A holistic optimization framework for virtual power plants with physics-informed battery degradation and probabilistic stability constraints

The operation of Virtual Power Plants (VPPs) is impacted by both the uncertainty of markets and the limitations of physical assets, affecting the financial reliability and asset longevity of VPPs. This paper outlines a new two-stage stochastic optimization method for the co-optimization of the VPP's financial performance, its battery degradation, and its ability to provide primary frequency response. Key aspects of this method include: (1) a real-time, physics based electrochemical model to estimate the marginal cost of battery degradation in real time; (2) a multivariate ARIMA-GARCH model to forecast correlated market price and renewable power production forecasts; and (3) a Conditional Value at Risk (CVaR) probabilistic constraint to insure reliable frequency response. A detailed case study demonstrates that employing a degradation-aware strategy, rather than a traditional profit-maximizing approach, results in a 5.4% increase in annual net profit alongside a significant extension of battery lifetime. The proposed method will provide utilities with a strategic decision-making tool to balance their short-term revenue requirements, their long-term asset health needs, and their obligation to maintain grid stability.