Automated demand response for residential consumers

Bhosale, Vaibhav; Hadawale, Prasad; Borole, Akash; Kinhekar, Nandkishor

doi:10.1109/npsc.2016.7858960

Cited by 4 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Cuckoo search and symbiotic organisms search algorithms are used to efficiently schedule timeshiftable loads in a task scheduling-based DR technique, and the results obtained are compared. The basic hardware model for the DR implementation for consumer loads has been developed by the authors in [25], but the results are obtained without optimization. PLC-based advanced metering infrastructure and the OpenADR 2.0 protocol are used for developing the automated demand response system for the control of residential loads.…”

Section: Literature Review Of Related Workmentioning

confidence: 99%

Optimal scheduling and demand response implementation for home energy management

Priolkar,

Sreeraj

2024

IJECE

View full text Add to dashboard Cite

The optimal scheduling of the loads based on dynamic tariffs and implementation of a direct load control (DLC) based demand response program for the domestic consumer is proposed in this work. The load scheduling is carried out using binary particle swarm optimization and a newly prefaced nature-inspired discrete elephant herd optimization technique, and their effectiveness in minimization of cost and the peak-to-average ratio is analyzed. The discrete elephant herd optimization algorithm has acceptable characteristics compared to the conventional algorithms and has determined better exploring properties for multi-objective problems. A prototype hardware model for a home energy management system is developed to demonstrate and analyze the optimal load scheduling and DLC-based demand response program. The controller effectively schedules and implements DLC on consumer devices. The load scheduling optimization helps to improve PAR by a value of 2.504 and results in energy cost savings of ₹ 12.05 on the scheduled day. Implementation of DLC by 15% results in monthly savings of ₹ 204.18. The novelty of the work is the implementation of discrete elephant herd optimization for load scheduling and the development of the prototype hardware model to show effects of both optimal load scheduling and the DLC-based demand response program implementation.

show abstract

Section: Literature Review Of Related Workmentioning

confidence: 99%