A trading optimization model for virtual power plants in day-ahead power market considering uncertainties

Abstract

Background: The day-ahead power market is an important part of the spot market. In the day-ahead market, participants make short-term forecasts of the load and output to propose the bidding curve more precisely. As energy aggregators that have regulatory resources, virtual power plants (VPPs) need to consider the uncertainty of distributed renewable energy output when participating in power market transactions. Methods: This paper analyzes the uncertainty and built an optimization model for VPP in day-ahead power market considering the uncertainty from both inner parts and the market environment. To verify the model, a simulation study is ran. Results: And the study results show the following: 1) the forecasting model is more efficient than the traditional algorithm in terms of accuracy, and 2) the confidence levels are not fully positive with the benefit of VPPs. Discussion: Improving the confidence level could reduce the uncertainty brought by renewable energy, but could also cause conservative trading behavior and affect the consumption of renewable energy.