Multi-Objective Optimization Scheduling of Microgrids Based on Particle Swarm Optimization Algorithm

In order to gain a deeper comprehension of the microgrid scheduling method based on multi-objective optimization, the author proposes research into scheduling microgrids for multi-objective optimization. Before controlling the microgrid, the author needs information on each energy storage device's remaining capacity, maximum discharge power, and maximum charging power. The mathematical model of the microgrid is built on top of this. On the basis of the created microgrid mathematical model, a multi-objective optimization microgrid operation model for multiple microgrid states, including the island state and the grid-connected state, is built. The actual output power of each distributed power source, as well as the discharge and charging power of batteries and supercapacitors, are all optimization variables. Second, the multi-objective optimization microgrid job is addressed using the modified particle swarm optimization technique, allowing multi-objective optimization to be used to schedule microgrids. The designed method was then subjected to experimental testing. In addition, set the experiment's parameters for the improved particle swarm optimization algorithm to 150, 250, and 100 for the maximum number of iterations and external file sizes. The comparison of the microgrid's photovoltaic and wind power consumption can demonstrate that this approach has a higher photovoltaic and wind power consumption, thereby increasing the microgrid's overall consumption of renewable energy and successfully achieving multi-objective optimization scheduling. The writer laid out a microgrid enhancement planning model and tackled it utilizing molecule swarm streamlining calculation. The outcomes demonstrated the method's efficiency as well as the rationality and practicality of the author's suggested approach.