Joint maintenance scheduling for generators and energy storage systems in sustainable power systems
Year: 2025
Type: article
Abstract: 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 ESS profit-seeking behavior, 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, we formulate the problem as a bi-level distributionally robust optimization model, which is solved using a strong duality theorem. Case studies on a modified provincial power system with increasing renewable energy penetration, which demonstrating the framework’s effectiveness. The case study shows a reduction in yearly load shaving by 18.74% and renewable energy curtailment by 3.23% when renewable penetration reaches 40%.
Authors: Nian Liu, Yubing Chen, Kai Jiang, Jie Huang, Zhen Wang
Language: English
Subfield: Safety, Risk, Reliability and Quality
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,920
Award: G8826514033
OpenAlex ID: W7114787635