Optimal Bidding Strategy for Electric Vehicle Aggregators in Electricity Markets

TLDR

The study develops an optimal bidding strategy for an electric vehicle aggregator in day‑ahead energy and regulation markets through stochastic optimization and introduces a new battery model for improved charging representation. The stochastic optimization model incorporates uncertainties from day‑ahead/real‑time bid deviations, regulation signal energy and price variations, and classifies energy deviations as uninstructed or instructed to maximize profit and reserve reliability. Simulation results for an aggregator of one thousand EVs demonstrate the effectiveness of the proposed strategy and battery model.

Abstract

This paper determines the optimal bidding strategy of an electric vehicle (EV) aggregator participating in day-ahead energy and regulation markets using stochastic optimization. Key sources of uncertainty affecting the bidding strategy are identified and incorporated in the stochastic optimization model. The aggregator portfolio optimization model should include inevitable deviations between day-ahead cleared bids and actual real-time energy purchases as well as uncertainty for the energy content of regulation signals in order to ensure profit maximization and reliable reserve provision. Energy deviations are characterized as "uninstructed" or "instructed" depending on whether or not the responsibility resides with the aggregator. Price deviations and statistical characteristics of regulation signals are also investigated. Finally, a new battery model is proposed for better approximation of the battery charging characteristic. Test results with an EV aggregator representing one thousand EVs are presented and discussed.