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Title: Automated Classification of Power Plants by Generation Type
Generation type of power plant (e.g. steam, wind) is an important attribute in power grid and energy market studies such as bidding strategy, audit of generation mix, and accounting for load- generation matching. Recently, regional transmission organizations (RTOs) and independent system operators (ISOs) are increasingly redacting a wide range of grid and market data attributes to protect their participants’ business interests. Lack of this information can prevent important power grid research. We propose techniques to infer power plant generation types based on publicly-available market data. We develop and evaluate these techniques on data available from the Midcontinent Independent System Operator (MISO). Evaluation shows successful classification of power plants, achieving 100% precision and 99.5% recall for wind plants, and 91.7% overall accuracy. On the basis of generated power, our classification shows 100% precision and 99.8% recall for wind plants and 93.2% overall accuracy. Our ultimate goal is to generalize to a wide range of RTOs/ISOs. We explore three feature types (bid pattern, capability, and opera- tion), and evaluate their classification value for MISO. We also assess applicability to other RTOs/ISOs based on available market data. These studies inform the efficacy of the features for generation-type inference in other RTOs/ISOs.
Authors:
;
Award ID(s):
1832230 1901466
Publication Date:
NSF-PAR ID:
10155310
Journal Name:
The Eleventh ACM International Conference on Future Energy Systems (e-Energy ’20)
Sponsoring Org:
National Science Foundation
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