A Domestic Microgrid with Optimized Home Energy Management System

Zafar Iqbal, Nadeem Javaid, Saleem Iqbal, Sheraz Aslam, Zahoor Ali Khan, Wadood Abdul, Ahmad Almogren and Atif Alamri
Additional contact information
Zafar Iqbal: PMAS Arid Agriculture University, Rawalpindi 4600, Pakistan
Nadeem Javaid: COMSATS Institute of Information Technology, Islamabad 44000, Pakistan
Saleem Iqbal: PMAS Arid Agriculture University, Rawalpindi 4600, Pakistan
Sheraz Aslam: COMSATS Institute of Information Technology, Islamabad 44000, Pakistan
Zahoor Ali Khan: CIS, Higher Colleges of Technology, Fujairah 4114, UAE
Wadood Abdul: Research Chair of Pervasive and Mobile Computing College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia
Ahmad Almogren: Research Chair of Pervasive and Mobile Computing College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia
Atif Alamri: Research Chair of Pervasive and Mobile Computing College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia

Energies, 2018, vol. 11, issue 4, 1-39

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.

Keywords: microgrid; heuristic algorithm; energy management; demand side management; demand response (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

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