Highlights: What are the main findings? A two-stage stochastic MILP framework is proposed, integrating distributed energy resources (DERs), microgrids, and remotely controlled switches to optimize real-time operation and enhance resilience in distribution systems. The first stage focuses on economic day-ahead scheduling of DERs, load management, and network reconfiguration based on real-time market data. The second stage re-optimizes operations under specific disruption scenarios, leveraging DER dispatch, microgrid formation, and prioritized load shedding to maximize system resilience. What is the implication of the main finding? The proposed method allows distribution system operators (DSOs) to account for uncertainties in renewable generation, market prices, and component vulnerabilities, resulting in reduced load curtailment, improved voltage stability, and faster post-event recovery during extreme weather conditions. This framework contributes to developing smart, climate-resilient urban infrastructure by supporting the coordinated operation of decentralized energy resources and enabling adaptive, cost-effective grid management under stress conditions. Climate change has emerged as a significant driver of the increasing frequency and severity of power outages. Rising global temperatures place additional stress on electrical grids that must meet substantial electricity demands, while extreme weather events such as hurricanes, floods, heatwaves, and wildfires frequently damage vulnerable electrical infrastructure. Ensuring the resilient operation of distribution systems under these conditions poses a major challenge. This paper presents a comprehensive two-stage techno-economic strategy to enhance the resilience of modern distribution systems. The approach optimizes the scheduling of distributed energy resources—including distributed generation (DG), wind turbines (WTs), battery energy storage systems (BESSs), and electric vehicle (EV) charging stations—along with the strategic placement of remotely controlled switches. Key objectives include preventing damage propagation through the isolation of affected areas, maintaining power supply via islanding, and implementing prioritized load shedding during emergencies. Since improving resilience incurs additional costs, it is essential to strike a balance between resilience and economic factors. The performance of our two-stage multi-objective mixed-integer linear programming approach, which accounts for uncertainties in vulnerability modeling based on thresholds for line damage, market prices, and renewable energy sources, was evaluated using the IEEE 33-bus test system. The results demonstrated the effectiveness of the proposed methodology, highlighting its ability to improve resilience by enhancing system robustness, enabling faster recovery, and optimizing operational costs in response to high-impact low-probability (HILP) natural events.
Enhancing Modern Distribution System Resilience: A Comprehensive Two-Stage Approach for Mitigating Climate Change Impact
Siano P.
2025
Abstract
Highlights: What are the main findings? A two-stage stochastic MILP framework is proposed, integrating distributed energy resources (DERs), microgrids, and remotely controlled switches to optimize real-time operation and enhance resilience in distribution systems. The first stage focuses on economic day-ahead scheduling of DERs, load management, and network reconfiguration based on real-time market data. The second stage re-optimizes operations under specific disruption scenarios, leveraging DER dispatch, microgrid formation, and prioritized load shedding to maximize system resilience. What is the implication of the main finding? The proposed method allows distribution system operators (DSOs) to account for uncertainties in renewable generation, market prices, and component vulnerabilities, resulting in reduced load curtailment, improved voltage stability, and faster post-event recovery during extreme weather conditions. This framework contributes to developing smart, climate-resilient urban infrastructure by supporting the coordinated operation of decentralized energy resources and enabling adaptive, cost-effective grid management under stress conditions. Climate change has emerged as a significant driver of the increasing frequency and severity of power outages. Rising global temperatures place additional stress on electrical grids that must meet substantial electricity demands, while extreme weather events such as hurricanes, floods, heatwaves, and wildfires frequently damage vulnerable electrical infrastructure. Ensuring the resilient operation of distribution systems under these conditions poses a major challenge. This paper presents a comprehensive two-stage techno-economic strategy to enhance the resilience of modern distribution systems. The approach optimizes the scheduling of distributed energy resources—including distributed generation (DG), wind turbines (WTs), battery energy storage systems (BESSs), and electric vehicle (EV) charging stations—along with the strategic placement of remotely controlled switches. Key objectives include preventing damage propagation through the isolation of affected areas, maintaining power supply via islanding, and implementing prioritized load shedding during emergencies. Since improving resilience incurs additional costs, it is essential to strike a balance between resilience and economic factors. The performance of our two-stage multi-objective mixed-integer linear programming approach, which accounts for uncertainties in vulnerability modeling based on thresholds for line damage, market prices, and renewable energy sources, was evaluated using the IEEE 33-bus test system. The results demonstrated the effectiveness of the proposed methodology, highlighting its ability to improve resilience by enhancing system robustness, enabling faster recovery, and optimizing operational costs in response to high-impact low-probability (HILP) natural events.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
