IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v316y2022i1d10.1007_s10479-021-04119-8.html
   My bibliography  Save this article

Residential power demand side management optimization based on fine-grained mixed frequency data

Author

Listed:
  • Bo wang

    (Beijing Institute of Technology
    Beijing Institute of Technology
    Beijing Institute of Technology)

  • Nana Deng

    (Beijing Institute of Technology
    Beijing Institute of Technology
    Beijing Institute of Technology)

  • Wenhui Zhao

    (Beijing Institute of Technology
    Beijing Institute of Technology
    Beijing Institute of Technology)

  • Zhaohua Wang

    (Beijing Institute of Technology
    Beijing Institute of Technology
    Beijing Institute of Technology)

Abstract

Demand-side response (DSR) measures, which facilitate the management of system reliability and maintain system resource adequacy of power grids, are gaining prominence. Traditional optimization methods for residential electricity usage in peak-demand hours often lack flexibility and effectiveness in solving practical problems due to a large amount of data and a high degree of data uncertainty. Our study aims to obtain a high degree of responsiveness for numerous and highly dispersed residential customers by integrating data-driven and model-driven methods. In this study, we conducted large-scale DSR controlled trials, collected 15-min intervals of electricity consumption data, and matched the survey data. A dynamic time-warping clustering-based difference-in-difference method was proposed for empirical analysis. The results indicate that monetary incentives induced a statistically significant reduction in electricity usage during peak periods in summer by 18.6%, while the response effect was not significant in winter. The heterogeneity analysis suggests that the reduction was mainly contributed by the middle-income group and the elderly group. Our study provides a basis for policymakers to formulate tailored policies to optimize residents’ electricity consumption during peak hours.

Suggested Citation

  • Bo wang & Nana Deng & Wenhui Zhao & Zhaohua Wang, 2022. "Residential power demand side management optimization based on fine-grained mixed frequency data," Annals of Operations Research, Springer, vol. 316(1), pages 603-622, September.
    • Handle: RePEc:spr:annopr:v:316:y:2022:i:1:d:10.1007_s10479-021-04119-8
    DOI: 10.1007/s10479-021-04119-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-021-04119-8
    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/s10479-021-04119-8?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. Jordehi, A. Rezaee, 2019. "Optimisation of demand response in electric power systems, a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 308-319.
    2. Mahmoudimehr, Javad & Sebghati, Parvin, 2019. "A novel multi-objective Dynamic Programming optimization method: Performance management of a solar thermal power plant as a case study," Energy, Elsevier, vol. 168(C), pages 796-814.
    3. Yu, Mengmeng & Hong, Seung Ho, 2017. "Incentive-based demand response considering hierarchical electricity market: A Stackelberg game approach," Applied Energy, Elsevier, vol. 203(C), pages 267-279.
    4. Nathan Nunn & Nancy Qian, 2011. "The Potato's Contribution to Population and Urbanization: Evidence From A Historical Experiment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 126(2), pages 593-650.
    5. Srinivasan, Dipti & Rajgarhia, Sanjana & Radhakrishnan, Bharat Menon & Sharma, Anurag & Khincha, H.P., 2017. "Game-Theory based dynamic pricing strategies for demand side management in smart grids," Energy, Elsevier, vol. 126(C), pages 132-143.
    6. Xiang, Yue & Cai, Hanhu & Gu, Chenghong & Shen, Xiaodong, 2020. "Cost-benefit analysis of integrated energy system planning considering demand response," Energy, Elsevier, vol. 192(C).
    7. 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.
    8. Bartusch, Cajsa & Alvehag, Karin, 2014. "Further exploring the potential of residential demand response programs in electricity distribution," Applied Energy, Elsevier, vol. 125(C), pages 39-59.
    9. Klaassen, E.A.M. & Kobus, C.B.A. & Frunt, J. & Slootweg, J.G., 2016. "Responsiveness of residential electricity demand to dynamic tariffs: Experiences from a large field test in the Netherlands," Applied Energy, Elsevier, vol. 183(C), pages 1065-1074.
    10. Lee V. White & Nicole D. Sintov, 2020. "Health and financial impacts of demand-side response measures differ across sociodemographic groups," Nature Energy, Nature, vol. 5(1), pages 50-60, January.
    11. Álvarez-Miranda, Eduardo & Salgado-Rojas, José & Hermoso, Virgilio & Garcia-Gonzalo, Jordi & Weintraub, Andrés, 2020. "An integer programming method for the design of multi-criteria multi-action conservation plans," Omega, Elsevier, vol. 92(C).
    12. Tang, Rui & Wang, Shengwei & Li, Hangxin, 2019. "Game theory based interactive demand side management responding to dynamic pricing in price-based demand response of smart grids," Applied Energy, Elsevier, vol. 250(C), pages 118-130.
    13. Kavaklioglu, Kadir, 2011. "Modeling and prediction of Turkey's electricity consumption using Support Vector Regression," Applied Energy, Elsevier, vol. 88(1), pages 368-375, January.
    14. Behl, Madhur & Smarra, Francesco & Mangharam, Rahul, 2016. "DR-Advisor: A data-driven demand response recommender system," Applied Energy, Elsevier, vol. 170(C), pages 30-46.
    15. Koichiro Ito, 2014. "Do Consumers Respond to Marginal or Average Price? Evidence from Nonlinear Electricity Pricing," American Economic Review, American Economic Association, vol. 104(2), pages 537-563, February.
    16. Alaqeel, T.A. & Suryanarayanan, S., 2019. "A comprehensive cost-benefit analysis of the penetration of Smart Grid technologies in the Saudi Arabian electricity infrastructure," Utilities Policy, Elsevier, vol. 60(C), pages 1-1.
    17. Shyama Ratnasiri & Clevo Wilson & Wasantha Athukorala & Maria A. Garcia-Valiñas & Benno Torgler & Robert Gifford, 2018. "Effectiveness of two pricing structures on urban water use and conservation: a quasi-experimental investigation," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 20(3), pages 547-560, July.
    18. 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).
    19. Hu, Maomao & Xiao, Fu, 2018. "Price-responsive model-based optimal demand response control of inverter air conditioners using genetic algorithm," Applied Energy, Elsevier, vol. 219(C), pages 151-164.
    20. Juan Yang & Man Gao, 2018. "The impact of education expansion on wage inequality," Applied Economics, Taylor & Francis Journals, vol. 50(12), pages 1309-1323, March.
    21. Chen, Yongbao & Xu, Peng & Chu, Yiyi & Li, Weilin & Wu, Yuntao & Ni, Lizhou & Bao, Yi & Wang, Kun, 2017. "Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings," Applied Energy, Elsevier, vol. 195(C), pages 659-670.
    22. 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.
    23. Wang, Yong & Li, Lin, 2013. "Time-of-use based electricity demand response for sustainable manufacturing systems," Energy, Elsevier, vol. 63(C), pages 233-244.
    24. Fotouhi Ghazvini, Mohammad Ali & Faria, Pedro & Ramos, Sergio & Morais, Hugo & Vale, Zita, 2015. "Incentive-based demand response programs designed by asset-light retail electricity providers for the day-ahead market," Energy, Elsevier, vol. 82(C), pages 786-799.
    25. Frank A. Wolak, 2011. "Do Residential Customers Respond to Hourly Prices? Evidence from a Dynamic Pricing Experiment," American Economic Review, American Economic Association, vol. 101(3), pages 83-87, May.
    26. Yating Li & William A. Pizer & Libo Wu, 2019. "Climate change and residential electricity consumption in the Yangtze River Delta, China," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 116(2), pages 472-477, January.
    27. Huiming Zhang & Kai Wu & Yueming Qiu & Gabriel Chan & Shouyang Wang & Dequn Zhou & Xianqiang Ren, 2020. "Solar photovoltaic interventions have reduced rural poverty in China," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    28. Koichiro Ito & Takanori Ida & Makoto Tanaka, 2018. "Moral Suasion and Economic Incentives: Field Experimental Evidence from Energy Demand," American Economic Journal: Economic Policy, American Economic Association, vol. 10(1), pages 240-267, February.
    29. Cayla, Jean-Michel & Maizi, Nadia & Marchand, Christophe, 2011. "The role of income in energy consumption behaviour: Evidence from French households data," Energy Policy, Elsevier, vol. 39(12), pages 7874-7883.
    Full references (including those not matched with items on IDEAS)

    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. Antonopoulos, Ioannis & Robu, Valentin & Couraud, Benoit & Kirli, Desen & Norbu, Sonam & Kiprakis, Aristides & Flynn, David & Elizondo-Gonzalez, Sergio & Wattam, Steve, 2020. "Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    2. Valeria Di Cosmo, Sean Lyons, and Anne Nolan, 2014. "Estimating the Impact of Time-of-Use Pricing on Irish Electricity Demand," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    3. Deng, Nana & Wang, Bo & Wang, Zhaohua, 2023. "Does targeted poverty alleviation improve households’ adaptation to hot weathers: Evidence from electricity consumption of poor households," Energy Policy, Elsevier, vol. 183(C).
    4. Xu, Bo & Wang, Jiexin & Guo, Mengyuan & Lu, Jiayu & Li, Gehui & Han, Liang, 2021. "A hybrid demand response mechanism based on real-time incentive and real-time pricing," Energy, Elsevier, vol. 231(C).
    5. 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.
    6. Dorothee Charlier and Sondes Kahouli, 2019. "From Residential Energy Demand to Fuel Poverty: Income-induced Non-linearities in the Reactions of Households to Energy Price Fluctuations," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    7. 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.
    8. Yunusov, Timur & Torriti, Jacopo, 2021. "Distributional effects of Time of Use tariffs based on electricity demand and time use," Energy Policy, Elsevier, vol. 156(C).
    9. Kuang, Yunming & Lin, Boqiang, 2021. "Performance of tiered pricing policy for residential natural gas in China: Does the income effect matter?," Applied Energy, Elsevier, vol. 304(C).
    10. Xiaojia Bao, 2016. "Water, Electricity and Weather Variability in Rural Northern China," Working Papers 2014-07-02, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    11. Wang, Bo & Deng, Nana & Li, Haoxiang & Zhao, Wenhui & Liu, Jie & Wang, Zhaohua, 2021. "Effect and mechanism of monetary incentives and moral suasion on residential peak-hour electricity usage," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    12. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    13. Anna Risch & Claire Salmon, 2017. "What matters in residential energy consumption: evidence from France," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 40(1/2), pages 79-116.
    14. Salomé Bakaloglou and Dorothée Charlier, 2019. "Energy Consumption in the French Residential Sector: How Much do Individual Preferences Matter?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
    15. Amit Shewale & Anil Mokhade & Nitesh Funde & Neeraj Dhanraj Bokde, 2022. "A Survey of Efficient Demand-Side Management Techniques for the Residential Appliance Scheduling Problem in Smart Homes," Energies, MDPI, vol. 15(8), pages 1-34, April.
    16. Lin, Boqiang & Zhu, Penghu, 2021. "Has increasing block pricing policy been perceived in China? Evidence from residential electricity use," Energy Economics, Elsevier, vol. 94(C).
    17. Alberini, Anna & Towe, Charles, 2015. "Information v. energy efficiency incentives: Evidence from residential electricity consumption in Maryland," Energy Economics, Elsevier, vol. 52(S1), pages 30-40.
    18. Frondel, Manuel & Kussel, Gerhard & Sommer, Stephan, 2019. "Heterogeneity in the price response of residential electricity demand: A dynamic approach for Germany," Resource and Energy Economics, Elsevier, vol. 57(C), pages 119-134.
    19. Tilov, Ivan & Farsi, Mehdi & Volland, Benjamin, 2020. "From frugal Jane to wasteful John: A quantile regression analysis of Swiss households’ electricity demand," Energy Policy, Elsevier, vol. 138(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:spr:annopr:v:316:y:2022:i:1:d:10.1007_s10479-021-04119-8. 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.