Comparing models of aggregate EV electricity market capacity when driver behavior is uncertain

What is it about?

Electric vehicles have flexibility in when, and sometimes at what rate, they can charge. Aggregating this flexibility across hundreds of vehicles can enable them to act as a virtual power plant (VPP) that can participate in the wholesale energy market, like generators and utilities do. Electric vehicle owners could save money and even turn a profit by optimizing when they charge and by providing grid services, like voltage or frequency regulation. The first step to making this possible is modeling the aggregate energy and power capacity of the vehicles in an efficient way that can be easily communicated while preserving privacy. Many models have been developed, but they might perform better or worse when driver behavior is uncertain. This paper provides a way to compare different electric vehicle aggregation models and their performance when there is uncertainty in when drivers arrive and depart and in how much they need to charge.

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Photo by Sevadis on Unsplash

Photo by Sevadis on Unsplash

Why is it important?

In addition to comparing four models that haven't been compared previously, this work provides a way to understand how models perform under uncertainty. Additionally, the results could provide a benchmark for more complex models that attempt to account for uncertainty.