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A review on the deployment of demand response programs with multiple aspects coexistence over smart grid platform

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  • Ibrahim, Charles
  • Mougharbel, Imad
  • Kanaan, Hadi Y.
  • Daher, Nivine Abou
  • Georges, Semaan
  • Saad, Maarouf

Abstract

A modern strategy for improving the control of generation, distribution and consumption of electrical energy consists of launching programs affecting the demand on the consumer side. Objectives related to the so-called Demand Response Programs (DRP) are of technical, economic and marketing order. Multiple DRPs are actually proposed and researchers keep on suggesting others. Although DRPs suggestions are still under tests for validation and implementation, an extensive literature exists in this domain. Multiple interdependent objectives are targeted and individual treatment might lead to undesired outcome. It is paramount to perform a holistic review showing the implications of the objectives on each other. The aim is to include them in a consolidated model with assigned weights where intensive what if scenarios will be applied to reach the optimal model settings. Existing reviews and literature in this domain focused on individual or partially correlated aspects. This paper presents a review providing a wide coverage on existing approaches and objectives for implementing DRPs and their relation. Therefore, authors decided to focus on the technical, economic and marketing aspects with the consideration of architectures and business intelligence. Model structures with their variability and dynamicity enable a financially viable model with various market options correlated with the economic and technical aspects. Hence, this mechanism assists in planning adequately and supporting the decision-making process.

Suggested Citation

  • Ibrahim, Charles & Mougharbel, Imad & Kanaan, Hadi Y. & Daher, Nivine Abou & Georges, Semaan & Saad, Maarouf, 2022. "A review on the deployment of demand response programs with multiple aspects coexistence over smart grid platform," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    • Handle: RePEc:eee:rensus:v:162:y:2022:i:c:s1364032122003525
    DOI: 10.1016/j.rser.2022.112446
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    References listed on IDEAS

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    1. Antonopoulos, Ioannis & Robu, Valentin & Couraud, Benoit & Kirli, Desen & Norbu, Sonam & Kiprakis, Aristides & Flynn, David & Elizondo-Gonzalez, Sergio & Wattam, Steve, 2020. "Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
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    Cited by:

    1. Kong, Xiangyu & Wang, Zhengtao & Liu, Chao & Zhang, Delong & Gao, Hongchao, 2023. "Refined peak shaving potential assessment and differentiated decision-making method for user load in virtual power plants," Applied Energy, Elsevier, vol. 334(C).

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