IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v52y2013icp37-43.html
   My bibliography  Save this article

Multi-agent simulation of the time-of-use pricing policy in an urban natural gas pipeline network: A case study of Zhengzhou

Author

Listed:
  • Li, Lanlan
  • Gong, Chengzhu
  • Wang, Deyun
  • Zhu, Kejun

Abstract

This paper establishes a multi-agent system comprising a government agent, a gas operator agent, and an industrial and commercial users agent. The system simulates the dynamic change process of demands and running states in an UGPN (urban gas pipeline network) and explores the optimal TOU (time-of-use) natural gas price to minimize the peak–valley load difference. The government agent plays a monitoring role of providing the upper and lower limits of natural gas price. The gas operator dynamically sets natural gas price based on user response, within the bounds established by a government authority. Then, the industrial and commercial users modify their consumption according to the price provided by the gas operator. This study considers the case of Zhengzhou, China to simulate the hourly gas-usage behavior of industrial and commercial users under the TOU pricing policy. The results indicate the existence of an optimal peak–valley price difference through which both the gas operator and users can gain benefits. Further, rising peak price also increases the benefits of the end-users, while those of the gas operator decrease; however, at some threshold value, the gas operator benefits from further increases in the peak price.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:52:y:2013:i:c:p:37-43
    DOI: 10.1016/j.energy.2013.02.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544213000972
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2013.02.002?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. Valenzuela, Jorge & Thimmapuram, Prakash R. & Kim, Jinho, 2012. "Modeling and simulation of consumer response to dynamic pricing with enabled technologies," Applied Energy, Elsevier, vol. 96(C), pages 122-132.
    2. Nikzad, Mehdi & Mozafari, Babak & Bashirvand, Mahdi & Solaymani, Soodabeh & Ranjbar, Ali Mohamad, 2012. "Designing time-of-use program based on stochastic security constrained unit commitment considering reliability index," Energy, Elsevier, vol. 41(1), pages 541-548.
    3. Meier, Helena & Rehdanz, Katrin, 2010. "Determinants of residential space heating expenditures in Great Britain," Energy Economics, Elsevier, vol. 32(5), pages 949-959, September.
    4. He, Yongxiu & Wang, Bing & Wang, Jianhui & Xiong, Wei & Xia, Tian, 2012. "Residential demand response behavior analysis based on Monte Carlo simulation: The case of Yinchuan in China," Energy, Elsevier, vol. 47(1), pages 230-236.
    5. Atkinson, Scott E., 1981. "Reshaping residential electricity load curves through time-of-use pricing," Resources and Energy, Elsevier, vol. 3(2), pages 175-194, October.
    6. Yu, Shi-wei & Zhu, Ke-jun, 2012. "A hybrid procedure for energy demand forecasting in China," Energy, Elsevier, vol. 37(1), pages 396-404.
    7. Cappers, Peter & Goldman, Charles & Kathan, David, 2010. "Demand response in U.S. electricity markets: Empirical evidence," Energy, Elsevier, vol. 35(4), pages 1526-1535.
    8. Luis Olmos & Sophia Ruester & Siok Jen Liong & Jean-Michel Glachant, 2010. "Energy Efficiency Actions Related to the Rollout of Smart Meters for Small Consumers," RSCAS Working Papers 2010/02fsr, European University Institute.
    9. 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.
    10. Tishler, Asher, 1983. "The industrial and commercial demand for electricity under time-of-use pricing," Journal of Econometrics, Elsevier, vol. 23(3), pages 369-384, December.
    11. 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.
    12. Olmos, Luis & Ruester, Sophia & Liong, Siok-Jen & Glachant, Jean-Michel, 2011. "Energy efficiency actions related to the rollout of smart meters for small consumers, application to the Austrian system," Energy, Elsevier, vol. 36(7), pages 4396-4409.
    13. 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.
    14. Lou, Sheldon X.C. & Keng, C.W.Kenneth & Jiang, Jiong & Cheng, Feng, 1992. "Time of use (TOU) rate effect using the pattern clustering method," Energy, Elsevier, vol. 17(11), pages 1093-1104.
    15. Tishler, Asher, 1984. "A model of industrial demand for electricity under time-of-use pricing and three labor shifts," Resources and Energy, Elsevier, vol. 6(2), pages 107-127, June.
    16. Wang, Jianhui & Bloyd, Cary N. & Hu, Zhaoguang & Tan, Zhongfu, 2010. "Demand response in China," Energy, Elsevier, vol. 35(4), pages 1592-1597.
    17. Bernard, Jean-Thomas & Bolduc, Denis & Yameogo, Nadège-Désirée, 2011. "A pseudo-panel data model of household electricity demand," Resource and Energy Economics, Elsevier, vol. 33(1), pages 315-325, January.
    18. Yousefi, Shaghayegh & Moghaddam, Mohsen Parsa & Majd, Vahid Johari, 2011. "Optimal real time pricing in an agent-based retail market using a comprehensive demand response model," Energy, Elsevier, vol. 36(9), pages 5716-5727.
    19. Faria, P. & Vale, Z., 2011. "Demand response in electrical energy supply: An optimal real time pricing approach," Energy, Elsevier, vol. 36(8), pages 5374-5384.
    20. Walawalkar, Rahul & Fernands, Stephen & Thakur, Netra & Chevva, Konda Reddy, 2010. "Evolution and current status of demand response (DR) in electricity markets: Insights from PJM and NYISO," Energy, Elsevier, vol. 35(4), pages 1553-1560.
    21. Frank Asche & Odd Bjarte Nilsen & Ragnar Tveteras, 2008. "Natural Gas Demand in the European Household Sector," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 27-46.
    22. 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. van Goor, Harm & Scholtens, Bert, 2014. "Modeling natural gas price volatility: The case of the UK gas market," Energy, Elsevier, vol. 72(C), pages 126-134.
    2. 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.
    3. 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.
    4. Kovačič, Miha & Šarler, Božidar, 2014. "Genetic programming prediction of the natural gas consumption in a steel plant," Energy, Elsevier, vol. 66(C), pages 273-284.
    5. Lin, Boqiang & Chen, Xing, 2018. "Is the implementation of the Increasing Block Electricity Prices policy really effective?--- Evidence based on the analysis of synthetic control method," Energy, Elsevier, vol. 163(C), pages 734-750.
    6. 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.
    7. He, Yongxiu & Liu, Yangyang & Wang, Jianhui & Xia, Tian & Zhao, Yushan, 2014. "Low-carbon-oriented dynamic optimization of residential energy pricing in China," Energy, Elsevier, vol. 66(C), pages 610-623.
    8. Jian Chai & Liqiao Wang, 2020. "Analysis and Design of Interruptible Gas Contract in China under Energy Market Reform," Sustainability, MDPI, vol. 12(2), pages 1-15, January.

    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, 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.
    2. 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.
    3. Cortés-Arcos, Tomás & Bernal-Agustín, José L. & Dufo-López, Rodolfo & Lujano-Rojas, Juan M. & Contreras, Javier, 2017. "Multi-objective demand response to real-time prices (RTP) using a task scheduling methodology," Energy, Elsevier, vol. 138(C), pages 19-31.
    4. 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.
    5. 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.
    6. Aghaei, Jamshid & Alizadeh, Mohammad-Iman, 2013. "Multi-objective self-scheduling of CHP (combined heat and power)-based microgrids considering demand response programs and ESSs (energy storage systems)," Energy, Elsevier, vol. 55(C), pages 1044-1054.
    7. Feuerriegel, Stefan & Neumann, Dirk, 2014. "Measuring the financial impact of demand response for electricity retailers," Energy Policy, Elsevier, vol. 65(C), pages 359-368.
    8. Zheng, Yingying & Jenkins, Bryan M. & Kornbluth, Kurt & Kendall, Alissa & Træholt, Chresten, 2018. "Optimization of a biomass-integrated renewable energy microgrid with demand side management under uncertainty," Applied Energy, Elsevier, vol. 230(C), pages 836-844.
    9. Alasseri, Rajeev & Tripathi, Ashish & Joji Rao, T. & Sreekanth, K.J., 2017. "A review on implementation strategies for demand side management (DSM) in Kuwait through incentive-based demand response programs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 617-635.
    10. 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.
    11. He, Yongxiu & Wang, Bing & Wang, Jianhui & Xiong, Wei & Xia, Tian, 2012. "Residential demand response behavior analysis based on Monte Carlo simulation: The case of Yinchuan in China," Energy, Elsevier, vol. 47(1), pages 230-236.
    12. Meyabadi, A. Fattahi & Deihimi, M.H., 2017. "A review of demand-side management: Reconsidering theoretical framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 367-379.
    13. 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.
    14. Faria, P. & Vale, Z., 2011. "Demand response in electrical energy supply: An optimal real time pricing approach," Energy, Elsevier, vol. 36(8), pages 5374-5384.
    15. Märkle-Huß, Joscha & Feuerriegel, Stefan & Neumann, Dirk, 2018. "Large-scale demand response and its implications for spot prices, load and policies: Insights from the German-Austrian electricity market," Applied Energy, Elsevier, vol. 210(C), pages 1290-1298.
    16. Hong, Seung Ho & Yu, Mengmeng & Huang, Xuefei, 2015. "A real-time demand response algorithm for heterogeneous devices in buildings and homes," Energy, Elsevier, vol. 80(C), pages 123-132.
    17. Ricci, Elena Claire & Banterle, Alessandro, 2020. "Do major climate change-related public events have an impact on consumer choices?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 126(C).
    18. Alagoz, B. Baykant & Kaygusuz, Asim & Akcin, Murat & Alagoz, Serkan, 2013. "A closed-loop energy price controlling method for real-time energy balancing in a smart grid energy market," Energy, Elsevier, vol. 59(C), pages 95-104.
    19. 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.
    20. Guo, Peiyang & Li, Victor O.K. & Lam, Jacqueline C.K., 2017. "Smart demand response in China: Challenges and drivers," Energy Policy, Elsevier, vol. 107(C), pages 1-10.

    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:52:y:2013:i:c:p:37-43. 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.