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Title: Risk-Averse Model Predictive Control Design for Battery Energy Storage Systems

Abstract

When batteries supply behind-the-meter services such as arbitrage or peak load management, an optimal controller can be designed to minimize the total electric bill. The limitations of the batteries, such as on voltage or state-of-charge, are represented in the model used to forecast the system’s state dynamics. Control model inaccuracy can lead to an optimistic shortfall, where the achievable schedule will be costlier than the schedule derived using the model. To improve control performance and avoid optimistic shortfall, we develop a novel methodology for high performance, risk-averse battery energy storage controller design. Our method is based on two contributions. First, the application of a more accurate, but non-convex, battery system model is enabled by calculating upper and lower bounds on the globally optimal control solution. Second, the battery model is then modified to consistently underestimate capacity by a statistically selected margin, thereby hedging its control decisions against normal variations in battery system performance. The proposed model predictive controller, developed using this methodology, performs better and is more robust than the state-of-the-art approach, achieving lower bills for energy customers and being less susceptible to optimistic shortfall.

Authors:
 [1];  [2];  [2]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. Univ. of Texas, Austin, TX (United States). Dept. of Electrical and Computer Engineering
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
1574700
Report Number(s):
SAND-2019-11836J
Journal ID: ISSN 1949-3053; 679911
Grant/Contract Number:  
AC04-94AL85000; NA0003525
Resource Type:
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; battery energy storage system (BESS); State-of-Charge (SoC); energy storage; optimal control; model predictive control; load management

Citation Formats

Rosewater, David, Baldick, Ross, and Santoso, Surya. Risk-Averse Model Predictive Control Design for Battery Energy Storage Systems. United States: N. p., 2019. Web. doi:10.1109/TSG.2019.2946130.
Rosewater, David, Baldick, Ross, & Santoso, Surya. Risk-Averse Model Predictive Control Design for Battery Energy Storage Systems. United States. doi:10.1109/TSG.2019.2946130.
Rosewater, David, Baldick, Ross, and Santoso, Surya. Thu . "Risk-Averse Model Predictive Control Design for Battery Energy Storage Systems". United States. doi:10.1109/TSG.2019.2946130. https://www.osti.gov/servlets/purl/1574700.
@article{osti_1574700,
title = {Risk-Averse Model Predictive Control Design for Battery Energy Storage Systems},
author = {Rosewater, David and Baldick, Ross and Santoso, Surya},
abstractNote = {When batteries supply behind-the-meter services such as arbitrage or peak load management, an optimal controller can be designed to minimize the total electric bill. The limitations of the batteries, such as on voltage or state-of-charge, are represented in the model used to forecast the system’s state dynamics. Control model inaccuracy can lead to an optimistic shortfall, where the achievable schedule will be costlier than the schedule derived using the model. To improve control performance and avoid optimistic shortfall, we develop a novel methodology for high performance, risk-averse battery energy storage controller design. Our method is based on two contributions. First, the application of a more accurate, but non-convex, battery system model is enabled by calculating upper and lower bounds on the globally optimal control solution. Second, the battery model is then modified to consistently underestimate capacity by a statistically selected margin, thereby hedging its control decisions against normal variations in battery system performance. The proposed model predictive controller, developed using this methodology, performs better and is more robust than the state-of-the-art approach, achieving lower bills for energy customers and being less susceptible to optimistic shortfall.},
doi = {10.1109/TSG.2019.2946130},
journal = {IEEE Transactions on Smart Grid},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {10}
}

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