Table 1.
List of symbols.
Fig 1.
Cgen[l], (black line): actual half-hourly carbon intensity measurements for UK National Grid electricity generation during the first week of January 2022, published by the National Energy System Operator (NESO) [25];
(black dotted line): NESO’s published sequence of one-step-ahead forecasts;
(blue line): our l-step-ahead forecast sequence (12), estimated by transferring CarbonCast side-information [21].
Fig 2.
Empirical distribution of relative one-step ahead forecast error, , observed in the NESO published forecasts (2022) [25].
Fig 3.
Mean absolute percentage error (MAPE) of the synthesized long-range carbon intensity forecast sequence, Cgen[l].
Table 2.
Aggregate simulation results (2022, UK National Grid). Mean ± standard deviation of average carbon intensity of energy charged by the EV, CEV (gCO2/kWh) and percentage improvements for different forecast horizons N.
Fig 4.
Sensitivity of CEV to forecast horizon N.
The error bars show one standard deviation around the mean.The sensitivity analysis suggests that CEV plateaus for approximately N > 3.
Fig 5.
Average carbon intensity of energy charged by the EV, CEV for each month of the year 2022.
Fig 6.
Charging Power, P (kW), during a period of one month for three different Charging Strategies:
(a) uncontrolled plug-and-charge strategy; (b) MPC (N = 1) charges the EV every night, but at the expected minimum carbon intensity during that night; (c) MPC (N = 4) can delay charging during nights where Cgen is high, resulting in fewer, but longer, charges during periods of low Cgen.
Fig 7.
State of Charge (SOC) profiles over six simulation days for three charging strategies: uncontrolled (blue), MPC with a 1-day horizon (orange), and MPC with a 4-day horizon (green).
The black line shows the carbon intensity signal (Cgen). Gray background indicates night charging windows, during which the EV is plugged in; The figure highlights how predictive strategies shift charging to periods of lower carbon intensity.
Fig 8.
Cumulative CO2 emissions over six simulation days for the same three charging strategies.
Emissions increase stepwise with each charging event. MPC strategies result in significantly lower cumulative emissions.
Fig 9.
Impact of user flexibility on average carbon intensity (CEV) during one month of simulated EV charging (January 2023, UK national carbon intensity data).
Each gray bar represents a different daily plug-in time window, ranging from 20 to 4 hours. Four lines correspond to daily energy demands of 5, 10, 20, and 30 kWh. Shorter plug-in durations and higher energy demands both increase average carbon intensity. Daytime charging scenarios (right side) typically result in significantly higher emissions than overnight charging at comparable plug-in durations.
Fig 10.
Regional variation in average carbon intensity (CEV) of EV charging across 14 UK regions during January 2023.
Results are shown for uncontrolled charging and three predictive MPC strategies with forecast horizons N = 1, 2, 4 days. Smart charging reduces emissions in all regions, with relative reductions ranging from 16% to 83.9%. Longer prediction horizons yield larger reductions across regions.