Load shifting is one means whereby buildings may reduce their peak demand and provide other services to the electric grid. Current rate tariffs penalize facility peak demand, and large commercial and industrial buildings may realize cost savings by reducing this facility peak demand. The successful shifting of electric load requires some knowledge or prediction of the peak demand. This prediction is generally imperfect, and the resulting load shifting control is sub-optimal. This paper develops the optimal load shifting operations using a battery energy storage system under certain assumptions. This optimal solution is then used to develop a generalized strategy for load shifting battery control with the purpose of reducing peak 15-minute demand. This generalized strategy involves a prediction of the target demand level and a prediction of the current 15 minute period. The method whereby these predictions are made is critical to the success of load shifting. The general control logic may be used to analyze the sensitivity of load shifting to prediction error and sampling rate..
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ASME 2017 Dynamic Systems and Control Conference
October 11–13, 2017
Tysons, Virginia, USA
Conference Sponsors:
- Dynamic Systems and Control Division
ISBN:
978-0-7918-5829-5
PROCEEDINGS PAPER
A Generalized Approach for Commercial and Industrial Load Shifting Control
Austin Rogers,
Austin Rogers
Texas A&M University, College Station, TX
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Bryan Rasmussen
Bryan Rasmussen
Texas A&M University, College Station, TX
Search for other works by this author on:
Austin Rogers
Texas A&M University, College Station, TX
Bryan Rasmussen
Texas A&M University, College Station, TX
Paper No:
DSCC2017-5191, V003T42A002; 9 pages
Published Online:
November 14, 2017
Citation
Rogers, A, & Rasmussen, B. "A Generalized Approach for Commercial and Industrial Load Shifting Control." Proceedings of the ASME 2017 Dynamic Systems and Control Conference. Volume 3: Vibration in Mechanical Systems; Modeling and Validation; Dynamic Systems and Control Education; Vibrations and Control of Systems; Modeling and Estimation for Vehicle Safety and Integrity; Modeling and Control of IC Engines and Aftertreatment Systems; Unmanned Aerial Vehicles (UAVs) and Their Applications; Dynamics and Control of Renewable Energy Systems; Energy Harvesting; Control of Smart Buildings and Microgrids; Energy Systems. Tysons, Virginia, USA. October 11–13, 2017. V003T42A002. ASME. https://doi.org/10.1115/DSCC2017-5191
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