Integrated energy microgrid dynamic scheduling method based on artificial intelligence

Abstract

The invention belongs to the field of energy scheduling, and particularly relates to a comprehensive energy microgrid dynamic scheduling method based on artificial intelligence, and the method comprises the steps: locally optimizing a commutation angle, optimizing a distributed energy unit start-stop strategy, searching an optimal position of a unit, and globally optimizing a microgrid structure. According to the scheme, the ant colony optimization algorithm is adopted to reduce torque ripple, improve unit efficiency, avoid current regulation delay, improve dynamic response speed, reduce power loss and improve a voltage curve in an energy network system; an energy micro-grid optimization method combining a genetic algorithm and a particle swarm optimization algorithm is creatively provided, the particle swarm optimization algorithm is responsible for global search, the genetic algorithm is responsible for local optimization, weight coefficients of the particle swarm optimization algorithm and the genetic algorithm are adjusted through fuzzy logic control, particles are guided to move to an optimal solution, the efficiency of the algorithm for searching the layout of a distributed energy unit is improved, and the optimization efficiency of the algorithm is improved. And the calculation complexity is reduced.

Description

Technical Field

[0001] The present invention relates to the field of energy dispatching, and specifically refers to a dynamic dispatching method for integrated energy microgrids based on artificial intelligence. Background Art

[0002] Improving energy efficiency is an important way to save energy and reduce emissions. The dynamic scheduling method of integrated energy microgrid based on artificial intelligence refers to a method that uses artificial intelligence technology to optimize the equipment configuration of energy units and the topological structure of the energy network. Among the existing approximate schemes, for example, the power optimization scheduling method, device and equipment in the scenario of new energy grid connection in CN119010022B, this scheme, aimed at the technical problem that the existing technology ignores the defects of differences between units, this scheme analyzes the frequency domain characteristics of the electric power data of each wind turbine in the wind power array, calculates the influence weight, measures the power volatility of the wind farm, and combines neural networks to predict future output power, thereby achieving the technical effect of improving the stability of the power grid and reducing the risk of power grid fluctuations. However, there is a technical problem that it only relies on the frequency domain analysis of the electric power vector, does not consider the spatial distribution and topological structure factors between wind turbines, and does not consider the startup response time and climbing rate of the energy unit, resulting in delayed energy scheduling; In addition, for example, CN118040789B discloses a method for dispatching a group of new energy microgrids taking into account stability constraints. This method aims to solve the technical problem of low-frequency oscillations in the prior art due to a high proportion of new energy access in the microgrid. The minimum damping ratio is calculated through the Lyapunov energy function, and the minimum damping ratio is used as a stability constraint. The multi-time domain rolling optimization dispatching scheme achieves the technical effect of preventing the problem of low-frequency oscillations in the microgrid due to a high proportion of new energy access, and solving the negative effects of power fluctuations, line overloads, voltage instability, and even system crashes caused by low-frequency oscillations. However, when multiple microgrids are interconnected through interconnection lines, there is a coupling effect of multiple sub-microgrids, and the dynamic response is more complicated, resulting in larger errors. In addition, the state variables, power constraints, and damping constraints of each sub-microgrid will increase the difficulty of multi-dimensional optimization, and the continuous collection of multiple time scales in the future will face the technical problem of high computational complexity. Summary of the invention

[0003] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides a dynamic scheduling method for integrated energy microgrids based on artificial intelligence. In view of the technical problems that the prior art only relies on the frequency domain analysis of the electric power vector, does not consider the spatial distribution and topological structure factors between wind turbines, does not consider the starting response time and climbing rate of the energy unit, and leads to delayed energy scheduling, this scheme adopts the ant colony optimization algorithm to reduce torque pulsation, improve unit efficiency, avoid current regulation delay, increase dynamic response speed, reduce power loss and improve the voltage curve in the energy grid system, optimize the start-stop strategy of distributed energy units, and improve the reliability and safety of the network; when multiple microgrids are interconnected through interconnection lines, there are multiple sub-microgrids The coupling effect makes the dynamic response more complicated, resulting in larger errors. In addition, the state variables, power constraints and damping constraints of each sub-microgrid will increase the difficulty of multi-dimensional optimization. Continuously collecting multiple time scales in the future will face the technical problem of high computational complexity. This scheme creatively proposes an energy microgrid optimization method that combines a genetic algorithm with a particle swarm optimization algorithm. The particle swarm optimization algorithm is responsible for global search, and the genetic algorithm is responsible for local optimization. It not only avoids premature convergence of the particle swarm optimization algorithm, but also prevents the genetic algorithm from excessively perturbing the solution space. The weight coefficients of the two are adjusted through fuzzy logic control to guide the particles to move to the optimal solution, achieve more stable optimization, improve the efficiency of the algorithm in searching for the layout of distributed energy units, and reduce computational complexity.

[0004] The technical solution adopted by the present invention is as follows: The present invention provides an integrated energy microgrid dynamic scheduling method based on artificial intelligence, the method comprising the following steps: Step S1: Locally optimize the commutation angle to maintain the stability of the commutation process of the distributed energy unit. Specifically, in the integrated energy microgrid, an ant colony optimization method is used to offline search and use a lookup table to store the optimal switching angle and angle change speed of the distributed energy unit. The switching angle includes a turn-on angle and a turn-off angle, and the combination of the switching angle and the angle change speed is recorded as a candidate solution; Step S2: Optimizing the start-stop strategy of the distributed energy unit, specifically, during the operation of the distributed energy unit, searching the lookup table online, using direct instantaneous torque control technology, optimizing the switch angle and angle change speed of the distributed energy unit, adjusting the start-stop strategy of the distributed energy unit, and optimizing the current waveform; Step S3: searching for the optimal position of the unit, specifically, using a method combining a genetic algorithm and a particle swarm optimization algorithm to cyclically optimize the position and network topology of the distributed energy units in the integrated energy microgrid to obtain the optimal position and network topology; Step S4: Globally optimize the microgrid structure and optimize the integrated energy microgrid according to the optimal location and network topology.

[0005] Furthermore, in step S1, the commutation angle is locally optimized, specifically comprising the following steps: Step S11: Determine the ant colony optimization target, specifically, calculate the objective function of the ant colony optimization method, calculate the integral average value of the instantaneous torque within an electrical cycle to obtain the average torque, record the difference between the maximum torque value and the minimum torque value within the same electrical cycle as the torque ripple, assign a weight corresponding to the torque ripple, and subtract the torque ripple with the weight assigned from the average torque to obtain the torque quality. The objective function calculation formula used is as follows: ; In the formula, represents the objective function of the ant colony optimization method, represents the weight corresponding to the torque ripple, Represents torque quality, represents the reference torque quality used for normalization, Indicates the efficiency of the unit in converting electrical energy into mechanical energy. represents the baseline efficiency; Step S12: determining the constraint conditions of the ant colony optimization method to prevent the distributed energy unit from generating negative torque; Step S13: Storing the optimal solution, specifically, using a lookup table to store the candidate solution after each iteration update of the ant colony optimization method, and finding the optimal candidate solution by maximizing the torque quality and the unit efficiency, and storing it in the lookup table.

[0006] Furthermore, in step S12, the constraint conditions of the ant colony optimization method are determined, which specifically includes the following steps: Step S121: Angle constraint, specifically, presetting a minimum conduction angle threshold to ensure that the difference between the turn-off angle and the turn-on angle is greater than the minimum conduction angle threshold; Step S122: region division, specifically, dividing the inductance positive slope region and the non-inductance positive slope region according to the inductance change rate, with time as the horizontal coordinate and inductance as the vertical coordinate, wherein the inductance positive slope region is a region where the inductance increases with the change of the rotor position; Step S123: Inductance constraint, specifically, setting the turn-off angle before the end of the inductance positive slope region, that is, turning off the current before the inductance reaches the maximum value, and turning on the current after the inductance reaches the maximum value.

[0007] Furthermore, in step S3, searching for the optimal position of the unit specifically includes the following steps: Step S31: Initializing the population, specifically, recording the distributed energy units as nodes, initializing the particle population of the particle swarm optimization algorithm, and the particles represent potential node positions and topological relationships; Step S32: setting constraints in the search process; Step S33: Initialize genetic weight parameters; Step S34: global search, specifically, using a particle swarm optimization algorithm to calculate fitness and perform a global search on the particle population; Step S35: local optimization, which is used to perform local optimization on the particle population using a genetic algorithm. Specifically, according to the genetic weight parameter, particles are randomly selected to perform crossover and mutation operations. The crossover operation refers to selecting two particles to exchange information with each other to generate new particles, and the mutation operation refers to randomly changing the particle information. Step S36: adjusting the influence of the genetic algorithm on the search process; Step S37: population merging, specifically, merging the new particle populations generated by the particle swarm optimization algorithm and the genetic algorithm, updating the particle population, and selecting particles according to fitness as the next generation of particle population.

[0008] Furthermore, in step S32, the constraint conditions in the search process are set, which specifically includes the following steps: Step S321: power constraint, specifically, ensuring that the active power output of any node must be equal to the active power demand of the node plus the sum of the active power transmitted through all lines of the node, and ensuring that the reactive power output of any node must be equal to the reactive power demand of the node plus the sum of the reactive power transmitted through all lines of the node; Step S322: Capacity limitation, specifically, presetting a voltage specified range and a power capacity limitation to ensure that the voltage is within the voltage specified range and that the active power output and reactive power output of each node do not exceed the power capacity limitation.

[0009] Furthermore, in step S34, the global search specifically includes the following steps: Step S341: updating the speed and position of the particle according to the historical best position of each particle and the best position of the group; Step S342: Calculate the fitness of each particle using the following formula: ; In the formula, represents fitness, represents the total number of nodes in the integrated energy microgrid, and Respectively represent nodes and The active power at and Respectively represent nodes and The reactive power at and Represents the network correlation coefficient.

[0010] Furthermore, in step S36, the influence of the genetic algorithm on the search process is adjusted, which specifically includes the following steps: Step S361: Calculate the contribution of the genetic algorithm to measure the optimization effect of the genetic algorithm. Specifically, the cumulative sum of the gain degree brought by the genetic algorithm in the most recent random round cycle is divided by the total number of particles affected by the genetic algorithm in the most recent random round cycle to obtain the optimization effect of the genetic algorithm. The cumulative sum of the gain degree refers to the fitness difference of the particles affected by the genetic algorithm and whose fitness is improved; Step S362: Calculate the contribution of the particle swarm optimization algorithm, repeat step S361, measure the optimization effect of the particle swarm optimization algorithm, and obtain the particle swarm optimization effect; Step S363: rule learning, specifically, using a fuzzy logic reasoning system to dynamically adjust the search process. If the genetic algorithm optimization effect is good, it means that the genetic algorithm contributes more to the optimization at the current stage, and the role of the genetic algorithm should be enhanced. If the particle swarm optimization effect is good, it means that the particle swarm optimization algorithm contributes more, and the role of the genetic algorithm should be reduced, and the genetic weight parameter is updated. The genetic weight parameter update formula is as follows: ; In the formula, represents the genetic weight parameter, represents the fuzzy logic reasoning system, It represents the optimization effect of the genetic algorithm.

[0011] The present invention provides a dynamic scheduling method for integrated energy microgrid based on artificial intelligence. The beneficial effects achieved by the present invention using the above scheme are as follows: (1) In view of the technical problem that the existing technology only relies on the frequency domain analysis of the electric power vector, does not consider the spatial distribution and topological structure factors between wind turbines, does not consider the start-up response time and ramp rate of the energy unit, and leads to the technical problem of energy scheduling lag, this solution adopts the ant colony optimization algorithm to reduce torque pulsation, improve unit efficiency, avoid current regulation delay, increase dynamic response speed, reduce power loss and improve the voltage curve in the energy grid system, optimize the start-stop strategy of distributed energy units, and improve the reliability and safety of the network; (2) When multiple microgrids are interconnected through interconnection lines, there is a coupling effect of multiple sub-microgrids, and the dynamic response is more complex, resulting in large errors. In addition, the state variables, power constraints and damping constraints of each sub-microgrid will increase the difficulty of multi-dimensional optimization. Continuously collecting multiple time scales in the future will face the technical problem of high computational complexity. This scheme creatively proposes an energy microgrid optimization method that combines a genetic algorithm with a particle swarm optimization algorithm. The particle swarm optimization algorithm is responsible for global search, and the genetic algorithm is responsible for local optimization. This avoids premature convergence of the particle swarm optimization algorithm and prevents the genetic algorithm from excessively perturbing the solution space. The weight coefficients of the two are adjusted through fuzzy logic control to guide the particles to move towards the optimal solution, achieving more stable optimization, improving the efficiency of the algorithm in searching for the layout of distributed energy units, and reducing computational complexity. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 A schematic diagram of the process of the dynamic scheduling method of the integrated energy microgrid based on artificial intelligence provided by the present invention; Figure 2 is a schematic diagram of step S1; Figure 3 is a schematic diagram of step S12; Figure 4 is a schematic diagram of step S3; Figure 5 is a schematic diagram of step S32; Figure 6 is a schematic diagram of step S34; Figure 7 is a schematic diagram of step S36.

[0013] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0014] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0015] Example 1, see Figures 1 to 7 The present invention provides a method for dynamic scheduling of integrated energy microgrids based on artificial intelligence, the method comprising the following steps: Step S1: Locally optimize the commutation angle to maintain the stability of the commutation process of the distributed energy unit. Specifically, in the integrated energy microgrid, an ant colony optimization method is used to offline search and use a lookup table to store the optimal switching angle and angle change speed of the distributed energy unit. The switching angle includes a turn-on angle and a turn-off angle, and the combination of the switching angle and the angle change speed is recorded as a candidate solution; Step S2: Optimizing the start-stop strategy of the distributed energy unit, specifically, during the operation of the distributed energy unit, searching the lookup table online, using direct instantaneous torque control technology, optimizing the switch angle and angle change speed of the distributed energy unit, adjusting the start-stop strategy of the distributed energy unit, and optimizing the current waveform; Step S3: searching for the optimal position of the unit, specifically, using a method combining a genetic algorithm and a particle swarm optimization algorithm to cyclically optimize the position and network topology of the distributed energy units in the integrated energy microgrid to obtain the optimal position and network topology; Step S4: Globally optimize the microgrid structure and optimize the integrated energy microgrid according to the optimal location and network topology.

[0016] Example 2, see Figure 1 to Figure 2 This embodiment is based on the above embodiment. In step S1, the commutation angle is locally optimized, specifically including the following steps: Step S11: Determine the ant colony optimization target, specifically, calculate the objective function of the ant colony optimization method, calculate the integral average value of the instantaneous torque within an electrical cycle to obtain the average torque, record the difference between the maximum torque value and the minimum torque value within the same electrical cycle as the torque ripple, assign a weight corresponding to the torque ripple, and subtract the torque ripple with the weight assigned from the average torque to obtain the torque quality. The objective function calculation formula used is as follows: ; In the formula, represents the objective function of the ant colony optimization method, represents the weight corresponding to the torque ripple, Represents torque quality, represents the reference torque quality used for normalization, Indicates the efficiency of the unit in converting electrical energy into mechanical energy. represents the baseline efficiency; Step S12: determining the constraint conditions of the ant colony optimization method to prevent the distributed energy unit from generating negative torque; Step S13: Storing the optimal solution, specifically, using a lookup table to store the candidate solution after each iteration update of the ant colony optimization method, and finding the optimal candidate solution by maximizing the torque quality and the unit efficiency, and storing it in the lookup table.

[0017] Example 3, see Figures 1 to 3 Based on the above embodiment, in step S12, the constraint conditions of the ant colony optimization method are determined, which specifically includes the following steps: Step S121: Angle constraint, specifically, presetting a minimum conduction angle threshold to ensure that the difference between the turn-off angle and the turn-on angle is greater than the minimum conduction angle threshold; Step S122: region division, specifically, dividing the inductance positive slope region and the non-inductance positive slope region according to the inductance change rate, with time as the horizontal coordinate and inductance as the vertical coordinate, wherein the inductance positive slope region is a region where the inductance increases with the change of the rotor position; Step S123: Inductance constraint, specifically, setting the turn-off angle before the end of the inductance positive slope region, that is, turning off the current before the inductance reaches the maximum value, and turning on the current after the inductance reaches the maximum value.

[0018] Example 4, see Figures 1 to 4 Based on the above embodiment, this embodiment searches for the optimal position of the unit in step S3, specifically including the following steps: Step S31: Initializing the population, specifically, recording the distributed energy units as nodes, initializing the particle population of the particle swarm optimization algorithm, and the particles represent potential node positions and topological relationships; Step S32: setting constraints in the search process; Step S33: Initialize genetic weight parameters; Step S34: global search, specifically, using a particle swarm optimization algorithm to calculate fitness and perform a global search on the particle population; Step S35: local optimization, which is used to perform local optimization on the particle population using a genetic algorithm. Specifically, according to the genetic weight parameter, particles are randomly selected to perform crossover and mutation operations. The crossover operation refers to selecting two particles to exchange information with each other to generate new particles, and the mutation operation refers to randomly changing the particle information. Step S36: adjusting the influence of the genetic algorithm on the search process; Step S37: population merging, specifically, merging the new particle populations generated by the particle swarm optimization algorithm and the genetic algorithm, updating the particle population, and selecting particles according to fitness as the next generation of particle population.

[0019] Example 5, see Figures 1 to 5 Based on the above embodiment, in step S32, the constraint conditions in the search process are set, which specifically includes the following steps: Step S321: power constraint, specifically, ensuring that the active power output of any node must be equal to the active power demand of the node plus the sum of the active power transmitted through all lines of the node, and ensuring that the reactive power output of any node must be equal to the reactive power demand of the node plus the sum of the reactive power transmitted through all lines of the node; Step S322: Capacity limitation, specifically, presetting a voltage specified range and a power capacity limitation to ensure that the voltage is within the voltage specified range and that the active power output and reactive power output of each node do not exceed the power capacity limitation.

[0020] Example 6, see Figures 1 to 6 This embodiment is based on the above embodiment. In step S34, the global search specifically includes the following steps: Step S341: updating the speed and position of the particle according to the historical best position of each particle and the best position of the group; Step S342: Calculate the fitness of each particle using the following formula: ; In the formula, represents fitness, represents the total number of nodes in the integrated energy microgrid, and Respectively represent nodes and The active power at and Respectively represent nodes and The reactive power at and Represents the network correlation coefficient.

[0021] Embodiment 7, see Figures 1 to 7 This embodiment is based on the above embodiment. Further, in step S36, the influence of the genetic algorithm on the search process is adjusted, which specifically includes the following steps: Step S361: Calculate the contribution of the genetic algorithm to measure the optimization effect of the genetic algorithm. Specifically, the cumulative sum of the gain degree brought by the genetic algorithm in the most recent random round cycle is divided by the total number of particles affected by the genetic algorithm in the most recent random round cycle to obtain the optimization effect of the genetic algorithm. The cumulative sum of the gain degree refers to the fitness difference of the particles affected by the genetic algorithm and whose fitness is improved; Step S362: Calculate the contribution of the particle swarm optimization algorithm, repeat step S361, measure the optimization effect of the particle swarm optimization algorithm, and obtain the particle swarm optimization effect; Step S363: rule learning, specifically, using a fuzzy logic reasoning system to dynamically adjust the search process. If the genetic algorithm optimization effect is good, it means that the genetic algorithm contributes more to the optimization at the current stage, and the role of the genetic algorithm should be enhanced. If the particle swarm optimization effect is good, it means that the particle swarm optimization algorithm contributes more, and the role of the genetic algorithm should be reduced, and the genetic weight parameter is updated. The genetic weight parameter update formula is as follows: ; In the formula, represents the genetic weight parameter, represents the fuzzy logic reasoning system, It represents the optimization effect of the genetic algorithm.

[0022] Embodiment 8, see Figures 1 to 7 This embodiment is based on the above embodiment. In step S1, this embodiment uses a 12 / 8-pole three-phase switched reluctance motor as a distributed energy unit, the minimum conduction angle threshold is set to 15°, and the turn-off angle is set before the end of the inductance positive slope area. The inequality used is as follows: ; In the formula, represents the cut-off angle, is the rotor position corresponding to the maximum inductance.

[0023] Embodiment 9, see Figures 1 to 7 This embodiment is based on the above embodiment. In step S3, The integrated energy microgrid consists of 5 nodes, 3 of which are 12 / 8-pole three-phase switched reluctance motors, and 2 are load devices. The voltage of each node is specified to range from 0.9pu to 1.1pu. , establish the following power balance equation: ; In the formula, Representation Node The active power output, Representation Node The active power demand, Represents a slave node To all nodes connected to it Active power transmission; ; In the formula, Representation Node The reactive power output, Representation Node The reactive power demand, Represents a slave node To all nodes connected to it Reactive power transmission.

[0024] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0025] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

[0026] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. A dynamic dispatching method for integrated energy microgrid based on artificial intelligence, characterized by: The method comprises the following steps: Step S1: Locally optimize the commutation angle, specifically, in the integrated energy microgrid, use the ant colony optimization method to offline search and use a lookup table to store the optimal switching angle and angle change speed of the individual distributed energy units, the switching angle includes a turn-on angle and a turn-off angle, and record the combination of the switching angle and the angle change speed as a candidate solution; Step S2: Optimizing the start-stop strategy of the distributed energy unit, specifically, during the operation of the distributed energy unit, searching the lookup table online, using direct instantaneous torque control technology, optimizing the switch angle and angle change speed of the distributed energy unit, adjusting the start-stop strategy of the distributed energy unit, and optimizing the current waveform; Step S3: searching for the optimal position of the unit, specifically, using a method combining a genetic algorithm and a particle swarm optimization algorithm to cyclically optimize the position and network topology of the distributed energy units in the integrated energy microgrid to obtain the optimal position and network topology; Step S4: Globally optimize the microgrid structure and optimize the integrated energy microgrid according to the optimal location and network topology.

2. The method for dynamic scheduling of integrated energy microgrid based on artificial intelligence according to claim 1, characterized in that: In step S3, searching for the optimal position of the unit specifically includes the following steps: Step S31: Initializing the population, specifically, recording the distributed energy units as nodes, initializing the particle population of the particle swarm optimization algorithm, and the particles represent potential node positions and topological relationships; Step S32: setting constraints in the search process; Step S33: Initialize genetic weight parameters; Step S34: global search, specifically, using a particle swarm optimization algorithm to calculate fitness and perform a global search on the particle population; Step S35: local optimization, which is used to perform local optimization on the particle population using a genetic algorithm, specifically, randomly selecting particles for crossover and mutation operations according to genetic weight parameters; Step S36: adjusting the influence of the genetic algorithm on the search process; Step S37: merging populations, specifically, merging the new particle populations generated by the particle swarm optimization algorithm and the genetic algorithm to update the particle population.

3. The method for dynamic scheduling of integrated energy microgrid based on artificial intelligence according to claim 2 is characterized in that: In step S32, the constraints in the search process are set, which specifically includes the following steps: Step S321: power constraint, specifically, ensuring that the active power output of any node must be equal to the active power demand of the node plus the sum of the active power transmitted through all lines of the node, and ensuring that the reactive power output of any node must be equal to the reactive power demand of the node plus the sum of the reactive power transmitted through all lines of the node; Step S322: Capacity limitation.

4. The method for dynamic scheduling of integrated energy microgrid based on artificial intelligence according to claim 2 is characterized in that: In step S34, the global search specifically includes the following steps: Step S341: Update the speed and position of the particle; Step S342: Calculate the fitness of each particle using the following formula: ； In the formula, represents fitness, represents the total number of nodes in the integrated energy microgrid, and Respectively represent nodes and The active power at and Respectively represent nodes and The reactive power at and Represents the network correlation coefficient.

5. The method for dynamic scheduling of integrated energy microgrid based on artificial intelligence according to claim 4 is characterized in that: In step S36, the influence of the genetic algorithm on the search process is adjusted, which specifically includes the following steps: Step S361: Calculate the contribution of the genetic algorithm, specifically, divide the cumulative sum of the gain degree brought by the genetic algorithm in the most recent random round cycle by the total number of particles affected by the genetic algorithm in the most recent random round cycle to obtain the optimization effect of the genetic algorithm; Step S362: Calculate the contribution of the particle swarm optimization algorithm, repeat step S361, and obtain the particle swarm optimization effect; Step S363: rule learning, specifically, using a fuzzy logic reasoning system to dynamically adjust the search process, and updating the genetic weight parameters by comparing the optimization effect of the genetic algorithm and the particle swarm optimization effect. The genetic weight parameter update formula is as follows: ； In the formula, represents the genetic weight parameter, represents the fuzzy logic reasoning system, It represents the optimization effect of the genetic algorithm.

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