Renewable energy (RE) resources are relatively unpredictable and dependent on climatic conditions. The negative effects of existing randomness in RE resources can be reduced by the integration of RE resources into what is called Hybrid Renewable Energy Systems (HRES). The design of HRES remains as a complicated problem since there is uncertainty in energy prices, demand, and RE sources. In addition, it is a multi-objective design since several conflicting objectives must be considered. In this thesis, an optimal sizing approach has been proposed to aid decision makers in sizing and performance analysis of this kind of energy supply systems.
First, a straightforward methodology based on ε-constraint method is proposed for optimal sizing of HRESs containing RE power generators and two storage devices. The ε-constraint method has been applied to minimize simultaneously the total net present cost of the system, unmet load, and fuel emission. A simulation-based particle swarm optimization approach has been used to tackle the multi-objective optimization problem.
In the next step, a Pareto-based search technique, named dynamic multi-objective particle swarm optimization, has been performed to improve the quality of the Pareto front (PF) approximated by the ε-constraint method. The proposed method is examined for a case study including wind turbines, photovoltaic panels, diesel generators, batteries, fuel cells, electrolyzers, and hydrogen tanks. Well-known metrics from the literature are used to evaluate the generated PF.
Afterward, a multi-objective approach is presented to consider the economic, reliability and environmental issues at various renewable energy ratio values when optimizing the design of building energy supply systems. An existing commercial apartment building operating in a cold Canadian climate has been described to apply the proposed model. In this test application, the model investigates the potential use of RE resources for the building. Furthermore, the
application of plug-in electric vehicles instead of gasoline car for transportation is studied. Comparing model results against two well-known reported multi-objective algorithms has also been examined.
Finally, the existing uncertainties in RE and load are explicitly incorporated into the model to give more accurate and realistic results. An innovative and easy to implement stochastic multi-objective approach is introduced for optimal sizing of an HRES. / February 2016
| Identifer | oai:union.ndltd.org:MANITOBA/oai:mspace.lib.umanitoba.ca:1993/31040 |
| Date | January 2014 |
| Creators | Sharafi, Masoud |
| Contributors | ElMekkawy, Tarek (Mechanical Engineering), Peng, Qingjin (Mechanical Engineering) Filizadeh, Shaahin (Electrical and Computer Engineering) Elkamel, Ali (University of Waterloo) |
| Publisher | Elsevier, John Wiley&Sons |
| Source Sets | University of Manitoba Canada |
| Detected Language | English |
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