Abstract
With the emergence of the smart grid (SG), real-time interaction is favorable for both residents and power companies in optimal load scheduling to alleviate electricity cost and peaks in demand. In this paper, a modular framework is introduced for efficient load scheduling. The proposed framework is comprised of four modules: power company module, forecaster module, home energy management controller (HEMC) module, and resident module. The forecaster module receives a demand response (DR), information (real-time pricing scheme (RTPS) and critical peak pricing scheme (CPPS)), and load from the power company module to forecast pricing signals and load. The HEMC module is based on our proposed hybrid gray wolf-modified enhanced differential evolutionary (HGWmEDE) algorithm using the output of the forecaster module to schedule the household load. Each appliance of the resident module receives the schedule from the HEMC module. In a smart home, all the appliances operate according to the schedule to reduce electricity cost and peaks in demand with the affordable waiting time. The simulation results validated that the proposed framework handled the uncertainties in load and supply and provided optimal load scheduling, which facilitates both residents and power companies.
Highlights
With the emergence of information and communication technology (ICT), smart grid (SG) can make a robust and reliable system for the energy management of residential homes
The pricing signals used for load scheduling is forecasted using restricted Boltzmann machine (RBM)
The home energy management controller (HEMC) is responsible for scheduling the appliances using the forecasted real-time pricing scheme (RTPS) and critical peak pricing scheme (CPPS) pricing signals
Summary
With the emergence of information and communication technology (ICT), smart grid (SG) can make a robust and reliable system for the energy management of residential homes. ICT and sensors have moved the world towards automation. Excessive use of electricity for every activity has increased demand-side energy consumption. The high demand for electricity and limited fossils fuels lead to increased penetration of renewable energy resources (RERs) [1]. RERs is not a part of this discussion. Through scheduling and coordination of appliances, this high energy consumption can be managed. In [2], the authors reported that 38% increase in electricity consumption of power sector and 16% increase in electricity consumption of both residential and commercial sectors are expected by the year 2020
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
