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Demand Response Programs in Multi-Energy Systems: A Review

Author

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  • Morteza Vahid-Ghavidel

    (Faculty of Engineering, University of Porto (FEUP), 4200-465 Porto, Portugal
    Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), 4200-465 Porto, Portugal)

  • Mohammad Sadegh Javadi

    (Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), 4200-465 Porto, Portugal)

  • Matthew Gough

    (Faculty of Engineering, University of Porto (FEUP), 4200-465 Porto, Portugal
    Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), 4200-465 Porto, Portugal)

  • Sérgio F. Santos

    (Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), 4200-465 Porto, Portugal)

  • Miadreza Shafie-khah

    (School of Technology and Innovations, University of Vaasa, 65200 Vaasa, Finland)

  • João P.S. Catalão

    (Faculty of Engineering, University of Porto (FEUP), 4200-465 Porto, Portugal
    Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), 4200-465 Porto, Portugal)

Abstract

A key challenge for future energy systems is how to minimize the effects of employing demand response (DR) programs on the consumer. There exists a diverse range of consumers with a variety of types of loads, such as must-run loads, and this can reduce the impact of consumer participation in DR programs. Multi-energy systems (MES) can solve this issue and have the capability to reduce any discomfort faced by all types of consumers who are willing to participate in the DRPs. In this paper, the most recent implementations of DR frameworks in the MESs are comprehensively reviewed. The DR modelling approach in such energy systems is investigated and the main contributions of each of these works are included. Notably, the amount of research in MES has rapidly increased in recent years. The majority of the reviewed works consider power, heat and gas systems within the MES. Over three-quarters of the papers investigated consider some form of energy storage system, which shows how important having efficient, cost-effective and reliable energy storage systems will be in the future. In addition, a vast majority of the works also considered some form of demand response programs in their model. This points to the need to make participating in the energy market easier for consumers, as well as the importance of good communication between generators, system operators, and consumers. Moreover, the emerging topics within the area of MES are investigated using a bibliometric analysis to provide insight to other researchers in this area.

Suggested Citation

  • Morteza Vahid-Ghavidel & Mohammad Sadegh Javadi & Matthew Gough & Sérgio F. Santos & Miadreza Shafie-khah & João P.S. Catalão, 2020. "Demand Response Programs in Multi-Energy Systems: A Review," Energies, MDPI, vol. 13(17), pages 1-17, August.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:17:p:4332-:d:402070
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    References listed on IDEAS

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    Cited by:

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    2. Alabi, Tobi Michael & Aghimien, Emmanuel I. & Agbajor, Favour D. & Yang, Zaiyue & Lu, Lin & Adeoye, Adebusola R. & Gopaluni, Bhushan, 2022. "A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems," Renewable Energy, Elsevier, vol. 194(C), pages 822-849.
    3. Yong, Jin Yi & Tan, Wen Shan & Khorasany, Mohsen & Razzaghi, Reza, 2023. "Electric vehicles destination charging: An overview of charging tariffs, business models and coordination strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    4. Yuriy Leonidovich Zhukovskiy & Margarita Sergeevna Kovalchuk & Daria Evgenievna Batueva & Nikita Dmitrievich Senchilo, 2021. "Development of an Algorithm for Regulating the Load Schedule of Educational Institutions Based on the Forecast of Electric Consumption within the Framework of Application of the Demand Response," Sustainability, MDPI, vol. 13(24), pages 1-26, December.
    5. Tsoumalis, Georgios I. & Bampos, Zafeirios N. & Biskas, Pandelis N. & Keranidis, Stratos D. & Symeonidis, Polychronis A. & Voulgarakis, Dimitrios K., 2022. "A novel system for providing explicit demand response from domestic natural gas boilers," Applied Energy, Elsevier, vol. 317(C).
    6. Khorramfar, Rahman & Mallapragada, Dharik & Amin, Saurabh, 2024. "Electric-gas infrastructure planning for deep decarbonization of energy systems," Applied Energy, Elsevier, vol. 354(PA).
    7. Mishra, Kakuli & Basu, Srinka & Maulik, Ujjwal, 2022. "Load profile mining using directed weighted graphs with application towards demand response management," Applied Energy, Elsevier, vol. 311(C).
    8. Bodong, Song & Wiseong, Jin & Chengmeng, Li & Khakichi, Aroos, 2023. "Economic management and planning based on a probabilistic model in a multi-energy market in the presence of renewable energy sources with a demand-side management program," Energy, Elsevier, vol. 269(C).
    9. Kiani-Moghaddam, Mohammad & Soltani, Mohsen N. & Kalogirou, Soteris A. & Mahian, Omid & Arabkoohsar, Ahmad, 2023. "A review of neighborhood level multi-carrier energy hubs—uncertainty and problem-solving process," Energy, Elsevier, vol. 281(C).

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