IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v221y2024ics0960148123016531.html
   My bibliography  Save this article

A stochastic vehicle schedule model for demand response and grid flexibility in a renewable-building-e-transportation-microgrid

Author

Listed:
  • Zhou, Yuekuan
  • Liu, Xiaohua
  • Zhao, Qianchuan

Abstract

Vehicle-to-building interaction as one of emerging and promising techniques shows powerful functions, to address intermittence of renewable energy, stochastic demand of buildings, power grid stability, and frequency regulation. The energy interaction magnitude is highly dependent on stochastic vehicle schedules, whereas a few studies focus on stochastic model development and guidelines on vehicle schedules. In this study, a series of stochastic models were developed to characterise the arrival time, departure time, and detention time-duration of vehicles in buildings. Considering the schedule difference between private cars and shuttle buses, both Chi-square distribution and Normal distribution were adopted, to quantify the distribution degree of vehicle numbers, together with parametrical analysis on standard deviation. Comparative analysis between different stochastic models can guide vehicle schedules and provide flexible vehicle-to-building interactions. Research results indicate that, normal distribution on arriving time and detention time-duration of vehicles shows the minimum annual import cost (ICannual) at 128.1 HK$/m2·a, while the normal distribution on arriving time and departure time with Chi-square distribution for detention time-duration shows the minimum equivalent CO2 emission (ECEannual) at 38.6 kg/m2·a. Furthermore, in respect to the distribution degree of vehicle numbers, the sharper curve for vehicle numbers in buildings (with lower standard deviation) will lead to a lower ECEannual and ICannual. Dynamic energy management strategy for the optimal stochastic vehicle schedule indicates that, marginal impacts on import cost saving can be provided to local power grid due to the limited difference between off-peak and peak grid electricity prices. This study provides new insight into the stochastic vehicle schedule model for an interactive vehicle-to-building energy interaction framework. Research results can provide frontier guidelines on stochastic vehicle schedules and flexible vehicle-to-building interactions for techno-economic-environmental performance improvement.

Suggested Citation

  • Zhou, Yuekuan & Liu, Xiaohua & Zhao, Qianchuan, 2024. "A stochastic vehicle schedule model for demand response and grid flexibility in a renewable-building-e-transportation-microgrid," Renewable Energy, Elsevier, vol. 221(C).
  • Handle: RePEc:eee:renene:v:221:y:2024:i:c:s0960148123016531
    DOI: 10.1016/j.renene.2023.119738
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148123016531
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2023.119738?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. Zhou, Yuekuan & Cao, Sunliang & Hensen, Jan L.M. & Lund, Peter D., 2019. "Energy integration and interaction between buildings and vehicles: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    2. Zhang, Bin & Hu, Weihao & Xu, Xiao & Li, Tao & Zhang, Zhenyuan & Chen, Zhe, 2022. "Physical-model-free intelligent energy management for a grid-connected hybrid wind-microturbine-PV-EV energy system via deep reinforcement learning approach," Renewable Energy, Elsevier, vol. 200(C), pages 433-448.
    3. Xiao, B. & Ruan, J. & Yang, W. & Walker, P.D. & Zhang, N., 2021. "A review of pivotal energy management strategies for extended range electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    4. Powell, Siobhan & Cezar, Gustavo Vianna & Rajagopal, Ram, 2022. "Scalable probabilistic estimates of electric vehicle charging given observed driver behavior," Applied Energy, Elsevier, vol. 309(C).
    5. Thomas, Dimitrios & Deblecker, Olivier & Ioakimidis, Christos S., 2018. "Optimal operation of an energy management system for a grid-connected smart building considering photovoltaics’ uncertainty and stochastic electric vehicles’ driving schedule," Applied Energy, Elsevier, vol. 210(C), pages 1188-1206.
    6. Li, Shuangqi & Zhao, Pengfei & Gu, Chenghong & Huo, Da & Zeng, Xianwu & Pei, Xiaoze & Cheng, Shuang & Li, Jianwei, 2022. "Online battery-protective vehicle to grid behavior management," Energy, Elsevier, vol. 243(C).
    7. Yu, Zhenyu & Lu, Fei & Zou, Yu & Yang, Xudong, 2022. "Quantifying the real-time energy flexibility of commuter plug-in electric vehicles in an office building considering photovoltaic and load uncertainty," Applied Energy, Elsevier, vol. 321(C).
    8. Quddus, Md Abdul & Shahvari, Omid & Marufuzzaman, Mohammad & Usher, John M. & Jaradat, Raed, 2018. "A collaborative energy sharing optimization model among electric vehicle charging stations, commercial buildings, and power grid," Applied Energy, Elsevier, vol. 229(C), pages 841-857.
    9. Shah, Talha Hussain & Shabbir, Altamash & Waqas, Adeel & Janjua, Abdul Kashif & Shahzad, Nadia & Pervaiz, Hina & Shakir, Sehar, 2023. "Techno-economic appraisal of electric vehicle charging stations integrated with on-grid photovoltaics on existing fuel stations: A multicity study framework," Renewable Energy, Elsevier, vol. 209(C), pages 133-144.
    10. Xiong, Yingqi & Wang, Bin & Chu, Chi-cheng & Gadh, Rajit, 2018. "Vehicle grid integration for demand response with mixture user model and decentralized optimization," Applied Energy, Elsevier, vol. 231(C), pages 481-493.
    11. Gao, Zhiming & Lin, Zhenhong & LaClair, Tim J. & Liu, Changzheng & Li, Jan-Mou & Birky, Alicia K. & Ward, Jacob, 2017. "Battery capacity and recharging needs for electric buses in city transit service," Energy, Elsevier, vol. 122(C), pages 588-600.
    12. Zaneti, Letícia A.L. & Arias, Nataly Bañol & de Almeida, Madson C. & Rider, Marcos J., 2022. "Sustainable charging schedule of electric buses in a University Campus: A rolling horizon approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    13. Hoehne, Christopher G. & Chester, Mikhail V., 2016. "Optimizing plug-in electric vehicle and vehicle-to-grid charge scheduling to minimize carbon emissions," Energy, Elsevier, vol. 115(P1), pages 646-657.
    14. Pareschi, Giacomo & Küng, Lukas & Georges, Gil & Boulouchos, Konstantinos, 2020. "Are travel surveys a good basis for EV models? Validation of simulated charging profiles against empirical data," Applied Energy, Elsevier, vol. 275(C).
    15. Liu, Jia & Yang, Hongxing & Zhou, Yuekuan, 2021. "Peer-to-peer trading optimizations on net-zero energy communities with energy storage of hydrogen and battery vehicles," Applied Energy, Elsevier, vol. 302(C).
    16. Seddig, Katrin & Jochem, Patrick & Fichtner, Wolf, 2019. "Two-stage stochastic optimization for cost-minimal charging of electric vehicles at public charging stations with photovoltaics," Applied Energy, Elsevier, vol. 242(C), pages 769-781.
    17. Zheng, Xinyao & Zhou, Yuekuan, 2024. "Dynamic heat-transfer mechanism and performance analysis of an integrated Trombe wall with radiant cooling for natural cooling energy harvesting and air-conditioning," Energy, Elsevier, vol. 288(C).
    18. Good, Clara & Shepero, Mahmoud & Munkhammar, Joakim & Boström, Tobias, 2019. "Scenario-based modelling of the potential for solar energy charging of electric vehicles in two Scandinavian cities," Energy, Elsevier, vol. 168(C), pages 111-125.
    19. Gong, Lili & Cao, Wu & Liu, Kangli & Yu, Yue & Zhao, Jianfeng, 2020. "Demand responsive charging strategy of electric vehicles to mitigate the volatility of renewable energy sources," Renewable Energy, Elsevier, vol. 156(C), pages 665-676.
    20. Zhou, Yuekuan, 2022. "Transition towards carbon-neutral districts based on storage techniques and spatiotemporal energy sharing with electrification and hydrogenation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    21. Zhou, Yuekuan, 2022. "Energy sharing and trading on a novel spatiotemporal energy network in Guangdong-Hong Kong-Macao Greater Bay Area," Applied Energy, Elsevier, vol. 318(C).
    22. Barone, Giovanni & Buonomano, Annamaria & Forzano, Cesare & Giuzio, Giovanni Francesco & Palombo, Adolfo & Russo, Giuseppe, 2022. "Energy virtual networks based on electric vehicles for sustainable buildings: System modelling for comparative energy and economic analyses," Energy, Elsevier, vol. 242(C).
    23. Ottesen, Stig Odegaard & Tomasgard, Asgeir, 2015. "A stochastic model for scheduling energy flexibility in buildings," Energy, Elsevier, vol. 88(C), pages 364-376.
    24. Zheng, Siqian & Huang, Gongsheng & Lai, Alvin CK., 2021. "Techno-economic performance analysis of synergistic energy sharing strategies for grid-connected prosumers with distributed battery storages," Renewable Energy, Elsevier, vol. 178(C), pages 1261-1278.
    25. Fretzen, Ulrich & Ansarin, Mohammad & Brandt, Tobias, 2021. "Temporal city-scale matching of solar photovoltaic generation and electric vehicle charging," Applied Energy, Elsevier, vol. 282(PA).
    26. Zakaria, A. & Ismail, Firas B. & Lipu, M.S. Hossain & Hannan, M.A., 2020. "Uncertainty models for stochastic optimization in renewable energy applications," Renewable Energy, Elsevier, vol. 145(C), pages 1543-1571.
    27. Martin, H. & Buffat, R. & Bucher, D. & Hamper, J. & Raubal, M., 2022. "Using rooftop photovoltaic generation to cover individual electric vehicle demand—A detailed case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    28. Zhou, Yuekuan, 2022. "Incentivising multi-stakeholders’ proactivity and market vitality for spatiotemporal microgrids in Guangzhou-Shenzhen-Hong Kong Bay Area," Applied Energy, Elsevier, vol. 328(C).
    29. Zhang, Xudong & Zou, Yuan & Fan, Jie & Guo, Hongwei, 2019. "Usage pattern analysis of Beijing private electric vehicles based on real-world data," Energy, Elsevier, vol. 167(C), pages 1074-1085.
    30. Luthander, Rasmus & Nilsson, Annica M. & Widén, Joakim & Åberg, Magnus, 2019. "Graphical analysis of photovoltaic generation and load matching in buildings: A novel way of studying self-consumption and self-sufficiency," Applied Energy, Elsevier, vol. 250(C), pages 748-759.
    31. Heredia, Willy Bernal & Chaudhari, Kalpesh & Meintz, Andrew & Jun, Myungsoo & Pless, Shanti, 2020. "Evaluation of smart charging for electric vehicle-to-building integration: A case study," Applied Energy, Elsevier, vol. 266(C).
    32. Han, Xiaojuan & Liang, Yubo & Ai, Yaoyao & Li, Jianlin, 2018. "Economic evaluation of a PV combined energy storage charging station based on cost estimation of second-use batteries," Energy, Elsevier, vol. 165(PA), pages 326-339.
    33. Fachrizal, Reza & Shepero, Mahmoud & Åberg, Magnus & Munkhammar, Joakim, 2022. "Optimal PV-EV sizing at solar powered workplace charging stations with smart charging schemes considering self-consumption and self-sufficiency balance," Applied Energy, Elsevier, vol. 307(C).
    34. Liu, Jia & Yang, Hongxing & Zhou, Yuekuan, 2021. "Peer-to-peer energy trading of net-zero energy communities with renewable energy systems integrating hydrogen vehicle storage," Applied Energy, Elsevier, vol. 298(C).
    35. Mansouri, Seyed Amir & Nematbakhsh, Emad & Jordehi, Ahmad Rezaee & Marzband, Mousa & Tostado-Véliz, Marcos & Jurado, Francisco, 2023. "An interval-based nested optimization framework for deriving flexibility from smart buildings and electric vehicle fleets in the TSO-DSO coordination," Applied Energy, Elsevier, vol. 341(C).
    36. Solanke, Tirupati U. & Khatua, Pradeep K. & Ramachandaramurthy, Vigna K. & Yong, Jia Ying & Tan, Kang Miao, 2021. "Control and management of a multilevel electric vehicles infrastructure integrated with distributed resources: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    37. Peng, Chao & Zou, Jianxiao & Lian, Lian, 2017. "Dispatching strategies of electric vehicles participating in frequency regulation on power grid: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 147-152.
    38. Cardoso, G. & Stadler, M. & Bozchalui, M.C. & Sharma, R. & Marnay, C. & Barbosa-Póvoa, A. & Ferrão, P., 2014. "Optimal investment and scheduling of distributed energy resources with uncertainty in electric vehicle driving schedules," Energy, Elsevier, vol. 64(C), pages 17-30.
    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. Zhou, Yuekuan, 2024. "AI-driven battery ageing prediction with distributed renewable community and E-mobility energy sharing," Renewable Energy, Elsevier, vol. 225(C).
    2. Marrasso, E. & Martone, C. & Pallotta, G. & Roselli, C. & Sasso, M., 2024. "Assessment of energy systems configurations in mixed-use Positive Energy Districts through novel indicators for energy and environmental analysis," Applied Energy, Elsevier, vol. 368(C).
    3. Dan, Zhaohui & Song, Aoye & Yu, Xiaojun & Zhou, Yuekuan, 2024. "Electrification-driven circular economy with machine learning-based multi-scale and cross-scale modelling approach," Energy, Elsevier, vol. 299(C).
    4. Zhou, Yuekuan & Zheng, Siqian & Hensen, Jan L.M., 2024. "Machine learning-based digital district heating/cooling with renewable integrations and advanced low-carbon transition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(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. Zhou, Yuekuan, 2023. "A dynamic self-learning grid-responsive strategy for battery sharing economy—multi-objective optimisation and posteriori multi-criteria decision making," Energy, Elsevier, vol. 266(C).
    2. Liu, Xiaochen & Fu, Zhi & Qiu, Siyuan & Li, Shaojie & Zhang, Tao & Liu, Xiaohua & Jiang, Yi, 2023. "Building-centric investigation into electric vehicle behavior: A survey-based simulation method for charging system design," Energy, Elsevier, vol. 271(C).
    3. Zhou, Yuekuan & Cao, Sunliang & Hensen, Jan L.M., 2021. "An energy paradigm transition framework from negative towards positive district energy sharing networks—Battery cycling aging, advanced battery management strategies, flexible vehicles-to-buildings in," Applied Energy, Elsevier, vol. 288(C).
    4. Zhou, Yuekuan, 2024. "AI-driven battery ageing prediction with distributed renewable community and E-mobility energy sharing," Renewable Energy, Elsevier, vol. 225(C).
    5. Zhou, Yuekuan, 2022. "Energy sharing and trading on a novel spatiotemporal energy network in Guangdong-Hong Kong-Macao Greater Bay Area," Applied Energy, Elsevier, vol. 318(C).
    6. Zhou, Yuekuan, 2022. "A regression learner-based approach for battery cycling ageing prediction―advances in energy management strategy and techno-economic analysis," Energy, Elsevier, vol. 256(C).
    7. Zhou, Yuekuan, 2023. "Sustainable energy sharing districts with electrochemical battery degradation in design, planning, operation and multi-objective optimisation," Renewable Energy, Elsevier, vol. 202(C), pages 1324-1341.
    8. Zhou, Yuekuan & Zheng, Siqian & Hensen, Jan L.M., 2024. "Machine learning-based digital district heating/cooling with renewable integrations and advanced low-carbon transition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    9. Liu, Xiaochen & Fu, Zhi & Qiu, Siyuan & Zhang, Tao & Li, Shaojie & Yang, Zhi & Liu, Xiaohua & Jiang, Yi, 2023. "Charging private electric vehicles solely by photovoltaics: A battery-free direct-current microgrid with distributed charging strategy," Applied Energy, Elsevier, vol. 341(C).
    10. Zhou, Yuekuan & Lund, Peter D., 2023. "Peer-to-peer energy sharing and trading of renewable energy in smart communities ─ trading pricing models, decision-making and agent-based collaboration," Renewable Energy, Elsevier, vol. 207(C), pages 177-193.
    11. He, Yingdong & Zhou, Yuekuan & Liu, Jia & Liu, Zhengxuan & Zhang, Guoqiang, 2022. "An inter-city energy migration framework for regional energy balance through daily commuting fuel-cell vehicles," Applied Energy, Elsevier, vol. 324(C).
    12. Zhou, Yuekuan, 2022. "Transition towards carbon-neutral districts based on storage techniques and spatiotemporal energy sharing with electrification and hydrogenation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    13. Zhou, Yuekuan & Zheng, Siqian, 2024. "A co-simulated material-component-system-district framework for climate-adaption and sustainability transition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    14. Niu, Jide & Li, Xiaoyuan & Tian, Zhe & Yang, Hongxing, 2024. "Uncertainty analysis of the electric vehicle potential for a household to enhance robustness in decision on the EV/V2H technologies," Applied Energy, Elsevier, vol. 365(C).
    15. Liu, Zhengxuan & Zhou, Yuekuan & Yan, Jun & Tostado-Véliz, Marcos, 2023. "Frontier ocean thermal/power and solar PV systems for transformation towards net-zero communities," Energy, Elsevier, vol. 284(C).
    16. Tostado-Véliz, Marcos & Rezaee Jordehi, Ahmad & Zhou, Yuekuan & Mansouri, Seyed Amir & Jurado, Francisco, 2024. "Best-case-aware planning of photovoltaic-battery systems for multi-mode charging stations," Renewable Energy, Elsevier, vol. 225(C).
    17. Buonomano, Annamaria, 2020. "Building to Vehicle to Building concept: A comprehensive parametric and sensitivity analysis for decision making aims," Applied Energy, Elsevier, vol. 261(C).
    18. He, Yingdong & Zhou, Yuekuan & Wang, Zhe & Liu, Jia & Liu, Zhengxuan & Zhang, Guoqiang, 2021. "Quantification on fuel cell degradation and techno-economic analysis of a hydrogen-based grid-interactive residential energy sharing network with fuel-cell-powered vehicles," Applied Energy, Elsevier, vol. 303(C).
    19. Barone, Giovanni & Buonomano, Annamaria & Forzano, Cesare & Giuzio, Giovanni Francesco & Palombo, Adolfo, 2020. "Increasing self-consumption of renewable energy through the Building to Vehicle to Building approach applied to multiple users connected in a virtual micro-grid," Renewable Energy, Elsevier, vol. 159(C), pages 1165-1176.
    20. Barone, Giovanni & Buonomano, Annamaria & Forzano, Cesare & Giuzio, Giovanni Francesco & Palombo, Adolfo & Russo, Giuseppe, 2022. "Energy virtual networks based on electric vehicles for sustainable buildings: System modelling for comparative energy and economic analyses," Energy, Elsevier, vol. 242(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:renene:v:221:y:2024:i:c:s0960148123016531. 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/renewable-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.