Thesis (Ph.D)-Cairo University, 2025.

Bibliography: pages 124-143.

In the pursuit of sustainable development, the integration of electric vehicles
(EVs) and renewable-based distributed generation (RDG) into distribution networks
(DNs) offers numerous advantages, including the reduction of harmful emissions, the
diversification of energy sources, and the promotion of renewable energy adoption.
However, the integration of RDGs and EVs into DNs presents substantial challenges,
particularly when not properly managed, posing risks to the safe and reliable operation
of the network. As a result, it is crucial for the distribution system operator (DSO) to
effectively define and assess two important terms: distributed generation hosting
capacity (DG-HC) and electric vehicles hosting capacity (EV-HC). Although there has
been a surge in research on hosting capacity (HC) approaches, there remains a
significant gap in the literature regarding models that integrate demand response (DR),
smart inverters (SIs) Volt/VAR control, DG-HC, and EV-HC objectives
simultaneously.

This thesis addresses this gap by proposing a hierarchical bi-level optimization
framework for enhancing hosting capacity, which incorporates both dynamic tariff-
based DR and SI Volt/VAR control. The lower level of the proposed framework
optimally adjusts the load curves of participating customers and the charging demands
of EV aggregators, based on the proposed dynamic tariff approach. In the upper level,
the DSO optimizes a multi-objective function that includes the maximization of DG-
HC and EV-HC, as well as the minimization of power losses, using the optimal load
curves from the lower level. Furthermore, the integration of DR with optimal
Volt/VAR control from SIs is used to further refine and support the optimization
process. The thesis also explores the impact of grid-connected EVs (GCEVs) on
increasing DG-HC, considering both uncoordinated and coordinated charging schemes.
To validate the robustness and effectiveness of the proposed approach, three types of
loads are taken into account, and the approach is tested on both the IEEE 33-bus test
system and a real distribution network with 59 buses located in Cairo, Egypt.



The results of the study lead to several key conclusions. First, the assessment of
DG-HC varies significantly depending on whether the EV charging demand is
considered. Specifically, for the IEEE 33-bus network, the average value of the optimal
DG-HC increased by over 133% when EV integration was included. Second, the
implementation of the dynamic pricing-based DR program in the IEEE 33-bus network
resulted in significant improvements in both DG-HC and EV-HC, with increases of
approximately 34% and 27%, respectively, leading to a substantial enhancement in the
combined DG-EV-HC. Finally, when the synergistic combination of DR and Volt/VAR
control was applied, the hosting capacity for DG and EVs saw remarkable
improvements in the IEEE 33-bus network, with DG-HC rising by approximately
49.2% and EV-HC increasing by 61.2%. These findings underscore the effectiveness of
the proposed synergistic enhancement technique in improving the overall hosting

تقدم هذه الأطروحة تقنيات جديدة لتعزيز سعة الاستضافة لكل من السيارات الكهربية والتوليد الموزع في وقت واحد. يتم فحص فعالية تقنية الاستجابة للطلب القائم على التسعيرة المتغيرة الجديد والتحكم في الفولت/فار للعواكس الذكية بهدف تحسين سعة الاستضافة المشتركة. تم اقتراح تصميم رياضي جديد متعدد الأهداف للأطروحة يهدف إلى تعظيم سعة الاستضافة وتقليل الفاقد الكلي في القدرة الكهربية في الوقت ذاته. تم تقييم مدى دقة المنهج المقترح عن طريق الأخذ في الاعتبار أنواع الأحمال المختلفة مثل الأحمال التجارية، والصناعية، والسكنية. أكدت نتائج المحاكاة على قدرة التقنية المقترحة علي تحسين سعة الاستضافة لكل من التوليد الموزع والسيارات الكهربية بشكل فعال مع تحقيق تقليل ملحوظ في القدرة الكهربية المفقودة.

Issues also as CD.

Text in English and abstract in Arabic & English.