IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v239y2022ipbs036054422102418x.html
   My bibliography  Save this article

Research on the contribution of regional Energy Internet emission reduction considering time-of-use tariff

Author

Listed:
  • Yang, Shu-Xia
  • Nie, Tian-qi
  • Li, Cheng-Cheng

Abstract

Energy Internet is expected to solve the problem of environmental pollution. This paper first analyzes the emission reduction mechanism of the time-of-use tariff on the regional Energy Internet, and then establishes a calculation model of regional Energy Internet emission reduction considering time-of-use tariff. Finally, a small-scale regional Energy Internet is simulated and calculated, and it is found that the carbon emission under the time-of-use tariff is reduced by 0.95% and the cost is reduced by 0.28%compared with that under the fixed electricity price. The results show that the system operation cost and pollutant emissions are lower under time-of-use tariff, and time-of-use tariff is more conducive to emission reduction. Moreover, it is also found that when the time-of-use tariff fluctuation ratio is less than 1, and the peak-to-valley electricity price ratio is between 2-3, the energy conservation and emission reduction is better, which provides a basis for the formulation of green electricity price.

Suggested Citation

  • Yang, Shu-Xia & Nie, Tian-qi & Li, Cheng-Cheng, 2022. "Research on the contribution of regional Energy Internet emission reduction considering time-of-use tariff," Energy, Elsevier, vol. 239(PB).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pb:s036054422102418x
    DOI: 10.1016/j.energy.2021.122170
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054422102418X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.122170?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. Haisheng Chen & Xinjing Zhang & Jinchao Liu & Chunqing Tan, 2013. "Compressed Air Energy Storage," Chapters, in: Ahmed F. Zobaa (ed.), Energy Storage - Technologies and Applications, IntechOpen.
    2. Jani Laine & Jukka Heinonen & Seppo Junnila, 2020. "Pathways to Carbon-Neutral Cities Prior to a National Policy," Sustainability, MDPI, vol. 12(6), pages 1-14, March.
    3. Ghanbari, Ali & Karimi, Hamid & Jadid, Shahram, 2020. "Optimal planning and operation of multi-carrier networked microgrids considering multi-energy hubs in distribution networks," Energy, Elsevier, vol. 204(C).
    4. Heydarian-Forushani, E. & Golshan, M.E.H. & Shafie-khah, M., 2015. "Flexible security-constrained scheduling of wind power enabling time of use pricing scheme," Energy, Elsevier, vol. 90(P2), pages 1887-1900.
    5. Massimo, Filippini, 2011. "Short- and long-run time-of-use price elasticities in Swiss residential electricity demand," Energy Policy, Elsevier, vol. 39(10), pages 5811-5817, October.
    6. Moghaddam, M. Parsa & Abdollahi, A. & Rashidinejad, M., 2011. "Flexible demand response programs modeling in competitive electricity markets," Applied Energy, Elsevier, vol. 88(9), pages 3257-3269.
    7. Torriti, Jacopo, 2012. "Price-based demand side management: Assessing the impacts of time-of-use tariffs on residential electricity demand and peak shifting in Northern Italy," Energy, Elsevier, vol. 44(1), pages 576-583.
    8. Bachrach, Devra, 2003. "Energy Efficiency Leadership in California: Preventing the Next Crisis," The Electricity Journal, Elsevier, vol. 16(6), pages 37-47, July.
    9. Wang, Yongli & Ma, Yuze & Song, Fuhao & Ma, Yang & Qi, Chengyuan & Huang, Feifei & Xing, Juntai & Zhang, Fuwei, 2020. "Economic and efficient multi-objective operation optimization of integrated energy system considering electro-thermal demand response," Energy, Elsevier, vol. 205(C).
    10. Aalami, H.A. & Moghaddam, M. Parsa & Yousefi, G.R., 2010. "Demand response modeling considering Interruptible/Curtailable loads and capacity market programs," Applied Energy, Elsevier, vol. 87(1), pages 243-250, January.
    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. Liu, Xinglei & Liu, Jun & Ren, Kezheng & Liu, Xiaoming & Liu, Jiacheng, 2022. "An integrated fuzzy multi-energy transaction evaluation approach for energy internet markets considering judgement credibility and variable rough precision," Energy, Elsevier, vol. 261(PB).
    2. Li, Hao & Wang, Zhao-Hua & Zhang, Bin, 2023. "How social interaction induce energy-saving behaviors in buildings: Interpersonal & passive interactions v.s. public & active interactions," Energy Economics, Elsevier, vol. 118(C).
    3. Huang, He & Wang, Honglei & Hu, Yu-Jie & Li, Chengjiang & Wang, Xiaolin, 2022. "Optimal plan for energy conservation and CO2 emissions reduction of public buildings considering users' behavior: Case of China," Energy, Elsevier, vol. 261(PA).

    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. Andruszkiewicz, Jerzy & Lorenc, Józef & Weychan, Agnieszka, 2020. "Seasonal variability of price elasticity of demand of households using zonal tariffs and its impact on hourly load of the power system," Energy, Elsevier, vol. 196(C).
    2. Li, Lanlan & Gong, Chengzhu & Wang, Deyun & Zhu, Kejun, 2013. "Multi-agent simulation of the time-of-use pricing policy in an urban natural gas pipeline network: A case study of Zhengzhou," Energy, Elsevier, vol. 52(C), pages 37-43.
    3. Talari, Saber & Shafie-khah, Miadreza & Osório, Gerardo J. & Aghaei, Jamshid & Catalão, João P.S., 2018. "Stochastic modelling of renewable energy sources from operators' point-of-view: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1953-1965.
    4. Boßmann, Tobias & Eser, Eike Johannes, 2016. "Model-based assessment of demand-response measures—A comprehensive literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1637-1656.
    5. Li, Bosong & Shen, Jingshuang & Wang, Xu & Jiang, Chuanwen, 2016. "From controllable loads to generalized demand-side resources: A review on developments of demand-side resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 936-944.
    6. Yang, Dechang & Wang, Ming & Yang, Ruiqi & Zheng, Yingying & Pandzic, Hrvoje, 2021. "Optimal dispatching of an energy system with integrated compressed air energy storage and demand response," Energy, Elsevier, vol. 234(C).
    7. Zheng, Menglian & Meinrenken, Christoph J. & Lackner, Klaus S., 2014. "Agent-based model for electricity consumption and storage to evaluate economic viability of tariff arbitrage for residential sector demand response," Applied Energy, Elsevier, vol. 126(C), pages 297-306.
    8. Dehnavi, Ehsan & Abdi, Hamdi, 2016. "Optimal pricing in time of use demand response by integrating with dynamic economic dispatch problem," Energy, Elsevier, vol. 109(C), pages 1086-1094.
    9. Buryk, Stephen & Mead, Doug & Mourato, Susana & Torriti, Jacopo, 2015. "Investigating preferences for dynamic electricity tariffs: The effect of environmental and system benefit disclosure," Energy Policy, Elsevier, vol. 80(C), pages 190-195.
    10. Wang, Yong & Li, Lin, 2015. "Time-of-use electricity pricing for industrial customers: A survey of U.S. utilities," Applied Energy, Elsevier, vol. 149(C), pages 89-103.
    11. Woo, C.K. & Li, R. & Shiu, A. & Horowitz, I., 2013. "Residential winter kWh responsiveness under optional time-varying pricing in British Columbia," Applied Energy, Elsevier, vol. 108(C), pages 288-297.
    12. Antonio Gabaldón & Carlos Álvarez & María Del Carmen Ruiz-Abellón & Antonio Guillamón & Sergio Valero-Verdú & Roque Molina & Ana García-Garre, 2018. "Integration of Methodologies for the Evaluation of Offer Curves in Energy and Capacity Markets through Energy Efficiency and Demand Response," Sustainability, MDPI, vol. 10(2), pages 1-27, February.
    13. Doostizadeh, Meysam & Ghasemi, Hassan, 2012. "A day-ahead electricity pricing model based on smart metering and demand-side management," Energy, Elsevier, vol. 46(1), pages 221-230.
    14. Ibrahim Alotaibi & Mohammed A. Abido & Muhammad Khalid & Andrey V. Savkin, 2020. "A Comprehensive Review of Recent Advances in Smart Grids: A Sustainable Future with Renewable Energy Resources," Energies, MDPI, vol. 13(23), pages 1-41, November.
    15. Shahryari, E. & Shayeghi, H. & Mohammadi-ivatloo, B. & Moradzadeh, M., 2018. "An improved incentive-based demand response program in day-ahead and intra-day electricity markets," Energy, Elsevier, vol. 155(C), pages 205-214.
    16. Heydarian-Forushani, E. & Golshan, M.E.H. & Siano, Pierluigi, 2017. "Evaluating the benefits of coordinated emerging flexible resources in electricity markets," Applied Energy, Elsevier, vol. 199(C), pages 142-154.
    17. Schroeder, Andreas, 2011. "Modeling storage and demand management in power distribution grids," Applied Energy, Elsevier, vol. 88(12), pages 4700-4712.
    18. Jing Liang & Yueming Qiu & Bo Xing, 2021. "Social Versus Private Benefits of Energy Efficiency Under Time-of-Use and Increasing Block Pricing," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 78(1), pages 43-75, January.
    19. Batalla-Bejerano, Joan & Trujillo-Baute, Elisa & Villa-Arrieta, Manuel, 2020. "Smart meters and consumer behaviour: Insights from the empirical literature," Energy Policy, Elsevier, vol. 144(C).
    20. Lu, Renzhi & Hong, Seung Ho & Zhang, Xiongfeng, 2018. "A Dynamic pricing demand response algorithm for smart grid: Reinforcement learning approach," Applied Energy, Elsevier, vol. 220(C), pages 220-230.

    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:239:y:2022:i:pb:s036054422102418x. 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.