A parallel meta-heuristic method for solving large scale unit commitment considering the integration of new energy sectors

Xiaodong Zhu, Shihao Zhao, Zhile Yang, Ning Zhang and Xinzhi Xu

Energy, 2022, vol. 238, issue PC

Abstract: In recent years, global warming impact are becoming increasingly severe due to the dramatic green house emission and severe environmental problem. The large integration of PEV and RGs directly affect the supply and demand balance of power grid, which bring challenges to the secure and economic operation of power system. This study proposes a novel parallel social learning particle swarm optimization method for solving the large scale power system scheduling problem with significant integration of RGs and PEVs. The novel algorithm combines the real value and binary decision variables obtained by social learning particle swarm optimization algorithm, aiming to solve large scale mixed integer unit commitment problem considering charging and discharging management of PEV with large RGs integration. To verify the effectiveness of the proposed algorithm, numerical examples are analyzed for multi scale unit numbers and various cases of RGs and PEVs. The results show that the proposed parallel social learning particle swarm optimization method has superior performance in solving UC problems considering new energy sectors. In addition, the case studies shows that the integration of new energy sources and flexible demand side management of plug-in electric vehicles have great potentials to alleviate power grid load and bring considerable economic benefits.

Keywords: Unit commitment; Large scale; Meta-heuristic; Electric vehicle; Renewable energy (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:238:y:2022:i:pc:s0360544221020776

DOI: 10.1016/j.energy.2021.121829

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