Optimal Operation of a Grid-Connected Hybrid Renewable Energy System for Residential Applications

Akbar Maleki, Marc A. Rosen and Fathollah Pourfayaz
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Akbar Maleki: Department of Renewable Energies, Faculty of New Science & Technologies, University of Tehran, POB 14395-1561, Tehran, Iran
Marc A. Rosen: Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, Oshawa, ON L1H 7K4, Canada
Fathollah Pourfayaz: Department of Renewable Energies, Faculty of New Science & Technologies, University of Tehran, POB 14395-1561, Tehran, Iran

Sustainability, 2017, vol. 9, issue 8, 1-20

Abstract: The results of a study on incorporating solar-thermal collectors into a hybrid renewable energy system are reported. A photovoltaic–wind turbine–fuel cell–solar-thermal collector system is designed and an economic model is introduced for supplying the residential thermal and electrical loads via the grid-connected hybrid system. Since determining the optimal operation of a hybrid system such as a combined heat and power system constitutes a complex optimization problem requiring a sophisticated optimization method, a modified heuristic approach-based particle swarm optimization is proposed for solving the optimization problem. The results are compared with those obtained by an efficient metaheuristic optimization method, namely a genetic algorithm, in terms of accuracy and run time. The results show that, using the grid-connected hybrid combined heat and power system, among the cases considered, decreases the total cost of the system. The results also demonstrate that the reductions in daily cost relative to the base case by the modified particle swarm optimization algorithm for Cases 1–4 are 5.01%, 25.59%, 19.42%, and 22.19%, respectively. Finally, Case 2 is the most cost-effective and reliable. Moreover, the modified particle swarm optimization algorithm leads to better results than the genetic algorithm.

Keywords: solar energy; wind energy; hydrogen; grid-connected renewable energy system; hybrid energy system; combined heat and power; particle swarm optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (22)

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