Planning for Resilient Power Distribution Systems using Risk-Based Quantification and Q-Learning

Conference · Mon Dec 20 04:00:00 EST 2021

DOI:https://doi.org/10.1109/PESGM46819.2021.9638213· OSTI ID:1844605

Paul, Shuva ^[1]; Poudel, Shiva ^[2]; Dubey, Anamika ^[3]

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1. Washington State University
2. BATTELLE (PACIFIC NW LAB)
3. WASHINGTON STATE UNIV

Grid hardening is one of the most effective approaches that reduce the component failures and restoration efforts thus increasing the resilience of the power systems against extreme events. However, hardening and upgrading the entire system is prohibitively expensive and hence the optimal design of a distribution network is challenging. This paper adopted a reinforcement learning algorithm to identify the optimal hardening strategy to enhance the resilience of power distribution systems. Adopting the Q-learning algorithm as the reinforcement learning technique, we found the sequential optimal action for hardening measures to enhance the grid's resilience for the given budget. To identify the optimal strategy through Q-learning, Conditional Value at Risk (CVaR) is used as a rewarding metric. A study on the IEEE 123-bus test feeder validate the effectiveness of the proposed model and show how to effectively allocate budget limited resources to plan a resilient power distribution network.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1844605
• Report Number(s): PNNL-SA-157696
• Country of Publication: United States
• Language: English