IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v109y2016icp310-325.html
   My bibliography  Save this article

Is real-time electricity pricing suitable for residential users without demand-side management?

Author

Listed:
  • Campillo, Javier
  • Dahlquist, Erik
  • Wallin, Fredrik
  • Vassileva, Iana

Abstract

The smart metering infrastructure in Sweden allows electricity providers to offer electricity RTP (real time pricing) to homeowners, together with other dynamic pricing contracts across the country. These contracts are supposed to encourage users to shift power consumption during peak hours to help balance the load in the power system. Of all the available contracts in Sweden, monthly-average price holds the largest share, in response to the low electricity prices during the last three years. It is not clear if RTP will become a popular dynamic pricing scheme since daily price fluctuations might keep customers away from this type of contract. Literature review suggests that RTP adoption is only beneficial when combined with the use of customer demand flexibility, but it does not provide enough information about users adopting RTP without changing their electricity usage profile. This paper studies the economic impact if customers would shift to RTP contracts without adopting demand-side management. To achieve this, electricity costs from a large group of households were calculated and compared between both pricing schemes using the hourly consumption data of a 7-year period. Results suggest that the RTP electricity contract offer a considerable economic savings potential even without enabling consumer demand-side management.

Suggested Citation

  • Campillo, Javier & Dahlquist, Erik & Wallin, Fredrik & Vassileva, Iana, 2016. "Is real-time electricity pricing suitable for residential users without demand-side management?," Energy, Elsevier, vol. 109(C), pages 310-325.
    • Handle: RePEc:eee:energy:v:109:y:2016:i:c:p:310-325
    DOI: 10.1016/j.energy.2016.04.105
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544216305187
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2016.04.105?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. Gyamfi, Samuel & Krumdieck, Susan, 2011. "Price, environment and security: Exploring multi-modal motivation in voluntary residential peak demand response," Energy Policy, Elsevier, vol. 39(5), pages 2993-3004, May.
    2. Shafiullah, G.M. & M.T. Oo, Amanullah & Shawkat Ali, A.B.M. & Wolfs, Peter, 2013. "Potential challenges of integrating large-scale wind energy into the power grid–A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 306-321.
    3. William J. Hausman & John L. Neufeld, 1984. "Time-of-Day Pricing in the U.S. Electric Power Industry at the Turn of the Century," RAND Journal of Economics, The RAND Corporation, vol. 15(1), pages 116-126, Spring.
    4. Thorsnes, Paul & Williams, John & Lawson, Rob, 2012. "Consumer responses to time varying prices for electricity," Energy Policy, Elsevier, vol. 49(C), pages 552-561.
    5. Darby, Sarah J. & McKenna, Eoghan, 2012. "Social implications of residential demand response in cool temperate climates," Energy Policy, Elsevier, vol. 49(C), pages 759-769.
    6. 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.
    7. Schaber, Katrin & Steinke, Florian & Mühlich, Pascal & Hamacher, Thomas, 2012. "Parametric study of variable renewable energy integration in Europe: Advantages and costs of transmission grid extensions," Energy Policy, Elsevier, vol. 42(C), pages 498-508.
    8. 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.
    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. Deng, Tingting & Yan, Wenzhou & Nojavan, Sayyad & Jermsittiparsert, Kittisak, 2020. "Risk evaluation and retail electricity pricing using downside risk constraints method," Energy, Elsevier, vol. 192(C).
    2. Desen Kirli & Maximilian Parzen & Aristides Kiprakis, 2021. "Impact of the COVID-19 Lockdown on the Electricity System of Great Britain: A Study on Energy Demand, Generation, Pricing and Grid Stability," Energies, MDPI, vol. 14(3), pages 1-25, January.
    3. Dong, Jun & Jiang, Yuzheng & Liu, Dongran & Dou, Xihao & Liu, Yao & Peng, Shicheng, 2022. "Promoting dynamic pricing implementation considering policy incentives and electricity retailers’ behaviors: An evolutionary game model based on prospect theory," Energy Policy, Elsevier, vol. 167(C).
    4. 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.
    5. Karunathilake, Hirushie & Hewage, Kasun & Sadiq, Rehan, 2018. "Opportunities and challenges in energy demand reduction for Canadian residential sector: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2005-2016.
    6. Anette Boom & Sebastian Schwenen, 2021. "Is real-time pricing smart for consumers?," Journal of Regulatory Economics, Springer, vol. 60(2), pages 193-213, December.
    7. Correia-da-Silva, João & Soares, Isabel & Fernández, Raquel, 2020. "Impact of dynamic pricing on investment in renewables," Energy, Elsevier, vol. 202(C).
    8. Pretto, Madeline, 2021. "Tail-risk Comprehension and Protection in Real-time Electricity Pricing : Experimental Evidence," Warwick-Monash Economics Student Papers 25, Warwick Monash Economics Student Papers.
    9. 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).
    10. Jonathan Gumz & Diego Castro Fettermann & Enzo Morosini Frazzon & Mirko Kück, 2022. "Using Industry 4.0’s Big Data and IoT to Perform Feature-Based and Past Data-Based Energy Consumption Predictions," Sustainability, MDPI, vol. 14(20), pages 1-34, October.
    11. Adlband, Nahid & Biguesh, Mehrzad & Mohammadi, Mohammad, 2020. "A privacy-preserving and aggregate load controlling decentralized energy consumption scheduling scheme," Energy, Elsevier, vol. 198(C).
    12. Kiguchi, Y. & Weeks, M. & Arakawa, R., 2021. "Predicting winners and losers under time-of-use tariffs using smart meter data," Energy, Elsevier, vol. 236(C).
    13. Tafakori, Laleh & Pourkhanali, Armin & Fard, Farzad Alavi, 2018. "Forecasting spikes in electricity return innovations," Energy, Elsevier, vol. 150(C), pages 508-526.
    14. Jun Dong & Huijuan Huo & Dongran Liu & Rong Li, 2017. "Evaluating the Comprehensive Performance of Demand Response for Commercial Customers by Applying Combination Weighting Techniques and Fuzzy VIKOR Approach," Sustainability, MDPI, vol. 9(8), pages 1-32, July.
    15. Ogunjuyigbe, A.S.O. & Ayodele, T.R. & Akinola, O.A., 2017. "User satisfaction-induced demand side load management in residential buildings with user budget constraint," Applied Energy, Elsevier, vol. 187(C), pages 352-366.
    16. Sasaki, Kento & Aki, Hirohisa & Ikegami, Takashi, 2022. "Application of model predictive control to grid flexibility provision by distributed energy resources in residential dwellings under uncertainty," Energy, Elsevier, vol. 239(PB).
    17. 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.
    18. Wang, Ziyang & Sun, Mei & Gao, Cuixia & Wang, Xin & Ampimah, Benjamin Chris, 2021. "A new interactive real-time pricing mechanism of demand response based on an evaluation model," Applied Energy, Elsevier, vol. 295(C).
    19. Pamulapati, Trinadh & Mallipeddi, Rammohan & Lee, Minho, 2020. "Multi-objective home appliance scheduling with implicit and interactive user satisfaction modelling," Applied Energy, Elsevier, vol. 267(C).
    20. Wang, Xinlin & Wang, Hao & Ahn, Sung-Hoon, 2021. "Demand-side management for off-grid solar-powered microgrids: A case study of rural electrification in Tanzania," Energy, Elsevier, vol. 224(C).
    21. Adil Amin & Wajahat Ullah Khan Tareen & Muhammad Usman & Haider Ali & Inam Bari & Ben Horan & Saad Mekhilef & Muhammad Asif & Saeed Ahmed & Anzar Mahmood, 2020. "A Review of Optimal Charging Strategy for Electric Vehicles under Dynamic Pricing Schemes in the Distribution Charging Network," Sustainability, MDPI, vol. 12(23), pages 1-28, December.
    22. Ioannis Panapakidis & Nikolaos Asimopoulos & Athanasios Dagoumas & Georgios C. Christoforidis, 2017. "An Improved Fuzzy C-Means Algorithm for the Implementation of Demand Side Management Measures," Energies, MDPI, vol. 10(9), pages 1-42, September.

    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. 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.
    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. Gils, Hans Christian, 2016. "Economic potential for future demand response in Germany – Modeling approach and case study," Applied Energy, Elsevier, vol. 162(C), pages 401-415.
    4. Haider, Haider Tarish & See, Ong Hang & Elmenreich, Wilfried, 2016. "A review of residential demand response of smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 166-178.
    5. Lopes, Marta A.R. & Henggeler Antunes, Carlos & Janda, Kathryn B. & Peixoto, Paulo & Martins, Nelson, 2016. "The potential of energy behaviours in a smart(er) grid: Policy implications from a Portuguese exploratory study," Energy Policy, Elsevier, vol. 90(C), pages 233-245.
    6. Kobus, Charlotte B.A. & Klaassen, Elke A.M. & Mugge, Ruth & Schoormans, Jan P.L., 2015. "A real-life assessment on the effect of smart appliances for shifting households’ electricity demand," Applied Energy, Elsevier, vol. 147(C), pages 335-343.
    7. 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).
    8. Andruszkiewicz, Jerzy & Lorenc, Józef & Weychan, Agnieszka, 2020. "Seasonal variability of price elasticity of demand of households using zonal tariffs and its impact on hourly load of the power system," Energy, Elsevier, vol. 196(C).
    9. 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.
    10. Julien Lancelot Michellod & Declan Kuch & Christian Winzer & Martin K. Patel & Selin Yilmaz, 2022. "Building Social License for Automated Demand-Side Management—Case Study Research in the Swiss Residential Sector," Energies, MDPI, vol. 15(20), pages 1-25, October.
    11. Martínez Ceseña, Eduardo A. & Good, Nicholas & Mancarella, Pierluigi, 2015. "Electrical network capacity support from demand side response: Techno-economic assessment of potential business cases for small commercial and residential end-users," Energy Policy, Elsevier, vol. 82(C), pages 222-232.
    12. 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.
    13. 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).
    14. 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.
    15. Anna Kowalska-Pyzalska & Katarzyna Maciejowska & Katarzyna Sznajd-Weron & Rafal Weron, 2013. "Going green: Agent-based modeling of the diffusion of dynamic electricity tariffs," HSC Research Reports HSC/13/05, Hugo Steinhaus Center, Wroclaw University of Technology.
    16. Kwon, Pil Seok & Østergaard, Poul, 2014. "Assessment and evaluation of flexible demand in a Danish future energy scenario," Applied Energy, Elsevier, vol. 134(C), pages 309-320.
    17. 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).
    18. 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.
    19. Cruz, Marco R.M. & Fitiwi, Desta Z. & Santos, Sérgio F. & Catalão, João P.S., 2018. "A comprehensive survey of flexibility options for supporting the low-carbon energy future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 97(C), pages 338-353.
    20. Ahmed Ismail & Mustafa Baysal, 2023. "Dynamic Pricing Based on Demand Response Using Actor–Critic Agent Reinforcement Learning," Energies, MDPI, vol. 16(14), pages 1-19, July.

    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:109:y:2016:i:c:p:310-325. 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.