Algorithmic Approaches to Output Prediction in a Virtual Power Plant

Abstract: Virtual Power Plants (VPPs) are an emerging form of technology that allows owners of electricity producing appliances, such as electric vehicles, to partake in a pool of producers of sustainable energy. The Swedish electricity grid owner Svenska Kraftnät hosts a platform where VPPs act as intermediaries between energy producing customers and third party buyers. A requirement to participate in these transactions, however, is to post a bid specifying the amount of power that can be produced from a VPP during a given hour at least 48 hours into the future. This is where forecasting comes into the picture. This report compares the accuracy of eight different machine learning models when tasked with forecasting power output using the same training data from an electric vehicle-based VPP. The study also examines which inferences about customer behavior can be drawn from the same data and give strategic recommendations to VPPs based on the findings of the study. Upon evaluating the results, it was found that deep learning models outperformed autoregressive models, which in turn outperformed Random Forest Regression and Support Vector Regression. As for customer behaviors found in the data, a small negative correlation between spot prices and delivered output was found, suggesting that customers limit their charging when spot prices are high. Further, more power is generally produced during nighttime and on weekends. The data also shows an autocorrelation with a lag of 24 hours, suggesting that charging behaviors on a given day influence charging behaviors the subsequent day.