Integrated Degradation-Aware and Uncertainty-Driven Techno-Economic Planning of Hybrid Renewable Microgrids
Abstract
Hybrid renewable microgrids are increasingly considered a practical solution for supplying reliable and sustainable electricity to remote regions. However, many planning studies do not consider the longer-term effects of battery degradation and the effect of changing operating conditions on system performance. This study develops an integrated techno-economic planning mathematical model for a self-sufficient PV/WT/DG/BESS microgrid using actual hourly meteorological and load-demand data from New Minia, Egypt. This framework integrates variability in renewable resources, battery degradation, reliability constraints, environmental factors, and uncertainty evaluation as part of a comprehensive assessment. The proposed approach identifies a system configuration that balances supply reliability, economic performance, renewable energy (RE) penetration, and long-term storage sustainability. The optimal configuration achieved a cost of energy (COE) of 0.1545 $/kWh and a net present cost of approximately 4.5 M$, while maintaining the RE contribution of 79.43%. The configuration achieved a low loss of power supply probability (LPSP) of 0.00593 and annual expected energy not served of 13,516.52 kWh. The resultant configuration reduced dependence on diesel generation and decreased yearly CO2 emissions to 483.05 ton/year. A degradation-aware battery model was incorporated to represent long-term storage behavior, yielding an estimated battery service life of approximately 12.16 years under the adopted operating assumptions. Furthermore, deterministic sensitivity analysis was conducted to evaluate the influence of key economic and system parameters on the techno-economic performance of the proposed microgrid. Overall, the findings demonstrate the effectiveness and practical potential of the proposed degradation-aware and uncertainty-driven framework in supporting reliable and economically sustainable hybrid-microgrid planning.