Abstract

Traditional microgrid planning often overlooks PV and BESS degradation or relies on complex, downscaled models, leading to unreliable, costly and suboptimal investment decisions. This paper presents a degradation-based investment optimization (DBIO) methodology for long-term microgrid planning. The model optimally sizes and schedules PV, BESS, and controllable distributed energy resources, while considering technical, financial, and degradation characteristics. We first developed a cumulative multi-year optimization model as a benchmark, excluding BESS efficiency fade and capacity degradation that would be captured in the next step, to ensure convergence. Subsequently, a yearly validation model was iteratively solved for each year in the planning horizon, updating energy efficiencies of PV and BESS, along with BESS capacity, based on annual degradation, ensuring the reliability of initial solution. An iterative refinement process further adjusts BESS capacity to eliminate load shedding while minimizing costs. Sensitivity analyses on PV efficiency degradation rates, second-life battery (SLB) capital cost, and grid tariffs further explore their economic implications. Results show that degradation significantly impacts resource allocation, with ignored degradation risking reliability, potential load shedding, and blackout costs, while SLBs provide cost-saving opportunities. The DBIO framework offers a computationally efficient and scalable solution for microgrid planning, with broader applications in grid-scale asset management.

Index Terms

Battery energy storage systems, capacity fade, efficiency fade, iterative optimization, microgrid planning, mixed-integer linear programming, PV degradation, renewable energy integration, second-life batteries

Cite this paper:

Hassan Zahid Butt and Xingpeng Li, “Degradation-Aware Microgrid Optimal Planning: Integrating Dynamic Energy Efficiencies and Second-Life Battery Potential”, arXiv, Mar. 2025.