الفهرس 
Introduction and Literature SurveyOnly 14 pages are availabe for public view
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Abstract
In isolated rural regions it is difficult to provide energy to improve the
living environment of the people. Finding economical and practical solutions
poses significant challenges. An Integrated Renewable Energy System (IRES)
can effectively utilize locally available renewable resources to satisfy the
diversity of energy and other requirements. The purpose of this study is to design
an IRES to “energize” rural areas for varying energy sources and demands.
The first part of this thesis presents a comparative study between the gridconnected
and the off-grid IRES to match the power requirement of a remote
village in rural Egypt. Five scenarios have been suggested to resolve the village
energy issue. These include PV/Biomass/Battery IRES, PV/Wind/Battery IRES,
Wind/Biomass/Battery IRES, PV/Wind/Biomass/Battery IRES and a gridconnected
IRES. The scope of this study is to identify the best feasible
selection/option.
An optimization model in terms of four parameters has also been applied.
These parameters include the Loss of Power Supply Probability (LPSP), the
Renewable Energy Fraction (REF), the Waste Energy Fraction (WEF) and
economic feasibility. All calculations employed the MATLAB software with the
ultimate objective converging to an optimal solution for the sizing problem. The
selection of the optimum system was based on maximizing the REF, minimizing
the system Total Net Present Cost (TNPC), and the WEF for a specific LPSP.
The outcomes of the simulation show that the optimal configuration is
obtained for a grid-connected IRES comprising of two biomass power systems,
one wind turbine, and two Nickel Iron battery with a REF of 50.05% and a LCOE
of 0.0122 $/kWh. The second part of this thesis focuses on developing a grid-connected IRES in such a way to provide uninterrupted energy supply to a village connected to an unreliable grid with a random outage. The grid availability is modeled in this study randomly with the percentage of 100%, 90%, and 80% of total yearly grid
operation respectively.
The system adopts meta-heuristic optimization using six different
optimization techniques to achieve the optimum sizing of the system. In this case, the meta-heuristic algorithm with the best performance and minimal execution time will be directly selected. The optimization techniques included the Particle Swarm Optimization (PSO), the Flower Pollination Algorithm (FPA), the
Harmony Search (HS) algorithm, the Artificial Bee Colony (ABC) Algorithm,
the Fire-fly Algorithm (FA) and Hybrid Fire-Fly/Harmony Search Algorithm
(HFA/HS). The simulation results show that the Hybrid Fire-Fly/Harmony
Search Algorithm has the minimum execution time and best performance among the other algorithms.
Three different battery technologies, including Flooded lead-acid (FLA),
Lithium Ferro Phosphate (LFP) and Nickel Iron (Ni-Fe) have been considered in this study. Our study suggests that the Nickel Iron battery worth consideration in renewable applications.