Scheduling optimization of IES based on improved particle swarm algorithm

Abstract

Abstract The integrated energy system (IES) has been a qualified candidate for solving the problems of energy utilization and environmental pollution. In this study, an improved IES scheduling optimization model based on an improved particle swarm algorithm is established, and the daily operating cost is taken as the objective function. Constraints on the energy supply and consumption sides are fully considered. The results show that with the energy storage conversion scheduling strategy participating in the optimization, the daily cost is significantly decreased by 10.5%. Compared with the genetic algorithm with elite strategy, the particle swarm algorithm is more suitable for the IES, with the lowest daily cost of $ 6, 193. Besides, the improved annealing particle swarm algorithm has better adaptability when facing high-dimensional multimodal function problems, and performs better on global search and local optimization.