Portfolio optimization of generic energy storage-based virtual power plant under decision-dependent uncertainties

Abstract

Distributed energy resources (DERs) can be integrated into a smart and aggregated entity, namely a virtual power plant (VPP). This integration is beneficial to facilitate their flexibility utilization and optimally allocate their capacity to participate in competitive electricity markets. While various types of uncertainties exist in the market operation of VPP, such as the stochastic power of renewable energy resources (RES), etc., the literature generally overlooked the decision-dependent uncertainties (DDUs) with the operation of generic energy storage (GES). To be risk-hedging, this manuscript proposes a novel approach for risk-averse portfolio optimization of VPP with special attention to DDUs. To achieve tractable optimization, we propose a two-stage chance-constrained stochastic optimization model and three reformulations of different types of uncertainties. Furthermore, a modified and iterative progressive hedging algorithm is further proposed to handle the conditional value at risk (CVaR) and DDU items and efficiently solve the problem with large-scale GES portfolios. Exhaustive case studies based on real-world data demonstrate that VPP has to face the consequence of a significant real-time penalty (6.36 % loss in average profit) and losses in GES power (about 6.5 MW) when overlooking the DDUs' consequences. Moreover, it is more beneficial for greedy decision-makers to incorporate the DDUs' impact into their less risk-aversion strategy. Additionally, comparative results prove the validity of the proposed algorithm and its superiority in solving problems with large-scale GES portfolios and high-performance computing systems (over 30 % CPU time saved).