Smart Grid Stability Enhancement using Waterwheel Plant Algorithm for Demand Side Management

Demand-Side Management (DSM) within Internet of Things (IoT)-enabled Smart Grids (SGs) employs advanced communication automation and intelligent control techniques to manage electricity consumption. One of the disadvantages of DSM in IoT-enabled SGs is that demand forecasting is inaccurate due to un...

Full description

Saved in:
Bibliographic Details
Published in:2025 7th International Conference on Inventive Material Science and Applications (ICIMA) pp. 379 - 385
Main Authors: Askar, Sami, Uma, S., Sethi, Gaurav, Ramkumar, M. Siva, Dhivya, S., N, Rajesha
Format: Conference Proceeding
Language:English
Published: IEEE 28.05.2025
Subjects:
Accuracy
Demand side management
Energy efficiency
Internet of Things
Load Balancing
Load management
Mean Squared Percentage Error
Optimization
Power system stability
Renewable energy sources
Smart grids
Stability analysis
Waterwheel Plant Algorithm
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Demand-Side Management (DSM) within Internet of Things (IoT)-enabled Smart Grids (SGs) employs advanced communication automation and intelligent control techniques to manage electricity consumption. One of the disadvantages of DSM in IoT-enabled SGs is that demand forecasting is inaccurate due to uncertainties in consumption patterns of electricity and Renewable Energy (RE) generation, leading to inefficient load scheduling and increasing errors in energy distribution. To address these issues, this paper proposes a Waterwheel Plant Algorithm (WPA) is utilized to optimize DSM in SGs by enhancing energy efficiency, improving load balancing, and reducing forecasting errors for better grid stability. The WPA is used to minimize Mean Squared Percentage Error (MSPE) for securing DSM in SGs. Its adaptive optimization ensures that minimize forecasting errors across operating conditions thereby enhancing overall efficiency of the energy distribution system. By then proposed WPA method is implemented on the MATLAB platform, and its performance is evaluated by comparing it with existing optimization techniques such as Binary Orientation Search Algorithm (BOSA), Ant Colony Optimization (ACO), Binary Particle Swarm Optimization and Gravitational Search Algorithm (BPSOGSA), Modified Elephant Herd optimization algorithm (MEHOA), and Arithmetic Harris Hawks Optimization (AHHO). The results show that the proposed WPA method provides superior performance in minimizing MSPE, achieving the lowest MSPE value of 2.05%, indicating its enhanced accuracy DSM in SGs.
DOI:10.1109/ICIMA64861.2025.11073955