Abstract
Advanced distribution management systems (ADMS) provide a suite of tools to meet the needs of a modern grid: increased reliability and power quality, improved resiliency and security, reduced costs, and enhanced customer participation. A critical challenge that utilities face with adoption of ADMS is the quality of models and data that the ADMS uses for making control decisions. Data quality has a two-fold impact on ADMS adoption: 1. Data quality improvement might constitute up to 25% of ADMS deployment costs. 2. The accuracy of data and models used by the ADMS affects the utility's ability to meet its operational objectives. Thus, quantifying the data quality requirements and its impact on performance is critical to reducing the overall cost of deployment, enabling increased adoption and ensuring that the ADMS performs as specified. This paper offers the motivation, methodology, and evaluation strategy to fill this critical gap.
Original language | American English |
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Number of pages | 5 |
DOIs | |
State | Published - Feb 2019 |
Event | 2019 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2019 - Washington, United States Duration: 18 Feb 2019 → 21 Feb 2019 |
Conference
Conference | 2019 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2019 |
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Country/Territory | United States |
City | Washington |
Period | 18/02/19 → 21/02/19 |
Bibliographical note
See NREL/CP-5D00-72404 for preprintNREL Publication Number
- NREL/CP-5D00-74947
Keywords
- ADMS
- ADMS test bed
- data quality
- measurement density
- performance