Towards Financial Resilience: Smart Energy Management in Virtual Power Plants using Stochastic–Robust Optimization

Abstract

The dynamic coordination of Distributed Energy Resources (DERs) and price-responsive demands, orchestrated as a Virtual Power Plant (VPP), stands as a pivotal challenge in the electricity industry. This paper presents a novel contribution through the application of stochastic–robust optimization to address the intricate uncertainties associated with stochastic DERs, fluctuating energy prices, and varying Micro-Grid (MG) component availability. Leveraging smart grid technology enables real-time monitoring of the VPP, facilitating dynamic adjustments to energy management decisions in response to uncertainties. The focus is on maximizing the financial profit of the VPP, achieved through seamless integration of solar power stations, storage units, and price-responsive demands within a MG. The paper employs interval- and scenario-based forecasting to systematically model continuous random variables (e.g., energy price and solar generation) and discrete random variables (e.g., MG component availability). Through an in-depth case study, the proposed stochastic–robust optimization is rigorously analyzed, demonstrating superior performance, particularly during contingencies on days with highly volatile energy prices. This research provides valuable insights and a robust framework for navigating the intricate landscape of VPP energy management amid evolving industry dynamics and technological advancements. The stochastic robust model obtained in average 12.5% more profit in comparison with other models. Moreover, regarding the conditional value at risk in the healthy condition of MG, the profit reduced about 43.7% and 12.6% compared to deterministic with confidence levels 85% and 95%, respectively.