skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Analytical sizing methods for behind-the-meter battery storage

Abstract

In behind-the-meter application, battery storage system (BSS) is utilized to reduce a commercial or industrial customer’s payment for electricity use, including energy charge and demand charge. The potential value of BSS in payment reduction and the most economic size can be determined by formulating and solving standard mathematical programming problems. In this method, users input system information such as load profiles, energy/demand charge rates, and battery characteristics to construct a standard programming problem that typically involve a large number of constraints and decision variables. Such a large scale programming problem is then solved by optimization solvers to obtain numerical solutions. Such a method cannot directly link the obtained optimal battery sizes to input parameters and requires case-by-case analysis. In this paper, we present an objective quantitative analysis of costs and benefits of customer-side energy storage, and thereby identify key factors that affect battery sizing. Based on the analysis, we then develop simple but effective guidelines that can be used to determine the most cost-effective battery size or guide utility rate design for stimulating energy storage development. The proposed analytical sizing methods are innovative, and offer engineering insights on how the optimal battery size varies with system characteristics. We illustrate themore » proposed methods using practical building load profile and utility rate. The obtained results are compared with the ones using mathematical programming based methods for validation.« less

Authors:
ORCiD logo; ORCiD logo; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1361985
Report Number(s):
PNNL-SA-122123
Journal ID: ISSN 2352-152X; TE1400000
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Resource Relation:
Journal Name: Journal of Energy Storage; Journal Volume: 12; Journal Issue: C
Country of Publication:
United States
Language:
English
Subject:
25 ENERGY STORAGE

Citation Formats

Wu, Di, Kintner-Meyer, Michael, Yang, Tao, and Balducci, Patrick. Analytical sizing methods for behind-the-meter battery storage. United States: N. p., 2017. Web. doi:10.1016/j.est.2017.04.009.
Wu, Di, Kintner-Meyer, Michael, Yang, Tao, & Balducci, Patrick. Analytical sizing methods for behind-the-meter battery storage. United States. doi:10.1016/j.est.2017.04.009.
Wu, Di, Kintner-Meyer, Michael, Yang, Tao, and Balducci, Patrick. Tue . "Analytical sizing methods for behind-the-meter battery storage". United States. doi:10.1016/j.est.2017.04.009.
@article{osti_1361985,
title = {Analytical sizing methods for behind-the-meter battery storage},
author = {Wu, Di and Kintner-Meyer, Michael and Yang, Tao and Balducci, Patrick},
abstractNote = {In behind-the-meter application, battery storage system (BSS) is utilized to reduce a commercial or industrial customer’s payment for electricity use, including energy charge and demand charge. The potential value of BSS in payment reduction and the most economic size can be determined by formulating and solving standard mathematical programming problems. In this method, users input system information such as load profiles, energy/demand charge rates, and battery characteristics to construct a standard programming problem that typically involve a large number of constraints and decision variables. Such a large scale programming problem is then solved by optimization solvers to obtain numerical solutions. Such a method cannot directly link the obtained optimal battery sizes to input parameters and requires case-by-case analysis. In this paper, we present an objective quantitative analysis of costs and benefits of customer-side energy storage, and thereby identify key factors that affect battery sizing. Based on the analysis, we then develop simple but effective guidelines that can be used to determine the most cost-effective battery size or guide utility rate design for stimulating energy storage development. The proposed analytical sizing methods are innovative, and offer engineering insights on how the optimal battery size varies with system characteristics. We illustrate the proposed methods using practical building load profile and utility rate. The obtained results are compared with the ones using mathematical programming based methods for validation.},
doi = {10.1016/j.est.2017.04.009},
journal = {Journal of Energy Storage},
number = C,
volume = 12,
place = {United States},
year = {Tue Aug 01 00:00:00 EDT 2017},
month = {Tue Aug 01 00:00:00 EDT 2017}
}