW 10220 yuan / kW 0.0119 yuan / kWh 20 Wind turbine 100 kW 11200 yuan / kW 0.0273 yuan / kWh 20 Gas turbine 200 kW 8302 yuan / kW 0.0231 yuan / kWh 25 Battery 450 kWh 2184 yuan / kW 0.2261 yuan / kWh 12

[0109] The dispatching method of the wind-solar-storage microgrid based on the ant colony algorithm constructs a microgrid model of the integrated energy system. Taking the economic cost and environmental cost as indicators, it reasonably allocates the power outputs of each device in the system, thereby reasonably dispatching to reduce costs. As a small system that can realize power generation and distribution, the reasonable allocation of the microgrid can improve the utilization rate of clean energy, which plays an important role in energy conservation and environmental protection. Moreover, the present invention enhances the economy and environmental performance of the system, improves the multi-energy complementarity and conversion ability of the system, can achieve effective convergence in a short time, and reaches the optimal balance of economic and environmental performance. In addition, the cost generated by the microgrid optimal dispatching method using the ant colony algorithm is lower than that of the conventional dispatching method.

[0110] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A scheduling method for a wind-solar-storage microgrid based on the ant colony algorithm, characterized in that It includes the following steps: S1: Build a wind-solar-storage microgrid system and establish models for each device in the microgrid; S2: Establish a comprehensive cost model to form an objective function that takes into account both the economic cost and environmental cost of the system; S3: Establish various constraint conditions for the system; S4: Based on S1 - S3, establish a mathematical model for microgrid optimal scheduling and obtain various basic parameters; S5: Initialize the relevant parameters of the ant colony algorithm and the pheromone matrix; S6: Initialize the output of each device, that is, the decision variable; S7: Calculate the total electricity cost according to the output of each power source and the unit electricity cost, as well as the total amount of unmet load, calculate the objective function f, and determine the local optimal solution and the global optimal solution F; S8: Use the ant colony algorithm to update the pheromone and position of each ant in the ant colony; S9: Return to S7, update the local optimal solution, and compare it with the global optimal solution obtained in the previous cycle, and update the smaller one as the global optimal solution F until the number of iterations is reached; S10: Solve the model and obtain the optimal scheduling result.

2. The method for dispatching a wind-solar-storage microgrid based on the ant colony algorithm according to claim 1, wherein: In S1, each device model includes a wind power generation model, a photovoltaic power generation model, a gas turbine power generation model, and an energy storage device model. The wind power generation model is: ， where, v t is the instantaneous wind speed, v in , v r and v out are the cut-in wind speed, rated wind speed and cut-out wind speed respectively, and P r is the rated power of the wind turbine.

3. The method for dispatching a wind-solar-storage microgrid based on the ant colony algorithm according to claim 2, wherein: The photovoltaic power generation model is: ， Among them, P PV is the output power of the photovoltaic panel, G t is the solar irradiance, G STC is the irradiance under standard test conditions, is the temperature coefficient -0.0047, T STC is the surface temperature of the photovoltaic panel under standard test conditions, 25 °C, P STC is the output power of the photovoltaic panel under standard measurement.

4. A method for scheduling a wind-solar-storage microgrid based on the ant colony algorithm according to claim 3, characterized in that: The gas turbine power generation model is: ， Among them, V NG is the instantaneous consumption volume of natural gas, ŋ GT is the gas turbine output efficiency, K NG The power generation capacity of natural gas per cubic meter is 9.7 kWh / m 3 .

5. A method for scheduling a wind-solar-storage microgrid based on the ant colony algorithm according to claim 4, characterized in that: The energy storage device model is: ， Among them, SOC (t + 1) and SOC (t) are the state of charge of the battery at times t + 1 and t, and are the charging and discharging powers of the battery, respectively, and are the charging and discharging efficiencies of the battery, respectively.

6. The method for dispatching a wind-solar-storage microgrid based on the ant colony algorithm according to claim 1, wherein: In S2, the objective function is: ， Wherein, is the initial investment cost, is the operation and maintenance cost, is the energy cost, is the grid interaction cost and is the carbon emission cost; Among them, the investment cost model of the system is: ， Among them, and are the total amounts of equipment optimized according to the number and capacity of the equipment respectively; is the investment cost per unit equipment or unit power; is the actual rate of return, is the expected service life of equipment k, is the capacity of equipment k; The operation and maintenance cost model of the system is: ， Among them, Γ d is the total number of days for the design operation; k is the total number of devices; ξ k is the maintenance cost per kWh of device k, is the output power of device k; The energy cost model of the system is: ， Among them, is the unit price of natural gas; The grid interaction cost model of the system is: ， Among them, is the unit electricity purchase price; The carbon emission cost model of the system is: ， Among them, κ is the treatment price per kg of carbon dioxide; ， where ε ng and ε ng are the carbon emission coefficients of natural gas and purchased electricity.

7. A method for scheduling a wind-solar-storage microgrid based on the ant colony algorithm according to claim 1, characterized in that: In S3, the constraint conditions are: The operation of the microgrid system needs to satisfy the overall electric power balance constraint; The operation of the microgrid system needs to satisfy the upper and lower limit constraints of each device; The operation of the microgrid system needs to satisfy the operation constraints of each energy storage device; The operation of the microgrid system needs to satisfy the ramp rate constraint and penalty constraint of each energy storage device; specifically including: The operation of the integrated energy system needs to satisfy the overall electric balance, heat balance, and cold balance constraints: ， The operation of the integrated energy system needs to satisfy the upper and lower limit constraints of each device: ， The operation of the integrated energy system needs to satisfy the operation constraints of each energy storage device: ， The operation of the integrated energy system needs to satisfy the ramp rate constraint and penalty constraint of each energy storage device.

8. A method for scheduling a wind-solar-storage microgrid based on the ant colony algorithm according to claim 1, characterized in that: In S4, obtaining various basic parameters includes: initial electricity load data, annual meteorological historical data, time-of-use electricity price data, device operation efficiency, rated power, start-stop time.

9. A method for dispatching a wind-solar-storage microgrid based on the ant colony algorithm according to claim 1, characterized in that: In S5, the parameters related to the ant colony algorithm and the pheromone matrix include: the pheromone weight coefficient α, the heuristic function weight coefficient β, the ant colony size m, the distance heuristic function, the pheromone concentration function, the maximum number of iterations, and the maximum allowable exchanged power between the microgrid and the distribution network; after an ant completes a path search, under the action of the evaporation coefficient ρ, the residual pheromone concentration of the previous iteration on this path evaporates, while the ants in the current iteration leave new pheromone increments. A group of ants is randomly generated, and each ant represents a possible solution. The transition probability p ij (t) is calculated by the formula: ， in, ; , Where: α is the pheromone weight coefficient; β is the heuristic function weight coefficient; s is the number of visited cities; τ ij (t) is the pheromone concentration function; allowed(k) is the set of the next feasible nodes; η ij (t) is the distance heuristic function; (x i , y i ) are the coordinates of the current node i; (x j , y j ) are the coordinates of the next node j; d ij is the distance between the current node i and the next node j; Ants release pheromone on the path they pass through, but the pheromone left before also gradually volatilizes over time. Let ρ (0 < ρ < 1) represent the volatilization degree of the pheromone. When the ants traverse all nodes, they will update the pheromone remaining in the environment. The update mechanism of the pheromone (ant position) is as follows: ， Among them, , , Where: m is the total number of ants per round; Δτ ij k (t) is the pheromone generated by ant k on path (i, j) during this round of iteration; Δτ ij (t) is the total pheromone generated by all ants (ant colony) on path (i, j) during this iteration, Q is the total amount of pheromone, and L k is the total length of the path traversed by ant k during this cycle.

10. A method for scheduling a wind-solar-storage microgrid based on the ant colony algorithm according to claim 1, characterized in that: In S6, the output of each device includes: the SOC curve of the energy storage power source and the output of other power sources.

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