Congestion based dynamic pricing for charging of EVS

Abstract

The rapid increase in electric vehicles (EVs) adoption presents new challenges for distribution system operators (DSOs), particularly in managing local network congestion and capacity limit violations. Concentrated EV charging during peak times can momentarily overload the electricity distribution network and compromise electricity supply reliability and quality. This research addresses the challenge of mitigating grid congestion by exploring a new pricing strategy for EV charging stations. The core research question investigates how dynamic pricing, based on real-time network congestion levels, can be used to balance load distribution and prevent overloading in specific network sections. This article proposes a dynamic pricing model that adjusts charging costs in response to network utilization rates, incentivizing EV users to charge during less congested hours or in less congested areas. The proposed model employs machine learning techniques to predict congestion levels, balancing user satisfaction, grid reliability, and station profitability. The findings demonstrate that dynamic pricing significantly reduces congestion during peak hours, improves load distribution, and enhances grid stability without compromising user accessibility. This research offers valuable insights for DSOs and policymakers aiming to develop sustainable and resilient EV charging infrastructure.