INTELLIGENT LOAD MANAGEMENT SYSTEM WITH RENEWABLE ENERGY
DESIGN DETAILS
The optimization of energy consumption, with consequent cost reduction, is one of the main challenges for the present and future smart grid. Demand response (DR) program is expected to be vital in-home energy management system (HEMS) which aims to schedule the operation of appliances to save energy costs by considering customer convenience as well as characteristics of electric appliances. The DR program is a challenging optimization problem especially when the formulations are non-convex or NP-hard problems. Timing pricing i.e ToU, prices are well known in advance may be a year ahead and establishes a variable price structure for peak, shoulder, and off-peak hours and low peak hours. RTP vary on an hourly basis depending on the energy demand of the market. Variable peak pricing is a hybrid of the two and establishes variable pricing in the day. Energy can be efficiently consumed, and the power consumption can be efficiently minimized by voluntary reduction of home electric consumption based on energy awareness and automatic or manual reduction of home idle appliances. An optimal approach for scheduling the power usage of smart appliances based on the ToU pricing scheme is developed using Matlab. The model is simulated in time of use pricing environment for three cases: 1) traditional homes; 2) smart homes; and 3) smart homes with renewable energy sources. Matlab simulation results are evaluated for optimal schedules the appliances resulting in electricity bill and peaks reductions.
REFERENCES
Reference Paper-1: An Intelligent Load Management System with Renewable Energy Integration for Smart Homes
Author’s Name: Nadeem Javaid, Ihsan Ullah, Mariam Akbar and Zafar Iqbal
Source: IEEE: Special Section on The New Era of Smart Cities: Sensors, Communication Technologies, and Applications
Year:2017
Reference Paper-2: Memetic Algorithm: Hybridization of Hill Climbing with Selection Operator
Author’s Name: Rakesh Kumar, Sanjay Tyagi and, Manju Sharma
Source: International Journal of Soft Computing and Engineering (IJSCE)
Year:2013
DOWNLOAD the Matlab(P-code) encrypted code from the below URL,
https://drive.google.com/file/d/1UOi7R3fRNDtXde2Ht8uHCgsSqhRzIQos/view?usp=sharing
SIMULATION VIDEO DEMO
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