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This content will become publicly available on January 25, 2019

Title: Battery Energy Storage State-of-Charge Forecasting: Models, Optimization, and Accuracy

Battery energy storage systems (BESS) are a critical technology for integrating high penetration renewable power on an intelligent electrical grid. As limited energy restricts the steady-state operational state-of-charge (SoC) of storage systems, SoC forecasting models are used to determine feasible charge and discharge schedules that supply grid services. Smart grid controllers use SoC forecasts to optimize BESS schedules to make grid operation more efficient and resilient. This study presents three advances in BESS state-of-charge forecasting. First, two forecasting models are reformulated to be conducive to parameter optimization. Second, a new method for selecting optimal parameter values based on operational data is presented. Last, a new framework for quantifying model accuracy is developed that enables a comparison between models, systems, and parameter selection methods. The accuracies achieved by both models, on two example battery systems, with each method of parameter selection are then compared in detail. The results of this analysis suggest variation in the suitability of these models for different battery types and applications. Finally, the proposed model formulations, optimization methods, and accuracy assessment framework can be used to improve the accuracy of SoC forecasts enabling better control over BESS charge/discharge schedules.
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  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. Public Service Company of New Mexico, Albuquerque, NM (United States)
  3. Univ. of Texas, Austin, TX (United States). School of Electrical and Computer Engineering
Publication Date:
Report Number(s):
Journal ID: ISSN 1949-3053; 656290
Grant/Contract Number:
Accepted Manuscript
Journal Name:
IEEE Transactions on Smart Grid
Additional Journal Information:
Journal Name: IEEE Transactions on Smart Grid; Journal ID: ISSN 1949-3053
Research Org:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org:
USDOE National Nuclear Security Administration (NNSA); USDOE Office of Electricity Delivery and Energy Reliability (OE)
Country of Publication:
United States
25 ENERGY STORAGE; batteries; distributed energy resources; energy storage; battery energy storage system (BESS); forecasting; uncertainty; state-of-charge
OSTI Identifier: