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This paper develops a probabilistic earthquake risk assessment for the electric power transmis- sion system in the City of Los Angeles. Via a dc load flow analysis of a suite of damage scenarios that reflect the seismic risk in Los Angeles, we develop a probabilistic representation for load shed during the restoration process. This suite of damage scenarios and their associated annual probabilities of occurrence are developed from 351 risk-adjusted earthquake scenarios using ground motion that collectively represent the seismic risk in Los Angeles at the census tract level. For each of these 351 earthquake scenarios, 12 damage scenarios are developed that form a probabilistic representation of the consequences of the earthquake scenario on the components of the transmission system. This analysis reveals that substation damage is the key driver of load shed. Damage to generators has a substantial but still secondary impact, and damage to transmission lines has significantly less impact. We identify the census tracts that are substantially more vulnerable to power transmission outages during the restoration process. Further, we explore the impact of forecasted increases in penetration of residential storage paired with rooftop solar. The deployment of storage paired with rooftop solar is represented at the census tract level and is assumed to be able to generate and store power for residential demand during the restoration process. The deployment of storage paired with rooftop solar reduces the load shed during the restoration process, but the distribution of this benefit is correlated with household income and whether the dwelling is owned or rented.
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Risk‐neutral and risk‐averse transmission switching for load shed recovery with uncertain renewable generation and demand
One of the main desired capabilities of the smart grid is ‘self‐healing’, which is the ability to quickly restore power after a disturbance. Due to critical outage events, customer demand or load is at times disconnected or shed temporarily. While deterministic optimisation models have been devised to help operators expedite load shed recovery by harnessing the flexibility of the grid's topology (i.e. transmission line switching), an important issue that remains unaddressed is how to cope with the uncertainty in generation and demand encountered during the recovery process. This study introduces two‐stage stochastic models to deal with these uncertain parameters, and one of them incorporates conditional value‐at‐risk to measure the risk level of unrecovered load shed. The models are implemented using a scenario‐based approach where the objective is to maximise load shed recovery in the bulk transmission network by switching transmission lines and performing other corrective actions (e.g. generator re‐dispatch) after the topology is modified. The benefits of the proposed stochastic models are compared with a deterministic mean‐value model, using the IEEE 118‐ and 14‐bus test cases. Experiments highlight how the proposed approach can serve as an offline contingency analysis tool, and how this method aids self‐healing by recovering more load shedding.
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- Award ID(s):
- 1824897
- PAR ID:
- 10570778
- Publisher / Repository:
- DOI PREFIX: 10.1049
- Date Published:
- Journal Name:
- IET Generation, Transmission & Distribution
- Volume:
- 14
- Issue:
- 21
- ISSN:
- 1751-8687
- Format(s):
- Medium: X Size: p. 4936-4945
- Size(s):
- p. 4936-4945
- Sponsoring Org:
- National Science Foundation
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