IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v334y2023ics0306261923000612.html
   My bibliography  Save this article

A hierarchical two-level MILP optimization model for the management of grid-connected BESS considering accurate physical model

Author

Listed:
  • Nebuloni, Riccardo
  • Meraldi, Lorenzo
  • Bovo, Cristian
  • Ilea, Valentin
  • Berizzi, Alberto
  • Sinha, Snigdh
  • Tamirisakandala, Raviteja Bharadwaj
  • Raboni, Pietro

Abstract

This work proposes a new Energy Management System (EMS) for Battery Energy Storage Systems (BESS). The goal is to make a BESS profitable in the new environment considering massive use of batteries that can be foreseen in the next future, due to the predictive increase of clean energy resources. The developed EMS considers two levels of optimization. The first level models the participation of the BESS in an Ancillary Service Market and schedules the BESS. The second level, the most innovative, is responsible for optimally distributing the power set-points obtained previously among the various battery banks considering, in addition to the battery aging, also the different efficiencies of battery banks, converters, and transformers. Moreover, this second-level manages both active and reactive power flows, and losses. Both optimization algorithms have been modeled as Mixed Integer Linear Programming (MILP) and implemented in GAMS using CPLEX as a solver. The results are encouraging: compared with the common industrial practice in which the load profile is equally shared among the individual batteries within a BESS, the two new proposed EMS strategies guarantee for a long period of operation (10-years) a consistent reduction in the number of batteries replacement (around 47%), thus ensuring significant cost savings. Moreover, the proposed BESS model accurately approximates the real physical behavior of the system, leading to an average error in State-of Energy (SoE) evaluation below 0.6%, which is almost one order of magnitude lower than the ones obtained by simpler models from literature with degradation only SoE-dependent.

Suggested Citation

  • Nebuloni, Riccardo & Meraldi, Lorenzo & Bovo, Cristian & Ilea, Valentin & Berizzi, Alberto & Sinha, Snigdh & Tamirisakandala, Raviteja Bharadwaj & Raboni, Pietro, 2023. "A hierarchical two-level MILP optimization model for the management of grid-connected BESS considering accurate physical model," Applied Energy, Elsevier, vol. 334(C).
    • Handle: RePEc:eee:appene:v:334:y:2023:i:c:s0306261923000612
    DOI: 10.1016/j.apenergy.2023.120697
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923000612
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2023.120697?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. de Souza Dutra, Michael David & Alguacil, Natalia, 2020. "Optimal residential users coordination via demand response: An exact distributed framework," Applied Energy, Elsevier, vol. 279(C).
    2. Merten, Michael & Olk, Christopher & Schoeneberger, Ilka & Sauer, Dirk Uwe, 2020. "Bidding strategy for battery storage systems in the secondary control reserve market," Applied Energy, Elsevier, vol. 268(C).
    3. Ming-Hua Lin & John Gunnar Carlsson & Dongdong Ge & Jianming Shi & Jung-Fa Tsai, 2013. "A Review of Piecewise Linearization Methods," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-8, November.
    4. Engels, Jonas & Claessens, Bert & Deconinck, Geert, 2019. "Techno-economic analysis and optimal control of battery storage for frequency control services, applied to the German market," Applied Energy, Elsevier, vol. 242(C), pages 1036-1049.
    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. Mehrshad Pakjoo & Luigi Piegari & Giuliano Rancilio & Silvia Colnago & Joseph Epoupa Mengou & Federico Bresciani & Giacomo Gorni & Stefano Mandelli & Marco Merlo, 2023. "A Review on Testing of Electrochemical Cells for Aging Models in BESS," Energies, MDPI, vol. 16(19), pages 1-26, September.
    2. Liu, Feng & Lv, Tao & Meng, Yuan & Li, Cong & Hou, Xiaoran & Xu, Jie & Deng, Xu, 2023. "Potential analysis of BESS and CCUS in the context of China's carbon trading scheme toward the low-carbon electricity system," Renewable Energy, Elsevier, vol. 210(C), pages 462-471.
    3. Matteo Spiller & Giuliano Rancilio & Filippo Bovera & Giacomo Gorni & Stefano Mandelli & Federico Bresciani & Marco Merlo, 2023. "A Model-Aware Comprehensive Tool for Battery Energy Storage System Sizing," Energies, MDPI, vol. 16(18), pages 1-24, September.
    4. Wang, Jian & Ilea, Valentin & Bovo, Cristian & Xie, Ning & Wang, Yong, 2023. "Optimal self-scheduling for a multi-energy virtual power plant providing energy and reserve services under a holistic market framework," Energy, Elsevier, vol. 278(PB).
    5. Irina Picioroaga & Madalina Luca & Andrei Tudose & Dorian Sidea & Mircea Eremia & Constantin Bulac, 2023. "Resilience-Driven Optimal Sizing of Energy Storage Systems in Remote Microgrids," Sustainability, MDPI, vol. 15(22), pages 1-16, November.

    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. Nitsch, Felix & Deissenroth-Uhrig, Marc & Schimeczek, Christoph & Bertsch, Valentin, 2021. "Economic evaluation of battery storage systems bidding on day-ahead and automatic frequency restoration reserves markets," Applied Energy, Elsevier, vol. 298(C).
    2. Jeddi, Babak & Mishra, Yateendra & Ledwich, Gerard, 2021. "Distributed load scheduling in residential neighborhoods for coordinated operation of multiple home energy management systems," Applied Energy, Elsevier, vol. 300(C).
    3. Zheng, Ling & Zhou, Bin & Cao, Yijia & Wing Or, Siu & Li, Yong & Wing Chan, Ka, 2022. "Hierarchical distributed multi-energy demand response for coordinated operation of building clusters," Applied Energy, Elsevier, vol. 308(C).
    4. Kuttner, Leopold, 2022. "Integrated scheduling and bidding of power and reserve of energy resource aggregators with storage plants," Applied Energy, Elsevier, vol. 321(C).
    5. Xu, Fangyuan & Zhu, Weidong & Wang, Yi Fei & Lai, Chun Sing & Yuan, Haoliang & Zhao, Yujia & Guo, Siming & Fu, Zhengxin, 2022. "A new deregulated demand response scheme for load over-shifting city in regulated power market," Applied Energy, Elsevier, vol. 311(C).
    6. Fernández-Blanco, Ricardo & Morales, Juan Miguel & Pineda, Salvador, 2021. "Forecasting the price-response of a pool of buildings via homothetic inverse optimization," Applied Energy, Elsevier, vol. 290(C).
    7. Ahmed Mohamed & Rémy Rigo-Mariani & Vincent Debusschere & Lionel Pin, 2023. "Stacked Revenues for Energy Storage Participating in Energy and Reserve Markets with an Optimal Frequency Regulation Modeling," Post-Print hal-04182119, HAL.
    8. Collath, Nils & Cornejo, Martin & Engwerth, Veronika & Hesse, Holger & Jossen, Andreas, 2023. "Increasing the lifetime profitability of battery energy storage systems through aging aware operation," Applied Energy, Elsevier, vol. 348(C).
    9. Cai, Qiran & Xu, Qingyang & Qing, Jing & Shi, Gang & Liang, Qiao-Mei, 2022. "Promoting wind and photovoltaics renewable energy integration through demand response: Dynamic pricing mechanism design and economic analysis for smart residential communities," Energy, Elsevier, vol. 261(PB).
    10. Md. Shafiul Alam & Tanzi Ahmed Chowdhury & Abhishak Dhar & Fahad Saleh Al-Ismail & M. S. H. Choudhury & Md Shafiullah & Md. Ismail Hossain & Md. Alamgir Hossain & Aasim Ullah & Syed Masiur Rahman, 2023. "Solar and Wind Energy Integrated System Frequency Control: A Critical Review on Recent Developments," Energies, MDPI, vol. 16(2), pages 1-31, January.
    11. Mircea Stefan Simoiu & Ioana Fagarasan & Stephane Ploix & Vasile Calofir, 2021. "Sizing and Management of an Energy System for a Metropolitan Station with Storage and Related District Energy Community," Energies, MDPI, vol. 14(18), pages 1-22, September.
    12. Kristina Pandžić & Ivan Pavić & Ivan Andročec & Hrvoje Pandžić, 2020. "Optimal Battery Storage Participation in European Energy and Reserves Markets," Energies, MDPI, vol. 13(24), pages 1-21, December.
    13. Arrinda, M. & Berecibar, M. & Oyarbide, M. & Macicior, H. & Muxika, E. & Messagie, M., 2020. "Levelized cost of electricity calculation of the energy generation plant of a CO2 neutral micro-grid," Energy, Elsevier, vol. 208(C).
    14. Bitencourt, Leonardo & Dias, Bruno & Soares, Tiago & Borba, Bruno & Quirós-Tortós, Jairo, 2023. "e-Carsharing siting and sizing DLMP-based under demand uncertainty," Applied Energy, Elsevier, vol. 330(PB).
    15. Mashlakov, Aleksei & Pournaras, Evangelos & Nardelli, Pedro H.J. & Honkapuro, Samuli, 2021. "Decentralized cooperative scheduling of prosumer flexibility under forecast uncertainties," Applied Energy, Elsevier, vol. 290(C).
    16. 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.
    17. Zhang, Yi & Cheng, Chuntian & Cao, Rui & Li, Gang & Shen, Jianjian & Wu, Xinyu, 2021. "Multivariate probabilistic forecasting and its performance’s impacts on long-term dispatch of hydro-wind hybrid systems," Applied Energy, Elsevier, vol. 283(C).
    18. Yang, Wangwang & Shi, Jing & Li, Shujian & Song, Zhaofang & Zhang, Zitong & Chen, Zexu, 2022. "A combined deep learning load forecasting model of single household resident user considering multi-time scale electricity consumption behavior," Applied Energy, Elsevier, vol. 307(C).
    19. Paulo Rotella Junior & Luiz Célio Souza Rocha & Sandra Naomi Morioka & Ivan Bolis & Gianfranco Chicco & Andrea Mazza & Karel Janda, 2021. "Economic Analysis of the Investments in Battery Energy Storage Systems: Review and Current Perspectives," Energies, MDPI, vol. 14(9), pages 1-29, April.
    20. Glismann, Samuel, 2021. "Ancillary Services Acquisition Model: Considering market interactions in policy design," Applied Energy, Elsevier, vol. 304(C).

    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:eee:appene:v:334:y:2023:i:c:s0306261923000612. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.