A risk-constrained decision support tool for EV aggregators participating in energy and frequency regulation markets

The growing trend of electric vehicles (EVs) in recent years has led to the emergence of EV aggregators in electricity markets. Generally, the main goal of an EV aggregator is to buy electricity from the wholesale market in a cost-effective manner while satisfying the charging requirements of EV owners. Accordingly, this paper presents a decision support tool for EV aggregators which enables them to determine the optimal bidding strategy to effectively participate in the day-ahead and real-time energy, and frequency regulation markets. Indeed, the aggregator mainly obtains profit by selling energy during the high-price hours (via vehicle-to-grid (V2G) capability) and providing primary frequency regulation service to the system operator. The proposed approach is based on a two-stage stochastic programming method, where risk aversion is modeled through the conditional value-at-risk (CVaR). The underlying uncertainties including the real-time energy prices, real-time regulation service deployments, and the uncertainties associated with the EV owners (i.e., arrival time, departure time, and initial battery state-of-charge (SOC)) are modeled as stochastic processes that are represented by different sets of scenarios. The cardinality of the combined scenario set is reduced using backward probability distance algorithm to make the resulting mixed-integer linear programming (MILP) problem tractable in large-scale and real-world cases. Extensive numerical analysis with one thousand EVs and real-world market data are conducted to validate the efficiency of the proposed approach in terms of solution optimality, robustness, and computation efficiency.