Open AccessRobust DED based on bad scenario set considering wind, EV and battery switching station
Author(s) -
Zhigang Lu,
Hao Zhao,
Haifeng Xiao,
Jiangfeng Zhang,
Xueping Li,
Xiaofeng Sun
Publication year - 2017
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2016.0634
Subject(s) - robustness (evolution) , wind power , battery (electricity) , electric power system , automotive engineering , computer science , reliability (semiconductor) , reliability engineering , power (physics) , control theory (sociology) , mathematical optimization , engineering , electrical engineering , mathematics , biochemistry , chemistry , physics , control (management) , quantum mechanics , gene , artificial intelligence
With the increasing penetration of wind power and electric vehicle (EV) into the power system, system operators face new challenges for system reliability and generation cost due to the intermittent of wind power. Furthermore, the randomly connected EVs at different time periods and locations add more uncertainty to the power system. In this study, uncertainties in wind power generation and EV charging load are modelled into the day‐ahead dynamic economic dispatch (DED) problem, solution feasibility and robustness are discussed, and the bad scenario set is formulated for the day‐ahead DED problem. In the obtained model, parameters can be used to adjust the positive bias or conservative bias, charging/discharging power of battery switch stations are also controlled to optimise the total cost of power system. To solve the optimisation problem, the multi‐agent bacterial colony chemotaxis algorithm and a mutation strategy based on the cloud theory are developed. The simulation results show that the proposed method is effective, and battery switching station can help to reduce total cost by charging and discharging batteries.