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Determination of the Electricity Storage Power and Capacity for Cooperation with the Microgrid Implementing the Peak Shaving Strategy in Selected Industrial Enterprises

Author

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  • Rafał Kuźniak

    (Faculty of Management and Computer Modeling, Kielce University of Technology, Al. Tysiąclecia P.P.7, 25-314 Kielce, Poland)

  • Artur Pawelec

    (Faculty of Management and Computer Modeling, Kielce University of Technology, Al. Tysiąclecia P.P.7, 25-314 Kielce, Poland)

  • Artur Bartosik

    (Faculty of Management and Computer Modeling, Kielce University of Technology, Al. Tysiąclecia P.P.7, 25-314 Kielce, Poland)

  • Marek Pawełczyk

    (Faculty of Management and Computer Modeling, Kielce University of Technology, Al. Tysiąclecia P.P.7, 25-314 Kielce, Poland)

Abstract

The capacity limitations of distribution networks, the increasing demands for electricity installation in terms of electricity quality parameters, and the development of electromobility all increase the need to implement systems in order to stabilize and regulate loads for end users. Battery Energy Storage Systems (BESSs), that operate in internal microgrids of enterprises, allow smoothing of electrical power consumption profiles by actively reducing peak demand power. By using BESSs to implement a peak shaving strategy to reduce the peak power consumption of a company in an external network, energy efficiency can be improved by reducing contractual capacity. The aim of this study is to determine the conditions for the use of energy storage, in order to implement a peak shaving strategy for which the installation of the enterprise microgrid is economically efficient. The analysis of the operating conditions of the BESS should take into account the size of the energy storage, the characteristics of the demand profile for the demand systems, the charges related to electricity, and the costs of electricity storage. In the study, the size of the energy storage was related to the power and electrical capacity of the BESS that was used to implement the peak shaving strategy. The article presents the results of research on the method for determining an effective capacity and power of BESSs for enterprise microgrid systems. The technical and cost limitations of the actual microgrid system, which affected the decrease in economic efficiency of the peak shaving strategy in the company research, were taken into account. The simulations of the operation of the electricity storage system, based on real data of the demand of production companies, were based on the rules and market conditions in Poland.

Suggested Citation

  • Rafał Kuźniak & Artur Pawelec & Artur Bartosik & Marek Pawełczyk, 2022. "Determination of the Electricity Storage Power and Capacity for Cooperation with the Microgrid Implementing the Peak Shaving Strategy in Selected Industrial Enterprises," Energies, MDPI, vol. 15(13), pages 1-20, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4793-:d:852018
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    References listed on IDEAS

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    1. Ning Gao & Xin Lin & Weimin Wu & Frede Blaabjerg, 2021. "Grid Current Feedback Active Damping Control Based on Disturbance Observer for Battery Energy Storage Power Conversion System with LCL Filter," Energies, MDPI, vol. 14(5), pages 1-16, March.
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    3. Claudia Rahmann & Benjamin Mac-Clure & Vijay Vittal & Felipe Valencia, 2017. "Break-Even Points of Battery Energy Storage Systems for Peak Shaving Applications," Energies, MDPI, vol. 10(7), pages 1-13, June.
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    6. Mohamad Kharseh & Holger Wallbaum, 2018. "How Adding a Battery to a Grid-Connected Photovoltaic System Can Increase its Economic Performance: A Comparison of Different Scenarios," Energies, MDPI, vol. 12(1), pages 1-19, December.
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    1. Pablo Carrasco Ortega & Pablo Durán Gómez & Julio César Mérida Sánchez & Fernando Echevarría Camarero & Ángel Á. Pardiñas, 2023. "Battery Energy Storage Systems for the New Electricity Market Landscape: Modeling, State Diagnostics, Management, and Viability—A Review," Energies, MDPI, vol. 16(17), pages 1-51, August.
    2. Hongli Liu & Luoqi Wang & Ji Li & Lei Shao & Delong Zhang, 2023. "Research on Smart Power Sales Strategy Considering Load Forecasting and Optimal Allocation of Energy Storage System in China," Energies, MDPI, vol. 16(8), pages 1-18, April.
    3. Adam Krechowicz & Maria Krechowicz & Katarzyna Poczeta, 2022. "Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources," Energies, MDPI, vol. 15(23), pages 1-41, December.

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