Resilient Disaster Recovery Logistics of Distribution Systems: Co-Optimize Service Restoration with Repair Crew and Mobile Power Source Dispatch

Abstract: Repair crews (RCs) and mobile power sources (MPSs) are critical resources for distribution system (DS) outage management after a natural disaster. However, their logistics is not well investigated. We propose a resilient scheme for disaster recovery logistics to co-optimize DS restoration with dispatch of RCs and MPSs. A novel co-optimization model is formulated to route RCs and MPSs in the transportation network, schedule them in the DS, and reconfigure the DS for microgrid formation coordinately, etc. The model incorporates different timescales of DS restoration and RC/MPS dispatch, the coupling of transportation and power networks, etc. To ensure radiality of the DS with variable physical structure and MPS allocation, we also model topology constraints based on the concept of spanning forest. The model is convexified equivalently and linearized into a mixed-integer linear programming. To reduce its computation time, preprocessing methods are proposed to pre-assign a minimal set of repair tasks to depots and reduce the number of candidate nodes for MPS connection. Resilient recovery strategies thus are generated to enhance service restoration, especially by dynamic formation of microgrids that are powered by MPSs and topologized by repair actions of RCs and network reconfiguration of the DS. Case studies demonstrate the proposed methodology.

• The paper introduces a co-optimization model that synchronizes repair crew routing with mobile power source deployment to enhance restoration speed and reliability.
• It applies novel spanning forest constraints to maintain distribution system radiality during dynamic reconfigurations and temporary microgrid formations.
• Numerical simulations on IEEE 33-node and 123-node systems demonstrate substantial load restoration and operational efficiency improvements.

Co-Optimizing Disaster Recovery Logistics for Electrical Distribution Systems

The paper "Resilient Disaster Recovery Logistics of Distribution Systems: Co-Optimize Service Restoration with Repair Crew and Mobile Power Source Dispatch" addresses a critical aspect of power systems engineering—efficient recovery logistics in the wake of natural disasters. It proposes a method for optimizing the deployment of repair crews (RCs) and mobile power sources (MPSs) to enhance the resilience of electrical distribution systems.

Overview of the Approach

The research introduces a co-optimization model that integrates the logistics of RCs and MPSs with the operational strategies of distribution systems (DSs). This model is designed to accommodate the dynamic nature of system restoration, taking into account varying physical network structures and the temporal alignment of restoration activities. Key aspects of the approach include:

1. Repair Crew Dispatch: The model prioritizes and schedules repair tasks, optimizing the routing of repair crews based on task urgency and the interdependence of system components.
2. Mobile Power Source Deployment: Mobile generation assets such as truck-mounted emergency generators and mobile storage systems are strategically dispatched to provide localized power to critical loads during system outages.
3. Microgrid Formation: The paper outlines methodologies for forming temporary microgrids to isolate critical services and ensure continuity of supply, leveraging both repaired system infrastructure and mobile generators.
4. Spanning Forest Constraints for Radiality: The authors propose novel constraints to ensure the DS maintains a radial configuration, particularly noting the unique challenges posed by a system undergoing structural repairs and reconfiguration.

Numerical Results and Claims

The paper presents strong numerical results that demonstrate the effectiveness of the co-optimization approach in two different IEEE test systems (33-node and 123-node). It emphasizes the speed and efficiency gains achievable through the proposed preprocessing methods, which include pre-assigning repair tasks and optimizing candidate node selection for MPS connections. Notable findings from the simulations include:

• Significant load restoration improvements compared to traditional strategies that do not co-optimize RC and MPS dispatch with DS operations.
• Enhanced service restoration through dynamic microgrid formation techniques that make use of real-time system reconfiguration and mobile power interventions.

Implications and Future Directions

From a practical standpoint, the research contributes valuable insights into the integration of diverse logistical and operational elements of DS restoration. It stands to inform future frameworks for disaster management in power systems, potentially influencing policy and infrastructure investments. Theoretically, the work extends the field by introducing adaptable optimization techniques suitable for complex, real-world disaster recovery scenarios.

Looking forward, developments in AI and machine learning could further enhance models like this by providing predictive analytics for disaster scenarios, thereby refining logistics and restoration methodologies even further. Additionally, as renewable energy sources and decentralized generation technologies continue to evolve, integrating these elements into the co-optimization framework would be a logical and promising direction for future research. The study's focus on real-time adaptability and success in improving DS resilience marks a meaningful contribution to the power systems community, particularly in light of increasing climate-associated disaster risks.