Robust Optimal Operation of Virtual Power Plants Under Decision-Dependent Uncertainty of Price Elasticity
This work addresses the challenge of managing distributed energy resources for VPP operators, offering an incremental improvement by incorporating decision-dependent uncertainty into pricing models.
The paper tackles the problem of optimal pricing for virtual power plants (VPPs) under uncertainty in demand elasticity, which is influenced by pricing decisions, by proposing a robust operation model and an improved algorithm. Case studies using real London household data show the model's effectiveness, though no concrete numerical results are provided.
The rapid deployment of distributed energy resources (DERs) is one of the essential efforts to mitigate global climate change. However, a vast number of small-scale DERs are difficult to manage individually, motivating the introduction of virtual power plants (VPPs). A VPP operator coordinates a group of DERs by setting suitable prices, and aggregates them for interaction with the power grid. In this context, optimal pricing plays a critical role in VPP operation. This paper proposes a robust optimal operation model for VPPs that considers uncertainty in the price elasticity of demand. Specifically, the demand elasticity is found to be influenced by the pricing decision, giving rise to decision-dependent uncertainty (DDU). An improved column-and-constraint (C&CG) algorithm, together with tailored transformation and reformulation techniques, is developed to solve the robust model with DDU efficiently. Case studies based on actual electricity consumption data of London households demonstrate the effectiveness of the proposed model and algorithm.