IDEAS home Printed from https://ideas.repec.org/r/aen/journl/ej35-2-06.html
   My bibliography  Save this item

Estimating the Impact of Time-of-Use Pricing on Irish Electricity Demand

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Clastres, Cédric & Khalfallah, Haikel, 2021. "Dynamic pricing efficiency with strategic retailers and consumers: An analytical analysis of short-term market interactions," Energy Economics, Elsevier, vol. 98(C).
  2. Ciarreta, Aitor & Espinosa, Maria Paz & Pizarro-Irizar, Cristina, 2023. "Pricing policies for efficient demand side management in liberalized electricity markets," Economic Modelling, Elsevier, vol. 121(C).
  3. David Huckebrink & Valentin Bertsch, 2021. "Integrating Behavioural Aspects in Energy System Modelling—A Review," Energies, MDPI, vol. 14(15), pages 1-26, July.
  4. Bertsch, Valentin & Geldermann, Jutta & Lühn, Tobias, 2017. "What drives the profitability of household PV investments, self-consumption and self-sufficiency?," Applied Energy, Elsevier, vol. 204(C), pages 1-15.
  5. Kang, Jieyi & Reiner, David M., 2022. "What is the effect of weather on household electricity consumption? Empirical evidence from Ireland," Energy Economics, Elsevier, vol. 111(C).
  6. Valeria Di Cosmo & Elisa Trujillo-Baute, 2018. "From Forward to Spot Prices: Producers, Retailers and Loss Averse Consumers in Electricity Markets," Working Papers 2018.31, Fondazione Eni Enrico Mattei.
  7. Guo, Bowei & Weeks, Melvyn, 2022. "Dynamic tariffs, demand response, and regulation in retail electricity markets," Energy Economics, Elsevier, vol. 106(C).
  8. Carroll Paula & Murphy Tadhg & Hanley Michael & Dempsey Daniel & Dunne John, 2018. "Household Classification Using Smart Meter Data," Journal of Official Statistics, Sciendo, vol. 34(1), pages 1-25, March.
  9. Cédric Clastres & Haikel Khalfallah, 2015. "An Analytical Approach to Activating Demand Elasticity with a Demand Response Mechanism," Post-Print hal-01222582, HAL.
  10. Liu, Lucy & Workman, Mark & Hayes, Sarah, 2022. "Net Zero and the potential of consumer data - United Kingdom energy sector case study: The need for cross-sectoral best data practice principles," Energy Policy, Elsevier, vol. 163(C).
  11. Vallés, Mercedes & Bello, Antonio & Reneses, Javier & Frías, Pablo, 2018. "Probabilistic characterization of electricity consumer responsiveness to economic incentives," Applied Energy, Elsevier, vol. 216(C), pages 296-310.
  12. Kim, Jihyo & Lee, Soomin & Jang, Heesun, 2022. "Lessons from residential electricity demand analysis on the time of use pricing experiment in South Korea," Energy Economics, Elsevier, vol. 113(C).
  13. Cosmo, Valeria Di & O’Hora, Denis, 2017. "Nudging electricity consumption using TOU pricing and feedback: evidence from Irish households," Journal of Economic Psychology, Elsevier, vol. 61(C), pages 1-14.
  14. Rezaeimozafar, Mostafa & Monaghan, Rory F.D. & Barrett, Enda & Duffy, Maeve, 2022. "A review of behind-the-meter energy storage systems in smart grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 164(C).
  15. Yan, Xing & Ozturk, Yusuf & Hu, Zechun & Song, Yonghua, 2018. "A review on price-driven residential demand response," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 411-419.
  16. Devine, Mel T. & Bertsch, Valentin, 2018. "Examining the benefits of load shedding strategies using a rolling-horizon stochastic mixed complementarity equilibrium model," European Journal of Operational Research, Elsevier, vol. 267(2), pages 643-658.
  17. Dorothee Charlier and Berangere Legendre, 2019. "A Multidimensional Approach to Measuring Fuel Poverty," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
  18. Silva, Susana & Soares, Isabel & Pinho, Carlos, 2018. "Electricity residential demand elasticities: Urban versus rural areas in Portugal," Energy, Elsevier, vol. 144(C), pages 627-632.
  19. Kim, Kyungah & Choi, Jihye & Lee, Jihee & Lee, Jongsu & Kim, Junghun, 2023. "Public preferences and increasing acceptance of time-varying electricity pricing for demand side management in South Korea," Energy Economics, Elsevier, vol. 119(C).
  20. Guo, Peiyang & Lam, Jacqueline C.K. & Li, Victor O.K., 2019. "Drivers of domestic electricity users’ price responsiveness: A novel machine learning approach," Applied Energy, Elsevier, vol. 235(C), pages 900-913.
  21. Cappers, Peter A. & Todd-Blick, Annika, 2021. "Heterogeneity in own-price residential customer demand elasticities for electricity under time-of-use rates: Evidence from a randomized-control trial in the United States," Utilities Policy, Elsevier, vol. 73(C).
  22. Thakur, Jagruti & Chakraborty, Basab, 2016. "Demand side management in developing nations: A mitigating tool for energy imbalance and peak load management," Energy, Elsevier, vol. 114(C), pages 895-912.
  23. 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).
  24. Shirley Pon, 2017. "The Effect of Information on TOU Electricity Use: an Irish residential study," The Energy Journal, International Association for Energy Economics, vol. 0(Number 6).
  25. Lang, Corey & Qiu, Yueming (Lucy) & Dong, Luran, 2023. "Increasing voluntary enrollment in time-of-use electricity rates: Findings from a survey experiment," Energy Policy, Elsevier, vol. 173(C).
  26. Hortay, Olivér & Kökény, László, 2020. "A villamosenergia-fogyasztás elhalasztásával kapcsolatos lakossági attitűd felmérése Magyarországon [A survey of popular attitudes to deferment of electricity consumption in Hungary]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(7), pages 657-687.
  27. 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.
  28. Clastres, Cédric & Khalfallah, Haikel, 2015. "An analytical approach to activating demand elasticity with a demand response mechanism," Energy Economics, Elsevier, vol. 52(PA), pages 195-206.
  29. Curtis, John & Devitt, Niamh & di Cosmo, Valeria & Farrell, Niall & FitzGerald, John & Hyland, Marie & Lynch, Muireann & Lyons, Sean & McCoy, Daire & Malaguzzi Valeri, Laura & Walsh, Darragh, 2014. "Irish Energy Policy: An Analysis of Current Issues," Research Series, Economic and Social Research Institute (ESRI), number rs37 edited by FitzGerald, John & Malaguzzi Valeri, Laura, June.
  30. Eoghan O'Neill & Melvyn Weeks, 2018. "Causal Tree Estimation of Heterogeneous Household Response to Time-Of-Use Electricity Pricing Schemes," Papers 1810.09179, arXiv.org, revised Oct 2019.
  31. Cédric Clastres & Haikel Khalfallah, 2014. "An analytical approach for elasticity of demand activation with demand response mechanisms," Working Papers halshs-01019679, HAL.
  32. Cédric Clastres & Haikel Khalfallah, 2021. "Dynamic pricing efficiency with strategic retailers and consumers: An analytical analysis of short-term market interactions," Post-Print hal-03193212, HAL.
  33. O'Neill, E. & Weeks, M., 2018. "Causal Tree Estimation of Heterogeneous Household Response to Time-Of-Use Electricity Pricing Schemes," Cambridge Working Papers in Economics 1865, Faculty of Economics, University of Cambridge.
  34. Weber, Sylvain & Puddu, Stefano & Pacheco, Diana, 2017. "Move it! How an electric contest motivates households to shift their load profile," Energy Economics, Elsevier, vol. 68(C), pages 255-270.
  35. Cédric Clastres & Haikel Khalfallah, 2020. "Retailers' strategies facing demand response and markets interactions," Working Papers hal-03167543, HAL.
  36. Guo, P. & Lam, J. & Li, V., 2018. "A novel machine learning approach for identifying the drivers of domestic electricity users’ price responsiveness," Cambridge Working Papers in Economics 1844, Faculty of Economics, University of Cambridge.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.