Kumar, Vikram, Naizi, Muhammad Ahsan, Aslam, Usama, Saeed, Nagham ORCID: https://orcid.org/0000-0002-5124-7973, Aurangzeb, Muhammad and Shah, Syed Abid Ali (2026) A holistic optimization framework for virtual power plants with physics-informed battery degradation and probabilistic stability constraints. Future Batteries, 9.

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Abstract

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.

Item Type:	Article
Identifier:	10.1016/j.fub.2026.100146
Keywords:	Virtual power plant; Battery degradation; Stochastic optimization; Electrochemical modeling; Grid reliability; CvaR
Subjects:	Construction and engineering > Electrical and electronic engineering
Date Deposited:	30 Sep 2026
Dates:	Date Publication status 12 January 2026 Accepted 14 January 2026 Published Online
School, department or research centre:	School of Computing and Engineering
Keywords:	Virtual power plant; Battery degradation; Stochastic optimization; Electrochemical modeling; Grid reliability; CvaR
URI:	https://repository.uwl.ac.uk/id/eprint/15475

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