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

Abstract

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.

Authors:
 [1];  [1];  [1];  [2];  [3]
  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:
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)
OSTI Identifier:
1421619
Report Number(s):
SAND-2017-8759J
Journal ID: ISSN 1949-3053; 656290
Grant/Contract Number:  
NA0003525
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
IEEE Transactions on Smart Grid
Additional Journal Information:
Journal Name: IEEE Transactions on Smart Grid; Journal ID: ISSN 1949-3053
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
25 ENERGY STORAGE; batteries; distributed energy resources; energy storage; battery energy storage system (BESS); forecasting; uncertainty; state-of-charge

Citation Formats

Rosewater, David, Ferreira, Summer, Schoenwald, David, Hawkins, Jon, and Santoso, Surya. Battery Energy Storage State-of-Charge Forecasting: Models, Optimization, and Accuracy. United States: N. p., 2018. Web. doi:10.1109/tsg.2018.2798165.
Rosewater, David, Ferreira, Summer, Schoenwald, David, Hawkins, Jon, & Santoso, Surya. Battery Energy Storage State-of-Charge Forecasting: Models, Optimization, and Accuracy. United States. doi:10.1109/tsg.2018.2798165.
Rosewater, David, Ferreira, Summer, Schoenwald, David, Hawkins, Jon, and Santoso, Surya. Thu . "Battery Energy Storage State-of-Charge Forecasting: Models, Optimization, and Accuracy". United States. doi:10.1109/tsg.2018.2798165.
@article{osti_1421619,
title = {Battery Energy Storage State-of-Charge Forecasting: Models, Optimization, and Accuracy},
author = {Rosewater, David and Ferreira, Summer and Schoenwald, David and Hawkins, Jon and Santoso, Surya},
abstractNote = {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.},
doi = {10.1109/tsg.2018.2798165},
journal = {IEEE Transactions on Smart Grid},
number = ,
volume = ,
place = {United States},
year = {Thu Jan 25 00:00:00 EST 2018},
month = {Thu Jan 25 00:00:00 EST 2018}
}

Journal Article:
Free Publicly Available Full Text
This content will become publicly available on January 25, 2019
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