IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v318y2022ics0306261922005384.html
   My bibliography  Save this article

Techno-economic assessment of battery storage integrated into a grid-connected and solar-powered residential building under different battery ageing models

Author

Listed:
  • Shabani, Masoume
  • Wallin, Fredrik
  • Dahlquist, Erik
  • Yan, Jinyue

Abstract

Battery storage in solar residential applications has the potential to improve system flexibility under high renewable energy penetration. A better understanding of the dynamic operational conditions of batteries is of high importance for the technical and economic feasibility of the associated system. This study evaluates key parameters for the proper battery management design, control, and optimization of a battery system integrated into a grid-connected, solar-powered building. Three different battery modelling scenarios are proposed in terms of battery ageing and lifetimes, internal states, and control strategies. Each proposed scenario consists of a set of specific methods for the estimation of battery voltage-current characteristics, capacity degradation, remaining lifetime, states of charge, states of health, and states of power. A criteria-based operational strategy linked to a nondominated sorting genetic algorithm (NSGA_II) is constructed for the simulation and multiobjective optimization of the system. The self-sufficiency ratio and life-cycle cost of a battery are considered the technical and economic goals, which are influenced by the capacity degradation and achievable lifetime of the battery. Moreover, the annual battery degradation cost and self-consumption ratio are calculated over the project lifetime. The comparison between the techno-economic optimization results obtained under three battery modelling scenarios indicate that a more realistic design and a superior techno-economic assessment are obtained under Model 3, which is able to simulate battery degradation considering all ageing influence parameters under real operational conditions. In comparison with Model 3, Model 1 which neglects the battery degradation, techno-economically leads an overly optimistic result and also Model 2, which was based on linear capacity degradation regardless of the observed dynamic operational conditions, leads an excessively pessimistic result, implying that applying several simplifying assumptions for a battery operation simulation in a real-life application greatly affects the resulting battery state of charge, state of power, and state of health estimations, leading to an improper battery management system and consequently to the misestimation of techno-economic objective functions. The results prove that the real design and techno-economic assessment of a battery in a solar-powered application highly depend on battery operations in which the seasonal photovoltaic (PV) power production affects the rates of calendric and cyclic battery degradation.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:appene:v:318:y:2022:i:c:s0306261922005384
    DOI: 10.1016/j.apenergy.2022.119166
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922005384
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2022.119166?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. Jinyue Yan & Ying Yang & Pietro Elia Campana & Jijiang He, 2019. "City-level analysis of subsidy-free solar photovoltaic electricity price, profits and grid parity in China," Nature Energy, Nature, vol. 4(8), pages 709-717, August.
    2. Mulleriyawage, U.G.K. & Shen, W.X., 2020. "Optimally sizing of battery energy storage capacity by operational optimization of residential PV-Battery systems: An Australian household case study," Renewable Energy, Elsevier, vol. 160(C), pages 852-864.
    3. Ma, Weiwu & Xue, Xinpei & Liu, Gang, 2018. "Techno-economic evaluation for hybrid renewable energy system: Application and merits," Energy, Elsevier, vol. 159(C), pages 385-409.
    4. Zhang, Yijie & Ma, Tao & Elia Campana, Pietro & Yamaguchi, Yohei & Dai, Yanjun, 2020. "A techno-economic sizing method for grid-connected household photovoltaic battery systems," Applied Energy, Elsevier, vol. 269(C).
    5. 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.
    6. Mayer, Martin János & Szilágyi, Artúr & Gróf, Gyula, 2020. "Environmental and economic multi-objective optimization of a household level hybrid renewable energy system by genetic algorithm," Applied Energy, Elsevier, vol. 269(C).
    7. 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.
    8. 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.
    9. Shabani, Masoume & Dahlquist, Erik & Wallin, Fredrik & Yan, Jinyue, 2020. "Techno-economic comparison of optimal design of renewable-battery storage and renewable micro pumped hydro storage power supply systems: A case study in Sweden," Applied Energy, Elsevier, vol. 279(C).
    10. Benavente, Fabian & Lundblad, Anders & Campana, Pietro Elia & Zhang, Yang & Cabrera, Saúl & Lindbergh, Göran, 2019. "Photovoltaic/battery system sizing for rural electrification in Bolivia: Considering the suppressed demand effect," Applied Energy, Elsevier, vol. 235(C), pages 519-528.
    11. Altan, Aytaç & Karasu, Seçkin, 2020. "Recognition of COVID-19 disease from X-ray images by hybrid model consisting of 2D curvelet transform, chaotic salp swarm algorithm and deep learning technique," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    12. Ghorbani, Narges & Kasaeian, Alibakhsh & Toopshekan, Ashkan & Bahrami, Leyli & Maghami, Amin, 2018. "Optimizing a hybrid wind-PV-battery system using GA-PSO and MOPSO for reducing cost and increasing reliability," Energy, Elsevier, vol. 154(C), pages 581-591.
    13. Holger C. Hesse & Michael Schimpe & Daniel Kucevic & Andreas Jossen, 2017. "Lithium-Ion Battery Storage for the Grid—A Review of Stationary Battery Storage System Design Tailored for Applications in Modern Power Grids," Energies, MDPI, vol. 10(12), pages 1-42, December.
    14. Zhang, Yang & Campana, Pietro Elia & Lundblad, Anders & Yan, Jinyue, 2017. "Comparative study of hydrogen storage and battery storage in grid connected photovoltaic system: Storage sizing and rule-based operation," Applied Energy, Elsevier, vol. 201(C), pages 397-411.
    15. Li, Jiaming, 2019. "Optimal sizing of grid-connected photovoltaic battery systems for residential houses in Australia," Renewable Energy, Elsevier, vol. 136(C), pages 1245-1254.
    16. Karasu, Seçkin & Altan, Aytaç & Bekiros, Stelios & Ahmad, Wasim, 2020. "A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series," Energy, Elsevier, vol. 212(C).
    17. Bingham, Raymond D. & Agelin-Chaab, Martin & Rosen, Marc A., 2019. "Whole building optimization of a residential home with PV and battery storage in The Bahamas," Renewable Energy, Elsevier, vol. 132(C), pages 1088-1103.
    18. Maheri, Alireza, 2014. "Multi-objective design optimisation of standalone hybrid wind-PV-diesel systems under uncertainties," Renewable Energy, Elsevier, vol. 66(C), pages 650-661.
    19. Karasu, Seçkin & Altan, Aytaç, 2022. "Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization," Energy, Elsevier, vol. 242(C).
    20. Rui Xiong & Hongwen He & Fengchun Sun & Kai Zhao, 2012. "Online Estimation of Peak Power Capability of Li-Ion Batteries in Electric Vehicles by a Hardware-in-Loop Approach," Energies, MDPI, vol. 5(5), pages 1-15, May.
    21. Luo, Xing & Wang, Jihong & Dooner, Mark & Clarke, Jonathan, 2015. "Overview of current development in electrical energy storage technologies and the application potential in power system operation," Applied Energy, Elsevier, vol. 137(C), pages 511-536.
    22. Altan, Aytaç & Karasu, Seçkin & Bekiros, Stelios, 2019. "Digital currency forecasting with chaotic meta-heuristic bio-inspired signal processing techniques," Chaos, Solitons & Fractals, Elsevier, vol. 126(C), pages 325-336.
    23. Muhsen, Dhiaa Halboot & Nabil, Moamen & Haider, Haider Tarish & Khatib, Tamer, 2019. "A novel method for sizing of standalone photovoltaic system using multi-objective differential evolution algorithm and hybrid multi-criteria decision making methods," Energy, Elsevier, vol. 174(C), pages 1158-1175.
    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. Zhang, Xiaoxi & Pan, Yongjun & Xiong, Yue & Zhang, Yongzhi & Tang, Mao & Dai, Wei & Liu, Binghe & Hou, Liang, 2024. "Deep learning-based vibration stress and fatigue-life prediction of a battery-pack system," Applied Energy, Elsevier, vol. 357(C).
    2. Hoseinzadeh, Siamak & Astiaso Garcia, Davide & Huang, Lizhen, 2023. "Grid-connected renewable energy systems flexibility in Norway islands’ Decarbonization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(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. Yingyue Li & Hongjun Li & Rui Miao & He Qi & Yi Zhang, 2023. "Energy–Environment–Economy (3E) Analysis of the Performance of Introducing Photovoltaic and Energy Storage Systems into Residential Buildings: A Case Study in Shenzhen, China," Sustainability, MDPI, vol. 15(11), pages 1-25, June.
    5. Shabani, Mohadeseh & Kordrostami, Sohrab & Jahani Sayyad Noveiri, Monireh, 2023. "Renewable energy performance analysis using fuzzy dynamic directional distance function model under natural and managerial disposability," Applied Energy, Elsevier, vol. 352(C).
    6. Zhao, Yi-Bo & Dong, Xiao-Jian & Shen, Jia-Ni & He, Yi-Jun, 2024. "Simultaneous sizing and scheduling optimization for PV-wind-battery hybrid systems with a modified battery lifetime model: A high-resolution analysis in China," Applied Energy, Elsevier, vol. 360(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. 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).
    2. Mulleriyawage, U.G.K. & Shen, W.X., 2021. "Impact of demand side management on optimal sizing of residential battery energy storage system," Renewable Energy, Elsevier, vol. 172(C), pages 1250-1266.
    3. 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).
    4. Zhang, Yijie & Ma, Tao & Yang, Hongxing, 2022. "Grid-connected photovoltaic battery systems: A comprehensive review and perspectives," Applied Energy, Elsevier, vol. 328(C).
    5. Wu, Yaling & Liu, Zhongbing & Liu, Jiangyang & Xiao, Hui & Liu, Ruimiao & Zhang, Ling, 2022. "Optimal battery capacity of grid-connected PV-battery systems considering battery degradation," Renewable Energy, Elsevier, vol. 181(C), pages 10-23.
    6. Yao, Qijia & Alsaade, Fawaz W. & Al-zahrani, Mohammed S. & Jahanshahi, Hadi, 2023. "Fixed-time neural control for output-constrained synchronization of second-order chaotic systems," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).
    7. Wu, Yaling & Liu, Zhongbing & Li, Benjia & Liu, Jiangyang & Zhang, Ling, 2022. "Energy management strategy and optimal battery capacity for flexible PV-battery system under time-of-use tariff," Renewable Energy, Elsevier, vol. 200(C), pages 558-570.
    8. Arsalis, Alexandros & Papanastasiou, Panos & Georghiou, George E., 2022. "A comparative review of lithium-ion battery and regenerative hydrogen fuel cell technologies for integration with photovoltaic applications," Renewable Energy, Elsevier, vol. 191(C), pages 943-960.
    9. Zou, Bin & Peng, Jinqing & Yin, Rongxin & Li, Houpei & Li, Sihui & Yan, Jinyue & Yang, Hongxing, 2022. "Capacity configuration of distributed photovoltaic and battery system for office buildings considering uncertainties," Applied Energy, Elsevier, vol. 319(C).
    10. Rajpal, Sheetal & Lakhyani, Navin & Singh, Ayush Kumar & Kohli, Rishav & Kumar, Naveen, 2021. "Using handpicked features in conjunction with ResNet-50 for improved detection of COVID-19 from chest X-ray images," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).
    11. Ceran, Bartosz, 2019. "The concept of use of PV/WT/FC hybrid power generation system for smoothing the energy profile of the consumer," Energy, Elsevier, vol. 167(C), pages 853-865.
    12. Yu, Xihong & Bao, Han & Chen, Mo & Bao, Bocheng, 2023. "Energy balance via memristor synapse in Morris-Lecar two-neuron network with FPGA implementation," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
    13. Wu, Di & Ma, Xu & Balducci, Patrick & Bhatnagar, Dhruv, 2021. "An economic assessment of behind-the-meter photovoltaics paired with batteries on the Hawaiian Islands," Applied Energy, Elsevier, vol. 286(C).
    14. 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).
    15. Vaziri Rad, Mohammad Amin & Toopshekan, Ashkan & Rahdan, Parisa & Kasaeian, Alibakhsh & Mahian, Omid, 2020. "A comprehensive study of techno-economic and environmental features of different solar tracking systems for residential photovoltaic installations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 129(C).
    16. Chen, Xi & Yu, Ruyi & Ullah, Sajid & Wu, Dianming & Li, Zhiqiang & Li, Qingli & Qi, Honggang & Liu, Jihui & Liu, Min & Zhang, Yundong, 2022. "A novel loss function of deep learning in wind speed forecasting," Energy, Elsevier, vol. 238(PB).
    17. Kashkynbayev, Ardak & Cao, Jinde & Suragan, Durvudkhan, 2021. "Global Lagrange stability analysis of retarded SICNNs," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).
    18. Lucas Deotti & Wanessa Guedes & Bruno Dias & Tiago Soares, 2020. "Technical and Economic Analysis of Battery Storage for Residential Solar Photovoltaic Systems in the Brazilian Regulatory Context," Energies, MDPI, vol. 13(24), pages 1-30, December.
    19. 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).
    20. Liu, Jia & Cao, Sunliang & Chen, Xi & Yang, Hongxing & Peng, Jinqing, 2021. "Energy planning of renewable applications in high-rise residential buildings integrating battery and hydrogen vehicle storage," Applied Energy, Elsevier, vol. 281(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:318:y:2022:i:c:s0306261922005384. 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.