Joint maintenance scheduling for generators and energy storage systems in sustainable power systems

• A robust ESS-coordinated framework improves energy adequacy in power systems with a high share of renewable energy. • The maintenance and operation process is modeled using a monthly time-series simulation with hourly resolution. • Exogenous forecast errors are modeled with Wasserstein ambiguity sets, while endogenous ESS aging and failure uncertainties follow Weibull distributions. • A distributionally robust optimization method is applied to the bilevel model, which is efficiently solved using strong duality theory. As sustainable energy penetration continues to rise, challenges such as renewable curtailment and power shortages have become increasingly prominent. Generator maintenance plays a crucial role in addressing these issues, directly impacting the system’s ability to manage peak shaving and valley filling. However, traditional scheduling methods struggle to accommodate the uncertainties introduced by renewable energy sources and energy storage systems (ESSs). To bridge this gap, we propose an ESS-coordinated maintenance framework that integrates ESSs’ operational costs, robust optimization led by system operators, and multi-faceted uncertainties. The framework accounts for both exogenous forecasting errors and endogenous ESS failure rates. A Wasserstein-based ambiguity set with dynamic robustness adjustment is designed to capture exogenous uncertainties, while the Weibull distribution models the relationship between failure rates and the aging process. To ensure effective ESS coordination and manage these dual uncertainties, the problem is formulated as a bi-level distributionally robust optimization model, and solved with the strong duality theorem. Case studies on a modified provincial power system with increasing renewable energy penetration demonstrate the framework’s effectiveness. The case study reveals an 18.74% reduction in yearly unbalanced energy. These results highlight the potential of our approach to enhance system reliability and support renewable integration.