MPC-Based Real-Time Charging Coordination for Electric Vehicle Aggregator to Provide Regulation Service in a Market Environment

TL;DR

This paper presents an MPC-based optimization model for electric vehicle aggregators to participate in energy and regulation markets, considering future EV arrivals and risk aversion, validated through simulations on a large EV system.

Contribution

It introduces a novel MPC-based framework incorporating CVaR for EV aggregator operation in market environments, accounting for future arrivals and risk preferences.

Findings

Effective revenue generation demonstrated in simulations.

Successful satisfaction of EV charging requirements.

Risk-aware optimization improves market participation outcomes.

Abstract

The optimal operation problem of electric vehicle aggregator (EVA) is considered. An EVA can participate in energy and regulation markets with its current and upcoming EVs, thus reducing its total cost of purchasing energy to fulfill EVs' charging requirements. An MPC based optimization model is developed to consider future arrival of EVs as well as energy and regulation prices. The index of CVaR is used to model risk-averseness of an EVA. Simulations on the 1000-EV test system validate the effectiveness of our work in achieving a lucrative revenue while satisfying the charging requests from EV owners.