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Demand Response Implementation: Overview of Europe and United States Status

Author

Listed:
  • Cátia Silva

    (Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD), Intelligent Systems Associated Laboratory (LASI), Polytechnic of Porto, 4249-015 Porto, Portugal)

  • Pedro Faria

    (Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD), Intelligent Systems Associated Laboratory (LASI), Polytechnic of Porto, 4249-015 Porto, Portugal)

  • Zita Vale

    (Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD), Intelligent Systems Associated Laboratory (LASI), Polytechnic of Porto, 4249-015 Porto, Portugal)

Abstract

The authors review the efforts made in the last five years to implement Demand Response (DR) programs, considering and studying several models and countries. As motivation, climate change has been a topic widely discussed in the last decades, namely in the power and energy sectors. Therefore, it is crucial to substitute non-renewable fuels with more environment-friendly solutions. Enabling Distributed Generation (DG), namely using renewable resources such as wind and solar, can be part of the solution to reduce the greenhouse effects. However, their unpredictable behavior might result in several problems for network management. Therefore, the consumer should become more flexible towards this new paradigm where the generation no longer follows the demand requests. With this, Demand Response (DR) concept is created as part of this solution. This paper studies the European Union and United States’ current status, with over 50 references.

Suggested Citation

  • Cátia Silva & Pedro Faria & Zita Vale, 2023. "Demand Response Implementation: Overview of Europe and United States Status," Energies, MDPI, vol. 16(10), pages 1-20, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:10:p:4043-:d:1145288
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    References listed on IDEAS

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    1. Brown, Marilyn A. & Chapman, Oliver, 2021. "The size, causes, and equity implications of the demand-response gap," Energy Policy, Elsevier, vol. 158(C).
    2. Hainsch, Karlo & Löffler, Konstantin & Burandt, Thorsten & Auer, Hans & Crespo del Granado, Pedro & Pisciella, Paolo & Zwickl-Bernhard, Sebastian, 2022. "Energy transition scenarios: What policies, societal attitudes, and technology developments will realize the EU Green Deal?," Energy, Elsevier, vol. 239(PC).
    3. Chen, Yongbao & Chen, Zhe & Xu, Peng & Li, Weilin & Sha, Huajing & Yang, Zhiwei & Li, Guowen & Hu, Chonghe, 2019. "Quantification of electricity flexibility in demand response: Office building case study," Energy, Elsevier, vol. 188(C).
    4. Alireza Nouri & Shafi Khadem & Anna Mutule & Christina Papadimitriou & Rad Stanev & Mattia Cabiati & Andrew Keane & Paula Carroll, 2022. "Identification of Gaps and Barriers in Regulations, Standards, and Network Codes to Energy Citizen Participation in the Energy Transition," Energies, MDPI, vol. 15(3), pages 1-29, January.
    5. Omid Abrishambaf & Pedro Faria & Zita Vale, 2020. "Ramping of Demand Response Event with Deploying Distinct Programs by an Aggregator," Energies, MDPI, vol. 13(6), pages 1-18, March.
    6. Mohammad Shakeri & Jagadeesh Pasupuleti & Nowshad Amin & Md. Rokonuzzaman & Foo Wah Low & Chong Tak Yaw & Nilofar Asim & Nurul Asma Samsudin & Sieh Kiong Tiong & Chong Kok Hen & Chin Wei Lai, 2020. "An Overview of the Building Energy Management System Considering the Demand Response Programs, Smart Strategies and Smart Grid," Energies, MDPI, vol. 13(13), pages 1-15, June.
    7. Daiva Stanelyte & Neringa Radziukyniene & Virginijus Radziukynas, 2022. "Overview of Demand-Response Services: A Review," Energies, MDPI, vol. 15(5), pages 1-31, February.
    8. Khojasteh, Meysam & Faria, Pedro & Vale, Zita, 2022. "A robust model for aggregated bidding of energy storages and wind resources in the joint energy and reserve markets," Energy, Elsevier, vol. 238(PB).
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    Cited by:

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    2. Jaka Rober & Leon Maruša & Miloš Beković, 2023. "A Machine Learning Application for the Energy Flexibility Assessment of a Distribution Network for Consumers," Energies, MDPI, vol. 16(17), pages 1-20, August.
    3. Olga Bogdanova & Karīna Viskuba & Laila Zemīte, 2023. "A Review of Barriers and Enables in Demand Response Performance Chain," Energies, MDPI, vol. 16(18), pages 1-33, September.
    4. Zhiqiang Dai & Xun Pei & Yunuo Xu & Tianyi Zhang & Lanchun Lv, 2024. "Assessing Carbon Emission Reduction Potential: A Case Study of Low Carbon Demand Response Technology in Fangshan District, Beijing," Sustainability, MDPI, vol. 16(4), pages 1-16, February.

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