Hybrid Genetic Particle Swarm Optimization Based Economical Operation of Energy Hub

Energy hub (EH) is a significant model for analyzing multiple energy systems (MES), which is considered a useful model to improve the energy utilization rate and cut down the system’s energy cost. However, the optimization of EH is a complex nonlinear problem. In this paper, firstly, a systematic model of energy hub is established with a genetic algorithm. Then, a hybrid GA-PSO strategy combining particle swarm optimization (PSO) with genetic algorithm (GA) is proposed to solve the optimal operation of EH. Finally, the proposed hybrid algorithm is compared with the basic PSO algorithm based on considering the demand response when taking a typical summer day of a residential building as a case study. The results indicate that the proposed hybrid GA-PSO optimization program strategy not only greatly accelerates the convergence ability, but also shows better economic performance in solving EH’s optimal operation.