Demand Response for Renewable Generation in an IoT based Intelligent Smart Energy Management System

Rao, Challa Krishna; Sahoo, Sarat Kumar; Yanine, Franco Fernando

doi:10.1109/i-pact52855.2021.9696781

Cited by 11 publications

(2 citation statements)

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“…The primary focus of the proposed technique in this study is the design and production of real-time hardware prototypes. Unlike existing strategies that prioritize appliance functionality, this work introduces a customizable smart energy management system [27].…”

Section: Introductionmentioning

confidence: 99%

“…In traditional systems, power outages occur when generated energy falls short of customer demand [8]. Load shedding is strategically implemented by utilities to balance the system and minimize outages, shifting energy use to off-peak times [42]. With the increasing need for energy management systems, the focus is on optimizing power usage at consumer sites and enhancing resource management [9].…”

Section: Introductionmentioning

confidence: 99%

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Design and deployment of a novel Decisive Algorithm to enable real-time optimal load scheduling within an Intelligent Smart Energy Management System based on IoT

Rao,

Sahoo,

Yanine

2024

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Consumers routinely use electrical devices, leading to a disparity between consumer demand and the supply side a significant concern for the energy sector. Implementing demand-side energy management can enhance energy efficiency and mitigate substantial supply-side shortages. Current energy management practices focus on reducing power consumption during peak hours, enabling a decrease in overall electricity costs without sacrificing usage. To tackle the mentioned challenges and maintain system equilibrium, it is essential to develop a flexible and portable system. Introducing an intelligent energy management system could pre-empt power outages by implementing controlled partial load shedding based on consumer preferences. During a demand response event, the system adapts by imposing a maximum demand limit, considering various scenarios and adjusting appliance priorities. Experimental work, incorporating user comfort levels, sensor data, and usage times, is conducted using Smart Energy Management Systems (SEMS) integrated with cost-optimization algorithms.

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Section: Introductionmentioning

confidence: 99%