Wind power offer strategy in day-ahead market considering price bidding strategy for electric vehicle aggregators

In this paper, a new scheme is proposed whereby a wind power producer (WPP) can enhance its participation in the day-ahead markets (DAM). To do so, an energy exchange between the WPP and electric vehicle aggregators (EVAs) in the day-ahead energy, balancing, and regulation markets is required. Therefore, an optimal bidding/offering strategy is developed to reduce the unpredictability of WPP in the power markets and optimize electric vehicle (EV) charging profiles. EVAs bid price-energy packages to the WPP for either charging or not charging the EV to compensate power imbalance of the WPP. Generally, the WPP collects EVAs' bids to determine the share of each them in energy exchange contracts by profit function optimization. Stochastic hourly optimization is solved by a Genetic Algorithm (GA). Also, Conditional value at risk (CVaR) is used to risk assess WPP profit. Furthermore, there is a competition between EVAs to sell their services to WPP to satisfy EV owners. To model the EVAs competition a non-cooperative game is proposed. The Nash equilibrium is used to solve this non-cooperative game.