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

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  • 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.

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  • 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
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    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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).
    9. 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.
    10. 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.
    11. 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).
    12. 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).
    13. 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.
    14. 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).
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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).
    20. 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.
    21. 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.
    22. 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).
    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.
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