Impact Analysis of Optimal EV Bi-directional Charging with Spatial-temporal Constraints

TL;DR

This paper presents an optimization method for EV bi-directional charging that considers spatial-temporal constraints, aiming to reduce costs and grid stress through smart scheduling and V2G technology.

Contribution

It develops a mixed-integer linear programming model to evaluate the impact of spatial power constraints and real-time prices on EV charging strategies.

Findings

Minimal cost increase under extreme capacity constraints

Effective load shifting with smart scheduling

Potential grid support through V2G technology

Abstract

The growth in Electric Vehicle (EV) market share is expected to increase power demand on distribution networks. Uncoordinated residential EV charging, based on driving routines, creates peak demand at various zone substations depending on location and time. Leveraging smart charge scheduling and Vehicle-to-Grid (V2G) technologies offers opportunities to adjust charge schedules, allowing for load shifting and grid support, which can reduce both charging costs and grid stress. In this work, we develop a charge scheduling optimization method that can be used to assess the impact of spatial power capacity constraints and real-time price profiles. We formulate a mixed-integer linear programming problem to minimize overall charging costs, taking into account factors such as time-varying EV locations, EV charging requirements, and local power demands across different zones. Our analysis uses real data for pricing signals and local power demands, combined with simulated data for EV driving plans. Four metrics are introduced to assess impacts from the perspectives of both EV users and zones. Results indicate that overall EV charging costs are only minimally affected under extreme power capacity constraints.