Scalable Optimization for Mobility-Aware Coordinated Electric Vehicle Charging in Distribution Power Networks

TL;DR

This paper introduces MAC, a scalable framework for optimizing EV charging to maximize demand flexibility benefits, reducing overloads in distribution networks without disrupting travel needs.

Contribution

It presents a novel, scalable optimization method that couples EV charging over mobility horizons and provides a decentralized interpretation for regional power network planning.

Findings

MAC significantly reduces overload-driven upgrades in a case study.

The framework achieves near-optimal solutions with scalable ADMM-based decomposition.

Trajectory-coupled flexibility enhances the potential of EV demand management.

Abstract

Rapid growth in electric-vehicle (EV) charging demand is placing increasing stress on distribution power networks (DPNs), whose hosting capacity is often limited and spatially uneven. Beyond demonstrating that coordination can help, this paper answers an open question that is central for planners: what is the maximal achievable benefit of EV demand flexibility in reducing overload-driven distribution upgrades at a regional scale? Establishing such an upper bound is computationally challenging, as it entails solving and certifying near-optimal solutions to population-scale optimization problems with millions of variables and both spatial and temporal coupling. We introduce MAC (Mobility-Aware Coordinated EV charging), a framework that quantifies the maximum potential of leveraging EV demand flexibility to mitigate DPN overloading risk without interrupting drivers' travel needs. (i) MAC expands feasible scheduling by coupling charging decisions over a full mobility horizon: instead of enforcing per-session energy recovery, it only requires the EV state-of-charge (SOC) to remain sufficient for upcoming trips. (ii) MAC is computationally scalable via an ADMM-based decomposition with custom subproblem solvers, and admits a decentralized interpretation in which dual variables act as locational-temporal price signals that implement the social optimum as a competitive equilibrium. Using high-resolution mobility trajectories and feeder hosting-capacity data in a future-oriented 30% EV adoption scenario for the San Francisco Bay Area, we show that MAC can dramatically reduce overload-driven upgrade requirements relative to unmanaged charging. This paper illustrates how trajectory-coupled flexibility and scalable, certifiable optimization can provide actionable best-case benchmarks for DPN planning and operations.