Multiobjective Pareto-Optimal Intelligent Electric Vehicle Charging Schedule in a Commercial Charging Station: A Stochastic Convex Optimization Approach

This article presents real-time Pareto-optimal scheduling for bidirectional electric vehicle (EV) charging in a commercial charging station with on-site renewable energy and battery energy storage to optimize several objectives. To incorporate the inherent uncertainty in the model, mixture density neural networks are presented to estimate the parameters of the probability distribution of demands and deadlines using a negative-log-likelihood loss function. From the joint distribution of demands and deadlines, future EV charging requests are estimated. Furthermore, we formulate the control problem as a multiobjective stochastic convex optimization problem from the perspective of the charging station operator, which simultaneously aims to minimize the total cost of charging, frequent change in charging rates, maximum demand of the charging station and battery degradation costs subject to various system constraints. We empirically evaluate the proposed scheduling policy for optimality gap, competitive ratio, and robustness, and show that the proposed scheduling policy reduces cost by about
$30 \%$
over the benchmark scheduling policies.