Preventive Power Outage Estimation Based on a Novel Scenario Clustering Strategy

Wang, Xiaofei; Liu, Weijia; Ding, Fei; Yao, Yiyun; Zhao, Junbo

doi:10.1109/PESGM51994.2024.10760283

Preventive Power Outage Estimation Based on a Novel Scenario Clustering Strategy

Conference · Tue Nov 26 04:00:00 EST 2024

DOI:https://doi.org/10.1109/PESGM51994.2024.10760283· OSTI ID:2497734

Wang, Xiaofei; Liu, Weijia; Ding, Fei; ; Zhao, Junbo

The increasing occurrence of extreme weather events is challenging power grid operation. For extreme weather events, the system operator is responsible for estimating the power outages and scheduling the restoration resources. This paper proposes an outage evaluation framework to identify the possible unserved load profiles, vulnerable areas, and mobile energy adequacy. The outputs of an outage prediction model tool are used to generate numerous faulted line scenarios. Next, each scenario's nodal unserved load profile is obtained by solving a three-phase restoration model that considers repair crews and mobile energy resources (MERs). Then, a novel scenario clustering strategy is developed to cluster the unserved load profiles into multiple representative profiles which the system operator can focus on. Finally, case studies on a distribution system evaluate the damage caused by an extreme weather event and verify the effectiveness of the proposed scenario clustering strategy.

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:: 2497734

Report Number(s):: NREL/CP-5D00-92629; MainId:94410; UUID:d489ba62-00f9-470e-993b-09bd040be787; MainAdminId:75662

Country of Publication:: United States

Language:: English

References (12)

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POWER TRANSMISSION AND DISTRIBUTION
SOLAR ENERGY
mobile energy resources (MERs)
power system resilience
preventive outage evaluation
scenario clustering

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References (12)

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