A Framework for Resilient Power System Optimization under Renewable Uncertainty

Thesis Abstract

The increasing integration of renewable energy sources into power systems has introduced significant variability and uncertainty, posing challenges to ensuring reliability, stability, and optimal operation of electrical networks. This study aims to develop a comprehensive framework for enhancing the resilience of power system optimization processes under the pervasive uncertainties associated with renewable energy generation, focusing on wind and solar power variability. The specific objectives include (1) to analyze the influence of renewable energy variability on power system operational efficiency; (2) to identify and model critical uncertainty factors affecting system reliability; (3) to design an optimization framework incorporating resilience objectives through multi-criteria decision-making; and (4) to validate the proposed framework through simulation on a representative regional power grid. The research adopts a mixed-methods approach, employing a quantitative research design complemented by qualitative insights. The primary data comprises voltage, power flow, and generation variability datasets collected over a two-year period from a regional grid operator serving approximately 2 million consumers. A stratified random sampling technique was used to select 150 measurement points across different regions and generation sites for data collection using smart meters and Supervisory Control and Data Acquisition (SCADA) systems. Additional information was obtained through expert interviews with 20 grid operation engineers, providing qualitative insights into system flexibility and resilience strategies. Data analysis involves advanced statistical techniques, including Monte Carlo simulations to assess uncertainty impacts, regression analysis to determine the significance of identified variables, and multi-criteria decision analysis (MCDA) to evaluate the optimality of new operational strategies. The study proposes a resilience-oriented power system optimization model integrating stochastic programming with robust optimization techniques, grounded in the Resilience Theory and the Adaptive Capacity Theory. The stochastic programming component captures the probabilistic nature of renewable generation, while robust optimization ensures solutions maintain efficacy under worst-case scenarios. The model’s framework incorporates grid flexibility measures, such as energy storage, demand response, and transmission upgrades, to enhance system adaptability. Simulation results indicate that the proposed framework improves system reliability indices by an average of 15% compared to conventional optimization models, reduces power outages by 12%, and enhances renewable energy utilization efficiency by 8%. Sensitivity analyses reveal the model’s robustness under various uncertainty intensities and operational constraints. This research contributes novel insights into the strategic integration of resilience criteria within power system optimization, extending existing models by explicitly addressing renewable uncertainty through an integrated stochastic-robust framework. It advances theoretical understanding by empirically demonstrating the effectiveness of combining resilience and flexibility measures in operational contexts, thus filling a significant gap in current literature. The framework offers practical implementation pathways for grid operators to balance economic costs with resilience objectives, supporting more reliable and sustainable energy transitions. It also provides a methodological template adaptable to different regional frameworks and renewable profiles. In conclusion, the study underscores the importance of incorporating resilience considerations into power system optimization to cope effectively with renewable variability. It recommends that policymakers and grid managers adopt the proposed framework to enhance system robustness, integrate energy storage solutions strategically, and invest in grid modernization initiatives. Future research should explore the integration of emerging technologies such as artificial intelligence and machine learning to further enhance system resilience, as well as extend the framework’s application to multi-energy systems integrating electricity, heat, and gas networks. The findings systematize resilience-enhanced optimization practices, offering a vital tool for the sustainable and reliable integration of renewables into modern power networks.