Risk‐averse integrated demand response and dynamic G2V charge scheduling of an electric vehicle aggregator to support grid stability

Abstract

Electric vehicle aggregator (EVA) trades in energy and ancillary service markets for profit maximisation through grid-to-vehicle (G2V) charge scheduling of EVs. It can provide regulation services to system operator (SO) through coordinated and distributed charging of EVs. However, EV owner aim at minimum charging cost. Price-based demand response (PBDR) integrated charge scheduling can motivate EVs by off peak charging for reduced cost along with system load levelling. Real-time (RT) PBDR, adopted in literature, has low acceptance rate by EVs for being too dynamic to respond and being infeasible for few charge cycles in a day. Time of use (TOU) PBDR, being less dynamic, is a natural price signal for EVs and shields them from RTP volatility as well. Additionally, EV charging cost constrained EVA's charge scheduling is a realistic formulation and is subjected to multiple uncertainties like EVs' mobility behaviour and market prices. Considering this, risk-averse charge scheduling of EVA with TOU PBDR and EV charging cost constraint is proposed. EVs' mobility behaviour and market prices' uncertainties are modeled through stochastic programming. Agglomerative hierarchical clustering is proposed to develop TOU price for EVA from RTP. Conditional value at risk (CVaR) is used as risk measure. Results of a case study of EVA with 1000 EVs illustrate a trade-off among EVA profit and risk with optimal values of EV charging cost and regulation provision to SO for risk-neutral and risk-averse cases.