The optimization model of wind-optical co-storage configuration for campus microgrid based on improved particle swarm optimization algorithm

As the world gradually faces problems such as fossil energy exhaustion and environmental pollution, the demand for new energy has been greatly spawned. In this paper, the energy storage configuration optimization problem of a park microgrid under the combined power supply mode of solar power generation and main grid is studied. Through the combination of multi-objective optimization and algorithm, the energy storage configuration is optimized, and the optimal wind and solar storage coordination optimization configuration scheme is given. In this paper, the energy storage configuration scheme independently operated by each park is developed according to the requirements and the economy is analyzed. The average wind, optical power and load power per hour are obtained by using the Simpson method to integrate the data. After data analysis, a power purchase scheme was developed to reduce the amount of abandoned power, and the four economic index values h of the three parks were calculated based on the power purchase scheme. The sensitivity analysis of the factors affecting the four indexes is carried out to find the key factors affecting the economic indexes in each park. Then the optimization model based on particle swarm optimization algorithm is established. Then the energy storage configuration scheme of the joint park is developed and its economy is analyzed. First, the load power and wind and solar power of the joint park are combined as joint Park D, and the problem 1.2 model is introduced to solve the relevant economic indicators without energy storage. Then, the advantages of the joint park are analyzed, and it is found that the wind-light power generation consortium of the joint park is conducive to the complementary power generation, the load consortium is conducive to the full use of wind-light power generation, and the use of batteries is conducive to the spatio-temporal transfer of wind-light resources. Finally, the future landscape storage coordination configuration plan of the park is formulated and its economy is analyzed. An optimization model aiming at the total cost of power purchase in the park within 5 years is established. A set of investment plan with considerable return is obtained by solving it, and an economic analysis and demonstration report of the plan is given according to its economic indicators.