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Multi-Agent Reinforcement Learning for Distribution System Critical Load Restoration

Conference ·
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Grid resilience has become a critical topic recently because of the increasing occurrence of extreme events and the growing integration of intermittent renewable energy sources. To build a resilient distribution system, this paper develops a multiagent reinforcement learning-based (MARL) method to coordinate distribution energy resources (DERs) dispatch, load pickup, and network reconfiguration for load restoration after a system outage. With the help of two types of control agents, namely critical load restoration (CLR) and coordination (COR) agents, system loads can be restored efficiently, given available resources. The effectiveness and superiority of the proposed algorithm are demonstrated through simulations and comparative studies on a real distribution feeder in Western Colorado.

Research Organization:
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
DOE Contract Number:
AC36-08GO28308
OSTI ID:
2229753
Report Number(s):
NREL/CP-5D00-88291; MainId:89066; UUID:36b0beaf-83fc-4875-b053-2322818ea16b; MainAdminID:71292
Resource Relation:
Conference: Presented at the the 2023 IEEE Power & Energy Society General Meeting (PESGM), 16-20 July 2023, Orlando, Florida; Related Information: 84636
Country of Publication:
United States
Language:
English

References (7)

Hierarchical Distribution System Adaptive Restoration With Diverse Distributed Energy Resources April 2021
A Fast and Scalable Genetic Algorithm-Based Approach for Planning of Microgrids in Distribution Networks July 2022
Integration of Preventive and Emergency Responses for Power Grid Resilience Enhancement November 2017
A Full Decentralized Multi-Agent Service Restoration for Distribution Network With DGs March 2020
Conceptual Framework for Developing Resilience Metrics for the Electricity, Oil, and Gas Sectors in the United States September 2014
Distribution System Resilience Under Asynchronous Information Using Deep Reinforcement Learning September 2021
Service restoration of active distribution systems with increasing penetration of renewable distributed generation June 2019

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Related Subjects

POWER TRANSMISSION AND DISTRIBUTION
distribution system
grid resilience
load restoration
multi-agent reinforcement learning