EnerNex Corporation was awarded a Small Business Innovative Research Phase II grant from the U.S. Department of Energy to develop Distribution State Estimation. The grant is supporting the creation of a software product that will enable load modeling and prediction tools for the improved resolution of customer loads; accurate sensitivity assessments of the impact of weather conditions, new technologies, demand response programs and load curtailment practices; and advanced algorithms and decision logics for the large data set analysis. The software will maximize use of current system capacity and meet contingency planning and response requirements. EnerNex is using the research of NCSU Professor, Mesut Baran, to produce better methods of load modeling from statistical data, with branch current state estimation and bad data identification. Using the load estimation toolbox, industry researchers can create and deploy new load estimation modules as add-ons to the OSIsoft PI System, the industry-leading data historian software.

Tom McDermott is the principal investigator for this project, funded by Phase I and Phase II small business and innovation research (SBIR) grants. Advanced load and state estimation algorithms are implemented in compiled MatLab applications. These algorithms are specially adapted to characteristics of distribution feeders, and the types of measurements typically available, which include automated meters. In turn, these modules interface to IEEE-standard Power Quality Data Interchange Format (PQDIF), and to the OSIsoft PI product used by many electric utilities.