Electrical Grid Intact Improved Maximum Demand Control Using IoT

Abstract

This research investigates the application of Internet of Things (IoT) technology to improve maximum demand control (MDC) within electrical grids, aiming to enhance grid stability and efficiency while maintaining grid integrity. The project develops a novel MDC system using real-time data acquisition and analysis from IoT-enabled smart meters and grid sensors. The system employs predictive algorithms to optimize energy consumption, minimizing peak demand and preventing potential outages. Results demonstrate a significant reduction in peak demand, improved grid stability, and reduced operational costs compared to traditional MDC methods. This approach offers a scalable and adaptable solution for modernizing existing power grids.

Introduction

The increasing integration of renewable energy sources and the growing demand for electricity pose significant challenges to electrical grid stability. Maximum demand control (MDC) plays a crucial role in managing peak loads and preventing grid overloads, but traditional methods often lack real-time responsiveness and granular control. The Internet of Things (IoT) offers a transformative opportunity to overcome these limitations by enabling widespread deployment of smart sensors and meters, providing real-time data for precise and adaptive load management. This research addresses the need for improved MDC strategies by leveraging the capabilities of IoT to enhance grid stability, reduce peak demand, and improve overall efficiency.

Objectives

• Develop an IoT-enabled MDC system using real-time data acquisition.
• Utilize predictive algorithms for load optimization and energy efficiency.
• Minimize peak demand to prevent outages and ensure grid integrity.
• Demonstrate scalability for large-scale grid applications.

Technical Details

• Microcontroller: ESP32 or NodeMCU for real-time data collection.
• Smart energy meters and load sensors connected via IoT.
• Cloud-based data analytics and storage.
• Dashboard for monitoring and prediction of demand.
• Automated load shedding and alert mechanisms.