UC Berkeley

Today's energy market is increasingly integrating time-varying tariffs, peak demand charges, and/or export tariffs. In this context, intelligent charging scheduling can considerably reduce the plug-in electric vehicle (PEV) charging cost. This is especially the case as more and more PEVs are charged in buildings that are also equipped with grid-connected intermittent energy resources (IERs) (e.g., photovoltaic systems and wind turbine generators). In this work, we propose a novel and complete intelligent PEV charging scheduling system (tailored for domestic settings) that can account for peak demand charges, time-varying tariffs, and/or export tariffs, appropriately considering both potential IER generation and the rest of a building's consumption. The backbone of our approach builds on adaptive model predictive control, and includes an efficient depth-first-search-based PEV charging planning algorithm that we propose. Importantly, our approach does not rely on a simplified linear modeling of the charging dynamics, which is a typical and limiting assumption of such systems. We evaluate our approach with real data, considering both solar and wind IER generation capacity, to show that it can reduce the cost of charging by up to ∼45% and ∼35% in the United States and the United Kingdom domestic settings, respectively, compared to standard PEV charging practices.