Next Article in Journal
Analysis of Energy Saving Potential in High-Performance Building Technologies under Korean Climatic Conditions
Next Article in Special Issue
Mitigating Household Energy Poverty through Energy Expenditure Affordability Algorithm in a Smart Grid
Previous Article in Journal
Simulation and Experimental Study on the Optical Performance of a Fixed-Focus Fresnel Lens Solar Concentrator Using Polar-Axis Tracking
Previous Article in Special Issue
Contributions of Bottom-Up Energy Transitions in Germany: A Case Study Analysis
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle
Energies 2018, 11(4), 888; https://doi.org/10.3390/en11040888

Scheduling Appliances with GA, TLBO, FA, OSR and Their Hybrids Using Chance Constrained Optimization for Smart Homes

1
School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad 44000, Pakistan
2
COMSATS Institute of Information Technology, Islamabad 44000, Pakistan
*
Author to whom correspondence should be addressed.
Received: 26 January 2018 / Revised: 22 March 2018 / Accepted: 2 April 2018 / Published: 10 April 2018
(This article belongs to the Special Issue Energy Efficient and Smart Cities)
View Full-Text   |   Download PDF [1041 KB, uploaded 3 May 2018]   |  

Abstract

In this paper, we design a controller for home energy management based on following meta-heuristic algorithms: teaching learning-based optimization (TLBO), genetic algorithm (GA), firefly algorithm (FA) and optimal stopping rule (OSR) theory. The principal goal of designing this controller is to reduce the energy consumption of residential sectors while reducing consumer’s electricity bill and maximizing user comfort. Additionally, we propose three hybrid schemes OSR-GA, OSR-TLBO and OSR-FA, by combining the best features of existing algorithms. We have also optimized the desired parameters: peak to average ratio, energy consumption, cost, and user comfort (appliance waiting time) for 20, 50, 100 and 200 heterogeneous homes in two steps. In the first step, we obtain the optimal scheduling of home appliances implementing our aforementioned hybrid schemes for single and multiple homes while considering user preferences and threshold base policy. In the second step, we formulate our problem through chance constrained optimization. Simulation results show that proposed hybrid scheduling schemes outperformed for single and multiple homes and they shift the consumer load demand exceeding a predefined threshold to the hours where the electricity price is low thus following the threshold base policy. This helps to reduce electricity cost while considering the comfort of a user by minimizing delay and peak to average ratio. In addition, chance-constrained optimization is used to ensure the scheduling of appliances while considering the uncertainties of a load hence smoothing the load curtailment. The major focus is to keep the appliances power consumption within the power constraint, while keeping power consumption below a pre-defined acceptable level. Moreover, the feasible regions of appliances electricity consumption are calculated which show the relationship between cost and energy consumption and cost and waiting time. View Full-Text
Keywords: smart grid; supply side management; demand response; demand side management; real time pricing; chance constrained optimization smart grid; supply side management; demand response; demand side management; real time pricing; chance constrained optimization
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Nadeem, Z.; Javaid, N.; Malik, A.W.; Iqbal, S. Scheduling Appliances with GA, TLBO, FA, OSR and Their Hybrids Using Chance Constrained Optimization for Smart Homes. Energies 2018, 11, 888.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top