Rolling Optimization of Mobile Energy Storage Fleets for Resilient Service Restoration (1905.06599v2)

Abstract: Mobile energy storage systems (MESSs) provide promising solutions to enhance distribution system resilience in terms of mobility and flexibility. This paper proposes a rolling integrated service restoration strategy to minimize the total system cost by coordinating the scheduling of MESS fleets, resource dispatching of microgrids and network reconfiguration of distribution systems. The integrated strategy takes into account damage and repair to both the roads in transportation networks and the branches in distribution systems. The uncertainties in load consumption and the status of roads and branches are modeled as scenario trees using Monte Carlo simulation method. The operation strategy of MESSs is modeled by a stochastic multi-layer time-space network technique. A rolling optimization framework is adopted to dynamically update system damage, and the coordinated scheduling at each time interval over the prediction horizon is formulated as a two-stage stochastic mixed-integer linear program with temporal-spatial and operation constraints. The proposed model is verified on two integrated test systems, one is with Sioux Falls transportation network and four 33-bus distribution systems, and the other is the Singapore transportation network-based test system connecting six 33-bus distribution systems. The results demonstrate the effectiveness of MESS mobility to enhance distribution system resilience due to the coordination of mobile and stationary resources.

Overview of "Rolling Optimization of Mobile Energy Storage Fleets for Resilient Service Restoration"

The paper focuses on an integrated approach to enhancing distribution system resilience through the use of Mobile Energy Storage Systems (MESSs). It leverages the flexibility and mobility of MESS fleets to synchronize with microgrids for effective and cost-efficient service restoration following utility outages, particularly those caused by extreme weather events.

Key Contributions and Methodological Advancements

1. Integrated Restoration Strategy: The authors propose a novel strategy that integrates the scheduling of MESS fleets, dispatch of microgrid resources, and network reconfiguration. The aim is to minimize overall system costs and restore electricity services efficiently.
2. Stochastic Multi-Layer Time-Space Network Modeling: The operation and scheduling of MESSs are modeled using an innovative stochastic multi-layer time-space network, which provides a comprehensive structure to simulate the temporal-spatial dynamics of MESS fleets across transportation networks. This reduces computational complexity compared to traditional models and results in fewer decision variables and constraints.
3. Rolling Optimization Framework: The study employs a rolling optimization framework to dynamically update and accommodate the real-time changes in the distribution and transportation systems. This is achieved by modeling uncertainties using Monte Carlo simulations, capturing potential damages and repairs to roads and distribution network branches.
4. Two-Stage Stochastic Mixed-Integer Linear Programming (MILP): The core optimization problem is structured as a two-stage stochastic MILP, where the first-stage decision variables are scenario-independent and second-stage decisions adjust based on realized uncertainties. This formulation addresses the inherent uncertainty in load predictions, road conditions, and network damages.

Numerical Results and Insights

Simulation studies are conducted on two test systems: one featuring the Sioux Falls transportation network connected to multiple 33-bus distribution systems, and another using the Singapore transportation network with additional distribution systems. By comparison across scenarios, the proposed integrated strategy shows a significant reduction in total system costs and enhancements in critical load restoration when MESSs are effectively coordinated with local microgrid resources.

Practical and Theoretical Implications

• Enhanced System Resilience: The research highlights the potential of MESS fleets to provide significant resilience benefits through energy transfer between microgrids. This mobility aspect allows MESSs to mitigate energy imbalances effectively across the network.
• Cost Efficiency: The substantial reduction in interruption and operational costs, as demonstrated in the results, indicates practical benefits for utilities looking to implement MESS as a strategic component in emergency response and disaster recovery plans.
• Innovative Network Modeling: The adoption of the multi-layer time-space network can influence future research, offering a scalable method to integrate mobile energy systems into power network operations considering spatial and temporal dynamics.

Prospective Developments in AI and Energy Systems

Future work could enrich the stochastic modeling with machine learning techniques for more accurate demand forecasting and predictive maintenance of infrastructure. Additionally, incorporating AI-driven adaptive algorithms in the rolling optimization process could further enhance decision-making speed and accuracy, leading to faster response times in real-world applications. The integration of Internet of Things (IoT) data can also improve situational awareness and contribute to the advancement of smart grid technologies.

In conclusion, this paper presents a detailed and sophisticated framework for leveraging MESSs to bolster the resilience and efficiency of power distribution systems under uncertain conditions. The findings elucidate key methods for optimizing energy storage dispatch and network reconfiguration, paving the way for future innovations in resilient smart grids and energy management systems.