Electric vehicle (EV) penetration has been increasing in the modern electricity grid and has been complemented by the growth of EV charging infrastructure. This paper addresses the gap in the literature on the EV effects of total electricity costs on commercial buildings by incorporating V0G, V1G, and V2B charging. The electricity costs are minimized in 14 commercial buildings with real load profiles, demand, and energy charges. The scientific contributions of this study are the incorporation of demand charges, quantification of EV, and smart charging electricity costs and benefits using several representative long-term datasets, and the derivation of approximate equations that simplify the estimation of EV economic impacts. Our analysis is primarily based on an idealized uniform EV commuter fleet case study. The V1G and V2B charging electricity costs as a function of the number of EVs initially diverge with increasing charging demand and then become parallel to one another with the V2B electricity costs being lower than V1G costs. A longer EV layover time leads to higher numbers of V2B charging stations that can be installed such that original (pre-EV) electricity costs are not exceeded as compared to a shorter layover time. Sensitivity analyses based on changing the final state of charge (SOC) of EVs between 90% and 80% and initial SOC between 50% and 40% (thereby keeping charging energy demand constant) show that the total electricity costs are the same for V0G and V1G charging, while for V2B charging, the total electricity costs decrease as final SOC decreases.

Open-source, high resolution power consumption data are scarce. We compiled, quality controlled, and released publicly a comprehensive power dataset of parts of the University of California, San Diego microgrid. The advanced microgrid contains several distributed energy resources (DERs), such as solar power plants, electric vehicles, buildings, a combined heat and power gas-fired power plant, and electric and thermal storage. Most datasets contain 15-min averages of real and reactive power from 1 January, 2015 until 29 February, 2020. We also include Python codes to fill missing data and flag and replace potentially erroneous data. The extensive dataset of conventional and new DERs is designed to accelerate research and development work in the area of sustainable microgrids.