Journal
APPLIED ENERGY
Volume 314, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2022.118715
Keywords
Unit commitment; Electric vehicle; Renewable energy; Rescheduling scheme
Categories
Funding
- National Science Foundation of China [52077213, 62003332]
- Outstanding Young Researcher Innovation Fund of Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences [2018-22]
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This paper proposes a novel power system rescheduling strategy to address the challenges posed by the uncertainty of renewable power generations. A particle swarm optimization algorithm based scheduling scheme is also introduced. Experimental results validate the effectiveness of the proposed framework and algorithm, achieving significant cost reduction and highlighting the importance of managing plug-in electric vehicle charging and discharging to alleviate the negative impact on grid stability caused by intermittent renewable power generations.
The significant penetration of renewable power generations (RGs) and the large-scale use of plug-in electric vehicles (PEVs) have brought tangible impacts in tackling the climate change challenge the mankind has been facing due to substantive green-house gas and pollutant emissions from fossil-fuel based thermal power generation plants. However, the uncertainty of RGs has also exerted significant challenges to the grid operation and control. Therefore, dynamic power system scheduling to accommodate the intermittent RGs and mass roll-out of PEVs has become extremely important. In this paper, a novel power system rescheduling strategy is proposed to tackle this problem. Considering the uncertainty of the wind energy, a set of indices according to different wind power application scenarios is proposed to initiate a rescheduling scheme for power generations. In addition, a social learning particle swarm optimization algorithm based on real-value and binary parallel is proposed to schedule the output of generator units and the charging and discharging of the PEV. The effectiveness of the proposed active rescheduling framework and solving algorithm has been verified by extensive experiments considering different number of generating units and scenarios, achieving up to over 5.3% cost reduction. The experimental results have also shown that through expropriate management of the charging and discharging of PEVs would be significantly alleviate the negative impact on the grid stability caused by the intermittent wind power generations.
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