Postdisaster Routing of Movable Energy Resources for Enhanced Distribution System Resilience: A Deep Reinforcement Learning-Based Approach

Journal Article · 01 November 2023 · IEEE Industry Applications Magazine

DOI: https://doi.org/10.1109/mias.2023.3325046 · OSTI ID:2204847

Gautam, Mukesh  ^[1]; Bhusal, Narayan  ^[2]; Ben-Idris, Mohammed  ^[3]

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1. Idaho National Laboratory (INL), Idaho Falls, ID (United States)
2. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
3. Michigan State Univ., East Lansing, MI (United States)

The deployment of movable energy resources (MERs) can be an effective strategy to restore critical loads to enhance power system resilience when no other energy sources are available after the occurrence of an extreme event. Since the optimal locations of MERs following an extreme event are dependent on system operating states (e.g., the loads at each node, on/off status of system branches, and so on), existing analytical and population-based approaches must repeat the entire analysis and calculation when the system operating states change. On the contrary, if deep reinforcement learning (DRL)-based algorithms are sufficiently trained with a wide range of scenarios, they can quickly find optimal or near-optimal locations irrespective of changes in system states. A deep Q-learning-based approach is proposed for optimal MER deployment to enhance power system resilience. MERs can be also utilized to complement other types of resources, if available. The proposed approach operates in two stages after the occurrence of extreme events. In the first stage, the distribution network is represented as a graph, and the network is then reconfigured using tie switches by using Kruskal’s spanning forest search algorithm (KSFSA). To maximize critical load recovery, the optimal or near-optimal locations of MERs are chosen in the second stage. Further, case studies on a 33-node distribution system and a modified IEEE 123-node system demonstrate the effectiveness of the proposed approach for postdisaster routing of MERs.

• Research Organization: Idaho National Laboratory (INL), Idaho Falls, ID (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE; National Science Foundation (NSF)
• Grant/Contract Number: AC07-05ID14517; NSF 1847578; AC05-00OR22725
• OSTI ID: 2204847
• Alternate ID(s): OSTI ID: 2283855
• Report Number(s): INL/JOU-23-74337-Rev.000; TRN: US2406860
• Journal Information: IEEE Industry Applications Magazine, Vol. 30, Issue 1; ISSN 1077-2618
• Publisher: IEEECopyright Statement
• Country of Publication: United States
• Language: English