The Promise of EV-Aware Multi-Period OPF Problem: Cost and Emission Benefits

TL;DR

This paper develops a comprehensive multi-period optimal power flow model incorporating electric vehicles and emission considerations, using second-order cone programming for globally optimal solutions, demonstrating significant emission and cost benefits.

Contribution

It introduces the first integrated model combining real datasets and a second-order cone programming approach for EV-aware multi-period optimal power flow.

Findings

High-quality solutions with small optimality gaps for large networks

Coordinated EV charging significantly reduces emissions and costs

Insights for policy incentives based on cost-emission trade-offs

Abstract

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.