Journal
IEEE TRANSACTIONS ON POWER SYSTEMS
Volume 36, Issue 6, Pages 5131-5142Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2021.3069443
Keywords
Uncertainty; Generators; Stochastic processes; Machine learning; Hurricanes; Load shedding; Computational modeling; Large-scale systems; load shedding; machine learning; ML-assisted power system operation; power outage; power system resiliency; preventive operation; stochastic unit commitment; severe weather; transmission line outage
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Funding
- NSF ECCS [1839833, 2004658]
- Direct For Computer & Info Scie & Enginr
- Office of Advanced Cyberinfrastructure (OAC) [2004658] Funding Source: National Science Foundation
- Directorate For Engineering
- Div Of Electrical, Commun & Cyber Sys [1839833] Funding Source: National Science Foundation
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This paper evaluates the application of machine learning in reducing the computational burden of stochastic unit commitment and proposes an algorithm to train and utilize a machine learning model for assistance, showing a significant reduction in solution time.
Stochastic unit commitment is an efficient method for grid operation in the presence of significant uncertainties. An example is an operation during a predicted hurricane with uncertain line out-ages. However, the solution quality comes at the cost of substantial computational burden, which makes its adoption challenging. This paper evaluates some possible ways that machine learning can be used to reduce this computational burden. First, a set of feasibility studies is conducted. Results suggest that using machine learning as an assistant to the stochastic unit commitment solver is more advantageous than using it as a standalone solver. In particular, the machine learning model is trained to facilitate solving the problem by determining the unnecessary constraints that can be removed from the original problem without affecting the final accuracy. The variables that can be used as input features/predictors or outputs for the machine learning model are determined through feasibility studies. Then, an algorithm to train and utilize a machine learning model is proposed. The method is tested on a 500-bus synthetic South Carolina system. Various test cases show an average reduction in solution time by more than 90% by using the trained machine learning model to assist the stochastic unit commitment solver.
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