The Impact of Smart Grid Prosumer Grouping on Forecasting Accuracy and Its Benefits for Local Electricity Market Trading

Abstract

Local electricity markets may emerge as a mechanism for managing the increasing numbers of distributed generation resources. However, in order to be successful, these markets will heavily rely on accurate forecasts of consumption and/or production from its participants. This issue has not been widely researched in the context of such markets, and it presents a clear roadblock for wide market adoption as forecasting errors result in penalty and opportunity costs. Forecasting individual demand often leads to large errors. However, these errors can be reduced through the creation of groups, however small. In the work presented here, we investigate the relationship between group size and forecast accuracy, based on Seasonal-Naïve and Holt-Winters algorithms, and the effects forecasting errors have on trading in an intra-day local electricity market composed of consumers and “prosumers.” Furthermore, we measure the performance of a group participating on the market, and demonstrate how it can be a mitigating strategy to enable even highly unpredictable individuals to reduce their costs, and participate more effectively in the market.