Intelligent Degradation-Aware Dispatch for Inverter-Based Photovoltaic–Battery Energy Systems

Solar photovoltaic (PV) and battery energy storage systems (BESS) are increasingly deployed in homes and communities. However, conventional operational strategies often prioritise short-term electricity costs without considering equipment ageing. Repeated battery cycling contributes to capacity degradation, while temperature-dependent PV losses affect available generation and system efficiency. This study develops an intelligent degradation-aware dispatch framework for inverter-interfaced PV–battery systems, integrating reduced-order battery ageing, PV degradation and thermal derating, inverter constraints, and adaptive supervisory control within a lightweight, rule-based architecture. The framework assesses electricity-cost and battery-preservation trade-offs under operating constraints. A full-year simulation at 15 min resolution compares four policies: Baseline, Battery-Aware, Battery+PV-Aware, and Adaptive-Supervisory. Relative to the Baseline, Battery-Aware and Battery+PV-Aware reduce annual equivalent full cycles (EFCs) by 18.03% and 29.10%, respectively, with electricity-cost changes of −0.11% and +1.88%. The Adaptive-Supervisory policy reduces EFCs by 8.49% and operating cost by 1.81%. Controlled ablations show that supervisory adaptation reduces cycling by 2.14% and 10.15% under supplementary synthetic tariffs, but does not alter dispatch under the original three-level tariff. Equal-constraints comparisons further reveal tariff-dependent trade-offs between fixed and adaptive degradation weighting, without establishing universal adaptive superiority. Supplementary simulations using UK-DALE residential demand and PVGIS meteorological inputs show consistent battery-cycling reductions of 19.27–20.67% over 198.25 days. The findings demonstrate that degradation-informed policies can reduce battery cycling with modest economic trade-offs, while adaptive supervision provides additional benefits under suitable tariff conditions. The framework offers a transparent, computationally tractable approach to lifecycle-aware energy management, with physical validation and calibrated ageing models remaining priorities for future work.