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

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