Optimal scheduling of ancillary services provided by an electric vehicle aggregator. (15th February 2023) - de la Torre, S.; Aguado, J.A.; Sauma, E.
Abstract: Massification of Electric vehicles (EVs) is becoming a worldwide reality as a means to combat climate change and local pollution. Considering that most of the time vehicles are in parking places, there is an opportunity for using EVs to provide some valuable services to the power network. In particular, EVs can provide ancillary services in electricity markets through an aggregating agent. To this end, EVs aggregators need to develop decision support tools to optimally allocate energy and regulation resources considering power network constraints. Unlike optimization models for EVs aggregators currently available in the literature, in this paper we propose an optimization approach for EVs aggregators that jointly considers the most important aspects influencing EVs profitability, such as uncertainty, drivers' patterns, capacity constraints, state of charge constraints, regulation demand constraints, regulation offer constraints, regulation bounds constraints, and power-system security constraints. The optimization problem is formulated as a mixed-integer linear programming problem, thus ensuring global optimality. Results are presented in the form of the hourly allocation for charging/discharging power profiles, distinguishing between day-ahead energy and capacity/energy for regulation, and the profit that can be reached, while accounting for network constraints. The proposed model is illustrated through a case study, which allows us to show that EVs aggregators allow for leading to a more reliable power system operation, avoiding transmission lines congestion, while providing important profits for EV owners who are able to provide regulation services. Highlights: EV aggregators allow for a more reliable power system operation. EV owners can attain profits from participating in energy markets. An EV model featuring uncertainty, driving patterns, state of charge and capacity. A relatively simple one-stage mixed-integer linear programming optimization model.