IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v96y2016icp48-58.html
   My bibliography  Save this article

The peak-shaving efficiency analysis of natural gas time-of-use pricing for residential consumers: Evidence from multi-agent simulation

Author

Listed:
  • Li, Lanlan
  • Gong, Chengzhu
  • Tian, Shizhong
  • Jiao, Jianling

Abstract

The present paper proposes a multi-agent simulation model for analyzing the peak-shaving efficiency of natural gas TOU (time-of-use) pricing for residential consumers. Firstly, a natural gas price sensitivity function has been explored according to gas consumption characteristics of residential users, which establishes the residential users demand response model from the perspective of consumer psychology. Secondly, a TOU pricing multi-agent simulation system has been developed, which mainly includes a government agent, a gas operator agent, and seven residential user agents of different income level. Finally, this study takes Zhengzhou city in China as an example to simulate the dynamic change process of running states in the UGPN (Urban Gas Pipeline Network) under the residential users TOU pricing policy. The simulation results indicate that the maximum peak-valley load difference can be reduced by 11.12%, given the residential users' response to TOU tariffs, by shifting load and electricity substitution. The TOU price also increases benefits of gas operators, and reduces residential consumers' energy expenditure. Furthermore, the small ratio of the residential users' consumption will lead to the low peak-valley load difference, but poor peak shaving efficiency and little gas operators' benefits. In addition, the expenditure on energy changes among different kinds of consumers; that is, the high and low income households have a large increased ratio, while the middle income households have a low increased ratio.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:96:y:2016:i:c:p:48-58
    DOI: 10.1016/j.energy.2015.12.042
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544215016849
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2015.12.042?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. Khan, Muhammad Arshad, 2015. "Modelling and forecasting the demand for natural gas in Pakistan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 1145-1159.
    2. Zareen, N. & Mustafa, M.W. & Sultana, U. & Nadia, R. & Khattak, M.A., 2015. "Optimal real time cost-benefit based demand response with intermittent resources," Energy, Elsevier, vol. 90(P2), pages 1695-1706.
    3. Alberini, Anna & Gans, Will & Velez-Lopez, Daniel, 2011. "Residential consumption of gas and electricity in the U.S.: The role of prices and income," Energy Economics, Elsevier, vol. 33(5), pages 870-881, September.
    4. 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.
    5. Wadud, Zia & Dey, Himadri S. & Kabir, Md. Ashfanoor & Khan, Shahidul I., 2011. "Modeling and forecasting natural gas demand in Bangladesh," Energy Policy, Elsevier, vol. 39(11), pages 7372-7380.
    6. Dagher, Leila, 2012. "Natural gas demand at the utility level: An application of dynamic elasticities," Energy Economics, Elsevier, vol. 34(4), pages 961-969.
    7. Caves, Douglas W. & Christensen, Laurits R. & Schoech, Philip E. & Hendricks, Wallace, 1984. "A comparison of different methodologies in a case study of residential time-of-use electricity pricing : Cost-Benefit Analysis," Journal of Econometrics, Elsevier, vol. 26(1-2), pages 17-34.
    8. Yu, Shiwei & Wei, Yi-Ming & Guo, Haixiang & Ding, Liping, 2014. "Carbon emission coefficient measurement of the coal-to-power energy chain in China," Applied Energy, Elsevier, vol. 114(C), pages 290-300.
    9. Maddala, G S, et al, 1997. "Estimation of Short-Run and Long-Run Elasticities of Energy Demand from Panel Data Using Shrinkage Estimators," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 90-100, January.
    10. Yu, Yihua & Zheng, Xinye & Han, Yi, 2014. "On the demand for natural gas in urban China," Energy Policy, Elsevier, vol. 70(C), pages 57-63.
    11. Alberini, Anna & Gans, Will & Velez-Lopez, Daniel, 2011. "Residential Consumption of Gas and Electricity in the U.S.: The Role of Prices and Income," Sustainable Development Papers 99637, Fondazione Eni Enrico Mattei (FEEM).
    12. 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.
    13. Kim, Byung-Do & Blattberg, Robert C & Rossi, Peter E, 1995. "Modeling the Distribution of Price Sensitivity and Implications for Optimal Retail Pricing," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 291-303, July.
    14. 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.
    15. Faruqui, Ahmad & Malko, J.Robert, 1983. "The residential demand for electricity by time-of-use: A survey of twelve experiments with peak load pricing," Energy, Elsevier, vol. 8(10), pages 781-795.
    16. Meier, Helena & Rehdanz, Katrin, 2010. "Determinants of residential space heating expenditures in Great Britain," Energy Economics, Elsevier, vol. 32(5), pages 949-959, September.
    17. Caves, Douglas W. & Christensen, Laurits R. & Herriges, Joseph A., 1984. "Consistency of residential customer response in time-of-use electricity pricing experiments," Journal of Econometrics, Elsevier, vol. 26(1-2), pages 179-203.
    18. Zhang, Tao & Zhang, David, 2007. "Agent-based simulation of consumer purchase decision-making and the decoy effect," Journal of Business Research, Elsevier, vol. 60(8), pages 912-922, August.
    19. Yoo, Seung-Hoon & Lim, Hea-Jin & Kwak, Seung-Jun, 2009. "Estimating the residential demand function for natural gas in Seoul with correction for sample selection bias," Applied Energy, Elsevier, vol. 86(4), pages 460-465, April.
    20. 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.
    21. Bartusch, Cajsa & Wallin, Fredrik & Odlare, Monica & Vassileva, Iana & Wester, Lars, 2011. "Introducing a demand-based electricity distribution tariff in the residential sector: Demand response and customer perception," Energy Policy, Elsevier, vol. 39(9), pages 5008-5025, September.
    22. Bernstein, Ronald & Madlener, Reinhard, 2011. "Residential Natural Gas Demand Elasticities in OECD Countries: An ARDL Bounds Testing Approach," FCN Working Papers 15/2011, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    23. Payne, James E. & Loomis, David G. & Wilson, Renardo, 2011. "Residential Natural Gas Demand in Illinois: Evidence from the ARDL Bounds Testing Approach," Journal of Regional Analysis and Policy, Mid-Continent Regional Science Association, vol. 41(2), pages 1-10.
    24. Garcia-Cerrutti, L. Miguel, 2000. "Estimating elasticities of residential energy demand from panel county data using dynamic random variables models with heteroskedastic and correlated error terms," Resource and Energy Economics, Elsevier, vol. 22(4), pages 355-366, October.
    25. repec:ags:jrapmc:133220 is not listed on IDEAS
    26. Wang, Yong & Li, Lin, 2013. "Time-of-use based electricity demand response for sustainable manufacturing systems," Energy, Elsevier, vol. 63(C), pages 233-244.
    27. 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.
    28. Zhu, Zhi-Shuang & Liao, Hua & Cao, Huai-Shu & Wang, Lu & Wei, Yi-Ming & Yan, Jinyue, 2014. "The differences of carbon intensity reduction rate across 89 countries in recent three decades," Applied Energy, Elsevier, vol. 113(C), pages 808-815.
    29. Sheikhi, Aras & Bahrami, Shahab & Ranjbar, Ali Mohammad, 2015. "An autonomous demand response program for electricity and natural gas networks in smart energy hubs," Energy, Elsevier, vol. 89(C), pages 490-499.
    30. Paltsev, Sergey & Zhang, Danwei, 2015. "Natural gas pricing reform in China: Getting closer to a market system?," Energy Policy, Elsevier, vol. 86(C), pages 43-56.
    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. Venizelou, Venizelos & Philippou, Nikolas & Hadjipanayi, Maria & Makrides, George & Efthymiou, Venizelos & Georghiou, George E., 2018. "Development of a novel time-of-use tariff algorithm for residential prosumer price-based demand side management," Energy, Elsevier, vol. 142(C), pages 633-646.
    2. 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.
    3. 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.
    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. Gao, Hongjun & Zhao, Yinbo & He, Shuaijia & Liu, Junyong, 2023. "Demand response management of community integrated energy system: A multi-energy retail package perspective," Applied Energy, Elsevier, vol. 330(PA).
    6. Jiajia Li & Jinfu Liu & Peigang Yan & Xingshuo Li & Guowen Zhou & Daren Yu, 2021. "Operation Optimization of Integrated Energy System under a Renewable Energy Dominated Future Scene Considering Both Independence and Benefit: A Review," Energies, MDPI, vol. 14(4), pages 1-36, February.
    7. Askari, S. & Montazerin, N. & Fazel Zarandi, M.H., 2016. "Gas networks simulation from disaggregation of low frequency nodal gas consumption," Energy, Elsevier, vol. 112(C), pages 1286-1298.
    8. Li, Lanlan & Ming, Huayang & Fu, Weizhong & Shi, Quan & Yu, Shiwei, 2021. "Exploring household natural gas consumption patterns and their influencing factors: An integrated clustering and econometric method," Energy, Elsevier, vol. 224(C).
    9. Wang, Tiantian & Zhang, Dayong & Ji, Qiang & Shi, Xunpeng, 2020. "Market reforms and determinants of import natural gas prices in China," Energy, Elsevier, vol. 196(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. 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.
    2. Burke, Paul J. & Yang, Hewen, 2016. "The price and income elasticities of natural gas demand: International evidence," Energy Economics, Elsevier, vol. 59(C), pages 466-474.
    3. Kostakis, Ioannis & Lolos, Sarantis & Sardianou, Eleni, 2021. "Residential natural gas demand: Assessing the evidence from Greece using pseudo-panels, 2012–2019," Energy Economics, Elsevier, vol. 99(C).
    4. Malzi, Mohamed Jaouad & Sohag, Kazi & Vasbieva, Dinara G. & Ettahir, Aziz, 2020. "Environmental policy effectiveness on residential natural gas use in OECD countries," Resources Policy, Elsevier, vol. 66(C).
    5. Yu, Yihua & Zheng, Xinye & Han, Yi, 2014. "On the demand for natural gas in urban China," Energy Policy, Elsevier, vol. 70(C), pages 57-63.
    6. Copiello, Sergio & Grillenzoni, Carlo, 2017. "Is the cold the only reason why we heat our homes? Empirical evidence from spatial series data," Applied Energy, Elsevier, vol. 193(C), pages 491-506.
    7. Khan, Muhammad Arshad, 2015. "Modelling and forecasting the demand for natural gas in Pakistan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 1145-1159.
    8. 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.
    9. 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.
    10. Halim Tatli, 2018. "Multiple Determinants of Household Natural Gas Demand: A Panel Data Analysis in OECD Countries," Asian Development Policy Review, Asian Economic and Social Society, vol. 6(4), pages 243-253, December.
    11. Hu, Wenhao & Ho, Mun S. & Cao, Jing, 2019. "Energy consumption of urban households in China," China Economic Review, Elsevier, vol. 58(C).
    12. 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.
    13. Zhang, Yi & Ji, Qiang & Fan, Ying, 2018. "The price and income elasticity of China's natural gas demand: A multi-sectoral perspective," Energy Policy, Elsevier, vol. 113(C), pages 332-341.
    14. John T. Cuddington and Leila Dagher, 2015. "Estimating Short and Long-Run Demand Elasticities: A Primer with Energy-Sector Applications," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    15. 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.
    16. Eshagh Mansourkiaee, 2023. "Estimating energy demand elasticities for gas exporting countries: a dynamic panel data approach," SN Business & Economics, Springer, vol. 3(1), pages 1-28, January.
    17. 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.
    18. Haider, Haider Tarish & See, Ong Hang & Elmenreich, Wilfried, 2016. "Residential demand response scheme based on adaptive consumption level pricing," Energy, Elsevier, vol. 113(C), pages 301-308.
    19. Jumah Ahmad Alzyadat, 2022. "The Price and Income Elasticity of Demand for Natural Gas Consumption in Saudi Arabia," International Journal of Energy Economics and Policy, Econjournals, vol. 12(6), pages 357-363, November.
    20. Csereklyei, Zsuzsanna, 2020. "Price and income elasticities of residential and industrial electricity demand in the European Union," Energy Policy, Elsevier, vol. 137(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:96:y:2016:i:c:p:48-58. 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.