GridDR: Enhancing Grid Reliability using Demand Response Program

Authors

  • Archana Y. Chaudhari, Prasad B. Dhore, Alok R. Kadu, Adarsh K. Satpute, Shrey Y. Wani, Bhushan G. Phule

Keywords:

Demand Response, Grid Reliability, Smart Meter Data, K-means, Hierarchical Clustering

Abstract

In developing nations, power disruptions are a major worry, and grid stability is essential. Utilities must encourage energy consumption reductions by consumers during prime hours in to achieve and maintain grid stability and avoid brownouts or blackouts. Finding suitable candidates for Demand Response (DR) events is essential. . In order to strategically select candidates for DR events based on the utility's goals, this work suggested "GridDR," which gives users the ability to monitor their energy usage trends, customize their choices for participation, and receive tailored advice or incentives for taking part in demand response. In addition, the platform offers distributors thorough visualizations of customer energy usage data, allowing for the early identification of high-usage customers for demand response involvement. The study makes use of a dataset that includes hourly energy usage data gathered over a one-year period from 39 apartments to assess consumption trends and find possible participants in demand response The study starts with a thorough project overview, emphasizing the importance of demand response programs in resolving grid stability and reliability issues. With the intention of offering insights into temporal fluctuations and consumption trends, graphical analytic techniques are used to show daily, weekly, and monthly energy use patterns based on the dataset. Subsequently, two clustering algorithms, namely K-means and hierarchical clustering are used in this research work. GridDR has the potential to completely change how distributors and customers communicate and work together to optimize energy use and improve grid reliability by bridging the gap between data analytics, user interface design, and demand response program execution. In the end, the study emphasizes how critical, is to employ innovative approaches to leverage data-driven insights for the purpose of managing the changing issues of grid reliability and energy management in the residential sector.

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Published

20.06.2024

How to Cite

Archana Y. Chaudhari. (2024). GridDR: Enhancing Grid Reliability using Demand Response Program. International Journal of Intelligent Systems and Applications in Engineering, 12(4), 532–540. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6255

Issue

Vol. 12 No. 4 (2024)

Section

Research Article

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