الفهرس 
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Abstract
Electrification of rural or isolated areas, where the extension of the electrical
grid is too expensive, usually relies on a diesel generator as a single electric
source, however fossil fuel has many drawbacks such as greenhouse gas
emissions, unavailability, and price fluctuations. Moreover, due to the
advantage of clean nature and sustainability of renewable energy source, it is
usually used side by side with batteries and Diesel generator leading to
technical and economic challenges in designing a hybrid microgrid. Therefore,
this thesis discusses the optimal sizing of isolated hybrid microgrid, which
consists of distributed renewable energy resources (photovoltaics and wind
turbines), diesel generation, and energy storage system (batteries), to obtain
the best economic, technical and environmental indices.
The mathematical modeling and control strategy of the proposed isolated
hybrid microgrid components have been implemented using MATLAB.
Furthermore, our case study is based on the Zafrana area, a site located on the
eastern coast of Egypt. Two topologies with different renewable sources have
been considered in this study where the area is once supplied by
PV/Diesel/Battery and another time is supplied by PV/WIND/Diesel/Battery.
Moreover, a multi objective function based on Annual System Cost (ASC),
Fuel cost, CO2 emissions and technical penalty functions such as Loss of
Power Supply Probability, and Excess Energy Ratio and Fraction renewable
has been incorporated. In this thesis, a new optimization algorithm, namely
Turbulence Flow of Water Optimizer (TFWO), claimed to be used for the first
time in sizing the hybrid renewable isolated microgrid. Results are compared
to four well-known optimizers, namely Harris Hawks Optimization Algorithm
(HHO), Whale Optimization Algorithm (WOA), Jellyfish Search Optimizer
(JSO), and Equilibrium Optimizer (EO), to validate the superiority of the
proposed algorithm.
Case study 1 (PV/Diesel/Battery) results show that TFWO has better
convergence and accuracy than other algorithms, while adhering to the
required technical and environmental constraint values. The Fuel cost is
reduced by 71.6% compared to the case where the whole load is supplied by a
single Diesel generator and consequently, the CO2 emissions are reduced as
well. The carried sensitivity analysis shows that the obtained optimized
microgrid guarantees that LPSP is maintained less than 1.1% in all the
sensitivity cases, even with load increase by 25%. Although the ASC and fuel
cost increase to compensate for load increase, irradiation decrease, or Diesel
generator efficiency decrease, the FR% in all cases is maintained higher than
59.7% (the optimized FR% is 73.77%).
Moreover, in case study 2 (PV/WIND/Diesel/Battery), the TFWO results still
have the best convergence and accuracy compared to the other used
optimization algorithms. Here the Fuel cost is reduced by 70.9% compared to
the case where the whole load is supplied by a single Diesel generator and
consequently, the CO2 emissions are reduced. Although the fuel cost in this
case is slightly higher than case study 1 (2.3% increase), the ASC of case study
2 is 3.9 % less than Case study 1. The sensitivity analysis carried out shows
that the obtained optimized microgrid guarantees that LPSP is maintained less
than 1.32% in all the sensitivity cases, even with load increase by 25%. The
ASC and fuel cost may increase to compensate for load increase, wind speed
decrease, irradiation decrease, or Diesel generator efficiency decrease, but the
FR% in all cases is maintained higher than 59.3% (the optimized FR% is
73.09%).