Chiwon Seo

This study proposes a novel robust low-carbon electric vehicle (EV) charging management algorithm for a photovoltaic-energy storage system-equipped electric vehicle charging station utilizing mobile energy storage systems. The proposed algorithm operates within a power distribution system that incorporates the carbon emission flow (CEF) model, explicitly addressing the dual uncertainties in power and carbon prices. In the proposed study, a carbon-aware objective function is introduced to prioritize low-carbon energy supply for EVs. To address price uncertainties, the problem is modeled using the Wasserstein metric-based distributionally robust optimization model and reformulated as a computationally tractable convex counterpart. Furthermore, to resolve the computational complexity arising from the bilinear terms in the CEF model, we propose a novel convex relaxation framework: sequential binary piecewise McCormick relaxation integrated with two-dimensional projection cuts. Numerical investigations demonstrate the superiority of the proposed framework, reducing the carbon consumption at the individual EVCSs by up to 18.94%, lowering the solution time from over 7,000 s to 232.6 s with a near-zero relaxation gap. In addition, the proposed DRO framework reduces the carbon emissions by 16.5% on average compared with robust optimization while keeping the out-of-sample cost within 1.6% of the stochastic benchmark.