Xiaoxu Fu*, Caixia Tan, Jiang Wang, Jinghan Zhou, Zhongfu Tan
School of Economics and Management, North China Electric Power University, Beijing 102206, China;
The randomness of wind generation is one of the main factors restricting gird connection of wind generation. The involvements of energy storage systems and resources used for demand response in the process of optimization for wind power are useful means to enhance its regulation capacity. Considering the uncertainty of wind generation in day-ahead plans, this paper proposes a coordinated scheduling optimization model for Wind-ES hybrid systems with demand response via electric vehicles. The model can be used to apply energy storage systems and electric vehicles simultaneously to both peak shaving/valley filling and wind generation plan tracking to achieve the coordination between the on-grid revenue and penalty cost of the hybrid system, so as to develop the optimal strategy for maximum benefits. The wind power is modeled by using scenario analysis method, and the mixed integer programming problem of this paper is solved via CPLEX software. The case study results show that the coordinated scheduling optimization model can not only earn additional revenue for electric vehicle owners, but also effectively improve the economy of wind power grid connection, which provides an important reference for scheduling the demand response resources of electric vehicles to consume wind generation.
V2G; Wind-ES hybrid systems; scenario reduction; uncertainty; mixed integer programming
Xiaoxu Fu, Caixia Tan, Jiang Wang, Jinghan Zhou, Zhongfu Tan. Optimital Decision-making Model of Mixed Ownership Reform in Power Grid Enterprise for Investing Hrbird Energy System Project. Academic Journal of Humanities & Social Sciences (2020) Vol. 3, Issue 2: 124-138. https://doi.org/10.25236/AJHSS.2020.030214.
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