Abstract
The implementation of advanced distribution management systems (ADMS) in today’s distribution networks (DNs) is critical for efficient operation. However, ADMS deployment poses significant challenges, particularly in gathering the extensive and diverse data required to model DNs. This paper presents a generalized, systematic, and algorithm-driven procedure for optimizing the missing data-gathering process during ADMS deployment. The procedure identifies the required DN model data by layers, considers distribution power utility (DPU) data sources, identifies missing data, and evaluates methods and the missing data-gathering ways, considering cost, duration, and specific constraints for data gathering. The developed approach provides DPUs with a clear, structured, and proactive approach to data gathering, significantly reducing complexity and enhancing efficiency. The practical application of this procedure is demonstrated using a real-world unbalanced DN example from a North American DPU, showcasing its potential to streamline ADMS deployment and deliver tangible operational benefits.
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