A trading optimization model for virtual power plants in day-ahead power market considering uncertainties - Weishang, Guo; Qiang, Wang; Haiying, Liu; Jing, Wu (Frontiers in Energy Research, 2023)
The day-ahead power market is an important part of the spot market. In the day-ahead market, market participants can 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 day-ahead power market transactions, as well as the risk inclination of decision makers. This paper analyzes the uncertainty related to VPPs’ participation in day-ahead power market transactions. and built an The optimization optimal model layout for VPP in the day-ahead power market of a VPP is simulated and calculated, including wind, PV, energy storage, and demand response when participating in power market trading considering the uncertainty from both inner parts and the market environment. To verify the model, a simulation study is ran. 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. 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.