IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v7y2014i1p130-149d31875.html
   My bibliography  Save this article

Optimal Sizing of Battery Storage Systems for Industrial Applications when Uncertainties Exist

Author

Listed:
  • Guido Carpinelli

    (Department of Electrical Engineering and Information Technology, University of Napoli Federico II, Naples 80125, Italy)

  • Anna Rita Di Fazio

    (Department of Electrical Engineering and Information, University of Cassino and Southern Lazio, Cassino 03043, Italy)

  • Shahab Khormali

    (Department of Electrical Engineering and Information Technology, University of Napoli Federico II, Naples 80125, Italy)

  • Fabio Mottola

    (Department of Electrical Engineering and Information Technology, University of Napoli Federico II, Naples 80125, Italy)

Abstract

Demand response (DR) can be very useful for an industrial facility, since it allows noticeable reductions in the electricity bill due to the significant value of energy demand. Although most industrial processes have stringent constraints in terms of hourly active power, DR only becomes attractive when performed with the contemporaneous use of battery energy storage systems (BESSs). When this option is used, an optimal sizing of BESSs is desirable, because the investment costs can be significant. This paper deals with the optimal sizing of a BESS installed in an industrial facility to reduce electricity costs. A four-step procedure, based on Decision Theory, was used to obtain a good solution for the sizing problem, even when facing uncertainties; in fact, we think that the sizing procedure must properly take into account the unavoidable uncertainties introduced by the cost of electricity and the load demands of industrial facilities. Three approaches provided by Decision Theory were applied, and they were based on: (1) the minimization of expected cost; (2) the regret felt by the sizing engineer; and (3) a mix of (1) and (2). The numerical applications performed on an actual industrial facility provided evidence of the effectiveness of the proposed procedure.

Suggested Citation

  • Guido Carpinelli & Anna Rita Di Fazio & Shahab Khormali & Fabio Mottola, 2014. "Optimal Sizing of Battery Storage Systems for Industrial Applications when Uncertainties Exist," Energies, MDPI, vol. 7(1), pages 1-20, January.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:1:p:130-149:d:31875
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/7/1/130/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/7/1/130/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Schroeder, Andreas, 2011. "Modeling storage and demand management in power distribution grids," Applied Energy, Elsevier, vol. 88(12), pages 4700-4712.
    2. Carpaneto, Enrico & Chicco, Gianfranco & Mancarella, Pierluigi & Russo, Angela, 2011. "Cogeneration planning under uncertainty. Part II: Decision theory-based assessment of planning alternatives," Applied Energy, Elsevier, vol. 88(4), pages 1075-1083, April.
    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. Mazhar Abbas & Eung-sang Kim & Seul-ki Kim & Yun-su Kim, 2016. "Comparative Analysis of Battery Behavior with Different Modes of Discharge for Optimal Capacity Sizing and BMS Operation," Energies, MDPI, vol. 9(10), pages 1-19, October.
    2. Keck, Felix & Lenzen, Manfred, 2021. "Drivers and benefits of shared demand-side battery storage – an Australian case study," Energy Policy, Elsevier, vol. 149(C).
    3. Carlos Suazo-Martínez & Eduardo Pereira-Bonvallet & Rodrigo Palma-Behnke, 2014. "A Simulation Framework for Optimal Energy Storage Sizing," Energies, MDPI, vol. 7(5), pages 1-23, May.
    4. Metz, Dennis & Saraiva, João Tomé, 2018. "Simultaneous co-integration of multiple electrical storage applications in a consumer setting," Energy, Elsevier, vol. 143(C), pages 202-211.
    5. Nahid-Al Masood & Md. Nahid Haque Shazon & Hasin Mussayab Ahmed & Shohana Rahman Deeba, 2020. "Mitigation of Over-Frequency through Optimal Allocation of BESS in a Low-Inertia Power System," Energies, MDPI, vol. 13(17), pages 1-23, September.
    6. Yongma Moon, 2014. "Optimal Time to Invest Energy Storage System under Uncertainty Conditions," Energies, MDPI, vol. 7(4), pages 1-19, April.
    7. Baker, T.E. & Epiney, A.S. & Rabiti, C. & Shittu, E., 2018. "Optimal sizing of flexible nuclear hybrid energy system components considering wind volatility," Applied Energy, Elsevier, vol. 212(C), pages 498-508.

    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. Haddadian, Hossein & Noroozian, Reza, 2017. "Optimal operation of active distribution systems based on microgrid structure," Renewable Energy, Elsevier, vol. 104(C), pages 197-210.
    2. McPherson, Madeleine & Stoll, Brady, 2020. "Demand response for variable renewable energy integration: A proposed approach and its impacts," Energy, Elsevier, vol. 197(C).
    3. Schachter, Jonathan A. & Mancarella, Pierluigi & Moriarty, John & Shaw, Rita, 2016. "Flexible investment under uncertainty in smart distribution networks with demand side response: Assessment framework and practical implementation," Energy Policy, Elsevier, vol. 97(C), pages 439-449.
    4. 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.
    5. Hanif, Sarmad & Alam, M.J.E. & Roshan, Kini & Bhatti, Bilal A. & Bedoya, Juan C., 2022. "Multi-service battery energy storage system optimization and control," Applied Energy, Elsevier, vol. 311(C).
    6. Finn, P. & O’Connell, M. & Fitzpatrick, C., 2013. "Demand side management of a domestic dishwasher: Wind energy gains, financial savings and peak-time load reduction," Applied Energy, Elsevier, vol. 101(C), pages 678-685.
    7. Capuder, Tomislav & Mancarella, Pierluigi, 2014. "Techno-economic and environmental modelling and optimization of flexible distributed multi-generation options," Energy, Elsevier, vol. 71(C), pages 516-533.
    8. Allard, Stéphane & Debusschere, Vincent & Mima, Silvana & Quoc, Tuan Tran & Hadjsaid, Nouredine & Criqui, Patrick, 2020. "Considering distribution grids and local flexibilities in the prospective development of the European power system by 2050," Applied Energy, Elsevier, vol. 270(C).
    9. Obara, Shin’ya & Konno, Daisuke & Utsugi, Yuta & Morel, Jorge, 2014. "Analysis of output power and capacity reduction in electrical storage facilities by peak shift control of PV system with bifacial modules," Applied Energy, Elsevier, vol. 128(C), pages 35-48.
    10. Woo, C.K. & Sreedharan, P. & Hargreaves, J. & Kahrl, F. & Wang, J. & Horowitz, I., 2014. "A review of electricity product differentiation," Applied Energy, Elsevier, vol. 114(C), pages 262-272.
    11. Tataraki, Kalliopi G. & Kavvadias, Konstantinos C. & Maroulis, Zacharias B., 2018. "A systematic approach to evaluate the economic viability of Combined Cooling Heating and Power systems over conventional technologies," Energy, Elsevier, vol. 148(C), pages 283-295.
    12. Apostolou, Dimitrios & Enevoldsen, Peter, 2019. "The past, present and potential of hydrogen as a multifunctional storage application for wind power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 917-929.
    13. Aghaei, Jamshid & Alizadeh, Mohammad-Iman, 2013. "Multi-objective self-scheduling of CHP (combined heat and power)-based microgrids considering demand response programs and ESSs (energy storage systems)," Energy, Elsevier, vol. 55(C), pages 1044-1054.
    14. Poudineh, Rahmatallah & Jamasb, Tooraj, 2014. "Distributed generation, storage, demand response and energy efficiency as alternatives to grid capacity enhancement," Energy Policy, Elsevier, vol. 67(C), pages 222-231.
    15. Giulia Sonetti, 2015. "What if We Adopt a Resilience Thinking Approach in the Urban Governance for Emission Reduction?," ERSA conference papers ersa15p1295, European Regional Science Association.
    16. Rajeev, T. & Ashok, S., 2015. "Dynamic load-shifting program based on a cloud computing framework to support the integration of renewable energy sources," Applied Energy, Elsevier, vol. 146(C), pages 141-149.
    17. Ersoz, Ibrahim & Colak, Uner, 2016. "Combined cooling, heat and power planning under uncertainty," Energy, Elsevier, vol. 109(C), pages 1016-1025.
    18. Masa-Bote, D. & Castillo-Cagigal, M. & Matallanas, E. & Caamaño-Martín, E. & Gutiérrez, A. & Monasterio-Huelín, F. & Jiménez-Leube, J., 2014. "Improving photovoltaics grid integration through short time forecasting and self-consumption," Applied Energy, Elsevier, vol. 125(C), pages 103-113.
    19. Sorrentino, Marco & Rizzo, Gianfranco & Sorrentino, Luca, 2014. "A study aimed at assessing the potential impact of vehicle electrification on grid infrastructure and road-traffic green house emissions," Applied Energy, Elsevier, vol. 120(C), pages 31-40.
    20. Fang, Xinli & Yang, Qiang & Wang, Jianhui & Yan, Wenjun, 2016. "Coordinated dispatch in multiple cooperative autonomous islanded microgrids," Applied Energy, Elsevier, vol. 162(C), pages 40-48.

    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:7:y:2014:i:1:p:130-149:d:31875. 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.