IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v90y2018i3d10.1007_s11069-017-3096-8.html
   My bibliography  Save this article

Multi-agent simulation-based residential electricity pricing schemes design and user selection decision-making

Author

Listed:
  • Chen Wang

    (Hefei University of Technology)

  • Kaile Zhou

    (Hefei University of Technology
    Ministry of Education)

  • Lanlan Li

    (Hefei University of Technology)

  • Shanlin Yang

    (Hefei University of Technology
    Ministry of Education)

Abstract

Multi-agent system employs the functions of communication, coordination and cooperation among intelligent agents to help people judge and analyze complex phenomena that cannot be directly observed, and it has become an important tool for solving large-scale complex problems. The problem of demand response (DR) in electric power system is difficult to be modeled due to the complicated environment and continuously evolving subjects. Multi-agent system can simulate the operation mechanism of electric power system, thus playing an important role in solving the DR problems. In this study, based on multi-agent simulation, we establish a multi-agent model of residential power market and propose a satisfaction function of residential users about electricity price. We focus on the interaction process among all the agents of power supply side, selling side and demand side and conduct simulation to obtain the selection and decision-making of residential users on different electricity pricing schemes. The results show that multi-agent system is beneficial to analyze, simulate and solve the DR problem in power market. Also, the satisfaction function of residential users on electricity price can support power selling enterprise to better understand the intention of residential users when choosing electricity pricing schemes and participating in DR program.

Suggested Citation

  • Chen Wang & Kaile Zhou & Lanlan Li & Shanlin Yang, 2018. "Multi-agent simulation-based residential electricity pricing schemes design and user selection decision-making," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 90(3), pages 1309-1327, February.
  • Handle: RePEc:spr:nathaz:v:90:y:2018:i:3:d:10.1007_s11069-017-3096-8
    DOI: 10.1007/s11069-017-3096-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-017-3096-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11069-017-3096-8?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. Xinhui Lu & Kaile Zhou & Felix T. S. Chan & Shanlin Yang, 2017. "Optimal scheduling of household appliances for smart home energy management considering demand response," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 88(3), pages 1639-1653, September.
    2. Sarwar, Suleman & Chen, Wei & Waheed, Rida, 2017. "Electricity consumption, oil price and economic growth: Global perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 9-18.
    3. Zhou, Kaile & Yang, Shanlin & Shen, Chao & Ding, Shuai & Sun, Chaoping, 2015. "Energy conservation and emission reduction of China’s electric power industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 10-19.
    4. Kiesel, Rüdiger & Paraschiv, Florentina, 2017. "Econometric analysis of 15-minute intraday electricity prices," Energy Economics, Elsevier, vol. 64(C), pages 77-90.
    5. Lin, Boqiang & Jiang, Zhujun, 2012. "Designation and influence of household increasing block electricity tariffs in China," Energy Policy, Elsevier, vol. 42(C), pages 164-173.
    6. Zhou, Kaile & Yang, Shanlin & Chen, Zhiqiang & Ding, Shuai, 2014. "Optimal load distribution model of microgrid in the smart grid environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 304-310.
    7. Wang, Chen & Zhou, Kaile & Yang, Shanlin, 2017. "A review of residential tiered electricity pricing in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 533-543.
    8. Friedman, Lee S., 2011. "The importance of marginal cost electricity pricing to the success of greenhouse gas reduction programs," Energy Policy, Elsevier, vol. 39(11), pages 7347-7360.
    9. Okajima, Shigeharu & Okajima, Hiroko, 2013. "Estimation of Japanese price elasticities of residential electricity demand, 1990–2007," Energy Economics, Elsevier, vol. 40(C), pages 433-440.
    10. Shen, Bo & Ghatikar, Girish & Lei, Zeng & Li, Jinkai & Wikler, Greg & Martin, Phil, 2014. "The role of regulatory reforms, market changes, and technology development to make demand response a viable resource in meeting energy challenges," Applied Energy, Elsevier, vol. 130(C), pages 814-823.
    11. Adom, Philip Kofi, 2017. "The long-run price sensitivity dynamics of industrial and residential electricity demand: The impact of deregulating electricity prices," Energy Economics, Elsevier, vol. 62(C), pages 43-60.
    12. Li, Lanlan & Gong, Chengzhu & Tian, Shizhong & Jiao, Jianling, 2016. "The peak-shaving efficiency analysis of natural gas time-of-use pricing for residential consumers: Evidence from multi-agent simulation," Energy, Elsevier, vol. 96(C), pages 48-58.
    13. Kwon, Sanguk & Cho, Seong-Hoon & Roberts, Roland K. & Kim, Hyun Jae & Park, KiHyun & Edward Yu, Tun-Hsiang, 2016. "Short-run and the long-run effects of electricity price on electricity intensity across regions," Applied Energy, Elsevier, vol. 172(C), pages 372-382.
    14. Gong, Chengzhu & Tang, Kai & Zhu, Kejun & Hailu, Atakelty, 2016. "An optimal time-of-use pricing for urban gas: A study with a multi-agent evolutionary game-theoretic perspective," Applied Energy, Elsevier, vol. 163(C), pages 283-294.
    15. Wang, Qiang & Chen, Xi, 2012. "China's electricity market-oriented reform: From an absolute to a relative monopoly," Energy Policy, Elsevier, vol. 51(C), pages 143-148.
    16. Shahbaz, Muhammad & Sarwar, Suleman & Chen, Wei & Malik, Muhammad Nasir, 2017. "Dynamics of electricity consumption, oil price and economic growth: Global perspective," Energy Policy, Elsevier, vol. 108(C), pages 256-270.
    17. Broadstock, David C. & Li, Jiajia & Zhang, Dayong, 2016. "Efficiency snakes and energy ladders: A (meta-)frontier demand analysis of electricity consumption efficiency in Chinese households," Energy Policy, Elsevier, vol. 91(C), pages 383-396.
    18. Stephenson, Janet & Barton, Barry & Carrington, Gerry & Gnoth, Daniel & Lawson, Rob & Thorsnes, Paul, 2010. "Energy cultures: A framework for understanding energy behaviours," Energy Policy, Elsevier, vol. 38(10), pages 6120-6129, October.
    19. Jang, Dongsik & Eom, Jiyong & Jae Park, Min & Jeung Rho, Jae, 2016. "Variability of electricity load patterns and its effect on demand response: A critical peak pricing experiment on Korean commercial and industrial customers," Energy Policy, Elsevier, vol. 88(C), pages 11-26.
    20. Yang, Liu & Dong, Ciwei & Wan, C.L. Johnny & Ng, Chi To, 2013. "Electricity time-of-use tariff with consumer behavior consideration," International Journal of Production Economics, Elsevier, vol. 146(2), pages 402-410.
    21. Dave, Saraansh & Sooriyabandara, Mahesh & Yearworth, Mike, 2013. "System behaviour modelling for demand response provision in a smart grid," Energy Policy, Elsevier, vol. 61(C), pages 172-181.
    22. Zhang, Chi & Zhou, Kaile & Yang, Shanlin & Shao, Zhen, 2017. "On electricity consumption and economic growth in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 353-368.
    23. Faruqui, Ahmad & George, Stephen S., 2002. "The Value of Dynamic Pricing in Mass Markets," The Electricity Journal, Elsevier, vol. 15(6), pages 45-55, July.
    24. Hensher, David A. & Shore, Nina & Train, Kenneth, 2014. "Willingness to pay for residential electricity supply quality and reliability," Applied Energy, Elsevier, vol. 115(C), pages 280-292.
    25. Gottwalt, Sebastian & Ketter, Wolfgang & Block, Carsten & Collins, John & Weinhardt, Christof, 2011. "Demand side management—A simulation of household behavior under variable prices," Energy Policy, Elsevier, vol. 39(12), pages 8163-8174.
    26. Eid, Cherrelle & Koliou, Elta & Valles, Mercedes & Reneses, Javier & Hakvoort, Rudi, 2016. "Time-based pricing and electricity demand response: Existing barriers and next steps," Utilities Policy, Elsevier, vol. 40(C), pages 15-25.
    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. Kerim Koc & Zeynep Işık, 2020. "A multi-agent-based model for sustainable governance of urban flood risk mitigation measures," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(1), pages 1079-1110, October.

    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. Wang, Chen & Zhou, Kaile & Yang, Shanlin, 2017. "A review of residential tiered electricity pricing in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 533-543.
    2. Zhou, Kaile & Yang, Shanlin, 2015. "Demand side management in China: The context of China’s power industry reform," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 954-965.
    3. Zhongdong Yu & Wei Liu & Liming Chen & Serkan Eti & Hasan Dinçer & Serhat Yüksel, 2019. "The Effects of Electricity Production on Industrial Development and Sustainable Economic Growth: A VAR Analysis for BRICS Countries," Sustainability, MDPI, vol. 11(21), pages 1-13, October.
    4. Gong, Chengzhu & Yu, Shiwei & Zhu, Kejun & Hailu, Atakelty, 2016. "Evaluating the influence of increasing block tariffs in residential gas sector using agent-based computational economics," Energy Policy, Elsevier, vol. 92(C), pages 334-347.
    5. Pandelara, Diego & Kristjanpoller, Werner & Michell, Kevin & Minutolo, Marcel C., 2022. "A fuzzy regression causality approach to analyze relationship between electrical consumption and GDP," Energy, Elsevier, vol. 239(PE).
    6. Jinning Wang & Fangxing Li & Hantao Cui & Qingxin Shi & Trey Mingee, 2022. "Electricity consumption variation versus economic structure during COVID-19 on metropolitan statistical areas in the US," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    7. Tomas Baležentis & Dalia Štreimikienė, 2019. "Sustainability in the Electricity Sector through Advanced Technologies: Energy Mix Transition and Smart Grid Technology in China," Energies, MDPI, vol. 12(6), pages 1-21, March.
    8. Ringler, Philipp & Keles, Dogan & Fichtner, Wolf, 2016. "Agent-based modelling and simulation of smart electricity grids and markets – A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 205-215.
    9. Huang, Liqing & Zhu, Bangzhu & Wang, Ping & Chevallier, Julien, 2022. "Energy out-of-poverty and inclusive growth: Evidence from the China health and nutrition survey," Structural Change and Economic Dynamics, Elsevier, vol. 60(C), pages 344-352.
    10. Cui, Wencong & Li, Jianyi & Xu, Wangtu & Güneralp, Burak, 2021. "Industrial electricity consumption and economic growth: A spatio-temporal analysis across prefecture-level cities in China from 1999 to 2014," Energy, Elsevier, vol. 222(C).
    11. Yang, Changhui & Meng, Chen & Zhou, Kaile, 2018. "Residential electricity pricing in China: The context of price-based demand response," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2870-2878.
    12. Oluwarotimi Ayokunnu Owolabi & Asa-Ruth Oboku Oku & Abidemi Alejo & Toun Ogunbiyi & Jeremiah Ifeanyi Ubah, 2021. "Access to Electricity, Information and Communications Technology (ICT), and Financial Development: Evidence From West Africa," International Journal of Energy Economics and Policy, Econjournals, vol. 11(2), pages 247-259.
    13. Sudati Nur Safiah & Rr. Retno Sugiharti & Rian Destiningsih & Putra Arif Budiman, 2021. "Dynamic Model for the Consumption of Electrical Energy in Indonesia," International Journal of Energy Economics and Policy, Econjournals, vol. 11(5), pages 356-362.
    14. Syed Hasan & Odmaa Narantungalag, & Martin Berka, 2022. "The intended and unintended consequences of large electricity subsidies: evidence from Mongolia," Discussion Papers 2202, School of Economics and Finance, Massey University, New Zealand.
    15. Sohag, Kazi & Sokhanvar, Amin & Belyaeva, Zhanna & Mirnezami, Seyed Reza, 2022. "Hydrocarbon prices shocks, fiscal stability and consolidation: Evidence from Russian Federation," Resources Policy, Elsevier, vol. 76(C).
    16. Solomon P. Nathaniel & Festus V. Bekun, 2020. "Electricity Consumption, Urbanization and Economic Growth in Nigeria: New Insights from Combined Cointegration amidst Structural Breaks," Research Africa Network Working Papers 20/013, Research Africa Network (RAN).
    17. Ashutosh Dash & Sangram Keshari Jena & Aviral Kumar Tiwari & Shawkat Hammoudeh, 2022. "Dynamics between Power Consumption and Economic Growth at Aggregated and Disaggregated (Sectoral) Level Using the Frequency Domain Causality," JRFM, MDPI, vol. 15(5), pages 1-18, May.
    18. Zafar, Muhammad Wasif & Shahbaz, Muhammad & Hou, Fujun & Sinha, Avik, 2018. "¬¬¬¬¬¬From Nonrenewable to Renewable Energy and Its Impact on Economic Growth: Silver Line of Research & Development Expenditures in APEC Countries," MPRA Paper 90611, University Library of Munich, Germany, revised 10 Dec 2018.
    19. Li, Na & Okur, Özge, 2023. "Economic analysis of energy communities: Investment options and cost allocation," Applied Energy, Elsevier, vol. 336(C).
    20. Li, Lanlan & Luo, Xuan & Zhou, Kaile & Xu, Tingting, 2018. "Evaluation of increasing block pricing for households' natural gas: A case study of Beijing, China," Energy, Elsevier, vol. 157(C), pages 162-172.

    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:spr:nathaz:v:90:y:2018:i:3:d:10.1007_s11069-017-3096-8. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.