Day-ahead bidding strategy for electric vehicle aggregator enabling multiple agent modes in uncertain electricity markets

Abstract

The electric vehicle (EV) aggregator is a new entity that coordinates the charging of an EV fleet to exploit the business opportunities in the electricity market. This paper explores the optimal bidding strategy for the EV aggregator participating in day-ahead markets. Two typical agent modes between the EV aggregator and the EV user are introduced based on the reality need of the EV user, viz. the centralized protocol management mode (CPMM) and the decentralized demand response mode (DDRM). After that, the stochastic optimization model is developed to maximize the expected profits of EV aggregator. The uncertainties from the electricity market and the EV charging are incorporated into the stochastic optimization model. The Guangdong electricity market, as the pioneer of China’s power reform, is first investigated as the target market to verify the rationality of the proposed approach. The possible real-time clearing scenarios are extracted from the historical market clearing data using the multi-class support vector machine (MSVM). Results reveal that the EV aggregator can make an optimal bidding strategy for the potential clearing scenarios when multiple agent modes are involved. Moreover, the comparison of two agent modes indicates that the EVs with the CPMM are more sensitive to the electricity price than those with the DDRM.