Electric-vehicle aggregators as cyber-physical energy systems: Optimal operation strategy for frequency regulation and energy arbitrage

Electric-vehicle aggregators are distributed storage systems that require coordination and control. Therefore, they are cyber-physical energy systems of practical relevance to the power industry as they are capable of providing valuable services regarding frequency regulation and demand displacement. This paper investigates the operation of an electric-vehicle aggregator for both energy arbitrage in the real-time market and frequency regulation. Modeling challenges arise from the fact that decisions in frequency regulation and energy arbitrage involve different time scales, and require accounting for uncertainties in market prices and electric-vehicle availability. To address these challenges, we propose a stochastic mixed-integer linear programming model to optimize the electric-vehicle aggregator operations. The model optimizes charging and discharging decisions across multiple timescales to maximize the profit of the aggregator. Two sources of uncertainty are considered: fluctuations in both energy and regulation prices, and variations in the power and energy capacities of the electric-vehicle aggregator. The proposed model is illustrated using a real-world case study, showing that utilizing the aggregator for both energy arbitrage and frequency regulation is more profitable than utilizing it just for any one of these two functions. Sensitivity and incremental analyses are also carried out.