<b>Cloud Computing Based Demand Response Management Using Deep Reinforcement Learning; Review </b>

This paper concentrates on using electric water heaters with the aim of demanding response. This is so as this method is considered most efficient when it comes to ensuring the safety and stabilization of power grids through the maintenance of balance between the demand and supply of the power grid. For this reason, the article investigates the overshoot temperature and the impact it has on demand response as well as to ensure the comfort and pricing variables as it happened in previous publications. The demand response process utilizing electric water heaters is explored as well as the impact of the physical parameters and the settings of the water heaters. In addition, a model is developed which puts into consideration the demand response requirements, the comfort of these water heater equipment, the power supply price in a simultaneous manner. Also, the effect of these factors on the end results of demand response is addressed. Experimental data is further used to demonstrate the efficiency of the suggested strategy.

Content

Author and article information

Journal

Title: ScienceOpen Preprints

Publisher: ScienceOpen

Publication date (Electronic preprint): 22 February 2023

Affiliations

[1 ] The University of Newcastle;

Author notes

[* ]Email: paperswritingpro@ 123456gmail.com .

Author information

Gabriel Katiwa https://orcid.org/0000-0003-3531-2402

Article

DOI: 10.14293/S2199-1006.1.SOR-.PPFI6OV.v1

SO-VID: a9dc719d-7b07-4a1b-a851-53cafacc9cd4

License:

This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

History

Date received : 22 February 2023

References

Gali Manvitha, Mahamkali Aditya. A Distributed Deep Meta Learning based Task Offloading Framework for Smart City Internet of Things with Edge-Cloud Computing. Journal of Internet Services and Information Security. Vol. 12(4):224–237. 2022. SASA Publications. [Cross Ref]
Ghanam Yaser, Ferreira Jennifer, Maurer Frank. Emerging Issues &amp; Challenges in Cloud Computing—A Hybrid Approach. Journal of Software Engineering and Applications. Vol. 05(11):923–937. 2012. Scientific Research Publishing, Inc. [Cross Ref]
Kadhim Qusay Kanaan, Yusof Robiah, Mahdi Hamid Sadeq, Ali Al-shami Sayed Samer, Selamat Siti Rahayu. A Review Study on Cloud Computing Issues. Journal of Physics: Conference Series. Vol. 1018:2018. IOP Publishing. [Cross Ref]
Cloud Computing. 2017. CRC Press. [Cross Ref]

Comments

Comment on this article

[1] Gali Manvitha, Mahamkali Aditya. A Distributed Deep Meta Learning based Task Offloading Framework for Smart City Internet of Things with Edge-Cloud Computing. Journal of Internet Services and Information Security. Vol. 12(4):224–237. 2022. SASA Publications. [Cross Ref]

[2] Ghanam Yaser, Ferreira Jennifer, Maurer Frank. Emerging Issues &amp; Challenges in Cloud Computing—A Hybrid Approach. Journal of Software Engineering and Applications. Vol. 05(11):923–937. 2012. Scientific Research Publishing, Inc. [Cross Ref]

[3] Kadhim Qusay Kanaan, Yusof Robiah, Mahdi Hamid Sadeq, Ali Al-shami Sayed Samer, Selamat Siti Rahayu. A Review Study on Cloud Computing Issues. Journal of Physics: Conference Series. Vol. 1018:2018. IOP Publishing. [Cross Ref]

[4] Cloud Computing. 2017. CRC Press. [Cross Ref]

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Cloud Computing Based Demand Response Management Using Deep Reinforcement Learning; Review