Demand Response Program applied for Electric Vehicle Charging in Distribution Networks

The growing penetration of Electric Vehicles (EVs) poses new challenges to distribution system operation, particularly regarding peak demand and asset overloading. This paper proposes an optimization model for EV charging profiles based on time-varying and location-sensitive signals derived from the electrical impedance matrix of the distribution network. Building on a previously published tariff-sensitivity framework, this study develops an EV charging optimization model that uses nodal and hourly signals to reflect the marginal impact of current injections on line congestion. The optimization minimizes the total EV charging cost while mitigating adverse grid impacts. Simulation results using the IEEE 123-bus system demonstrate reductions in losses and line loading, as well as cost savings for EV owners. By combining the proposed approach with the existing tariff-sensitivity framework, Distribution System Operators (DSOs) can better align incentives for EV owners, without compromising their charging profiles, and mitigate network issues, thereby supporting the scalable integration of EVs.