CSE Community Seminar

Abstract:

Legislation requires critical infrastructure to remain operational under extreme conditions—an imperative that can be rigorously captured through robust constraints in engineering design problems. In this talk, I will present a robust optimization framework that models the participation of electric vehicles in electricity markets. Specifically, we formulate a robust optimization problem that maximizes the expected profit of electric vehicles owners selling primary frequency regulation and guarantees that market commitments are met at all times for all frequency deviation trajectories in a functional uncertainty set that encodes applicable legislation. Faithfully modeling the energy conversion losses during battery charging and discharging renders this optimization problem nonconvex. By exploiting a total unimodularity property of the uncertainty set and an exact linear decision rule reformulation, we prove that this nonconvex robust optimization problem with functional uncertainties is equivalent to a tractable linear program. Through extensive numerical experiments using real-world data, we quantify the economic value of vehicle-to-grid and elucidate the financial incentives of vehicle owners, aggregators, equipment manufacturers, and regulators. We find that the prevailing penalties for nondelivery of promised regulation power are too low to incentivize vehicle owners to honor the delivery guarantees given to grid operators. This talk is based on the paper Reliable Frequency Regulation Through Vehicle-to-Grid: Encoding Legislation with Robust Constraints.