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

Author

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  • 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
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    References listed on IDEAS

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    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.
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