Validation of Synthetic U.S. Electric Power Distribution System Data Sets
| Autor: | Bryan Palmintier, Tarek Elgindy, Bruce Bugbee, Fernando Postigo, Jean-Sebastien Lacroix, Pablo Duenas, Venkat Krishnan, Tomás Gómez San Román, Carlos Mateo |
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| Rok vydání: | 2020 |
| Předmět: |
Electric power distribution
021103 operations research General Computer Science business.industry Computer science 020209 energy 0211 other engineering and technologies 02 engineering and technology computer.software_genre Synthetic data sets Critical infrastructure Set (abstract data type) Distribution system Distributed generation Metric (mathematics) 0202 electrical engineering electronic engineering information engineering Data mining business computer Test data |
| Zdroj: | IEEE Transactions on Smart Grid. 11:4477-4489 |
| ISSN: | 1949-3061 1949-3053 |
| Popis: | There is a strong need for synthetic yet realistic distribution system test data sets that are as diverse, large, and complex to solve as real systems. Such data sets can facilitate the development of advanced algorithms and the assessment of emerging distributed energy resources while avoiding the need to acquire proprietary critical infrastructure or private data. Such synthetic data sets, however, are useful only if they are realistic enough to look and behave similarly to actual systems. This paper presents a comprehensive framework for validating synthetic distribution data sets using a three-pronged statistical, operational, and expert validation approach. It also presents a set of statistical and operational metric targets for achieving realistic data sets based on detailed characterization of more than 10,000 real U.S. utility feeders. The paper demonstrates the use of the proposed validation approach to validate three large-scale synthetic data sets developed by the authors representing Santa Fe, New Mexico; Greensboro, North Carolina; and the San Francisco Bay Area, California. |
| Databáze: | OpenAIRE |
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