Stochastic control of a micro-grid using battery energy storage in solar-powered buildings

Ying Chen; Krystel K. Castillo-Villar; Bing Dong

doi:10.1007/s10479-019-03444-3

Stochastic control of a micro-grid using battery energy storage in solar-powered buildings

Ying Chen, Krystel K. Castillo-Villar, Bing Dong

Research output: Contribution to journal › Article › peer-review

Abstract

This paper presents an efficient data-driven building electricity management system that integrates a battery energy storage (BES) and photovoltaic panels to support decision-making capabilities. In this micro-grid (MG) system, solar panels and power grid supply the electricity to the building and the BES acts as a buffer to alleviate the uncertain effects of solar energy generation and the demands of the building. In this study, we formulate the problem as a Markov decision process and model the uncertainties in the MG system, using martingale model of forecast evolution method. To control the system, lookahead policies with deterministic/stochastic forecasts are implemented. In addition, wait-and-see, greedy and updated greedy policies are used to benchmark the performance of lookahead policies. Furthermore, by varying the charging/discharging rate, we obtain the different battery size (E_s) and transmission line power capacity (P_max) accordingly, and then we investigate how the different E_s and P_max affect the performance of control policies. The numerical experiments demonstrate that the lookahead policy with stochastic forecasts performs better than the lookahead policy with deterministic forecasts when the E_s and P_max are large enough, and the lookahead policies outperform the greedy and updated policies in all case studies.

Original language	English (US)
Pages (from-to)	197-216
Number of pages	20
Journal	Annals of Operations Research
Volume	303
Issue number	1-2
DOIs	https://doi.org/10.1007/s10479-019-03444-3
State	Published - Aug 2021
Externally published	Yes

Keywords

Building
Control
Lookahead policies
Micro-grid

ASJC Scopus subject areas

Decision Sciences(all)
Management Science and Operations Research

Access to Document

10.1007/s10479-019-03444-3

Cite this

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keywords = "Building, Control, Lookahead policies, Micro-grid",

author = "Ying Chen and Castillo-Villar, {Krystel K.} and Bing Dong",

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year = "2021",

month = aug,

doi = "10.1007/s10479-019-03444-3",

language = "English (US)",

volume = "303",

pages = "197--216",

journal = "Annals of Operations Research",

issn = "0254-5330",

publisher = "Springer Netherlands",

number = "1-2",

}

TY - JOUR

T1 - Stochastic control of a micro-grid using battery energy storage in solar-powered buildings

AU - Chen, Ying

AU - Castillo-Villar, Krystel K.

AU - Dong, Bing

N1 - Funding Information: This Project and the preparation of this publication were funded in part by monies provided by CPS. Energy through an agreement with The University of Texas at San Antonio. © CPS Energy and the University of Texas at San Antonio. 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\usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ t $$\end{document} (kW) g t + \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ g_{t}^{ + } $$\end{document} Power bought from the main grid at time period t \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ t $$\end{document} (kW) g t - \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ g_{t}^{ - } $$\end{document} Power from solar panels sold to the grid at time period t \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ t $$\end{document} (kW) T Total time periods δ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \delta $$\end{document} Yearly capital recovery factor a Battery equivalent capital cost with respect to energy size ($/kWh) cr Battery charge upper limit b Battery equivalent capital cost with respect to power size in $/kW Δ T \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \Delta T $$\end{document} Time step size I t \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ I_{t} $$\end{document} Inventory level of the battery at time period t \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ t $$\end{document} (kWh) D 2 t \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ D2_{t} $$\end{document} Demand satisfied by the battery at time period t \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ t $$\end{document} (kW) R t \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ R_{t} $$\end{document} Electricity from the battery sold back to the grid at time period t \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ t $$\end{document} (kW) B C t \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ BC_{t} $$\end{document} Amount of electricity increased in the battery due to charging at time period t \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ t $$\end{document} (kW) P V t \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ PV_{t} $$\end{document} Solar power generation at time period t \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ t $$\end{document} (kW) P max \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ P_{max} $$\end{document} Power capacity limit of the HVDC transmission system (kW) e Battery storage efficiency dc Battery discharge rate E s \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ E_{s} $$\end{document} Battery storage size (kWh) n \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ n $$\end{document} Total years of the battery usage life Funding Information: This Project and the preparation of this publication were funded in part by monies provided by CPS. Energy through an agreement with The University of Texas at San Antonio. ? CPS Energy and the University of Texas at San Antonio. Publisher Copyright: © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

PY - 2021/8

Y1 - 2021/8

N2 - This paper presents an efficient data-driven building electricity management system that integrates a battery energy storage (BES) and photovoltaic panels to support decision-making capabilities. In this micro-grid (MG) system, solar panels and power grid supply the electricity to the building and the BES acts as a buffer to alleviate the uncertain effects of solar energy generation and the demands of the building. In this study, we formulate the problem as a Markov decision process and model the uncertainties in the MG system, using martingale model of forecast evolution method. To control the system, lookahead policies with deterministic/stochastic forecasts are implemented. In addition, wait-and-see, greedy and updated greedy policies are used to benchmark the performance of lookahead policies. Furthermore, by varying the charging/discharging rate, we obtain the different battery size (Es) and transmission line power capacity (Pmax) accordingly, and then we investigate how the different Es and Pmax affect the performance of control policies. The numerical experiments demonstrate that the lookahead policy with stochastic forecasts performs better than the lookahead policy with deterministic forecasts when the Es and Pmax are large enough, and the lookahead policies outperform the greedy and updated policies in all case studies.

AB - This paper presents an efficient data-driven building electricity management system that integrates a battery energy storage (BES) and photovoltaic panels to support decision-making capabilities. In this micro-grid (MG) system, solar panels and power grid supply the electricity to the building and the BES acts as a buffer to alleviate the uncertain effects of solar energy generation and the demands of the building. In this study, we formulate the problem as a Markov decision process and model the uncertainties in the MG system, using martingale model of forecast evolution method. To control the system, lookahead policies with deterministic/stochastic forecasts are implemented. In addition, wait-and-see, greedy and updated greedy policies are used to benchmark the performance of lookahead policies. Furthermore, by varying the charging/discharging rate, we obtain the different battery size (Es) and transmission line power capacity (Pmax) accordingly, and then we investigate how the different Es and Pmax affect the performance of control policies. The numerical experiments demonstrate that the lookahead policy with stochastic forecasts performs better than the lookahead policy with deterministic forecasts when the Es and Pmax are large enough, and the lookahead policies outperform the greedy and updated policies in all case studies.

KW - Building

KW - Control

KW - Lookahead policies

KW - Micro-grid

UR - http://www.scopus.com/inward/record.url?scp=85074711951&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85074711951&partnerID=8YFLogxK

U2 - 10.1007/s10479-019-03444-3

DO - 10.1007/s10479-019-03444-3

M3 - Article

AN - SCOPUS:85074711951

SN - 0254-5330

VL - 303

SP - 197

EP - 216

JO - Annals of Operations Research

JF - Annals of Operations Research

IS - 1-2

ER -

Stochastic control of a micro-grid using battery energy storage in solar-powered buildings

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