Electric vehicle (EV) adoption rates are rising in California as a result of successful state air pollution mitigation regulations such as the Zero Emission Vehicle mandate. While this is a great feat for reducing local air pollution throughout the state, improperly managed EV charging can lead to peak electricity demand loads that can strain local distribution networks and potentially increase point source emissions from power plants. This problem can be solved if a straight forward algorithm can handle this uncertainty by estimating the accurate rate of load demand that is needed. Smart charging is one of the most promising approaches to prevent sub-optimal EV charging as it enables utility operators to control EV charging to reduce load spikes and take full advantage of renewable power. The researchers aim to develop an Artificial Neural Network (ANN) to forecast EVs’ trip destinations and charging behavior–information that is essential for electricity load aggregators to effectively manage charging loads.
