Future-Proofing Renewable Energy Systems: A Multi-Dimensional Resiliency Framework for Island Grids under Extreme Demand Stress
DOI:
https://doi.org/10.70917/ijcisim-2026-3593Keywords:
Power System Resiliency, Renewable Energy, Island Grid, Extreme Grid Stress, Reli- ability Indices, MindanaoAbstract
Objectives: To develop and empirically validate a multi-dimensional resiliency assessment framework for renewable-rich island power systems subjected to extreme real-world grid stress, using the isolated Mindanao grid of the Philippines during the 2020 demand disturbance as a full-scale natural experiment.
Methods: Six years (2015 to 2020) of official operational records from the system operator, the Department of Energy, and utility regulatory reports were consolidated, covering monthly energy delivery, system peak demand, 366 days of daily supply-demand balance, generation and rated capacity by fuel type, average system rates of more than thirty distribution utilities, and the reliability indices SAIFI, SAIDI, and MAIFI. Resiliency was evaluated across four dimensions, namely operational, generation, economic, and infrastructure, through pre-disturbance and during-disturbance comparative analysis.
Findings: The disturbance compressed monthly energy delivery by up to 9.3% and April peak demand by 11.6% year-on-year, yet annual energy delivery contracted by only 1.0% (10,180 GWh to 10,075 GWh) and annual generation by 0.4%. Renewable generation adapted flexibly: hydro output fell 10.3% while solar and biomass output rose 18.7% and 17.9%, respectively. The mean daily reserve margin widened from 44.4% in 2019 to 58.0% in 2020, average distribution rates declined 4.5% to PhP 9.35/kWh, and reliability improved, with SAIFI, SAIDI, and MAIFI changing by −4.3%, −4.3%, and −20.7%, respectively. All four dimensions were maintained or improved, demonstrating system-level resiliency.
Novelty: The study transforms a once-in-a-generation real-world disturbance into a reusable four- dimension resiliency assessment framework, validated entirely with measured operational data, that is applicable to future extreme stress scenarios such as cyberattacks, climate-induced disasters, and fuel crises in islanded and developing renewable-dominant power systems.