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Vehicle-To-Grid Plug-In Forecast To Aid Aggregator Participation In Ancillary Services Markets
Electric vehicle (EV) charge points (CPs) can be used by aggregators to provide frequency response services. Aggregators must have day-ahead forecasts of aggregate vehicle-to-grid (V2G) plug-in to produce meaningful bids for the day-ahead ancillary services market. However, there is a lack of understanding on what features should be considered and how complex the forecasting model should be. This paper explores the dependency of aggregate V2G plug-in on historic plug-in levels, calendar variables, and weather conditions. These investigations are used to develop three day-ahead forecasts of minimum aggregate V2G plug-in during 30-minute window. A neural network that considers previous V2G plug-in values the day before, three days before, and seven days before, in addition to day of the week, month, and hour, is found to be the most accurate.