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Residential demand response: Experimental evaluation and comparison of self-organizing techniques

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  • Dusparic, Ivana
  • Taylor, Adam
  • Marinescu, Andrei
  • Golpayegani, Fatemeh
  • Clarke, Siobhan

Abstract

Residential demand response (DR) has gained a significant increase in interest from industrial and academic communities as a means to contribute to more efficient operation of smart grids, with numerous techniques proposed to implement residential DR programmes. However, the proposed techniques have been evaluated in scenarios addressing different types of electrical devices with different energy requirements, on different scales, and have compared technique performance to different baselines. Furthermore, numerous review papers have been published comparing various characteristics of DR systems, but without comparing their performance. No existing work provides an experimental evaluation of residential DR techniques in a common scenario, side-by-side comparison of their properties and requirements derived from their behaviour in such a scenario and analysis of their suitability to various domain requirements. To address this gap, in this paper we present four self-organizing intelligent algorithms for residential DR, which we evaluate both quantitatively and qualitatively in a number of common residential DR scenarios, providing a performance comparison as well as a benchmark for further investigations of DR algorithms. The approaches implemented are: set-point, reinforcement learning, evolutionary computation, and Monte Carlo tree search. We compare the performance of approaches with regards to energy-use patterns (such as reduction in peak-time energy use), adaptivity to changes in the environment and device behaviour, communication requirements, computational complexity, scalability, and flexibility with respect to type of electric load to which it can be applied, and provide guidelines on their suitability based on specific DR requirements.

Suggested Citation

  • Dusparic, Ivana & Taylor, Adam & Marinescu, Andrei & Golpayegani, Fatemeh & Clarke, Siobhan, 2017. "Residential demand response: Experimental evaluation and comparison of self-organizing techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1528-1536.
  • Handle: RePEc:eee:rensus:v:80:y:2017:i:c:p:1528-1536
    DOI: 10.1016/j.rser.2017.07.033
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    References listed on IDEAS

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    1. Siano, Pierluigi, 2014. "Demand response and smart grids—A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 461-478.
    2. Haider, Haider Tarish & See, Ong Hang & Elmenreich, Wilfried, 2016. "A review of residential demand response of smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 166-178.
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    Cited by:

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    4. Lu, Renzhi & Hong, Seung Ho, 2019. "Incentive-based demand response for smart grid with reinforcement learning and deep neural network," Applied Energy, Elsevier, vol. 236(C), pages 937-949.
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    6. Wang, Chong & Ju, Ping & Wu, Feng & Pan, Xueping & Wang, Zhaoyu, 2022. "A systematic review on power system resilience from the perspective of generation, network, and load," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    7. Chen, Xiao & Zanocco, Chad & Flora, June & Rajagopal, Ram, 2022. "Constructing dynamic residential energy lifestyles using Latent Dirichlet Allocation," Applied Energy, Elsevier, vol. 318(C).
    8. Ribó-Pérez, D. & Carrión, A. & Rodríguez García, J. & Álvarez Bel, C., 2021. "Ex-post evaluation of Interruptible Load programs with a system optimisation perspective," Applied Energy, Elsevier, vol. 303(C).
    9. Golmohamadi, Hessam, 2022. "Demand-side management in industrial sector: A review of heavy industries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).

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