A hybrid storage-wind virtual power plant (VPP) participation in the electricity markets: A self-scheduling optimization considering price, renewable generation, and electric vehicles uncertainties

Abstract

The fast growth of technologies most of which depend on natural sources of energy has resulted in a huge consumption of fossil fuels. In this regard, many solutions have been suggested to alleviate the side effects such as air pollution and global warming. Among these solutions, mobile storages like electric vehicles (EVs) and renewable generations, have grown significantly due to being more applicable. But uncoordinated operation and uncertain nature of these distributed energy resources (DERs) can bring forward new challenges and issues to the operators of power system. Thus, in many cases it is more efficient to co-operate them in a hybrid system. In this study, we address a virtual power plant (VPP) that aggregates the EVs charging and discharging power into electricity markets, day-ahead (DA) energy and reserve, while enjoying wind power generation capacity. Not only we consider uncertainty of electricity market prices and the amount of energy produced by wind generation but also we present a novel EV uncertainty modelling in which we introduce a new facet that incorporates all the uncertain parameters of these vehicles into the whole stochastic optimization model along with other uncertainty sources. The proposed method can be utilized in case of a VPP that has wind generation and parking-lots accommodating EVs to optimally schedule its assets prior to participating in the electricity markets. The theoretical approach in developing the proposed self-scheduling model and its applicability is verified through several numerical simulations.