Optimizing Virtual Power Plant Operations in Energy and Frequency Regulation Reserve Markets: A Risk‐Averse Two‐Stage Scenario‐Oriented Stochastic Approach

The intermittent nature of distributed energy resources (DERs) has introduced significant challenges in power system operations, particularly in terms of flexibility, efficiency, and market participation. Aggregating DERs into a virtual power plant (VPP) offers a promising solution to these challenges, but it requires effective strategies to manage the inherent uncertainties and optimize operations across multiple energy markets. This paper develops an optimal bidding strategy for an aggregated multienergy virtual power plant (MEVPP) participating in both the day‐ahead (DA) energy market and the frequency regulation reserve market (FRRM). To effectively address these uncertainties, we propose a two‐stage scenario‐oriented stochastic optimization model that aims to maximize revenue and minimize operational costs by incorporating risk management strategies. Then, a novel fast forward selection and simultaneous reduction (FFS&SR) algorithm is proposed, which efficiently generates and refines scenarios, ensuring computational feasibility without compromising accuracy. The proposed VPP’s decision‐making problem considers the VPP’s risk‐averse nature, employing the conditional value at risk (CVaR) metric as a risk‐aversion parameter. Simulation results conducted over a 24‐h planning horizon validate the model’s performance, exhibiting superior performance in the bidding market scenarios. Furthermore, the numerical findings compare the risk‐neutral VPP framework with the proposed risk‐sensitive VPP strategy, revealing a trade‐off between expected profit and CvaR, indicating that as the risk aversion parameter escalates, expected profits decline while CVaR value rises, underscoring the importance of risk management in VPP optimization.