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An improved vehicle to the grid method with battery longevity management in a microgrid application

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  • Yang, Qingqing
  • Li, Jianwei
  • Cao, Wanke
  • Li, Shuangqi
  • Lin, Jie
  • Huo, Da
  • He, Hongwen

Abstract

This paper proposed an improved vehicle-to-grid (V2G) scheduling approach for the frequency control with the advantage of protecting the batteries hence saving the battery lifetime during grid connected service. The proposed methodology is improved in two ways. Firstly, to give a prediction of the available electric vehicle (EV) battery capacity in the control time-step for the V2G service, a deep learning based prediction is developed. Secondly, this study advances the previous V2G method by adding the quantitative analysis of the battery cycle life into the V2G optimization. The accurate prediction of the schedulable battery capacity based on the LSTM algorithm is shown very effective in the power system frequency control. Also, compared with the previous method that without battery lifetime control, the proposed method benefits in the reduction of charge/discharge cycles.

Suggested Citation

  • Yang, Qingqing & Li, Jianwei & Cao, Wanke & Li, Shuangqi & Lin, Jie & Huo, Da & He, Hongwen, 2020. "An improved vehicle to the grid method with battery longevity management in a microgrid application," Energy, Elsevier, vol. 198(C).
    • Handle: RePEc:eee:energy:v:198:y:2020:i:c:s0360544220304813
    DOI: 10.1016/j.energy.2020.117374
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    as
    1. Kim, Tae-Young & Cho, Sung-Bae, 2019. "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, Elsevier, vol. 182(C), pages 72-81.
    2. Liu, Jian & Zhong, Caifu, 2019. "An economic evaluation of the coordination between electric vehicle storage and distributed renewable energy," Energy, Elsevier, vol. 186(C).
    3. Lund, Henrik, 2018. "Renewable heating strategies and their consequences for storage and grid infrastructures comparing a smart grid to a smart energy systems approach," Energy, Elsevier, vol. 151(C), pages 94-102.
    4. Thé, Jesse & Yu, Hesheng, 2017. "A critical review on the simulations of wind turbine aerodynamics focusing on hybrid RANS-LES methods," Energy, Elsevier, vol. 138(C), pages 257-289.
    5. Ou, Xunmin & Zhang, Xiliang & Chang, Shiyan, 2010. "Scenario analysis on alternative fuel/vehicle for China's future road transport: Life-cycle energy demand and GHG emissions," Energy Policy, Elsevier, vol. 38(8), pages 3943-3956, August.
    6. Tan, Kang Miao & Padmanaban, Sanjeevikumar & Yong, Jia Ying & Ramachandaramurthy, Vigna K., 2019. "A multi-control vehicle-to-grid charger with bi-directional active and reactive power capabilities for power grid support," Energy, Elsevier, vol. 171(C), pages 1150-1163.
    7. Faddel, Samy & Aldeek, A. & Al-Awami, Ali T. & Sortomme, Eric & Al-Hamouz, Zakariya, 2018. "Ancillary Services Bidding for Uncertain Bidirectional V2G Using Fuzzy Linear Programming," Energy, Elsevier, vol. 160(C), pages 986-995.
    8. Li, Jianwei & Gee, Anthony M. & Zhang, Min & Yuan, Weijia, 2015. "Analysis of battery lifetime extension in a SMES-battery hybrid energy storage system using a novel battery lifetime model," Energy, Elsevier, vol. 86(C), pages 175-185.
    9. Liu, Jian, 2012. "Electric vehicle charging infrastructure assignment and power grid impacts assessment in Beijing," Energy Policy, Elsevier, vol. 51(C), pages 544-557.
    10. François, B. & Zoccatelli, D. & Borga, M., 2017. "Assessing small hydro/solar power complementarity in ungauged mountainous areas: A crash test study for hydrological prediction methods," Energy, Elsevier, vol. 127(C), pages 716-729.
    11. Li, Jianwei & Xiong, Rui & Mu, Hao & Cornélusse, Bertrand & Vanderbemden, Philippe & Ernst, Damien & Yuan, Weijia, 2018. "Design and real-time test of a hybrid energy storage system in the microgrid with the benefit of improving the battery lifetime," Applied Energy, Elsevier, vol. 218(C), pages 470-478.
    12. Laib, Oussama & Khadir, Mohamed Tarek & Mihaylova, Lyudmila, 2019. "Toward efficient energy systems based on natural gas consumption prediction with LSTM Recurrent Neural Networks," Energy, Elsevier, vol. 177(C), pages 530-542.
    13. Li, Jianwei & Yang, Qingqing & Mu, Hao & Le Blond, Simon & He, Hongwen, 2018. "A new fault detection and fault location method for multi-terminal high voltage direct current of offshore wind farm," Applied Energy, Elsevier, vol. 220(C), pages 13-20.
    14. Li, Shuangqi & He, Hongwen & Li, Jianwei, 2019. "Big data driven lithium-ion battery modeling method based on SDAE-ELM algorithm and data pre-processing technology," Applied Energy, Elsevier, vol. 242(C), pages 1259-1273.
    15. Rahman, Aowabin & Srikumar, Vivek & Smith, Amanda D., 2018. "Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 212(C), pages 372-385.
    16. Ou, Xunmin & Xiaoyu, Yan & Zhang, Xiliang, 2011. "Life-cycle energy consumption and greenhouse gas emissions for electricity generation and supply in China," Applied Energy, Elsevier, vol. 88(1), pages 289-297, January.
    17. Azizipanah-Abarghooee, Rasoul & Golestaneh, Faranak & Gooi, Hoay Beng & Lin, Jeremy & Bavafa, Farhad & Terzija, Vladimir, 2016. "Corrective economic dispatch and operational cycles for probabilistic unit commitment with demand response and high wind power," Applied Energy, Elsevier, vol. 182(C), pages 634-651.
    18. Kalogirou, S.A. & Agathokleous, R. & Barone, G. & Buonomano, A. & Forzano, C. & Palombo, A., 2019. "Development and validation of a new TRNSYS Type for thermosiphon flat-plate solar thermal collectors: energy and economic optimization for hot water production in different climates," Renewable Energy, Elsevier, vol. 136(C), pages 632-644.
    19. Li, Jianwei & Xiong, Rui & Yang, Qingqing & Liang, Fei & Zhang, Min & Yuan, Weijia, 2017. "Design/test of a hybrid energy storage system for primary frequency control using a dynamic droop method in an isolated microgrid power system," Applied Energy, Elsevier, vol. 201(C), pages 257-269.
    20. Li, Jianwei & Wang, Xudong & Zhang, Zhenyu & Le Blond, Simon & Yang, Qingqing & Zhang, Min & Yuan, Weijia, 2017. "Analysis of a new design of the hybrid energy storage system used in the residential m-CHP systems," Applied Energy, Elsevier, vol. 187(C), pages 169-179.
    21. Triviño-Cabrera, Alicia & Aguado, José A. & Torre, Sebastián de la, 2019. "Joint routing and scheduling for electric vehicles in smart grids with V2G," Energy, Elsevier, vol. 175(C), pages 113-122.
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    4. Cheng, Gong & Wang, Xinzhi & He, Yurong, 2021. "Remaining useful life and state of health prediction for lithium batteries based on empirical mode decomposition and a long and short memory neural network," Energy, Elsevier, vol. 232(C).
    5. Shipman, Rob & Roberts, Rebecca & Waldron, Julie & Naylor, Sophie & Pinchin, James & Rodrigues, Lucelia & Gillott, Mark, 2021. "We got the power: Predicting available capacity for vehicle-to-grid services using a deep recurrent neural network," Energy, Elsevier, vol. 221(C).
    6. Wu, Wei & Lin, Boqiang, 2021. "Benefits of electric vehicles integrating into power grid," Energy, Elsevier, vol. 224(C).
    7. Martín Antonio Rodríguez Licea & Francisco Javier Pérez Pinal & Allan Giovanni Soriano Sánchez, 2021. "An Overview on Electric-Stress Degradation Empirical Models for Electrochemical Devices in Smart Grids," Energies, MDPI, vol. 14(8), pages 1-23, April.

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