A Multi-Criterion Approach to Smart EV Charging with CO2 Emissions and Cost Minimization

TL;DR

This paper presents a multi-criterion linear programming approach for smart EV charging that balances CO2 emissions and costs, using Vietnam's data to demonstrate effectiveness and computational efficiency.

Contribution

It introduces a lightweight, multi-objective scheduling framework that effectively balances emissions and costs in fossil-dominated grids, adaptable to different carbon and economic priorities.

Findings

The scheduler reduces emissions by 7.3% while maintaining 19.8% cost savings.

It remains effective across different hydro reservoir budgets, maintaining cost and emission benefits.

The LP solves in milliseconds, enabling practical day-ahead planning.

Abstract

We study carbon-aware smart charging in a fossil-dominated grid by coupling a simplified hydro-thermal-renewable dispatch model with a tractable linear charging scheduler. The case study is informed by Vietnam's regional data. Thermal units remain dominant, renewables are time-varying, and hydropower is modeled through a single reservoir budget. From the day-ahead dispatch we derive hourly carbon intensity and a corresponding carbon-cost signal; these are combined with a local time-of-use tariff in the EV charging problem. The resulting weighted-sum linear program is multi-objective: by sweeping the trade-off coefficient, we recover the supported Pareto frontier between electricity cost and charging-associated emissions. In a 300-EV public-charging scenario with a 0.8 MW feeder cap, the proposed carbon-aware scheduler preserves the 19.8% bill reduction of a cost-only optimizer while lowering charging-associated emissions by 7.3%; a more carbon-focused tuning still remains 12.6% cheaper and 9.3% cleaner than a FIFO baseline. A hydro-sensitivity study shows that changing the reservoir budget by +/- 20% moves the mean grid carbon intensity from 360 to 466 g/kWh, yet the carbon-aware scheduler remains consistently cheaper and cleaner than FIFO. The dispatch and charging LPs solve in few milliseconds on a standard desktop computer, showing that the framework is lightweight enough for repeated day-ahead studies.