IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v165y2016icp592-600.html
   My bibliography  Save this article

True real time pricing and combined power scheduling of electric appliances in residential energy management system

Author

Listed:
  • Anees, Amir
  • Chen, Yi-Ping Phoebe

Abstract

This paper proposed a new smart home community architecture in power system, in which community controller will acts as a virtual power distribution company. The traditional real time pricing schemes may not be effectively implemented in terms of reduction of power peak to average ratio over the large number of end consumers. To overcome this problem, a true real time pricing between community controller and community end users is developed based on real time pricing and inclining block rates. The proposed pricing scheme implemented in the community is charged at the end of a day according to the combined load of the community. To schedule the electric appliances in a combined way, we have developed a power scheduling algorithm as well. The simulation results have revealed that by applying anticipated technique of pricing scheme in group of households, the consumption cost of end consumers decreases and the overall power peak to average ratio reduces as well which will be beneficial for the utilities.

Suggested Citation

  • Anees, Amir & Chen, Yi-Ping Phoebe, 2016. "True real time pricing and combined power scheduling of electric appliances in residential energy management system," Applied Energy, Elsevier, vol. 165(C), pages 592-600.
    • Handle: RePEc:eee:appene:v:165:y:2016:i:c:p:592-600
    DOI: 10.1016/j.apenergy.2015.12.103
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261915016761
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2015.12.103?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. Venkatesan, Naveen & Solanki, Jignesh & Solanki, Sarika Khushalani, 2012. "Residential Demand Response model and impact on voltage profile and losses of an electric distribution network," Applied Energy, Elsevier, vol. 96(C), pages 84-91.
    2. Farzan, Farbod & Jafari, Mohsen A. & Gong, Jie & Farzan, Farnaz & Stryker, Andrew, 2015. "A multi-scale adaptive model of residential energy demand," Applied Energy, Elsevier, vol. 150(C), pages 258-273.
    3. Peter O. Steiner, 1957. "Peak Loads and Efficient Pricing," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 71(4), pages 585-610.
    4. Torriti, Jacopo, 2012. "Demand Side Management for the European Supergrid: Occupancy variances of European single-person households," Energy Policy, Elsevier, vol. 44(C), pages 199-206.
    5. Soares, Ana & Gomes, Álvaro & Antunes, Carlos Henggeler, 2014. "Categorization of residential electricity consumption as a basis for the assessment of the impacts of demand response actions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 490-503.
    6. Javed, Fahad & Arshad, Naveed & Wallin, Fredrik & Vassileva, Iana & Dahlquist, Erik, 2012. "Forecasting for demand response in smart grids: An analysis on use of anthropologic and structural data and short term multiple loads forecasting," Applied Energy, Elsevier, vol. 96(C), pages 150-160.
    7. Kyriakarakos, George & Piromalis, Dimitrios D. & Dounis, Anastasios I. & Arvanitis, Konstantinos G. & Papadakis, George, 2013. "Intelligent demand side energy management system for autonomous polygeneration microgrids," Applied Energy, Elsevier, vol. 103(C), pages 39-51.
    8. Severin Borenstein, 2005. "The Long-Run Efficiency of Real-Time Electricity Pricing," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 93-116.
    9. 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.
    10. Finn, Paddy & Fitzpatrick, Colin, 2014. "Demand side management of industrial electricity consumption: Promoting the use of renewable energy through real-time pricing," Applied Energy, Elsevier, vol. 113(C), pages 11-21.
    11. Grünewald, Philipp & Torriti, Jacopo, 2013. "Demand response from the non-domestic sector: Early UK experiences and future opportunities," Energy Policy, Elsevier, vol. 61(C), pages 423-429.
    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. Yasuaki Miyazato & Hayato Tahara & Kosuke Uchida & Cirio Celestino Muarapaz & Abdul Motin Howlader & Tomonobu Senjyu, 2016. "Multi-Objective Optimization for Smart House Applied Real Time Pricing Systems," Sustainability, MDPI, vol. 8(12), pages 1-22, December.
    2. Artvin-Darien Gonzalez-Abreu & Roque-Alfredo Osornio-Rios & Arturo-Yosimar Jaen-Cuellar & Miguel Delgado-Prieto & Jose-Alfonso Antonino-Daviu & Athanasios Karlis, 2022. "Advances in Power Quality Analysis Techniques for Electrical Machines and Drives: A Review," Energies, MDPI, vol. 15(5), pages 1-26, March.
    3. Ibrahim Alotaibi & Mohammed A. Abido & Muhammad Khalid & Andrey V. Savkin, 2020. "A Comprehensive Review of Recent Advances in Smart Grids: A Sustainable Future with Renewable Energy Resources," Energies, MDPI, vol. 13(23), pages 1-41, November.
    4. Chreim, Bashar & Esseghir, Moez & Merghem-Boulahia, Leila, 2022. "LOSISH—LOad Scheduling In Smart Homes based on demand response: Application to smart grids," Applied Energy, Elsevier, vol. 323(C).
    5. Reynolds, Jonathan & Ahmad, Muhammad Waseem & Rezgui, Yacine & Hippolyte, Jean-Laurent, 2019. "Operational supply and demand optimisation of a multi-vector district energy system using artificial neural networks and a genetic algorithm," Applied Energy, Elsevier, vol. 235(C), pages 699-713.
    6. Lu, Qing & Lü, Shuaikang & Leng, Yajun & Zhang, Zhixin, 2020. "Optimal household energy management based on smart residential energy hub considering uncertain behaviors," Energy, Elsevier, vol. 195(C).
    7. Abdulaal, Ahmed & Moghaddass, Ramin & Asfour, Shihab, 2017. "Two-stage discrete-continuous multi-objective load optimization: An industrial consumer utility approach to demand response," Applied Energy, Elsevier, vol. 206(C), pages 206-221.
    8. Reihani, Ehsan & Motalleb, Mahdi & Thornton, Matsu & Ghorbani, Reza, 2016. "A novel approach using flexible scheduling and aggregation to optimize demand response in the developing interactive grid market architecture," Applied Energy, Elsevier, vol. 183(C), pages 445-455.
    9. Emmanuel Guillen-Garcia & Angel L. Zorita-Lamadrid & Oscar Duque-Perez & Luis Morales-Velazquez & Roque Alfredo Osornio-Rios & Rene De Jesus Romero-Troncoso, 2017. "Power Consumption Analysis of Electrical Installations at Healthcare Facility," Energies, MDPI, vol. 10(1), pages 1-14, January.
    10. Ghulam Hafeez & Nadeem Javaid & Sohail Iqbal & Farman Ali Khan, 2018. "Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units," Energies, MDPI, vol. 11(3), pages 1-27, March.
    11. Liang, Yile & Liu, Feng & Wang, Cheng & Mei, Shengwei, 2017. "Distributed demand-side energy management scheme in residential smart grids: An ordinal state-based potential game approach," Applied Energy, Elsevier, vol. 206(C), pages 991-1008.
    12. Lu, Tianguang & Ai, Qian & Wang, Zhaoyu, 2018. "Interactive game vector: A stochastic operation-based pricing mechanism for smart energy systems with coupled-microgrids," Applied Energy, Elsevier, vol. 212(C), pages 1462-1475.
    13. Anees, Amir & Dillon, Tharam & Chen, Yi-Ping Phoebe, 2019. "A novel decision strategy for a bilateral energy contract," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    14. Cao, GangCheng & Fang, Debin & Wang, Pengyu, 2021. "The impacts of social learning on a real-time pricing scheme in the electricity market," Applied Energy, Elsevier, vol. 291(C).
    15. 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).
    16. Zhao, Xueyuan & Gao, Weijun & Qian, Fanyue & Ge, Jian, 2021. "Electricity cost comparison of dynamic pricing model based on load forecasting in home energy management system," Energy, Elsevier, vol. 229(C).
    17. Yoon, Ah-Yun & Kim, Young-Jin & Zakula, Tea & Moon, Seung-Ill, 2020. "Retail electricity pricing via online-learning of data-driven demand response of HVAC systems," Applied Energy, Elsevier, vol. 265(C).
    18. Song, Chunhe & Jing, Wei & Zeng, Peng & Rosenberg, Catherine, 2017. "An analysis on the energy consumption of circulating pumps of residential swimming pools for peak load management," Applied Energy, Elsevier, vol. 195(C), pages 1-12.
    19. Pirouzi, Sasan & Aghaei, Jamshid & Niknam, Taher & Farahmand, Hossein & Korpås, Magnus, 2018. "Exploring prospective benefits of electric vehicles for optimal energy conditioning in distribution networks," Energy, Elsevier, vol. 157(C), pages 679-689.

    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. 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.
    2. Müller, Theresa & Möst, Dominik, 2018. "Demand Response Potential: Available when Needed?," Energy Policy, Elsevier, vol. 115(C), pages 181-198.
    3. Gils, Hans Christian, 2014. "Assessment of the theoretical demand response potential in Europe," Energy, Elsevier, vol. 67(C), pages 1-18.
    4. Paterakis, Nikolaos G. & Erdinç, Ozan & Catalão, João P.S., 2017. "An overview of Demand Response: Key-elements and international experience," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 871-891.
    5. 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.
    6. Nezamoddini, Nasim & Wang, Yong, 2017. "Real-time electricity pricing for industrial customers: Survey and case studies in the United States," Applied Energy, Elsevier, vol. 195(C), pages 1023-1037.
    7. Pechmann, Agnes & Shrouf, Fadi & Chonin, Max & Steenhusen, Nanke, 2017. "Load-shifting potential at SMEs manufacturing sites: A methodology and case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 431-438.
    8. Allcott, Hunt, 2011. "Rethinking real-time electricity pricing," Resource and Energy Economics, Elsevier, vol. 33(4), pages 820-842.
    9. 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.
    10. Kühnlenz, Florian & Nardelli, Pedro H.J. & Karhinen, Santtu & Svento, Rauli, 2018. "Implementing flexible demand: Real-time price vs. market integration," Energy, Elsevier, vol. 149(C), pages 550-565.
    11. Xu, Bing & Nayak, Amar & Gray, David & Ouenniche, Jamal, 2016. "Assessing energy business cases implemented in the North Sea Region and strategy recommendations," Applied Energy, Elsevier, vol. 172(C), pages 360-371.
    12. Klaucke, Franziska & Hoffmann, Christian & Hofmann, Mathias & Tsatsaronis, George, 2020. "Impact of the chlorine value chain on the demand response potential of the chloralkali process," Applied Energy, Elsevier, vol. 276(C).
    13. Vesterberg, Mattias, 2016. "The hourly income elasticity of electricity," Energy Economics, Elsevier, vol. 59(C), pages 188-197.
    14. Tahir, Muhammad Faizan & Chen, Haoyong & Khan, Asad & Javed, Muhammad Sufyan & Cheema, Khalid Mehmood & Laraik, Noman Ali, 2020. "Significance of demand response in light of current pilot projects in China and devising a problem solution for future advancements," Technology in Society, Elsevier, vol. 63(C).
    15. Paul L. Joskow, 2012. "Creating a Smarter U.S. Electricity Grid," Journal of Economic Perspectives, American Economic Association, vol. 26(1), pages 29-48, Winter.
    16. Anees, Amir & Dillon, Tharam & Chen, Yi-Ping Phoebe, 2019. "A novel decision strategy for a bilateral energy contract," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    17. Dranka, Géremi Gilson & Ferreira, Paula, 2019. "Review and assessment of the different categories of demand response potentials," Energy, Elsevier, vol. 179(C), pages 280-294.
    18. Olsthoorn, Mark & Schleich, Joachim & Klobasa, Marian, 2015. "Barriers to electricity load shift in companies: A survey-based exploration of the end-user perspective," Energy Policy, Elsevier, vol. 76(C), pages 32-42.
    19. Enrich, Jacint & Li, Ruoyi & Mizrahi, Alejandro & Reguant, Mar, 2024. "Measuring the impact of time-of-use pricing on electricity consumption: Evidence from Spain," Journal of Environmental Economics and Management, Elsevier, vol. 123(C).
    20. Viana, Matheus Sabino & Manassero, Giovanni & Udaeta, Miguel E.M., 2018. "Analysis of demand response and photovoltaic distributed generation as resources for power utility planning," Applied Energy, Elsevier, vol. 217(C), pages 456-466.

    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:appene:v:165:y:2016:i:c:p:592-600. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.