Multi-objective Capacity Optimization of Integrated Energy System with Compressed Air Energy Storage

This paper proposes a multi-objective capacity optimization method to determine the size of the integrated energy system (IES). A novel IES configuration composed of wind turbine, solar photovoltaic, combined cooling, heating, and power system, and compressed air energy storage is designed, which could improve energy efficiency and reduce emissions. Based on a developed modeling of all sub-systems composing the IES, the capacity is optimized to minimize the net present cost and environmental cost simultaneously. Non-dominated sorting genetic algorithm-II (NSCA-II) is applied to find the Parcto frontier of the constructed multi-objective formulas. Finally, taking an industrial park located in Jinan, China, as an example. The optimization results using the proposed approach provided a set of design solutions for the investor to select the optimal configuration.