Evolutionary Optimization in Smart Grids - Demand Response
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Keywords

Evolutionary Optimization
Smart Grids
Demand Response
Energy Consumption

How to Cite

[1]
Ji-won Park, “Evolutionary Optimization in Smart Grids - Demand Response”, Journal of AI in Healthcare and Medicine, vol. 3, no. 1, pp. 27–36, Apr. 2023, Accessed: Nov. 23, 2024. [Online]. Available: https://healthsciencepub.com/index.php/jaihm/article/view/10

Abstract

The integration of renewable energy sources and the increasing electrification of various sectors have led to significant challenges in the management of modern power grids. Smart grids, equipped with advanced technologies and communication systems, offer promising solutions to enhance grid efficiency and reliability. Among the key aspects of smart grid management, demand response plays a crucial role in balancing supply and demand by incentivizing consumers to adjust their electricity usage patterns.

This research paper investigates the application of evolutionary optimization techniques for demand response management in smart grids. The primary objective is to optimize energy consumption and reduce peak load demand, thereby enhancing grid stability and efficiency. The paper provides a comprehensive review of the existing literature on evolutionary optimization and its application in smart grids, focusing on demand response strategies.

References

Veronin, Michael A., et al. "Opioids and frequency counts in the US Food and Drug Administration Adverse Event Reporting System (FAERS) database: A quantitative view of the epidemic." Drug, Healthcare and Patient Safety (2019): 65-70.

Reddy, Byrapu, and Surendranadha Reddy. "Evaluating The Data Analytics For Finance And Insurance Sectors For Industry 4.0." Tuijin Jishu/Journal of Propulsion Technology 44.4 (2023): 3871-3877.

Dixit, Rohit R. "Investigating Healthcare Centers' Willingness to Adopt Electronic Health Records: A Machine Learning Perspective." Eigenpub Review of Science and Technology 1.1 (2017): 1-15.

Pillai, Aravind Sasidharan. "Multi-label chest X-ray classification via deep learning." arXiv preprint arXiv:2211.14929 (2022).

Venigandla, Kamala. "Integrating RPA with AI and ML for Enhanced Diagnostic Accuracy in Healthcare." Power System Technology 46.4 (2022).

Khan, Mohammad Shahbaz, et al. "Improving Multi-Organ Cancer Diagnosis through a Machine Learning Ensemble Approach." 2023 7th International Conference on Electronics, Communication and Aerospace Technology (ICECA). IEEE, 2023.

Kumar, Bonda Kiran, et al. "Predictive Classification of Covid-19: Assessing the Impact of Digital Technologies." 2023 7th International Conference on Electronics, Communication and Aerospace Technology (ICECA). IEEE, 2023.

Vemuri, Navya, and Kamala Venigandla. "Autonomous DevOps: Integrating RPA, AI, and ML for Self-Optimizing Development Pipelines." Asian Journal of Multidisciplinary Research & Review 3.2 (2022): 214-231.

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