  In this paper, we study the Multi-Period Optimal Power Flow problem (MOPF)
with electric vehicles (EV) under emission considerations. We integrate three
different real-world datasets: household electricity consumption, marginal
emission factors, and EV driving profiles. We present a systematic solution
approach based on second-order cone programming to find globally optimal
solutions for the resulting nonconvex optimization problem. To the best of our
knowledge, our paper is the first to propose such a comprehensive model
integrating multiple real datasets and a promising solution method for the
EV-aware MOPF problem. Our computational experiments on various instances with
up to 2000 buses demonstrate that our solution approach leads to high-quality
feasible solutions with provably small optimality gaps. In addition, we show
the importance of coordinated EV charging to achieve significant emission
savings and reductions in cost. In turn, our findings can provide insights to
decision-makers on how to incentivize EV drivers depending on the trade-off
between cost and emission.
