IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i23p4450-d289864.html
   My bibliography  Save this article

Optimal Operation of Battery Storage for a Subscribed Capacity-Based Power Tariff Prosumer—A Norwegian Case Study

Author

Listed:
  • Frida Berglund

    (Department of Electric Power Engineering, Norwegian University of Science and Technology, 7491 Trondheim, Norway)

  • Salman Zaferanlouei

    (Department of Electric Power Engineering, Norwegian University of Science and Technology, 7491 Trondheim, Norway)

  • Magnus Korpås

    (Department of Electric Power Engineering, Norwegian University of Science and Technology, 7491 Trondheim, Norway)

  • Kjetil Uhlen

    (Department of Electric Power Engineering, Norwegian University of Science and Technology, 7491 Trondheim, Norway)

Abstract

The cost of peak power for end-users subject to a demand charge may be substantial, expecting to increase further with the vast growth of power-demanding devices. In cases where load-shifting is not a viable option for cost reduction, battery storage systems used for peak shaving purposes are emerging as a promising solution. In this paper, the economic benefits of implementing battery storage into an existing grid-connected photovoltaic system for a medium-scale swimming facility is studied. The objective is to minimize the total cost of electricity for the facility, including the cost of energy and peak power demand, while ensuring the longevity of the battery. An optimization model based on multi-integer linear programming is built, and simulated using a one-year time horizon in GAMS and Matlab. The main results reveal that installing a battery storage system is economically attractive today, with net savings on the total system cost of 0.64% yearly. The cost of peak power is reduced by 13.9%, and the savings from peak shaving operation alone is enough to compensate for the yearly cost of the battery. Moreover, the battery ensures additional revenue by performing price arbitrage operations. When simulating the system for an assumed 2030 scenario, the battery is found to be more profitable with a yearly net savings of 4.15%.

Suggested Citation

  • Frida Berglund & Salman Zaferanlouei & Magnus Korpås & Kjetil Uhlen, 2019. "Optimal Operation of Battery Storage for a Subscribed Capacity-Based Power Tariff Prosumer—A Norwegian Case Study," Energies, MDPI, vol. 12(23), pages 1-24, November.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:23:p:4450-:d:289864
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/23/4450/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/12/23/4450/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Omar, Noshin & Monem, Mohamed Abdel & Firouz, Yousef & Salminen, Justin & Smekens, Jelle & Hegazy, Omar & Gaulous, Hamid & Mulder, Grietus & Van den Bossche, Peter & Coosemans, Thierry & Van Mierlo, J, 2014. "Lithium iron phosphate based battery – Assessment of the aging parameters and development of cycle life model," Applied Energy, Elsevier, vol. 113(C), pages 1575-1585.
    2. Nottrott, A. & Kleissl, J. & Washom, B., 2013. "Energy dispatch schedule optimization and cost benefit analysis for grid-connected, photovoltaic-battery storage systems," Renewable Energy, Elsevier, vol. 55(C), pages 230-240.
    3. Holger C. Hesse & Rodrigo Martins & Petr Musilek & Maik Naumann & Cong Nam Truong & Andreas Jossen, 2017. "Economic Optimization of Component Sizing for Residential Battery Storage Systems," Energies, MDPI, vol. 10(7), pages 1-19, June.
    4. Ranaweera, Iromi & Midtgård, Ole-Morten, 2016. "Optimization of operational cost for a grid-supporting PV system with battery storage," Renewable Energy, Elsevier, vol. 88(C), pages 262-272.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Morales Sandoval, Daniel A. & Saikia, Pranaynil & De la Cruz-Loredo, Ivan & Zhou, Yue & Ugalde-Loo, Carlos E. & Bastida, Héctor & Abeysekera, Muditha, 2023. "A framework for the assessment of optimal and cost-effective energy decarbonisation pathways of a UK-based healthcare facility11The short version of the paper was presented at ICAE2022, Bochum, German," Applied Energy, Elsevier, vol. 352(C).
    2. Eleonora Achiluzzi & Kirushaanth Kobikrishna & Abenayan Sivabalan & Carlos Sabillon & Bala Venkatesh, 2020. "Optimal Asset Planning for Prosumers Considering Energy Storage and Photovoltaic (PV) Units: A Stochastic Approach," Energies, MDPI, vol. 13(7), pages 1-20, April.
    3. Urbano, Eva M. & Martinez-Viol, Victor & Kampouropoulos, Konstantinos & Romeral, Luis, 2022. "Risk assessment of energy investment in the industrial framework – Uncertainty and Sensitivity Analysis for energy design and operation optimisation," Energy, Elsevier, vol. 239(PA).
    4. Carlo Baron & Ameena S. Al-Sumaiti & Sergio Rivera, 2020. "Impact of Energy Storage Useful Life on Intelligent Microgrid Scheduling," Energies, MDPI, vol. 13(4), pages 1-23, February.
    5. Hector Beltran & Pablo Ayuso & Emilio Pérez, 2020. "Lifetime Expectancy of Li-Ion Batteries used for Residential Solar Storage," Energies, MDPI, vol. 13(3), pages 1-18, January.
    6. Seward, William & Qadrdan, Meysam & Jenkins, Nick, 2022. "Quantifying the value of distributed battery storage to the operation of a low carbon power system," Applied Energy, Elsevier, vol. 305(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Park, Alex & Lappas, Petros, 2017. "Evaluating demand charge reduction for commercial-scale solar PV coupled with battery storage," Renewable Energy, Elsevier, vol. 108(C), pages 523-532.
    2. Azuatalam, Donald & Paridari, Kaveh & Ma, Yiju & Förstl, Markus & Chapman, Archie C. & Verbič, Gregor, 2019. "Energy management of small-scale PV-battery systems: A systematic review considering practical implementation, computational requirements, quality of input data and battery degradation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 555-570.
    3. Tervo, Eric & Agbim, Kenechi & DeAngelis, Freddy & Hernandez, Jeffrey & Kim, Hye Kyung & Odukomaiya, Adewale, 2018. "An economic analysis of residential photovoltaic systems with lithium ion battery storage in the United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 1057-1066.
    4. Despeghel, Jolien & Tant, Jeroen & Driesen, Johan, 2024. "Convex optimization of PV-battery system sizing and operation with non-linear loss models," Applied Energy, Elsevier, vol. 353(PA).
    5. Liao, Wei & Xiao, Fu & Li, Yanxue & Zhang, Hanbei & Peng, Jinqing, 2024. "A comparative study of demand-side energy management strategies for building integrated photovoltaics-battery and electric vehicles (EVs) in diversified building communities," Applied Energy, Elsevier, vol. 361(C).
    6. Gopinath Subramani & Vigna K. Ramachandaramurthy & Sanjeevikumar Padmanaban & Lucian Mihet-Popa & Frede Blaabjerg & Josep M. Guerrero, 2017. "Grid-Tied Photovoltaic and Battery Storage Systems with Malaysian Electricity Tariff—A Review on Maximum Demand Shaving," Energies, MDPI, vol. 10(11), pages 1-17, November.
    7. Mostafa Farrokhabadi, 2019. "Data-Driven Mitigation of Energy Scheduling Inaccuracy in Renewable-Penetrated Grids: Summerside Electric Use Case," Energies, MDPI, vol. 12(12), pages 1-23, June.
    8. Zou, Bin & Peng, Jinqing & Li, Sihui & Li, Yi & Yan, Jinyue & Yang, Hongxing, 2022. "Comparative study of the dynamic programming-based and rule-based operation strategies for grid-connected PV-battery systems of office buildings," Applied Energy, Elsevier, vol. 305(C).
    9. Nguyen, Su & Peng, Wei & Sokolowski, Peter & Alahakoon, Damminda & Yu, Xinghuo, 2018. "Optimizing rooftop photovoltaic distributed generation with battery storage for peer-to-peer energy trading," Applied Energy, Elsevier, vol. 228(C), pages 2567-2580.
    10. Li, Yanxue & Gao, Weijun & Ruan, Yingjun, 2018. "Performance investigation of grid-connected residential PV-battery system focusing on enhancing self-consumption and peak shaving in Kyushu, Japan," Renewable Energy, Elsevier, vol. 127(C), pages 514-523.
    11. Cagnano, A. & De Tuglie, E. & Mancarella, P., 2020. "Microgrids: Overview and guidelines for practical implementations and operation," Applied Energy, Elsevier, vol. 258(C).
    12. Dimitar Bozalakov & Mohannad J. Mnati & Joannes Laveyne & Jan Desmet & Lieven Vandevelde, 2019. "Battery Storage Integration in Voltage Unbalance and Overvoltage Mitigation Control Strategies and Its Impact on the Power Quality," Energies, MDPI, vol. 12(8), pages 1-26, April.
    13. Adrian Grimm & Patrik Schönfeldt & Herena Torio & Peter Klement & Benedikt Hanke & Karsten von Maydell & Carsten Agert, 2021. "Deduction of Optimal Control Strategies for a Sector-Coupled District Energy System," Energies, MDPI, vol. 14(21), pages 1-13, November.
    14. Ghorbanzadeh, Milad & Astaneh, Majid & Golzar, Farzin, 2019. "Long-term degradation based analysis for lithium-ion batteries in off-grid wind-battery renewable energy systems," Energy, Elsevier, vol. 166(C), pages 1194-1206.
    15. Georgiou, Giorgos S. & Christodoulides, Paul & Kalogirou, Soteris A., 2019. "Real-time energy convex optimization, via electrical storage, in buildings – A review," Renewable Energy, Elsevier, vol. 139(C), pages 1355-1365.
    16. Berecibar, M. & Gandiaga, I. & Villarreal, I. & Omar, N. & Van Mierlo, J. & Van den Bossche, P., 2016. "Critical review of state of health estimation methods of Li-ion batteries for real applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 572-587.
    17. Zhou, P. & Jin, R.Y. & Fan, L.W., 2016. "Reliability and economic evaluation of power system with renewables: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 537-547.
    18. Ester Vasta & Tommaso Scimone & Giovanni Nobile & Otto Eberhardt & Daniele Dugo & Massimiliano Maurizio De Benedetti & Luigi Lanuzza & Giuseppe Scarcella & Luca Patanè & Paolo Arena & Mario Cacciato, 2023. "Models for Battery Health Assessment: A Comparative Evaluation," Energies, MDPI, vol. 16(2), pages 1-34, January.
    19. Hartmann, Bálint & Divényi, Dániel & Vokony, István, 2018. "Evaluation of business possibilities of energy storage at commercial and industrial consumers – A case study," Applied Energy, Elsevier, vol. 222(C), pages 59-66.
    20. Vieira, Filomeno M. & Moura, Pedro S. & de Almeida, Aníbal T., 2017. "Energy storage system for self-consumption of photovoltaic energy in residential zero energy buildings," Renewable Energy, Elsevier, vol. 103(C), pages 308-320.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:23:p:4450-:d:289864. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.