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Energies 2018, 11(4), 1002; https://doi.org/10.3390/en11041002

A Domestic Microgrid with Optimized Home Energy Management System

1
PMAS Arid Agriculture University, Rawalpindi 4600, Pakistan
2
COMSATS Institute of Information Technology, Islamabad 44000, Pakistan
3
CIS, Higher Colleges of Technology, Fujairah 4114, UAE
4
Research Chair of Pervasive and Mobile Computing College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia
*
Author to whom correspondence should be addressed.
Received: 28 February 2018 / Revised: 10 April 2018 / Accepted: 13 April 2018 / Published: 20 April 2018
(This article belongs to the Special Issue Building Energy Use: Modeling and Analysis)
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Abstract

Microgrid is a community-based power generation and distribution system that interconnects smart homes with renewable energy sources (RESs). Microgrid efficiently and economically generates power for electricity consumers and operates in both islanded and grid-connected modes. In this study, we proposed optimization schemes for reducing electricity cost and minimizing peak to average ratio (PAR) with maximum user comfort (UC) in a smart home. We considered a grid-connected microgrid for electricity generation which consists of wind turbine and photovoltaic (PV) panel. First, the problem was mathematically formulated through multiple knapsack problem (MKP) then solved by existing heuristic techniques: grey wolf optimization (GWO), binary particle swarm optimization (BPSO), genetic algorithm (GA) and wind-driven optimization (WDO). Furthermore, we also proposed three hybrid schemes for electric cost and PAR reduction: (1) hybrid of GA and WDO named WDGA; (2) hybrid of WDO and GWO named WDGWO; and (3) WBPSO, which is the hybrid of BPSO and WDO. In addition, a battery bank system (BBS) was also integrated to make our proposed schemes more cost-efficient and reliable, and to ensure stable grid operation. Finally, simulations were performed to verify our proposed schemes. Results show that our proposed scheme efficiently minimizes the electricity cost and PAR. Moreover, our proposed techniques, WDGA, WDGWO and WBPSO, outperform the existing heuristic techniques. View Full-Text
Keywords: microgrid; heuristic algorithm; energy management; demand side management; demand response microgrid; heuristic algorithm; energy management; demand side management; demand response
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Iqbal, Z.; Javaid, N.; Iqbal, S.; Aslam, S.; Khan, Z.A.; Abdul, W.; Almogren, A.; Alamri, A. A Domestic Microgrid with Optimized Home Energy Management System. Energies 2018, 11, 1002.

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