A micro-grid optimal scheduling method based on improved red-billed blue magpie optimization algorithm

Abstract

The present application belongs to the technical field of power dispatch, especially a micro-grid optimization dispatching method based on an improved red-billed blue magpie optimization algorithm. The method comprises the following steps: S1: constructing a micro-grid system architecture; S2: establishing a micro-grid mathematical physical model; S3: constructing a micro-grid optimization dispatching model considering economy and environmental protection; and S4: solving the micro-grid optimization dispatching model by using the improved red-billed blue magpie optimization algorithm. The present application deeply reconstructs the optimization mechanism: in the initialization stage, a composite chaotic mapping strategy is used to mine potential high-quality solutions, significantly enhancing population diversity; in the exploration and development stage of core evolution, an anti-predation mechanism is introduced to improve the algorithm's response ability to environmental pressure, combined with a multi-stage fine foraging strategy, achieving a precise balance between local search precision and global development capability; in addition, a dynamic fault-tolerant mechanism is constructed by fusing the simulated annealing idea, effectively avoiding premature convergence.

Description

Technical Field

[0001] This invention relates to the field of power dispatching technology, specifically to a microgrid optimization dispatching method based on an improved Red-billed Blue Magpie optimization algorithm. Background Technology

[0002] With the rapid growth of the global economy, heavy reliance on fossil fuels has triggered a severe greenhouse effect and climate crisis. Vigorously developing renewable energy sources, such as wind and solar power, has become an important way to break this predicament. However, the inherent intermittency and volatility of renewable energy sources increase the uncontrollability of power generation output, exacerbating the risk of system supply and demand imbalance and posing a serious challenge to the real-time balance and safe and stable operation of large power grids.

[0003] Microgrids, as a new type of power system capable of efficiently integrating distributed energy resources, can effectively realize the local consumption and flexible management of distributed energy. Through optimized dispatching, microgrids can effectively mitigate the random fluctuations of renewable energy and achieve supply and demand balance within the region. This not only mitigates grid security risks but also provides a feasible path for the energy system to transition to a low-carbon and clean energy model.

[0004] In the operation and management of microgrid systems, optimized dispatch is a core element in ensuring their efficient and green operation. The system needs to rationally allocate the output of distributed power sources while meeting the dynamic load balance and physical constraints of various equipment operations. Traditional single-economic dispatch is no longer sufficient to meet the low-carbon development needs of new power systems. Therefore, incorporating pollutant treatment costs into the optimization objectives and implementing economic and environmentally friendly dispatch to minimize overall costs can truly achieve a new balance between economic, environmental, and technological constraints. Summary of the Invention

[0005] The purpose of this section is to outline some aspects of the embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0006] To address the aforementioned technical problems, according to one aspect of the present invention, the present invention provides the following technical solution:

[0007] A microgrid optimization scheduling method based on an improved red-billed blue magpie optimization algorithm includes the following steps:

[0008] S1: Construct a microgrid system architecture: integrate photovoltaics, wind turbines, micro gas turbines, fuel cells, and energy storage batteries. The photovoltaics, wind turbines, micro gas turbines, fuel cells, and energy storage batteries are connected to the microgrid bus through corresponding converters to achieve stable conversion and transmission of electrical energy.

[0009] S2: Establish mathematical and physical models for microgrids: establish photovoltaic models, wind turbine models, micro gas turbine models, fuel cell models, and energy storage battery models;

[0010] S3: Constructing a microgrid optimization scheduling model that balances economic efficiency and environmental protection: including constructing a comprehensive cost objective function and establishing microgrid operation constraints;

[0011] S4: An improved Red-beaked Blue Magpie optimization algorithm is used to solve the microgrid optimal scheduling model: The comprehensive cost objective function of the microgrid system is used as the fitness evaluation standard of the algorithm, and the output decision variables of each distributed power source and energy storage unit are mapped to the solution space for the algorithm to find the best solution. Under the premise of strictly satisfying the dynamic power balance of the system and the operating constraints of various physical equipment, through intelligent iterative evolution, the power scheduling strategy of each component is finally captured and output to achieve the optimal trade-off between the total operating cost and the environmental governance cost of the system.

[0012] As a preferred embodiment of the microgrid optimization scheduling method based on the improved Red-billed Blue Magpie optimization algorithm described in this invention, the method for establishing the photovoltaic model in S2 is as follows: The photovoltaic power output model is as follows:

[0013] (1)

[0014] (2)

[0015] in, It is the operating temperature; It is the ambient temperature; It is the intensity of light; This is the nominal operating temperature; It is the output power; It is the maximum output power of PV under standard test conditions; It is the light intensity under standard test conditions; It is the power temperature coefficient; For reference temperature;

[0016] The method for establishing the wind turbine model is as follows: The power output model of the wind turbine is as follows:

[0017] (3)

[0018] in, It is the output power; It is the rated power; and These are the actual wind speed and the rated wind speed at the wheel hub, respectively. and These are the cut-in wind speed and the cut-out wind speed, respectively.

[0019] The method for establishing the micro gas turbine model is as follows: The relationship between the operating efficiency and output power of the micro gas turbine is as follows:

[0020] (4)

[0021] (5)

[0022] in, It is the unit efficiency of MT per unit time; It is the active power output; It's the cost of fuel; It's the price of natural gas; It has the low calorific value of natural gas;

[0023] The method for establishing the fuel cell model is as follows: The relationship between the fuel cost and output power of the fuel cell is as follows:

[0024] (6)

[0025] in, It's the cost of fuel; It is the unit efficiency of FC per unit time; It is the active power output;

[0026] The method for establishing the energy storage battery model is as follows: During the charging and discharging process, the battery model equation characterizing the state of charge of the energy storage battery is as follows:

[0027] (7)

[0028] in, and These are energy storage batteries in and The state of charge at any given moment; and These are the charging efficiency and discharging efficiency of the energy storage battery, respectively. It is an energy storage battery in Output power at any given moment; It refers to the capacity of the energy storage battery; It refers to a unit of time.

[0029] As a preferred embodiment of the microgrid optimization scheduling method based on the improved Red-billed Blue Magpie optimization algorithm described in this invention, the method for constructing the comprehensive cost objective function in S3 is as follows: A multi-objective microgrid scheduling model is constructed with operating costs and pollutant treatment costs as objectives.

[0030] (8)

[0031] (9)

[0032] in, It is the overall cost of operating a microgrid; It is operating cost; It is the cost of environmental governance; setting = =1, treating economic costs and environmental impacts as equals, and transforming the multi-objective problem into a single-objective problem for solution; It's the cost of fuel; It's the maintenance cost; It is the interaction cost between the microgrid and the main grid;

[0033] The fuel cost of a microgrid mainly includes the fuel consumption costs of the micro-turbines and fuel cells during their operating cycle, namely:

[0034] (10)

[0035] The maintenance cost of a microgrid depends on the output power of each distributed generation device during its operating cycle, i.e.:

[0036] (11)

[0037] in, , , , and These are the maintenance cost coefficients for wind turbines, photovoltaics, micro gas turbines, fuel cells, and energy storage batteries, respectively. and They are The discharge and charging power of the energy storage battery at all times;

[0038] Energy exchange between the microgrid and the main grid is achieved through electricity purchase and sale, with costs calculated as follows:

[0039] (12)

[0040] in, Is The electricity purchase price between the microgrid and the main grid at any given time; Is The electricity sales price between the microgrid and the main grid at any given time; It is the microgrid and the main grid in Interaction power at any given moment;

[0041] The environmental governance costs of microgrids mainly involve the treatment costs of gaseous pollutants.

[0042] (13)

[0043] in, It is the first step in improving the quality of governance units. The costs required for this type of pollutant; It is the first The power generation unit for the first Emission coefficients of pollutants; It refers to the type of pollutant; It is the first Each power generation unit is in Power generation at any given moment.

[0044] As a preferred embodiment of the microgrid optimization scheduling method based on the improved Red-billed Blue Magpie optimization algorithm described in this invention, the specific method for establishing microgrid operation constraints in S3 is as follows: to ensure microgrid stability, the total power generation of the system needs to achieve dynamic balance with the total power consumption, that is:

[0045] (14)

[0046] in, Is the system in Load power at any given time; when When >0, the microgrid purchases electricity from the main grid. When <0, the microgrid sells electricity to the main grid; when When the value is greater than 0, the energy storage battery discharges. When the value is less than 0, the energy storage battery is charged;

[0047] The active power output constraints for four types of power generation units—wind turbines, photovoltaics, micro gas turbines, and fuel cells—are as follows:

[0048] (15)

[0049] (16)

[0050] in, and It is the first The upper and lower limits of the power of each power generation unit; CG is a controllable power generation unit, namely a micro gas turbine and a fuel cell; and It refers to the downward and upward ramp rates of the controllable power generation unit;

[0051] The capacity and power limitations of energy storage batteries are as follows:

[0052] (17)

[0053] in, and These are the upper and lower limits of the SOC of SB, respectively; and These are the upper and lower limits of the energy storage battery's output;

[0054] The power exchange limits between the microgrid and the main grid are as follows:

[0055] (18)

[0056] in, and These are the upper and lower limits of the transmission power of the tie line, respectively.

[0057] As a preferred embodiment of the microgrid optimization scheduling method based on the improved red-billed blue magpie optimization algorithm described in this invention, the mathematical model of the standard red-billed blue magpie optimization algorithm includes three core behaviors: finding food, attacking prey, and storing food.

[0058] The food-finding phase is simulated by the following equation (19) to model the exploration behavior of a small group in the solution space, and by equation (20) to model the wide-area search behavior of the cluster:

[0059] (19)

[0060] (20)

[0061] in, This represents the current iteration number; This represents the current position of the individual. This represents the position of a random individual in the current iteration. This indicates the individual's position in the next iteration; and The numbers of red-billed blue magpies in small groups and large groups, respectively; For the first A randomly selected individual; The search agent is randomly selected for the current iteration; and All are random numbers within the range [0,1].

[0062] The prey-attacking phase is simulated by the following equation (21) to represent the pecking behavior of a small group on small prey, and by the following equation (22) to represent the siege behavior of a large group on large prey:

[0063] (twenty one)

[0064] (twenty two)

[0065] (twenty three)

[0066] in, It is the optimal individual in the current iteration; The operator is nonlinearly decreasing from 1 to 0, as shown in formula (23); and To generate random numbers that follow a standard normal distribution; This represents the maximum number of iterations.

[0067] During the food storage phase, the red-billed blue magpie will store excess food in tree holes or other hidden locations for future use, as shown in formula (24):

[0068] (twenty four)

[0069] in, and The first The fitness values ​​of the Red-billed Blue Magpie before and after the location update.

[0070] As a preferred embodiment of the microgrid optimization scheduling method based on the improved red-billed blue magpie optimization algorithm described in this invention, the improved red-billed blue magpie optimization algorithm includes introducing a composite chaotic mapping strategy in the initialization stage, an anti-predation strategy in the food-finding stage, a multi-stage fine foraging strategy in the prey-attacking stage, and a simulated annealing strategy in the food-storing stage.

[0071] As a preferred embodiment of the microgrid optimization scheduling method based on the improved red-beaked blue magpie optimization algorithm described in this invention, the specific form of the composite chaotic mapping strategy introduced in the initialization phase is shown in formula (25):

[0072] (25)

[0073] in, For the first The location of each individual; and These are the upper and lower bounds of the solution space, respectively; A composite chaotic mapping strategy;

[0074] Composite chaotic mapping strategy uses control parameters The generation probabilities of Cubic and Sine mappings are adjusted to form chaotic sequences. Cubic mapping is shown in Equation (26), and Sine mapping is shown in Equation (27).

[0075] (26)

[0076] (27)

[0077] (28)

[0078] Among them, control parameters The value is 0.5; The control parameter for Cubic mapping takes the value 2.595; The control parameter for Sine mapping takes the value 4; This is the current chaotic mapping value; This is the output after chaotic mapping.

[0079] As a preferred embodiment of the microgrid optimization scheduling method based on the improved red-billed blue magpie optimization algorithm described in this invention, the introduction of an anti-predation strategy during the food-finding stage is as follows: Red-billed blue magpies are constantly threatened by predators, and when attacked, they will adopt different avoidance methods according to the size of the population: as shown in formula (29), small groups of red-billed blue magpies adopt a random jumping strategy of scattering and fleeing to confuse predators; as shown in formula (30), clusters of red-billed blue magpies quickly gather in the known safest area, attempting to use the group advantage to resist danger; subsequently, through a greedy selection mechanism, the fitness of the conventional foraging location and the improved location are compared, and a solution that is more conducive to survival is selected to ensure the efficiency and robustness of the algorithm's optimization.

[0080] (29)

[0081] (30)

[0082] (31)

[0083] (32)

[0084] in, The escape location of an individual after adopting an anti-predation strategy; as well as The escape search boundary dynamically shrinks with the number of iterations; A positive integer randomly generated by the chaotic mapping strategy; This is the individual's usual foraging location.

[0085] As a preferred embodiment of the microgrid optimization scheduling method based on the improved red-billed blue magpie optimization algorithm described in this invention, the multi-stage fine foraging strategy introduced in the prey attack stage is a multi-stage fine foraging strategy combining Brownian motion and Levy flight. The specific development process is divided into three stages: in the early stage of optimization, the population adopts Brownian motion with a stable step length to move towards the food-dense area, and its position update is shown in formula (33); in the middle stage of optimization, in order to balance the exploration and development capabilities of the algorithm, the population is divided into two: as shown in formula (34), the first half of the individuals use the alternating long and short steps of Levy flight to jump and scan the periphery; the second half of the individuals use Brownian motion to carry out fine hunting while the foraging radius is constantly shrinking; in the later stage of optimization, as shown in formula (35), the red-billed blue magpies concentrate on attacking prey near the optimal food source by adopting the excellent development capability of Levy flight.

[0086] (33)

[0087] (34)

[0088] (35)

[0089] in, The Brownian motion vector follows a standard normal distribution; For Levi's flight vector; The constant coefficient for controlling the step size is set to 0.5.

[0090] As a preferred embodiment of the microgrid optimization scheduling method based on the improved red-billed blue magpie optimization algorithm described in this invention, the specific method of introducing a simulated annealing strategy during the food storage stage is as follows: as shown in formula (36), a temperature parameter that decays nonlinearly with iteration is constructed to simulate the natural survival pressure faced by the red-billed blue magpie in different foraging seasons; as shown in formula (37), when the red-billed blue magpie explores a new location with poor food abundance, it no longer directly discards it, but simulates the trial-and-error instinct of birds and uses the Metropolis criterion to determine the probability of annealing. They are accepted as backup food storage sites, providing impetus for the population to escape local optima;

[0091] (36)

[0092]

[0093] in, For temperature parameters; This represents the average fitness of the current population. The probability of accepting a new solution; and These are the old and new food storage sites for the flock of birds.

[0094] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention focuses on the economic and environmentally friendly dispatching problem of microgrids and proposes an IRBMO algorithm. To overcome the defect of traditional algorithms easily getting trapped in local optima when dealing with high-dimensional nonlinear dispatching models, this algorithm deeply reconstructs the optimization mechanism: In the initialization phase, a composite chaotic mapping strategy is adopted to mine potential high-quality solutions, significantly enhancing population diversity; in the exploration and development phase of core evolution, an anti-predation mechanism is introduced to improve the algorithm's responsiveness to environmental pressures, and combined with a multi-stage fine-grained foraging strategy, a precise balance between local search accuracy and global development capability is achieved; in addition, a dynamic fault-tolerant mechanism is constructed by integrating simulated annealing, effectively avoiding premature convergence. Attached Figure Description

[0095] To more clearly illustrate the technical solutions of the embodiments of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and detailed embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0096] Figure 1 This is a schematic diagram of a microgrid system structure for a microgrid optimization scheduling method based on an improved red-billed blue magpie optimization algorithm according to the present invention.

[0097] Figure 2 The following are convergence curves of the reference functions in an embodiment of the microgrid optimization scheduling method based on the improved Red-billed Blue Magpie optimization algorithm of the present invention: (a) is the convergence curve of function F2; (b) is the convergence curve of function F3; (c) is the convergence curve of function F6; and (d) is the convergence curve of function F9.

[0098] Figure 3 This is an example of an optimized scheduling flowchart in an embodiment of a microgrid optimized scheduling method based on an improved red-billed blue magpie optimization algorithm according to the present invention.

[0099] Figure 4 The images show typical daily operating scenarios in summer and winter in an embodiment of a microgrid optimization scheduling method based on an improved red-billed blue magpie optimization algorithm of the present invention. (a) is a typical daily operating scenario data diagram in summer; (b) is a typical daily operating scenario data diagram in winter.

[0100] Figure 5 This is a time-of-use electricity price diagram in an embodiment of a microgrid optimization scheduling method based on an improved red-billed blue magpie optimization algorithm according to the present invention.

[0101] Figure 6The following are convergence result diagrams of various algorithms in an embodiment of the microgrid optimization scheduling method based on the improved Red-billed Blue Magpie optimization algorithm of the present invention. Among them, (a) is the convergence result diagram of various algorithms in summer; (b) is the convergence result diagram of various algorithms in winter.

[0102] Figure 7 The diagram shows the optimal scheduling scheme in an embodiment of the microgrid optimization scheduling method based on the improved Red-billed Blue Magpie optimization algorithm of the present invention. (a) is the convergence result diagram of each algorithm in summer; (b) is the convergence result diagram of each algorithm in winter. Detailed Implementation

[0103] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0104] Secondly, the present invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of the present invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not according to the usual scale. Furthermore, the schematic diagrams are merely examples and should not limit the scope of protection of the present invention. In addition, actual fabrication should include three-dimensional spatial dimensions of length, width, and depth.

[0105] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0106] The core of this invention lies in finding the optimal operating strategy that balances economy and environmental protection under complex and ever-changing constraints. However, due to the complex characteristics of microgrid scheduling models, such as high dimensionality, nonlinearity, and multiple constraints, traditional analysis methods and conventional intelligent optimization algorithms generally suffer from inherent defects such as slow convergence speed, easy getting trapped in local optima, and low search efficiency. These algorithmic shortcomings make it difficult for microgrid scheduling schemes to achieve true global optima, making it impossible for the system to achieve an optimal trade-off between operating costs and pollutant emission control costs in actual operation. The core of this invention aims to effectively overcome the problem of solving high-dimensional complex scheduling models, and efficiently and stably capture the global optimal strategy while strictly satisfying various physical and operational constraints of the microgrid. This will minimize the total operating cost and environmental control cost of the microgrid system under complex operating conditions, providing reliable technical support for the low-carbon, economical, and efficient operation of microgrids with a high proportion of renewable energy.

[0107] Specifically, a microgrid optimization scheduling method based on an improved red-billed blue magpie optimization algorithm includes the following steps:

[0108] S1: Constructing a microgrid system architecture:

[0109] The microgrid structure constructed in this invention is as follows: Figure 1As shown, it integrates photovoltaic (PV), wind turbine (WT), micro gas turbine (MT), fuel cell (FC), and battery storage (SB). These units are connected to the microgrid bus through corresponding converters to achieve stable conversion and transmission of electrical energy. The solid red line in the figure represents the flow of energy, and the dashed blue line represents the real-time information communication and control between the energy management system and each unit.

[0110] S2: Establishing a mathematical and physical model for the microgrid:

[0111] S2.1: Establishing a Photovoltaic Model: PV utilizes the photovoltaic effect of semiconductors to convert solar energy into electrical energy. The output power of a PV system is affected by light intensity and temperature. Its power output model is shown below:

[0112] (1)

[0113] (2)

[0114] in, It is the operating temperature; It is the ambient temperature; It is the intensity of light; This is the nominal operating temperature; It is the output power; It is the maximum output power of PV under standard test conditions; It is the light intensity under standard test conditions; It is the power temperature coefficient; This is a reference temperature.

[0115] S2.2: Establishing the wind turbine model: The WT converts wind energy into mechanical energy and then generates electricity. Its output power is directly affected by the wind speed at the WT hub height and the aerodynamic characteristics of the wind turbine. Its power output model is shown below:

[0116] (3)

[0117] in, It is the output power; It is the rated power; and These are the actual wind speed and the rated wind speed at the wheel hub, respectively. and These are the cut-in wind speed and the cut-out wind speed, respectively.

[0118] S2.3: Establishing a Micro Gas Turbine Model: A micro gas turbine (MT) is a new type of small-capacity generator that converts chemical energy into kinetic energy using natural gas or similar fuels. It boasts advantages such as high reliability, high power generation efficiency, and low pollution. Furthermore, unlike other new energy power generation units, its high flexibility allows for precise control to achieve optimal operating conditions. The relationship between its operating efficiency and output power is shown below:

[0119] (4)

[0120] (5)

[0121] in, It is the unit efficiency of MT per unit time; It is the active power output; It's the cost of fuel; It's the price of natural gas; It has the low calorific value of natural gas.

[0122] S2.4: Establishing a fuel cell model: FC is a type of power generation battery device that stores fuel and oxidant, converting some chemical energy into electrical energy through a chemical reaction. Unlike traditional combustion power generation methods, it theoretically emits almost no pollutants. The relationship between its fuel cost and output power is shown below:

[0123] (6)

[0124] in, It's the cost of fuel; It is the unit efficiency of FC per unit time; It is the active power output.

[0125] S2.5: Establishing the Energy Storage Battery Model: The battery (SB) acts as an energy buffer in a microgrid, mitigating the impact of renewable energy generation units on the grid and improving the reliability of the microgrid. The battery model equations characterizing the state of charge (SBC) during charging and discharging are shown below:

[0126] (7)

[0127] in, and SB is in and The state of charge at any given moment; and These are the charging efficiency and discharging efficiency of SB, respectively. It's an idiot. Output power at any given moment; That is the capacity of SB; It refers to a unit of time.

[0128] S3: Constructing a microgrid optimal scheduling model that balances economic efficiency and environmental friendliness:

[0129] S3.1: Constructing a Comprehensive Cost Objective Function: According to the "no free lunch" theorem, no single algorithm can simultaneously satisfy the requirements of all optimization problems. To balance the economic efficiency and cleanliness of microgrid operation, this invention constructs a multi-objective microgrid scheduling model with operating costs and pollution control costs as objectives.

[0130] (8)

[0131] (9)

[0132] in, It is the overall cost of operating a microgrid; It is operating cost; It is the cost of environmental governance; the invention sets = =1, treating economic costs and environmental impacts as equals, and transforming the multi-objective problem into a single-objective problem for solution; It's the cost of fuel; It's the maintenance cost; It is the interaction cost between the microgrid and the main grid.

[0133] The fuel cost of a microgrid mainly includes the fuel consumption costs of the micro-turbines and fuel cells during their operating cycle, i.e.

[0134] (10)

[0135] The maintenance cost of a microgrid depends on the output power of each distributed generation device during its operating cycle, i.e.

[0136] (11)

[0137] in, , , , and These are the maintenance cost coefficients for wind turbines, photovoltaics, micro gas turbines, fuel cells, and energy storage batteries, respectively. and They are The discharge and charging power of the energy storage battery at all times.

[0138] Energy exchange between the microgrid and the main grid is achieved through electricity purchase and sale, with costs calculated as follows:

[0139] (12)

[0140] in, Is The electricity purchase price between the microgrid and the main grid at any given time; Is The electricity sales price between the microgrid and the main grid at any given time; It is the microgrid and the main grid in Interaction power at any given moment.

[0141] The environmental governance costs of microgrids mainly refer to the treatment costs of gaseous pollutants.

[0142] (13)

[0143] in, It is the first step in improving the quality of governance units. The costs required for this type of pollutant; It is the first The power generation unit for the first Emission coefficients of pollutants; The types of pollutants include CO2, SO2, and NO. x Three categories of pollutants; It is the first Each power generation unit is in Power generation at any given moment.

[0144] S3.2: Establishing Microgrid Operational Constraints: To ensure microgrid stability, the system's total power generation must achieve dynamic balance with its total power consumption, i.e.

[0145] (14)

[0146] in, Is the system in Load power at any given time; when When >0, the microgrid purchases electricity from the main grid. When <0, the microgrid sells electricity to the main grid; when When the value is greater than 0, the energy storage battery discharges. When the value is less than 0, the energy storage battery is charged.

[0147] The active power output constraints for the four types of power generation units—WT, PV, MT, and FC—are as follows:

[0148] (15)

[0149] (16)

[0150] in, and It is the first The upper and lower limits of the power output of each power generation unit; CG is a controllable power generation unit, namely MT and FC; and It refers to the downward and upward ramp rates of the controllable power generation unit.

[0151] The capacity and power limitations of the SB are as follows:

[0152] (17)

[0153] in, and These are the upper and lower limits of the SOC of SB, respectively; and These are the upper and lower limits of SB's output, respectively.

[0154] The power interaction limits between the microgrid and the main grid are as follows:

[0155] (18)

[0156] in, and These are the upper and lower limits of the transmission power of the tie line, respectively.

[0157] S4: Solving the microgrid optimal scheduling model using an improved red-beaked blue magpie optimization algorithm:

[0158] After constructing a microgrid optimization scheduling model that balances economic efficiency and environmental protection, the core of this step lies in using the proposed improved Red-beaked Blue Magpie optimization algorithm to perform global and efficient optimization on this complex scheduling model, which is high-dimensional, nonlinear, and contains multiple constraints. This step uses the overall cost objective function of the microgrid system as the fitness evaluation criterion for the algorithm, mapping the output decision variables of each distributed power source and energy storage unit to the solution space for optimization. Under the premise of strictly satisfying the system's dynamic power balance and the operational constraints of various physical devices, through intelligent iterative evolution, it ultimately captures and outputs the power scheduling strategies of each component that achieve the optimal trade-off between the total system operating cost and environmental governance cost.

[0159] To fully and clearly explain the core optimization mechanism of this invention, the basic principles of the algorithm will be explained first.

[0160] S4.1: Introduction to the Standard Red-billed Blue Magpie Optimization Algorithm: The Red-billed Blue Magpie Optimization Algorithm is a metaheuristic optimization algorithm based on swarm intelligence. This algorithm mimics the efficient cooperative hunting process of the Red-billed Blue Magpie in nature, and its mathematical model mainly includes three core behaviors: finding food, attacking prey, and storing food.

[0161] S4.1.1: Food-finding stage: Red-billed blue magpies usually move in small groups or clusters. Equation (19) simulates the exploration behavior of small groups in the solution space, and equation (20) simulates the wide-area search behavior of clusters.

[0162] (19)

[0163] (20)

[0164] in, This represents the current iteration number; This represents the current position of the individual. This represents the position of a random individual in the current iteration. This indicates the individual's position in the next iteration; and The numbers of red-billed blue magpies in small groups and large groups, respectively; For the first A randomly selected individual; The search agent is randomly selected for the current iteration; and All are random numbers within the range [0,1].

[0165] S4.1.2: Attacking prey stage: After discovering prey, the red-billed blue magpie chooses different types of prey depending on the size of the group. Equation (21) simulates the pecking behavior of a small group on small prey, and equation (22) simulates the siege behavior of a group on large prey.

[0166] (twenty one)

[0167] (twenty two)

[0168] (twenty three)

[0169] in, It is the optimal individual in the current iteration; The operator is nonlinearly decreasing from 1 to 0, as shown in formula (23); and To generate random numbers that follow a standard normal distribution; This represents the maximum number of iterations.

[0170] S4.1.3: Food storage stage: Red-billed blue magpies will store excess food in tree holes or other hidden places for future use, as shown in formula (24).

[0171] (twenty four)

[0172] in, and The first The fitness values ​​of the Red-billed Blue Magpie before and after the location update.

[0173] S4.2: Improved Red-billed Blue Magpie Optimization Algorithm:

[0174] S4.2.1: Introducing a composite chaotic mapping strategy in the initialization phase: RBMO uses a random initialization strategy for population initialization, but it lacks an effective traversal of the search space, making it difficult to guarantee that the initial individuals are evenly distributed in the solution space, affecting the convergence speed and optimization accuracy of the algorithm, and is not conducive to the algorithm's optimization. In order to obtain a more uniform initial population distribution and ensure the diversity of initial solutions, this invention proposes a composite chaotic mapping strategy for the initialization of the RBMO algorithm, the specific form of which is shown in formula (25).

[0175] (25)

[0176] in, For the first The location of each individual; and These are the upper and lower bounds of the solution space, respectively; This is a composite chaotic mapping strategy.

[0177] Composite chaotic mapping strategy uses control parameters The generation probabilities of Cubic and Sine mappings are adjusted to form chaotic sequences. Cubic mapping is shown in Equation (26), and Sine mapping is shown in Equation (27).

[0178] (26)

[0179] (27)

[0180] (28)

[0181] Among them, control parameters In this invention, the value is 0.5; In this invention, the control parameter for Cubic mapping is set to 2.595; In this invention, the control parameter for Sine mapping is set to 4; This is the current chaotic mapping value; This is the output after chaotic mapping.

[0182] S4.2.2: Introducing anti-predation strategies during the food-finding phase: RBMO simulates the foraging behavior of red-billed blue magpies at different group sizes based on formulas (19) and (20). However, this single exploration mode lacks an effective response to external environmental pressures when facing complex multimodal optimization problems, easily leading to premature convergence and difficulty in obtaining the global optimal solution. To overcome this limitation, this invention introduces anti-predation mechanisms found in nature.

[0183] Red-billed blue magpies are constantly threatened by predators such as hawks, falcons, and cats. When attacked, they adopt different avoidance strategies depending on the size of the population: as shown in formula (29), small groups of red-billed blue magpies use a random jumping strategy of scattering and fleeing to confuse predators; as shown in formula (30), large groups of red-billed blue magpies quickly gather in the safest known area, attempting to use their group advantage to resist danger. Subsequently, the fitness of the conventional foraging location and the improved location is compared through a greedy selection mechanism to select the solution that is more conducive to survival, so as to ensure the efficiency and robustness of the algorithm's optimization.

[0184] (29)

[0185] (30)

[0186] (31)

[0187] (32)

[0188] in, The escape location of an individual after adopting an anti-predation strategy; as well as The escape search boundary dynamically shrinks with the number of iterations; A positive integer randomly generated by the chaotic mapping strategy; This is the individual's usual foraging location.

[0189] S4.2.3: Introducing a multi-stage refined foraging strategy in the prey attack phase: As shown in formulas (21) and (22), RBMO selects the optimal individual as the center and attacks prey within its neighborhood. However, this strategy, which relies excessively on the guidance of the optimal individual, will limit the local development capability of the algorithm to some extent. To this end, this invention introduces a multi-stage refined foraging strategy that combines Brownian motion and Levy flight.

[0190] This invention divides the development process into three stages: In the early stage of optimization, the population moves towards the food-dense area using Brownian motion with a stable step length, and its position is updated as shown in formula (33); In the middle stage of optimization, in order to balance the exploration and development capabilities of the algorithm, the population is divided into two: As shown in formula (34), the first half of the individuals use the alternating long and short steps of Levy's flight to jump and scan the periphery; the second half of the individuals use Brownian motion to refine their hunting while the foraging radius is constantly shrinking; In the later stage of optimization, as shown in formula (35), the red-billed blue magpie concentrates near the optimal food source and quickly attacks the prey using the excellent development capability of Levy's flight.

[0191] (33)

[0192] (34)

[0193] (35)

[0194] in, The Brownian motion vector follows a standard normal distribution; For Levi's flight vector; To control the constant coefficient of the step size, it is set to 0.5 in this invention.

[0195] S4.2.4: Introducing a simulated annealing strategy in the food storage stage: As shown in formula (24), in the food storage stage of RBMO, the greedy selection mechanism is too conservative in position updates, lacks fault tolerance, and is not conducive to exploring new potential solutions. Therefore, this invention introduces the idea of ​​simulated annealing to construct a new food storage mechanism.

[0196] As shown in formula (36), the algorithm constructs a temperature parameter that decays nonlinearly with iteration to simulate the natural survival pressure faced by the red-billed blue magpie in different foraging seasons; as shown in formula (37), when the red-billed blue magpie explores a new location with poor food abundance, it no longer directly abandons it, but simulates the trial-and-error instinct of birds, and according to the Metropolis criterion, it probabilistically... They are accepted as backup food storage sites, providing impetus for the population to escape local optima.

[0197] (36)

[0198]

[0199] in, For temperature parameters; This represents the average fitness of the current population. The probability of accepting a new solution; and These are the old and new food storage sites for the flock of birds.

[0200] Example:

[0201] Algorithm performance testing:

[0202] Experimental Design:

[0203] All experiments in this invention were programmed using MATLAB R2023a software, and the experimental environment was set up on a computer equipped with an Intel Core i5-10400 CPU (2.90GHz), 16.0GB of memory, and a Windows 11 64-bit operating system.

[0204] To verify the effectiveness of the proposed algorithm, comparative experiments were conducted using RBMO, PSO, SO, DBO, and HO algorithms. To ensure fairness, the population size for all algorithms was set to 50, and the maximum number of iterations was set to 500. The specific parameter settings for each algorithm are shown in Table 1 (where IRBMO is the improved Red-billed Blue Magpie optimization algorithm, RBMO is the Red-billed Blue Magpie optimization algorithm, PSO is the Particle Swarm Optimization algorithm, SO is the Snake optimization algorithm, DBO is the Dung Beetle optimization algorithm, and HO is the Hippopotamus optimization algorithm). Each algorithm was run independently 30 times on each test function, and the corresponding mean and standard deviation were recorded.

[0205] Table 1: Algorithm Parameter Settings

[0206]

[0207] The experiment used the CEC2019 benchmark set, which contains 10 complex single-objective test functions. Among them, F1~F3 are unimodal functions; F4~F10 are multimodal functions. This invention specifically selects F2 and F3 to verify the convergence accuracy of the algorithm, and selects F6 and F9 to verify the algorithm's ability to escape local optima.

[0208] Performance analysis of test functions:

[0209] Table 2 lists the statistical results of each algorithm on four benchmark functions. The results show that IRBMO achieved the best average convergence accuracy on all test functions. Particularly on the F2 function, IRBMO's solution accuracy is two orders of magnitude higher than other algorithms, demonstrating its excellent search capability. Except for the F2 function, IRBMO maintains the lowest standard deviation, indicating strong optimization stability when dealing with complex problems.

[0210] Table 2: Comparison of Benchmark Function Optimization Results

[0211]

[0212] Depend on Figure 2 As can be seen from the convergence curve, in the early stage of iteration, IRBMO decreases faster than the comparison algorithm, showing strong convergence ability; in the middle and late stages of iteration, even when facing multi-peak functions, IRBMO can still effectively escape local extrema and continue to approach a better solution.

[0213] Overall, IRBMO demonstrates significant advantages over the comparison algorithms, validating the effectiveness of the proposed improvement strategy and showcasing its application potential in solving complex engineering optimization problems.

[0214] Case Study:

[0215] Figure 3 This paper demonstrates the optimized scheduling process of a microgrid. The microgrid encompasses various distributed power sources, and its scheduling strategy considers time-of-use pricing mechanisms and seasonal variations in renewable energy output. This invention employs IRBMO (Inter-Rich Power Registry Model) for solution, achieving optimal overall cost for the microgrid while satisfying system operational constraints.

[0216] Experimental data:

[0217] To verify the effectiveness of the proposed model and algorithm, this invention selects a specific region as the research case area. This region possesses abundant wind and solar energy resources, as well as a solid industrial base, making it an important area for conducting research on clean energy integration and regional energy system optimization. Figure 4 Provides data on wind and solar power output, load, and temperature for typical days in the region during summer and winter.

[0218] Energy exchange between microgrids and the main grid is achieved through electricity procurement and sales. To guide users to rationally schedule their electricity consumption and ensure a balance between electricity supply and demand, a time-of-use pricing strategy is often adopted, charging fees based on the average marginal cost of system operation. Figure 5 This refers to the time-of-use electricity price for this region.

[0219] Table 3 lists the costs of pollutant treatment and their correlation coefficients, while Tables 4 and 5 provide parameters for distributed power sources and energy storage batteries.

[0220] Table 3: Pollutant Treatment Costs and Correlation Coefficients

[0221]

[0222] Table 4: Distributed Power Generation Parameters

[0223]

[0224] Table 5: Energy Storage Battery Parameters

[0225]

[0226] Simulation Result Analysis:

[0227] This invention designs a typical scenario to verify the effectiveness of IRBMO in solving the optimal scheduling of grid-connected microgrids: the microgrid consists of PV, WT, MT, FC and SB.

[0228] To evaluate the solution performance of different optimization algorithms, IRBMO was compared with the five algorithms mentioned above. The population size of each algorithm was 50, the maximum number of iterations was 200, each scenario was run independently 20 times, and the optimal value was selected as the scheduling basis.

[0229] Depend on Figure 6 As can be seen from the cost convergence curve, the IRBMO algorithm exhibits a relatively fast convergence speed in the early stage of iteration and effectively avoids premature convergence stagnation in the later stage of iteration until it stably converges to the global optimum.

[0230] Figure 7 This demonstrates the dispatch strategy for microgrid equipment output during 24 hours under basic operating conditions. The line graph shows that during the day, solar photovoltaic (PV) output is abundant, and the microgrid prioritizes the full absorption of renewable energy, while MT (Metal Transfer Mode) and FC (Fuel Concentrator) outputs remain at lower levels, effectively reducing fuel consumption and pollutant emissions. As PV output disappears at night and the evening peak arrives, power supply pressure increases, and MT and FC respond quickly, increasing unit output to ensure power balance. The bar chart shows that SB (Standard Grid Controller) implements an arbitrage strategy based on time-of-use pricing throughout the dispatch cycle. It charges during off-peak hours in the early morning and during peak PV generation at noon; while discharging during peak pricing periods. The main grid supplements power shortages when supply is insufficient and accepts surplus electricity into the grid when renewable energy output is excessive.

[0231] The core of microgrid EED lies in balancing operating and environmental governance costs. In the optimization process, unilaterally pursuing a single performance indicator often leads to the degradation of another objective. For example, while DBO and similar algorithms achieve the lowest operating costs in some scenarios, they sacrifice environmental benefits; whereas IRBMO can effectively achieve the optimal trade-off between economic efficiency and environmental friendliness. As shown in Table 6, the IRBMO algorithm achieved the lowest overall cost in both summer and winter, at RMB 1385.80 and RMB 1358.55, respectively.

[0232] Table 6: Simulation results of each algorithm

[0233]

[0234] Although the present invention has been described above with reference to embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, as long as there is no structural conflict, the features in the disclosed embodiments can be combined with each other in any manner. The lack of an exhaustive description of these combinations in this specification is merely for the sake of brevity and resource conservation. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A microgrid optimal scheduling method based on an improved red-billed blue magpie optimization algorithm, characterized in that, Includes the following steps: S1: Construct a microgrid system architecture: integrate photovoltaics, wind turbines, micro gas turbines, fuel cells, and energy storage batteries. The photovoltaics, wind turbines, micro gas turbines, fuel cells, and energy storage batteries are connected to the microgrid bus through corresponding converters to achieve stable conversion and transmission of electrical energy. S2: Establish mathematical and physical models for microgrids: establish photovoltaic models, wind turbine models, micro gas turbine models, fuel cell models, and energy storage battery models; S3: Constructing a microgrid optimization scheduling model that balances economic efficiency and environmental protection: including constructing a comprehensive cost objective function and establishing microgrid operation constraints; S4: An improved Red-beaked Blue Magpie optimization algorithm is used to solve the microgrid optimal scheduling model: The comprehensive cost objective function of the microgrid system is used as the fitness evaluation standard of the algorithm, and the output decision variables of each distributed power source and energy storage unit are mapped to the solution space for the algorithm to find the best solution. Under the premise of strictly satisfying the dynamic power balance of the system and the operating constraints of various physical equipment, through intelligent iterative evolution, the power scheduling strategy of each component is finally captured and output to achieve the optimal trade-off between the total operating cost and the environmental governance cost of the system.

2. The microgrid optimization scheduling method based on the improved red-billed blue magpie optimization algorithm according to claim 1, characterized in that, The photovoltaic model in S2 is established as follows: The power output model of photovoltaics is as follows: （1） in, It is the operating temperature; It is the ambient temperature; It is the intensity of light; This is the nominal operating temperature; It refers to output power; It is the maximum output power of PV under standard test conditions; It is the light intensity under standard test conditions; It is the power temperature coefficient; For reference temperature; The method for establishing the wind turbine model is as follows: The power output model of the wind turbine is as follows: in, It refers to output power; It is the rated power; and These are the actual wind speed and the rated wind speed at the wheel hub, respectively. and These are the cut-in wind speed and the cut-out wind speed, respectively. The method for establishing the micro gas turbine model is as follows: The relationship between the operating efficiency and output power of the micro gas turbine is as follows: in, It is the unit efficiency of MT per unit time; It is the active power output; It's the cost of fuel; It's the price of natural gas; It has the low calorific value of natural gas; The method for establishing the fuel cell model is as follows: The relationship between the fuel cost and output power of the fuel cell is as follows: in, It's the cost of fuel; It is the unit efficiency of FC per unit time; It is the active power output; The method for establishing the energy storage battery model is as follows: During the charging and discharging process, the battery model equation characterizing the state of charge of the energy storage battery is as follows: in, and These are energy storage batteries in and The state of charge at any given moment; and These are the charging efficiency and discharging efficiency of the energy storage battery, respectively. It is an energy storage battery in Output power at any given moment; It refers to the capacity of the energy storage battery; It refers to a unit of time.

3. The microgrid optimization scheduling method based on the improved red-billed blue magpie optimization algorithm according to claim 1, characterized in that, The method for constructing the comprehensive cost objective function in S3 is as follows: A multi-objective microgrid scheduling model is constructed with operating costs and pollution treatment costs as objectives. in, It is the overall cost of operating a microgrid; It is operating cost; It is the cost of environmental governance; setting = =1, treating economic costs and environmental impacts as equals, and transforming the multi-objective problem into a single-objective problem for solution; It's the cost of fuel; It's the maintenance cost; It is the interaction cost between the microgrid and the main grid; The fuel cost of a microgrid mainly includes the fuel consumption costs of the micro-turbines and fuel cells during their operating cycle, namely: The maintenance cost of a microgrid depends on the output power of each distributed generation device during its operating cycle, i.e.: in, , , , and These are the maintenance cost coefficients for wind turbines, photovoltaics, micro gas turbines, fuel cells, and energy storage batteries, respectively. and They are The discharge and charging power of the energy storage battery at all times; Energy exchange between the microgrid and the main grid is achieved through electricity purchase and sale, with costs calculated as follows: in, Is The electricity purchase price between the microgrid and the main grid at any given time; Is The electricity sales price between the microgrid and the main grid at any given time; It is the microgrid and the main grid in Interaction power at any given moment; The environmental governance costs of microgrids mainly involve the treatment costs of gaseous pollutants. in, It is the first step in improving the quality of governance units. The costs required for this type of pollutant; It is the first The power generation unit for the first Emission coefficients of pollutants; It refers to the types of pollutants; It is the first Each power generation unit is in Power generation at any given moment.

4. A microgrid optimization scheduling method based on an improved red-billed blue magpie optimization algorithm according to claim 1, characterized in that, The specific method for establishing microgrid operation constraints in S3 is as follows: To ensure microgrid stability, the total power generation of the system must achieve dynamic balance with the total power consumption, that is: in, Is the system in Load power at any given time; when When >0, the microgrid purchases electricity from the main grid. When <0, the microgrid sells electricity to the main grid; when When the value is >0, the energy storage battery discharges. When the value is less than 0, the energy storage battery is charged; The active power output constraints for four types of power generation units—wind turbines, photovoltaics, micro gas turbines, and fuel cells—are as follows: in, and It is the first The upper and lower limits of the power of each power generation unit; CG is a controllable power generation unit, namely a micro gas turbine and a fuel cell; and It refers to the downward and upward ramp rates of the controllable power generation unit; The capacity and power limitations of energy storage batteries are as follows: in, and These are the upper and lower limits of the SOC of SB, respectively; and These are the upper and lower limits of the energy storage battery's output; The power exchange limits between the microgrid and the main grid are as follows: in, and These are the upper and lower limits of the transmission power of the tie line, respectively.

5. A microgrid optimization scheduling method based on an improved red-billed blue magpie optimization algorithm according to claim 1, characterized in that, The mathematical model of the standard red-billed blue magpie optimization algorithm includes three core behaviors: finding food, attacking prey, and storing food. The food-finding phase is simulated by the following equation (19) to model the exploration behavior of a small group in the solution space, and by equation (20) to model the wide-area search behavior of the cluster: in, This represents the current iteration number; This represents the current position of the individual. This represents the position of a random individual in the current iteration. This indicates the individual's position in the next iteration; and The numbers of red-billed blue magpies in small groups and large groups, respectively; For the first A randomly selected individual; The search agent is randomly selected for the current iteration; and All are random numbers within the range [0,1]. The prey-attacking phase is simulated by the following equation (21) to represent the pecking behavior of a small group on small prey, and by the following equation (22) to represent the siege behavior of a large group on large prey: in, It is the optimal individual in the current iteration; The operator is nonlinearly decreasing from 1 to 0, as shown in formula (23); and To generate random numbers that follow a standard normal distribution; This represents the maximum number of iterations. During the food storage phase, the red-billed blue magpie will store excess food in tree holes or other hidden locations for future use, as shown in formula (24): in, and The first The fitness values ​​of the Red-billed Blue Magpie before and after the location update.

6. A microgrid optimization scheduling method based on an improved red-billed blue magpie optimization algorithm according to claim 5, characterized in that, The improved red-billed blue magpie optimization algorithm includes introducing a composite chaotic mapping strategy in the initialization stage, an anti-predation strategy in the food-finding stage, a multi-stage fine foraging strategy in the prey-attacking stage, and a simulated annealing strategy in the food storage stage.

7. A microgrid optimization scheduling method based on an improved red-billed blue magpie optimization algorithm according to claim 6, characterized in that, The specific form of the composite chaotic mapping strategy introduced in the initialization phase is shown in formula (25): in, For the first The location of each individual; and These are the upper and lower bounds of the solution space, respectively; A composite chaotic mapping strategy; Composite chaotic mapping strategy uses control parameters The generation probabilities of Cubic and Sine mappings are adjusted to form chaotic sequences. Cubic mapping is shown in Equation (26), and Sine mapping is shown in Equation (27). Among them, control parameters The value is 0.5; The control parameter for Cubic mapping takes the value 2.595; The control parameter for Sine mapping takes the value 4; This is the current chaotic mapping value; This is the output after chaotic mapping.

8. A microgrid optimization scheduling method based on an improved red-billed blue magpie optimization algorithm according to claim 6, characterized in that, The anti-predation strategy introduced during the food search phase is as follows: Red-billed blue magpies are constantly threatened by natural enemies, and when attacked, they will adopt different avoidance methods according to the size of the population: as shown in formula (29), small groups of red-billed blue magpies adopt a random jumping strategy of scattering and fleeing to confuse predators; as shown in formula (30), clusters of red-billed blue magpies quickly gather in the safest known area, trying to use the group advantage to resist danger. Subsequently, a greedy selection mechanism is used to compare the fitness of conventional foraging locations with the improved locations, and a solution that is more conducive to survival is selected to ensure the efficiency and robustness of the algorithm's optimization. in, The escape location of an individual after adopting an anti-predation strategy; as well as The escape search boundary dynamically shrinks with the number of iterations; A positive integer randomly generated by the chaotic mapping strategy; This is the individual's usual foraging location.

9. A microgrid optimization scheduling method based on an improved red-billed blue magpie optimization algorithm according to claim 6, characterized in that, The multi-stage refined foraging strategy introduced in the prey attack stage is a multi-stage refined foraging strategy that combines Brownian motion and Levy flight. The specific development process is divided into three stages: In the early stage of optimization, the population adopts Brownian motion with a stable step length to move towards the food-dense area, and its position update is shown in formula (33); In the middle stage of optimization, in order to balance the exploration and development capabilities of the algorithm, the population is divided into two: As shown in formula (34), the first half of the individuals use the alternating long and short steps of Levy flight to jump and scan the periphery; the second half of the individuals use Brownian motion to refine their hunting while the foraging radius is constantly shrinking; In the later stage of optimization, as shown in formula (35), the excellent development capability of Levy flight is used, and the red-billed blue magpie concentrates near the optimal food source to quickly attack the prey. in, The Brownian motion vector follows a standard normal distribution; For Levi's flight vector; The constant coefficient for controlling the step size is set to 0.

5.

10. A microgrid optimization scheduling method based on an improved red-billed blue magpie optimization algorithm according to claim 6, characterized in that, The specific method for introducing a simulated annealing strategy during the food storage stage is as follows: as shown in formula (36), a temperature parameter that decays nonlinearly with iteration is constructed to simulate the natural survival pressure faced by the red-billed blue magpie in different foraging seasons; as shown in formula (37), when the red-billed blue magpie explores a new location with poor food abundance, it no longer directly discards it, but simulates the trial-and-error instinct of birds and uses the Metropolis criterion to determine the annealing parameters based on probability. They are accepted as backup food storage sites, providing impetus for the population to escape local optima; in, For temperature parameters; This represents the average fitness of the current population. The probability of accepting a new solution; and These are the old and new food storage sites for the flock of birds.

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