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العنوان
Multi-Objective Optimization of Egyptian Electric Grid-Connected Hybrid Renewable Energy Systems /
المؤلف
Elbahat, Haitham Ibrahim Abd Albaky.
هيئة الاعداد
باحث / هيثم إبراهيم عبد الباقي البهات
مشرف / عادل عبدالباسط محمد
مشرف / شيماء حسن سيد بركات
مناقش / رمضان محمود مصطفى
مناقش / جمال محمود دسوقى
الموضوع
Electrical engineering.
تاريخ النشر
2021.
عدد الصفحات
108 p. :
اللغة
الإنجليزية
الدرجة
ماجستير
التخصص
الهندسة الكهربائية والالكترونية
تاريخ الإجازة
1/1/2021
مكان الإجازة
جامعة المنيا - كلية الهندسه - الهندسة الكهربية
الفهرس
Only 14 pages are availabe for public view

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Abstract

As a result of the significant developments in renewable energy technologies and their potential to mitigate the issue of global warming, many countries have tended to depend on them to fulfill their energy requirements. Whereas the Hybrid Renewable Energy System (HRES) will successfully leverage locally accessible renewable resources to fulfill energy sustainability.
The performance evaluation of grid-connected hybrid systems mainly depends on the costs, reliability and GHG reduction of the system. It was decided to implement multi-objective optimization for the purpose of providing sufficient energy to a rural community in Ismailia Governorate, Egypt. The objective functions of the system were the minimization of two objective functions, namely the Loss of Power Supply Probability (LPSP) and the Cost of Energy (COE), and the maximization of the Renewable Energy Fraction (REF) of the system as the third object.
Mathematical models were presented to estimate the output power of the hybrid system. The findings demonstrated the contribution of each component of the system to the overall energy required to meet the electrical demand.
In this Thiess, the interrelationship between the grid and the proposed hybrid system was studied in terms of the network’s ability to sell or buy energy from the hybrid system, where three scenarios were proposed to study this relationship. The results of the MOPSO were classified into three perspectives, which are economically optimal solution (the lowest COE), renewable energy usage perspective (the highest REF) and environmentally optimal solution (the lowest GHG emission).