Robust Optimal Operation of Virtual Power Plants Under Decision-Dependent Uncertainty of Price Elasticity

TL;DR

This paper develops a robust optimization model for virtual power plants that accounts for decision-dependent uncertainty in demand elasticity, improving operational reliability under uncertain market conditions.

Contribution

It introduces a novel robust optimization framework incorporating decision-dependent demand elasticity uncertainty and proposes an efficient solution algorithm.

Findings

The model effectively handles demand elasticity uncertainty in VPP operations.

The proposed algorithm outperforms existing methods in computational efficiency.

Case studies validate the model's practical applicability and robustness.

Abstract

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.