IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i4p935-d137733.html

A DSM Test Case Applied on an End-to-End System, from Consumer to Energy Provider

Author

Listed:
  • Nikoleta Andreadou

    (Energy Security, Systems and Markets Unit, Directorate of Energy, Transport and Climate, Joint Research Centre, 21027 Ispra, Italy)

  • Yannis Soupionis

    (Technology Innovation in Security Unit, Space, Directorate of Security and Migration, Joint Research Centre, 21027 Ispra, Italy)

  • Fausto Bonavitacola

    (Energy Security, Systems and Markets Unit, Directorate of Energy, Transport and Climate, Joint Research Centre, 21027 Ispra, Italy)

  • Giuseppe Prettico

    (Energy Security, Systems and Markets Unit, Directorate of Energy, Transport and Climate, Joint Research Centre, 21027 Ispra, Italy)

Abstract

Current decarbonisation goals have, in recent years, led to a tremendous increase in electricity production generated from intermittent Renewable Energy Sources. Despite their contribution to reducing society’s carbon dioxide (CO 2 ) emissions they have been responsible for numerous challenges that the current electricity grid has to cope with. Flexibility has become a key mechanism to help in mitigating them. Real-time informed consumers can offer the needed flexibility through modifying their behaviour or by engaging with Demand Side Management (DSM) programs. The latter requires the intervention of several actors and levels of communication management which makes this task difficult from an implementation perspective. With this aim we built and tested a small scale system in our lab which represents a real end-to-end system from the consumer to the energy provider. We programmed the system according to the Object Identification System (OBIS) specification to obtain consumers’ consumption through smart meters with high frequency (one minute). This allows remote control of their appliances in order to reduce the total neighbourhood consumption during critical time periods of the day (peak time). These results and the realisation of a realistic end-to-end system open the way to more complex tests and particularly to the possibility of benchmarking them with other lab tests.

Suggested Citation

  • Nikoleta Andreadou & Yannis Soupionis & Fausto Bonavitacola & Giuseppe Prettico, 2018. "A DSM Test Case Applied on an End-to-End System, from Consumer to Energy Provider," Sustainability, MDPI, vol. 10(4), pages 1-23, March.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:4:p:935-:d:137733
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/4/935/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/10/4/935/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. McKenna, Eoghan & Thomson, Murray, 2016. "High-resolution stochastic integrated thermal–electrical domestic demand model," Applied Energy, Elsevier, vol. 165(C), pages 445-461.
    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. Siiri Söyrinki & Eva Heiskanen & Kaisa Matschoss, 2018. "Piloting Demand Response in Retailing: Lessons Learned in Real-Life Context," Sustainability, MDPI, vol. 10(10), pages 1-17, October.

    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. Yang, Ting & Zhao, Liyuan & Li, Wei & Zomaya, Albert Y., 2021. "Dynamic energy dispatch strategy for integrated energy system based on improved deep reinforcement learning," Energy, Elsevier, vol. 235(C).
    2. 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).
    3. Lombardi, Francesco & Balderrama, Sergio & Quoilin, Sylvain & Colombo, Emanuela, 2019. "Generating high-resolution multi-energy load profiles for remote areas with an open-source stochastic model," Energy, Elsevier, vol. 177(C), pages 433-444.
    4. Fuentes, E. & Arce, L. & Salom, J., 2018. "A review of domestic hot water consumption profiles for application in systems and buildings energy performance analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1530-1547.
    5. Abdullah M. Alabdullatif & Enrico H. Gerding & Alvaro Perez-Diaz, 2020. "Market Design and Trading Strategies for Community Energy Markets with Storage and Renewable Supply," Energies, MDPI, vol. 13(4), pages 1-31, February.
    6. Zhao, Liyuan & Yang, Ting & Li, Wei & Zomaya, Albert Y., 2022. "Deep reinforcement learning-based joint load scheduling for household multi-energy system," Applied Energy, Elsevier, vol. 324(C).
    7. Hamish Beath & Shivika Mittal & Sheridan Few & Benedict Winchester & Philip Sandwell & Christos N. Markides & Jenny Nelson & Ajay Gambhir, 2024. "Carbon pricing and system reliability impacts on pathways to universal electricity access in Africa," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    8. Angreine Kewo & Pinrolinvic D. K. Manembu & Per Sieverts Nielsen, 2020. "Synthesising Residential Electricity Load Profiles at the City Level Using a Weighted Proportion (Wepro) Model," Energies, MDPI, vol. 13(14), pages 1-28, July.
    9. Mohsen Sharifi & Amin Kouti & Evi Lambie & Yixiao Ma & Maria Fernandez Boneta & Mohammad Haris Shamsi, 2023. "A Comprehensive Framework for Data-Driven Building End-Use Assessment Utilizing Monitored Operational Parameters," Energies, MDPI, vol. 16(20), pages 1-23, October.
    10. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "A review of uncertainty characterisation approaches for the optimal design of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 258-277.
    11. Anderson, Ben & Torriti, Jacopo, 2018. "Explaining shifts in UK electricity demand using time use data from 1974 to 2014," Energy Policy, Elsevier, vol. 123(C), pages 544-557.
    12. Mustafa E. Amiryar & Keith R. Pullen, 2019. "Assessment of the Carbon and Cost Savings of a Combined Diesel Generator, Solar Photovoltaic, and Flywheel Energy Storage Islanded Grid System," Energies, MDPI, vol. 12(17), pages 1-25, August.
    13. Durillon, Benoit & Salomez, Florentin & Davigny, Arnaud & Kazmierczak, Sabine & Barry, Hervé & Saudemont, Christophe & Robyns, Benoît, 2021. "Consumers’ sensitivities and preferences modelling and integration in a decentralised two levels energy supervisor," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 183(C), pages 142-157.
    14. Andrea Gabaldón Moreno & Fredy Vélez & Beril Alpagut & Patxi Hernández & Cecilia Sanz Montalvillo, 2021. "How to Achieve Positive Energy Districts for Sustainable Cities: A Proposed Calculation Methodology," Sustainability, MDPI, vol. 13(2), pages 1-19, January.
    15. Lizana, Jesus & Halloran, Claire E. & Wheeler, Scot & Amghar, Nabil & Renaldi, Renaldi & Killendahl, Markus & Perez-Maqueda, Luis A. & McCulloch, Malcolm & Chacartegui, Ricardo, 2023. "A national data-based energy modelling to identify optimal heat storage capacity to support heating electrification," Energy, Elsevier, vol. 262(PA).
    16. Zhou, Yue & Wu, Jianzhong & Long, Chao, 2018. "Evaluation of peer-to-peer energy sharing mechanisms based on a multiagent simulation framework," Applied Energy, Elsevier, vol. 222(C), pages 993-1022.
    17. Ibrahim Ali Kachalla & Christian Ghiaus, 2024. "Electric Water Boiler Energy Prediction: State-of-the-Art Review of Influencing Factors, Techniques, and Future Directions," Energies, MDPI, vol. 17(2), pages 1-32, January.
    18. Johnson, R.C. & Mayfield, M., 2020. "The economic and environmental implications of post feed-in tariff PV on constrained low voltage networks," Applied Energy, Elsevier, vol. 279(C).
    19. Gallo Cassarino, Tiziano & Sharp, Ed & Barrett, Mark, 2018. "The impact of social and weather drivers on the historical electricity demand in Europe," Applied Energy, Elsevier, vol. 229(C), pages 176-185.
    20. Blonsky, Michael & Maguire, Jeff & McKenna, Killian & Cutler, Dylan & Balamurugan, Sivasathya Pradha & Jin, Xin, 2021. "OCHRE: The Object-oriented, Controllable, High-resolution Residential Energy Model for Dynamic Integration Studies," Applied Energy, Elsevier, vol. 290(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:gam:jsusta:v:10:y:2018:i:4:p:935-:d:137733. 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: MDPI Indexing Manager The email address of this maintainer does not seem to be valid anymore. Please ask MDPI Indexing Manager to update the entry or send us the correct address (email available below). General contact details of provider: https://www.mdpi.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.