Multiple group search optimization based on decomposition for multi-objective dispatch with electric vehicle and wind power uncertainties

Xian Zhang, Ka Wing Chan, Huaizhi Wang, Bin Zhou, Guibin Wang and Jing Qiu

Applied Energy, 2020, vol. 262, issue C, No S0306261920300192

Abstract: While the number of plug-in electric vehicles (PEVs) increases rapidly, the application potential of PEVs should be accounted in electric power dispatch with several conflicting and competing objectives such as providing vehicle-to-grid (V2G) service or coordinating with wind power. To solve this highly constrained multi-objective optimization problem (MOOP), a multiple group search optimization based on decomposition (MGSO/D) is proposed considering the uncertainties of PEVs and wind power. Specifically, the decomposition approach effectively reduces the computational complexity, and the innovatively incorporated producer-scrounger model effectively improves the diversity and spanning of the Pareto-optimal front (PF). Meanwhile, the estimation error punishment is utilized to take into account of uncertainties. The performance of MGSO/D and the effectiveness of the uncertainty model are investigated on the IEEE 30-bus and 118-bus system with wind farms and PEV aggregators. Simulation results demonstrate the superiority of MGSO/D to solve this MOOP with practical uncertainties by comparing with well-established Pareto heuristic methods.

Keywords: Multi-objective optimization; Plug-in electric vehicles; Multiple group search optimization based on decomposition; Pareto-optimal front (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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DOI: 10.1016/j.apenergy.2020.114507

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