Abstract
This paper proposes a deep reinforcement learning (DRL) based approach for post-disaster critical load restoration in active distribution systems to form microgrids through network reconfiguration to minimize critical load curtailments. Distribution networks are represented as graph networks, and optimal network configurations with microgrids are obtained by searching for the optimal spanning forest. The constraints to the research question being explored are the radial topology and power balance. Unlike existing analytical and population-based approaches, which necessitate the repetition of entire analyses and computation for each outage scenario to find the optimal spanning forest, the proposed approach, once properly trained, can quickly determine the optimal, or near-optimal, spanning forest even when outage scenarios change. When multiple lines fail in the system, the proposed approach forms microgrids with distributed energy resources in active distribution systems to reduce critical load curtailment. The proposed DRL-based model learns the action-value function using the REINFORCE algorithm, which is a model-free reinforcement learning technique based on stochastic policy gradients. A case study was conducted on a 33-node distribution test system, demonstrating the effectiveness of the proposed approach for post-disaster critical load restoration.
Original language | American English |
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Number of pages | 6 |
DOIs | |
State | Published - 2022 |
Event | 2022 Resilience Week, RWS 2022 - National Harbor, United States Duration: 26 Sep 2022 → 29 Sep 2022 |
Conference
Conference | 2022 Resilience Week, RWS 2022 |
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Country/Territory | United States |
City | National Harbor |
Period | 26/09/22 → 29/09/22 |
Bibliographical note
Publisher Copyright:© 2022 IEEE.
NREL Publication Number
- NREL/CP-5R00-85275
Keywords
- Active distribution systems
- microgrid formation
- network reconfiguration
- reinforcement learning
- resilience