IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i19p6967-d922763.html
   My bibliography  Save this article

Review of Battery Energy Storage Systems Modeling in Microgrids with Renewables Considering Battery Degradation

Author

Listed:
  • Nataliia Shamarova

    (Department of Power Supply and Electrical Engineering, Irkutsk National Research Technical University, 664074 Irkutsk, Russia)

  • Konstantin Suslov

    (Department of Power Supply and Electrical Engineering, Irkutsk National Research Technical University, 664074 Irkutsk, Russia
    Department of Hydropower and Renewable Energy, National Research University “Moscow Power Engineering Institute”, 111250 Moscow, Russia)

  • Pavel Ilyushin

    (Department of Hydropower and Renewable Energy, National Research University “Moscow Power Engineering Institute”, 111250 Moscow, Russia
    Department of Research on the Relationship between Energy and the Economy, Energy Research Institute of the Russian Academy of Sciences, 117186 Moscow, Russia)

  • Ilia Shushpanov

    (Department of Power Supply and Electrical Engineering, Irkutsk National Research Technical University, 664074 Irkutsk, Russia)

Abstract

The modeling of battery energy storage systems (BESS) remains poorly researched, especially in the case of taking into account the power loss due to degradation that occurs during operation in the power system with a large penetration of generation from renewables and stochastic load from electric vehicles (EV). Meanwhile, the lifetime varies considerably from the manufacturer’s claim due to different operating conditions, and also depends on the level of renewable energy sources (RES) penetration, cyclic operation, temperature, discharge/charge rate, and depth of discharge. Choosing a simplistic approach to the degradation model can lead to unreliable conclusions in choosing the best management strategy and significant investment and operating costs. Most existing BESS models in stationary applications either assume zero degradation costs for storage or simplify battery life to a linear function of depth of discharge (DOD), which can lead to additional error in estimating the cost of BESS degradation. The complexity of constructing a lifetime model of BESS is due to the presence of nonlinear degradation of BESS at the beginning and at the end of the lifetime, as well as the difficulty in obtaining a large amount of experimental data that are close to the real-world operating conditions for the construction of most models. This article analyzes the features of BESS that are specific to their operation in microgrids in terms of the influence of the main stress factors on the degree of BESS degradation. This study also provides a review of existing models for assessing battery degradation.

Suggested Citation

  • Nataliia Shamarova & Konstantin Suslov & Pavel Ilyushin & Ilia Shushpanov, 2022. "Review of Battery Energy Storage Systems Modeling in Microgrids with Renewables Considering Battery Degradation," Energies, MDPI, vol. 15(19), pages 1-18, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:6967-:d:922763
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/19/6967/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/19/6967/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Paolo Scarabaggio & Raffaele Carli & Graziana Cavone & Mariagrazia Dotoli, 2020. "Smart Control Strategies for Primary Frequency Regulation through Electric Vehicles: A Battery Degradation Perspective," Energies, MDPI, vol. 13(17), pages 1-19, September.
    2. Valentin Silvera Diaz & Daniel Augusto Cantane & André Quites Ordovás Santos & Oswaldo Hideo Ando Junior, 2021. "Comparative Analysis of Degradation Assessment of Battery Energy Storage Systems in PV Smoothing Application," Energies, MDPI, vol. 14(12), pages 1-16, June.
    3. Zhang, Yajun & Liu, Yajie & Wang, Jia & Zhang, Tao, 2022. "State-of-health estimation for lithium-ion batteries by combining model-based incremental capacity analysis with support vector regression," Energy, Elsevier, vol. 239(PB).
    4. Huo, Da & Santos, Marcos & Sarantakos, Ilias & Resch, Markus & Wade, Neal & Greenwood, David, 2022. "A reliability-aware chance-constrained battery sizing method for island microgrid," Energy, Elsevier, vol. 251(C).
    5. Yasser Diab & François Auger & Emmanuel Schaeffer & Moutassem Wahbeh, 2017. "Estimating Lithium-Ion Battery State of Charge and Parameters Using a Continuous-Discrete Extended Kalman Filter," Energies, MDPI, vol. 10(8), pages 1-19, July.
    6. Tobajas, Javier & Garcia-Torres, Felix & Roncero-Sánchez, Pedro & Vázquez, Javier & Bellatreche, Ladjel & Nieto, Emilio, 2022. "Resilience-oriented schedule of microgrids with hybrid energy storage system using model predictive control," Applied Energy, Elsevier, vol. 306(PB).
    7. Narayan, Nishant & Papakosta, Thekla & Vega-Garita, Victor & Qin, Zian & Popovic-Gerber, Jelena & Bauer, Pavol & Zeman, Miroslav, 2018. "Estimating battery lifetimes in Solar Home System design using a practical modelling methodology," Applied Energy, Elsevier, vol. 228(C), pages 1629-1639.
    8. Li, Xiaoyu & Yuan, Changgui & Li, Xiaohui & Wang, Zhenpo, 2020. "State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression," Energy, Elsevier, vol. 190(C).
    9. Abdullah Dik & Siddig Omer & Rabah Boukhanouf, 2022. "Electric Vehicles: V2G for Rapid, Safe, and Green EV Penetration," Energies, MDPI, vol. 15(3), pages 1-26, January.
    10. George Baure & Matthieu Dubarry, 2020. "Durability and Reliability of EV Batteries under Electric Utility Grid Operations: Impact of Frequency Regulation Usage on Cell Degradation," Energies, MDPI, vol. 13(10), pages 1-11, May.
    11. Cardoso, Gonçalo & Brouhard, Thomas & DeForest, Nicholas & Wang, Dai & Heleno, Miguel & Kotzur, Leander, 2018. "Battery aging in multi-energy microgrid design using mixed integer linear programming," Applied Energy, Elsevier, vol. 231(C), pages 1059-1069.
    12. Petit, Martin & Prada, Eric & Sauvant-Moynot, Valérie, 2016. "Development of an empirical aging model for Li-ion batteries and application to assess the impact of Vehicle-to-Grid strategies on battery lifetime," Applied Energy, Elsevier, vol. 172(C), pages 398-407.
    13. Wiljan Vermeer & Gautham Ram Chandra Mouli & Pavol Bauer, 2020. "Real-Time Building Smart Charging System Based on PV Forecast and Li-Ion Battery Degradation," Energies, MDPI, vol. 13(13), pages 1-25, July.
    14. Yang Yang & Chong Lian & Chao Ma & Yusheng Zhang, 2019. "Research on Energy Storage Optimization for Large-Scale PV Power Stations under Given Long-Distance Delivery Mode," Energies, MDPI, vol. 13(1), pages 1-20, December.
    15. Saman Korjani & Mario Mureddu & Angelo Facchini & Alfonso Damiano, 2017. "Aging Cost Optimization for Planning and Management of Energy Storage Systems," Energies, MDPI, vol. 10(11), pages 1-17, November.
    16. Chen, Lin & Wang, Huimin & Liu, Bohao & Wang, Yijue & Ding, Yunhui & Pan, Haihong, 2021. "Battery state-of-health estimation based on a metabolic extreme learning machine combining degradation state model and error compensation," Energy, Elsevier, vol. 215(PA).
    17. Fei, Zicheng & Yang, Fangfang & Tsui, Kwok-Leung & Li, Lishuai & Zhang, Zijun, 2021. "Early prediction of battery lifetime via a machine learning based framework," Energy, Elsevier, vol. 225(C).
    18. Bernhard Faessler, 2021. "Stationary, Second Use Battery Energy Storage Systems and Their Applications: A Research Review," Energies, MDPI, vol. 14(8), pages 1-19, April.
    19. Saurabh Saxena & Darius Roman & Valentin Robu & David Flynn & Michael Pecht, 2021. "Battery Stress Factor Ranking for Accelerated Degradation Test Planning Using Machine Learning," Energies, MDPI, vol. 14(3), pages 1-17, January.
    20. Wei He & Michael Pecht & David Flynn & Fateme Dinmohammadi, 2018. "A Physics-Based Electrochemical Model for Lithium-Ion Battery State-of-Charge Estimation Solved by an Optimised Projection-Based Method and Moving-Window Filtering," Energies, MDPI, vol. 11(8), pages 1-23, August.
    21. Monika Sandelic & Daniel-Ioan Stroe & Florin Iov, 2018. "Battery Storage-Based Frequency Containment Reserves in Large Wind Penetrated Scenarios: A Practical Approach to Sizing," Energies, MDPI, vol. 11(11), pages 1-19, November.
    22. Muhammad Sufyan & Nasrudin Abd Rahim & ChiaKwang Tan & Munir Azam Muhammad & Siti Rohani Sheikh Raihan, 2019. "Optimal sizing and energy scheduling of isolated microgrid considering the battery lifetime degradation," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-28, February.
    23. Ilia Shushpanov & Konstantin Suslov & Pavel Ilyushin & Denis N. Sidorov, 2021. "Towards the Flexible Distribution Networks Design Using the Reliability Performance Metric," Energies, MDPI, vol. 14(19), pages 1-24, September.
    24. Uddin, Kotub & Dubarry, Matthieu & Glick, Mark B., 2018. "The viability of vehicle-to-grid operations from a battery technology and policy perspective," Energy Policy, Elsevier, vol. 113(C), pages 342-347.
    25. Bizhong Xia & Zheng Zhang & Zizhou Lao & Wei Wang & Wei Sun & Yongzhi Lai & Mingwang Wang, 2018. "Strong Tracking of a H-Infinity Filter in Lithium-Ion Battery State of Charge Estimation," Energies, MDPI, vol. 11(6), pages 1-20, June.
    26. H. Lan & S. Wen & Q. Fu & D. C. Yu & L. Zhang, 2015. "Modeling Analysis and Improvement of Power Loss in Microgrid," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-8, March.
    27. Wang, Shuoqi & Guo, Dongxu & Han, Xuebing & Lu, Languang & Sun, Kai & Li, Weihan & Sauer, Dirk Uwe & Ouyang, Minggao, 2020. "Impact of battery degradation models on energy management of a grid-connected DC microgrid," Energy, Elsevier, vol. 207(C).
    28. Peter Haidl & Armin Buchroithner & Bernhard Schweighofer & Michael Bader & Hannes Wegleiter, 2019. "Lifetime Analysis of Energy Storage Systems for Sustainable Transportation," Sustainability, MDPI, vol. 11(23), pages 1-21, November.
    29. Ziming Xu & Jun Xu & Zhechen Guo & Haitao Wang & Zheng Sun & Xuesong Mei, 2022. "Design and Optimization of a Novel Microchannel Battery Thermal Management System Based on Digital Twin," Energies, MDPI, vol. 15(4), pages 1-20, February.
    30. Peng, Chao & Zou, Jianxiao & Lian, Lian & Li, Liying, 2017. "An optimal dispatching strategy for V2G aggregator participating in supplementary frequency regulation considering EV driving demand and aggregator’s benefits," Applied Energy, Elsevier, vol. 190(C), pages 591-599.
    31. Alexandros Nikolian & Yousef Firouz & Rahul Gopalakrishnan & Jean-Marc Timmermans & Noshin Omar & Peter Van den Bossche & Joeri Van Mierlo, 2016. "Lithium Ion Batteries—Development of Advanced Electrical Equivalent Circuit Models for Nickel Manganese Cobalt Lithium-Ion," Energies, MDPI, vol. 9(5), pages 1-23, May.
    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. 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.
    2. Vladislav Volnyi & Pavel Ilyushin & Konstantin Suslov & Sergey Filippov, 2023. "Approaches to Building AC and AC–DC Microgrids on Top of Existing Passive Distribution Networks," Energies, MDPI, vol. 16(15), pages 1-26, August.
    3. Aleksandr Kulikov & Pavel Ilyushin & Aleksandr Sevostyanov & Sergey Filippov & Konstantin Suslov, 2024. "Estimation of an Extent of Sinusoidal Voltage Waveform Distortion Using Parametric and Nonparametric Multiple-Hypothesis Sequential Testing in Devices for Automatic Control of Power Quality Indices," Energies, MDPI, vol. 17(5), pages 1-24, February.
    4. Pavel Ilyushin & Vladislav Volnyi & Konstantin Suslov & Sergey Filippov, 2023. "State-of-the-Art Literature Review of Power Flow Control Methods for Low-Voltage AC and AC-DC Microgrids," Energies, MDPI, vol. 16(7), pages 1-35, March.
    5. Nisitha Padmawansa & Kosala Gunawardane & Samaneh Madanian & Amanullah Maung Than Oo, 2023. "Battery Energy Storage Capacity Estimation for Microgrids Using Digital Twin Concept," Energies, MDPI, vol. 16(12), pages 1-18, June.

    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. Shahjalal, Mohammad & Roy, Probir Kumar & Shams, Tamanna & Fly, Ashley & Chowdhury, Jahedul Islam & Ahmed, Md. Rishad & Liu, Kailong, 2022. "A review on second-life of Li-ion batteries: prospects, challenges, and issues," Energy, Elsevier, vol. 241(C).
    2. Ospina Agudelo, Brian & Zamboni, Walter & Monmasson, Eric, 2021. "Application domain extension of incremental capacity-based battery SoH indicators," Energy, Elsevier, vol. 234(C).
    3. Shabani, Masoume & Wallin, Fredrik & Dahlquist, Erik & Yan, Jinyue, 2023. "The impact of battery operating management strategies on life cycle cost assessment in real power market for a grid-connected residential battery application," Energy, Elsevier, vol. 270(C).
    4. Tepe, Benedikt & Figgener, Jan & Englberger, Stefan & Sauer, Dirk Uwe & Jossen, Andreas & Hesse, Holger, 2022. "Optimal pool composition of commercial electric vehicles in V2G fleet operation of various electricity markets," Applied Energy, Elsevier, vol. 308(C).
    5. Wei, Jingwen & Chen, Chunlin, 2021. "A multi-timescale framework for state monitoring and lifetime prognosis of lithium-ion batteries," Energy, Elsevier, vol. 229(C).
    6. Ko, Chi-Jyun & Chen, Kuo-Ching, 2024. "Using tens of seconds of relaxation voltage to estimate open circuit voltage and state of health of lithium ion batteries," Applied Energy, Elsevier, vol. 357(C).
    7. Liang Zhang & Shunli Wang & Daniel-Ioan Stroe & Chuanyun Zou & Carlos Fernandez & Chunmei Yu, 2020. "An Accurate Time Constant Parameter Determination Method for the Varying Condition Equivalent Circuit Model of Lithium Batteries," Energies, MDPI, vol. 13(8), pages 1-12, April.
    8. Shen, Dongxu & Wu, Lifeng & Kang, Guoqing & Guan, Yong & Peng, Zhen, 2021. "A novel online method for predicting the remaining useful life of lithium-ion batteries considering random variable discharge current," Energy, Elsevier, vol. 218(C).
    9. Zhaosheng Zhang & Shuo Wang & Ni Lin & Zhenpo Wang & Peng Liu, 2023. "State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles Based on Regional Capacity and LGBM," Sustainability, MDPI, vol. 15(3), pages 1-20, January.
    10. Andre Leippi & Markus Fleschutz & Michael D. Murphy, 2022. "A Review of EV Battery Utilization in Demand Response Considering Battery Degradation in Non-Residential Vehicle-to-Grid Scenarios," Energies, MDPI, vol. 15(9), pages 1-22, April.
    11. Woo-Yong Kim & Pyeong-Yeon Lee & Jonghoon Kim & Kyung-Soo Kim, 2019. "A Nonlinear-Model-Based Observer for a State-of-Charge Estimation of a Lithium-Ion Battery in Electric Vehicles," Energies, MDPI, vol. 12(17), pages 1-20, September.
    12. Guo, Yongfang & Yu, Xiangyuan & Wang, Yashuang & Huang, Kai, 2024. "Health prognostics of lithium-ion batteries based on universal voltage range features mining and adaptive multi-Gaussian process regression with Harris Hawks optimization algorithm," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    13. Li, Alan G. & West, Alan C. & Preindl, Matthias, 2022. "Towards unified machine learning characterization of lithium-ion battery degradation across multiple levels: A critical review," Applied Energy, Elsevier, vol. 316(C).
    14. Shabani, Masoume & Wallin, Fredrik & Dahlquist, Erik & Yan, Jinyue, 2022. "Techno-economic assessment of battery storage integrated into a grid-connected and solar-powered residential building under different battery ageing models," Applied Energy, Elsevier, vol. 318(C).
    15. Hossain, Md Alamgir & Pota, Hemanshu Roy & Squartini, Stefano & Zaman, Forhad & Guerrero, Josep M., 2019. "Energy scheduling of community microgrid with battery cost using particle swarm optimisation," Applied Energy, Elsevier, vol. 254(C).
    16. Wu, Muyao & Wang, Li & Wu, Ji, 2023. "State of health estimation of the LiFePO4 power battery based on the forgetting factor recursive Total Least Squares and the temperature correction," Energy, Elsevier, vol. 282(C).
    17. Lin, Chuanping & Xu, Jun & Shi, Mingjie & Mei, Xuesong, 2022. "Constant current charging time based fast state-of-health estimation for lithium-ion batteries," Energy, Elsevier, vol. 247(C).
    18. Paolo Scarabaggio & Raffaele Carli & Graziana Cavone & Mariagrazia Dotoli, 2020. "Smart Control Strategies for Primary Frequency Regulation through Electric Vehicles: A Battery Degradation Perspective," Energies, MDPI, vol. 13(17), pages 1-19, September.
    19. Mao, Jiachen & Jafari, Mehdi & Botterud, Audun, 2022. "Planning low-carbon distributed power systems: Evaluating the role of energy storage," Energy, Elsevier, vol. 238(PA).
    20. Haber, Marc & Azaïs, Philippe & Genies, Sylvie & Raccurt, Olivier, 2023. "Stress factor identification and Risk Probabilistic Number (RPN) analysis of Li-ion batteries based on worldwide electric vehicle usage," Applied Energy, Elsevier, vol. 343(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:gam:jeners:v:15:y:2022:i:19:p:6967-:d:922763. 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.