IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v198y2020ics0360544220304813.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220304813
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2020.117374?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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Kim, Tae-Young & Cho, Sung-Bae, 2019. "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, Elsevier, vol. 182(C), pages 72-81.
    7. Liu, Jian & Zhong, Caifu, 2019. "An economic evaluation of the coordination between electric vehicle storage and distributed renewable energy," Energy, Elsevier, vol. 186(C).
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. Liu, Jian, 2012. "Electric vehicle charging infrastructure assignment and power grid impacts assessment in Beijing," Energy Policy, Elsevier, vol. 51(C), pages 544-557.
    19. 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.
    20. 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.
    21. 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.
    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. Muhammad Ahsan Khan & Akhtar Hussain & Woon-Gyu Lee & Hak-Man Kim, 2023. "An Incentive-Based Mechanism to Enhance Energy Trading among Microgrids, EVs, and Grid," Energies, MDPI, vol. 16(17), pages 1-23, September.
    2. Connor Scott & Mominul Ahsan & Alhussein Albarbar, 2021. "Machine Learning Based Vehicle to Grid Strategy for Improving the Energy Performance of Public Buildings," Sustainability, MDPI, vol. 13(7), pages 1-22, April.
    3. Faris Adnan Padhilah & Kyeong-Hwa Kim, 2021. "A Centralized Power Flow Control Scheme of EV-Connected DC Microgrid to Satisfy Multi-Objective Problems under Several Constraints," Sustainability, MDPI, vol. 13(16), pages 1-37, August.
    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.

    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. 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.
    2. Jason Runge & Radu Zmeureanu, 2021. "A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings," Energies, MDPI, vol. 14(3), pages 1-26, January.
    3. Wu, Tian & Shang, Zhe & Tian, Xin & Wang, Shouyang, 2016. "How hyperbolic discounting preference affects Chinese consumers’ consumption choice between conventional and electric vehicles," Energy Policy, Elsevier, vol. 97(C), pages 400-413.
    4. Ding, Jia & Zhao, Yuxuan & Jin, Junyang, 2023. "Forecasting natural gas consumption with multiple seasonal patterns," Applied Energy, Elsevier, vol. 337(C).
    5. 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.
    6. Sun, Qixing & Xing, Dong & Alafnan, Hamoud & Pei, Xiaoze & Zhang, Min & Yuan, Weijia, 2019. "Design and test of a new two-stage control scheme for SMES-battery hybrid energy storage systems for microgrid applications," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    7. Guelpa, Elisa & Bischi, Aldo & Verda, Vittorio & Chertkov, Michael & Lund, Henrik, 2019. "Towards future infrastructures for sustainable multi-energy systems: A review," Energy, Elsevier, vol. 184(C), pages 2-21.
    8. Jiang, Sufan & Gao, Shan & Pan, Guangsheng & Zhao, Xin & Liu, Yu & Guo, Yasen & Wang, Sicheng, 2020. "A novel robust security constrained unit commitment model considering HVDC regulation," Applied Energy, Elsevier, vol. 278(C).
    9. Lu, Yakai & Tian, Zhe & Zhou, Ruoyu & Liu, Wenjing, 2021. "A general transfer learning-based framework for thermal load prediction in regional energy system," Energy, Elsevier, vol. 217(C).
    10. Peng, Tianduo & Ou, Xunmin & Yuan, Zhiyi & Yan, Xiaoyu & Zhang, Xiliang, 2018. "Development and application of China provincial road transport energy demand and GHG emissions analysis model," Applied Energy, Elsevier, vol. 222(C), pages 313-328.
    11. Hyunsoo Kim & Jiseok Jeong & Changwan Kim, 2022. "Daily Peak-Electricity-Demand Forecasting Based on Residual Long Short-Term Network," Mathematics, MDPI, vol. 10(23), pages 1-17, November.
    12. Ren, Lei & Zhou, Sheng & Peng, Tianduo & Ou, Xunmin, 2022. "Greenhouse gas life cycle analysis of China's fuel cell medium- and heavy-duty trucks under segmented usage scenarios and vehicle types," Energy, Elsevier, vol. 249(C).
    13. Yi, Tao & Cheng, Xiaobin & Chen, Yaxuan & Liu, Jinpeng, 2020. "Joint optimization of charging station and energy storage economic capacity based on the effect of alternative energy storage of electric vehicle," Energy, Elsevier, vol. 208(C).
    14. Khan, Zulfiqar Ahmad & Khan, Shabbir Ahmad & Hussain, Tanveer & Baik, Sung Wook, 2024. "DSPM: Dual sequence prediction model for efficient energy management in micro-grid," Applied Energy, Elsevier, vol. 356(C).
    15. Pablo Fernández-Bustamante & Oscar Barambones & Isidro Calvo & Cristian Napole & Mohamed Derbeli, 2021. "Provision of Frequency Response from Wind Farms: A Review," Energies, MDPI, vol. 14(20), pages 1-24, October.
    16. Li, Ao & Xiao, Fu & Zhang, Chong & Fan, Cheng, 2021. "Attention-based interpretable neural network for building cooling load prediction," Applied Energy, Elsevier, vol. 299(C).
    17. Guanglin Zhang & Yu Cao & Yongsheng Cao & Demin Li & Lin Wang, 2017. "Optimal Energy Management for Microgrids with Combined Heat and Power (CHP) Generation, Energy Storages, and Renewable Energy Sources," Energies, MDPI, vol. 10(9), pages 1-18, August.
    18. Zhu, Lingfeng & Wang, Yinshun & Guo, Yuetong & Liu, Wei & Hu, Chengyang, 2023. "Current decay and compensation of a closed-loop HTS magnet in non-uniform magnetic fields based on electro-magneto-thermal semi-analytical analysis," Energy, Elsevier, vol. 277(C).
    19. Xu, Wanrong & Kou, Chuanfu & E, Jiaqiang & Feng, Changling & Tan, Yan, 2024. "Effect analysis on the flow uniformity and pressure drop characteristics of the rotary diesel particulate filter for heavy-duty truck," Energy, Elsevier, vol. 288(C).
    20. Li, Shuangqi & Gu, Chenghong & Zeng, Xianwu & Zhao, Pengfei & Pei, Xiaoze & Cheng, Shuang, 2021. "Vehicle-to-grid management for multi-time scale grid power balancing," Energy, Elsevier, vol. 234(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:energy:v:198:y:2020:i:c:s0360544220304813. 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.journals.elsevier.com/energy .

    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.