Managing load congestion in electric vehicle charging stations under power demand uncertainty

Abstract

Electric vehicles (EV) have received considerable attention in recent years due to their low operating cost, potential for energy sustainability, and zero tailpipe emissions. This study presents a novel two-stage stochastic programming model integrating long- and short-term decisions to design and manage EV charging stations with renewable energy generation capability. The model captures the non-linear load congestion effect that increases exponentially as the electricity consumed by plugged-in EVs approaches the capacity of the charging station and linearizes it. The study proposes a hybrid decomposition algorithm that utilizes a Sample Average Approximation and an enhanced Progressive Hedging algorithm (PHA) inside a Constraint Generation algorithmic framework to efficiently solve the proposed optimization model. A case study based on Washington, D.C. is presented to visualize and validate the modeling results. Computational experiments demonstrate the effectiveness of the proposed algorithm in solving the problem in a practical amount of time. Finding of the study include that incorporating the load congestion factor encourages the opening of large-sized charging stations, increases the number of stored batteries, and that higher congestion costs call for a decrease in the opening of new charging stations.