Abstract

The transition to renewable energy has positioned photovoltaic (PV) systems and battery energy storage systems (BESS) as essential assets in microgrids, particularly for remote installations. However, traditional planning models often neglect dynamic degradation costs or rely on complex or non-linear approaches, limiting their scalability and practical applicability. This paper introduces a microgrid planning model that integrates adaptive degradation cost modeling to enable accurate, efficient, and scalable long-term resource allocation. The proposed model employs the iterative post-optimization correction (IPOC) framework, solving a sequence of mixed-integer linear programming problems. Each iteration refines BESS degradation costs based on observed depth-of-discharge profiles and incorporates PV degradation costs to ensure realistic asset performance assessments. Sensitivity analysis of PV and BESS capital costs further underscores the model’s robustness under varying economic conditions, with the IPOC framework achieving up to ~1% additional cost savings for the given test system compared to static approaches. The results demonstrate that by iteratively adjusting degradation penalties based on actual usage, the methodology optimizes BESS performance, ensures precise resource allocation, resolves issues of under- or overutilization, enhances system reliability, and facilitates scalable, sustainable microgrid planning.

Index Terms

Battery energy storage system, battery degradation, microgrid, MILP, optimal resource sizing, PV degradation, renewable energy.

Cite this paper:

Hassan Zahid Butt and Xingpeng Li, “Enhancing Optimal Microgrid Planning with Adaptive BESS Degradation Costs and PV Asset Management: An Iterative Post-Optimization Correction Framework”, Electric Power Systems Research, vol. 247, Oct. 2025.