Next Article in Journal
Efficiency Optimization of a Variable Bus Voltage DC Microgrid
Previous Article in Journal
Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy
Previous Article in Special Issue
Diverse Schemes of Cost Pooling for Carbon-Reduction Outsourcing in Low-Carbon Supply Chains
Article Menu

Export Article

Open AccessArticle
Energies 2018, 11(11), 3091; https://doi.org/10.3390/en11113091

An Inventive Method for Eco-Efficient Operation of Home Energy Management Systems

Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
*
Author to whom correspondence should be addressed.
Received: 21 August 2018 / Revised: 12 October 2018 / Accepted: 29 October 2018 / Published: 8 November 2018
(This article belongs to the Special Issue Energy Economy, Sustainable Energy and Energy Saving)
Full-Text   |   PDF [942 KB, uploaded 15 November 2018]   |  

Abstract

A demand response (DR) based home energy management systems (HEMS) synergies with renewable energy sources (RESs) and energy storage systems (ESSs). In this work, a three-step simulation based posteriori method is proposed to develop a scheme for eco-efficient operation of HEMS. The proposed method provides the trade-off between the net cost of energy ( C E n e t ) and the time-based discomfort ( T B D ) due to shifting of home appliances (HAs). At step-1, primary trade-offs for C E n e t , T B D and minimal emissions T E M i s s are generated through a heuristic method. This method takes into account photovoltaic availability, the state of charge, the related rates for the storage system, mixed shifting of HAs, inclining block rates, the sharing-based parallel operation of power sources, and selling of the renewable energy to the utility. The search has been driven through multi-objective genetic algorithm and Pareto based optimization. A filtration mechanism (based on the trends exhibited by T E M i s s in consideration of C E n e t and T B D ) is devised to harness the trade-offs with minimal emissions. At step-2, a constraint filter based on the average value of T E M i s s is used to filter out the trade-offs with extremely high values of T E M i s s . At step-3, another constraint filter (made up of an average surface fit for T E M i s s ) is applied to screen out the trade-offs with marginally high values of T E M i s s . The surface fit is developed using polynomial models for regression based on the least sum of squared errors. The selected solutions are classified for critical trade-off analysis to enable the consumer choice for the best options. Furthermore, simulations validate our proposed method in terms of aforementioned objectives. View Full-Text
Keywords: eco-efficient home energy management, dispatch of renewables and energy storage systems, load-shedding-compensating dispatchable generators, optimization using surface fitting techniques, multi-objective genetic algorithm, Pareto optimization eco-efficient home energy management, dispatch of renewables and energy storage systems, load-shedding-compensating dispatchable generators, optimization using surface fitting techniques, multi-objective genetic algorithm, Pareto 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

Hussain, B.; Javaid, N.; Hasan, Q.U.; Javaid, S.; Khan, A.; Malik, S.A. An Inventive Method for Eco-Efficient Operation of Home Energy Management Systems. Energies 2018, 11, 3091.

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