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

  • S.I. : Data Mining and Decision Analytics
  • Published:
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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 \( \left( {E_{s} } \right) \) 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.

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Abbreviations

\( EMP_{t} \) :

Electricity market price at time period \( t \) ($/kWh)

\( D_{t} \) :

Total demand at time period \( t \) (kW)

\( g_{t}^{ + } \) :

Power bought from the main grid at time period \( t \) (kW)

\( g_{t}^{ - } \) :

Power from solar panels sold to the grid at time period \( t \) (kW)

T :

Total time periods

\( \delta \) :

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

\( \Delta T \) :

Time step size

\( I_{t} \) :

Inventory level of the battery at time period \( t \) (kWh)

\( D2_{t} \) :

Demand satisfied by the battery at time period \( t \) (kW)

\( R_{t} \) :

Electricity from the battery sold back to the grid at time period \( t \) (kW)

\( BC_{t} \) :

Amount of electricity increased in the battery due to charging at time period \( t \) (kW)

\( PV_{t} \) :

Solar power generation at time period \( t \) (kW)

\( P_{max} \) :

Power capacity limit of the HVDC transmission system (kW)

e :

Battery storage efficiency

dc :

Battery discharge rate

\( E_{s} \) :

Battery storage size (kWh)

\( n \) :

Total years of the battery usage life

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Acknowledgements

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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Authors and Affiliations

  1. Mechanical Engineering Department & Texas Sustainable Energy Research Institute, University of Texas at San Antonio, San Antonio, TX, 78249, USA

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

  2. School of Economics and Management, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China

    Ying Chen

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  1. Ying Chen
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  2. Krystel K. Castillo-Villar
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Correspondence to Krystel K. Castillo-Villar.

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Chen, Y., Castillo-Villar, K.K. & Dong, B. Stochastic control of a micro-grid using battery energy storage in solar-powered buildings. Ann Oper Res 303, 197–216 (2021). https://doi.org/10.1007/s10479-019-03444-3

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  • DOI: https://doi.org/10.1007/s10479-019-03444-3

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