Optimal Charge Control of Plug-In Hybrid Electric Vehicles in Deregulated Electricity Markets

TLDR

Plug‑in hybrid electric vehicles can reduce oil dependence, but uncontrolled large‑scale deployment risks grid overload due to increased peak load. The study proposes and analyzes two algorithms to mitigate peak‑load risks from plug‑in hybrid electric vehicles. Both algorithms use price forecasts and dynamic programming, with one optimizing charging schedules and the other adding vehicle‑to‑grid ancillary service participation while respecting utilization and technical constraints. The algorithms substantially lower daily electricity costs—from $0.43 to $0.20 per driving day—and, when providing regulating power, increase daily profits to $1.71 while avoiding extra battery wear.

Abstract

Plug-in hybrid electric vehicles are a midterm solution to reduce the transportation sector's dependency on oil. However, if implemented in a large scale without control, peak load increases significantly and the grid may be overloaded. Two algorithms to address this problem are proposed and analyzed. Both are based on a forecast of future electricity prices and use dynamic programming to find the economically optimal solution for the vehicle owner. The first optimizes the charging time and energy flows. It reduces daily electricity cost substantially without increasing battery degradation. The latter also takes into account vehicle to grid support as a means of generating additional profits by participating in ancillary service markets. Constraints caused by vehicle utilization as well as technical limitations are taken into account. An analysis, based on data of the California independent system operator, indicates that smart charge timing reduces daily electricity costs for driving from $0.43 to $0.2. Provision of regulating power substantially improves plug-in hybrid electric vehicle economics and the daily profits amount to $1.71, including the cost of driving.