Multi-objective optimization of a novel combined cooling, dehumidification and power system using improved M-PSO algorithm

Jinwei Chang, Zhi Li, Yan Huang, Xiaonan Yu, Ruicheng Jiang, Rui Huang and Xiaoli Yu

Energy, 2022, vol. 239, issue PE

Abstract: This study proposes a novel combined cooling, dehumidification and power system based on internal combustion engine (ICE) for high temperature and humidity regions. In the proposed system, the electricity demand is mainly provided by ICE while the cooling and dehumidification demands are totally satisfied by absorption chiller (AC) and liquid desiccant dehumidification (LDD) units driven by waste heat of engine exhaust and jacket water. In addition, the electricity demand can be supplemented by battery and Organic Rankine Cycle (ORC) units driven by abundant waste heat of ICE. The aim of this system is designed to succeed the highly efficient utilization of ICE waste heat among AC, LDD and ORC units, and satisfy the multi-energy demands of users by optimizing the operation strategy. Firstly, a case study based on the actual power, cooling and dehumidification demands of a hotel building in Singapore is conducted to assess the performance of the proposed system. The parametric study of prominent design parameters in this system is investigated first to explore their effects on the system performance. Next, considering annual total cost and CO2 emissions as evaluation objectives, augmented ε-constraint method combined with improved Mutation particle swarm optimization (M-PSO) algorithm is used to conduct the multi-objective optimization of equipment capacity and operation. Finally, a sensitivity analysis of significant uncertainty factors is researched. The optimal results show that, compared to the traditional energy supply system, the annual carbon dioxide emission reduction ratio and total cost saving ratio can reach 35.91% and 25.46%, respectively.

Keywords: Distributed energy system; Multi-objective optimization; Liquid desiccant dehumidification; Mutation particle swarm optimization algorithm (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:239:y:2022:i:pe:s0360544221027365

DOI: 10.1016/j.energy.2021.122487

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