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Distributed scheduling for multi-energy synergy system considering renewable energy generations and plug-in electric vehicles:A level-based coupled optimization method

引用

Energy and AI 2024年第2期16卷 213-226页

作者： Linxin Zhang Zhile Yang Qinge Xiao Yuanjun Guo Zuobin Ying Tianyu Hu Xiandong Xu Sohail Khan Kang Li Faculty of Data Science City University of MacaoTaipa999078Macao Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhenGuangdong518055China Tianjin University Tianjin 300072China School of Electronic and Electrical Engineering University of LeedsLeedsLS29JTUK Institute of Applied Sciences&Technology Pak-Austria FachhochschuleHaripurKhyber PakhtunkhwaPakistan

Multi-energy synergy systems integrating high-penetration large-scale plug-in electric vehicles, distributed renewable energy generations, and battery energy storage systems have great potential to reduce the reliance of the grid on traditional fossil fuels. However, the random charging characteristics of plug-in electric vehicles and the uncertainty of photovoltaics may impose an additional burden on the grid and affect the supply–demand equilibrium. To address this issue, judicious scheduling optimization offers an effective solution. In this study, considering charge and discharge management of plug-in electric vehicles and intermittent photovoltaics, a novel Multi-energy synergy systems scheduling framework is developed for solving grid instability and unreliability issues. This formulates a large-scale mixed-integer problem, which calls for a powerful and effective optimizer. The new binary level-based learning optimization algorithm is proposed to address nonlinear large-scale high-coupling unit commitment problems. To investigate the feasibility of the proposed scheme, numerical experiments have been carried out considering multiple scales of unit numbers and various scenarios. Finally, the results confirm that the proposed scheduling framework is reasonable and effective in solving unit commitment problems, can achieve 3.3% cost reduction and demonstrates superior performance in handling large-scale energy optimization problems. The integration of plug-in electric vehicles, distributed renewable energy generations, and battery energy storage systems is verified to reduce the output power of 192.72 MW units during peak periods to improve grid stability. Therefore, optimizing energy utilization and distribution will become an indispensable part of future power systems.

关键词： Electric vehicle Unit commitment Renewable energy Battery energy storage system synergy optimization

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