Integrating Aging-Awareness into Price-Optimized PV-Battery Controllers Using Stochastic Dynamic Programming

This study presents the integration of a battery aging model into a non-linear optimization framework, with the primary goal of optimizing electricity costs through the use of Stochastic Dynamic Programming (SDP) as the solver. By incorporating aging effects into the existing algorithm, the study focuses on enhancing smart energy management in battery systems while extending their operational lifespan. The proposed methodology seamlessly integrates an aging-awareness model into the optimization process, maintaining the mathematical complexity of the original system while incorporating aging dynamics. The new algorithm aligns aging effects with price optimization by integrating them into a Stochastic Model Predictive Control (MPC) scheme. The aging-aware controller results in a significant improvement in the battery’s State of Health (SoH), increasing it from 95% to 98% compared to the non-aging-aware version, without substantially affecting the electricity bill. The findings underscore the potential of multi-objective optimization in real-time PV-battery systems, demonstrating how aging-aware strategies can substantially improve battery performance, cost-efficiency. This approach offers a pathway to more sustainable and efficient energy management systems in renewable energy applications, ensuring longer battery life and optimized economic benefits.