Optimal bidding strategy for virtual power plants in day-ahead markets: A quantile-based approach

This study addressed the critical challenge of optimizing bidding strategies for virtual power plants (VPPs) with high renewable energy integration in the day-ahead market (DAM), a setting where balancing energy supply and demand is complicated by the variable output of renewable sources. The proposed framework was designed to minimize the financial impacts of uncertainties inherent in renewable energy sources like wind and solar power, which are increasingly prevalent in VPPs. To achieve this, a robust optimization approach was develop that models uncertainties in power output and incorporates economic penalties for deviations. Key elements included diverse energy sources within VPP—such as electric vehicles (EVs), energy storage, gas turbines (GT), photovoltaic (PV) systems, and hydropower stations—which were carefully managed to maintain reserve capacities for balancing fluctuations. The study utilized a mixed-integer nonlinear programming model combined with quantile and super-quantile theory to allocate reserve capacity more effectively, distinguishing the current work from previous models that lacked this level of adaptability and economic consideration. Results indicate that this approach not only enhances profit margins for VPPs but also improves operational stability. The findings highlighted the strategic advantage of using a multi-source, uncertainty-aware bidding strategy, offering a novel contribution to the field by advancing beyond conventional reserve and penalty models. This work demonstrated that a diversified VPP with an optimized bidding strategy could successfully navigate the complexities of renewable integration, presenting a significant advancement over existing methods in the literature.