Participation of electric vehicle charging station aggregators in the day-ahead energy market using demand forecasting and uncertainty-based pricing

Abstract

This paper introduces a novel approach to the smart management of public EV charging infrastructure, combining day-ahead energy bidding with a dynamic end-user pricing model. It addresses critical challenges such as demand fluctuations and uncertainties in the day-ahead market while minimizing waiting times and maximizing profit and load distribution. Although charging prices do not need to directly mirror wholesale day-ahead market prices, they are based on these prices due to their availability and market relevance. Day-ahead energy procurement offers advantages such as liquidity and price stability; however, forecast errors can lead to overprocurement, negatively impacting profitability. To mitigate this, a pricing model that accounts for forecast uncertainty is proposed, ensuring profitability during demand fluctuations by setting higher prices during periods of greater uncertainty. Additionally, when a preferred station is occupied, the model offers lower prices at underutilized stations, improving load distribution and reducing waiting times. The proposed approach is compared to benchmark models, demonstrating improvements in load distribution (7.79%), reduced waiting times (83.02%), and increased profitability (27.81%). These results contribute to an enhanced user experience and more efficient use of public infrastructure, showcasing the effectiveness of the strategy in optimizing energy procurement and pricing for smart public charging infrastructure.