الفهرس 
Literature Overview of Hosting CapacityOnly 14 pages are availabe for public view
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Abstract
Improving the performance of distribution networks is an important goal for power system operators. Many technologies can make this required improvement, such as network reconfiguration or reinforcement, supplying the grid with active and reactive power compensators such as distributed power generation units (DGs), shunt capacitor banks (CBs), or installation of voltage conditioners such as voltage regulators (VRs). In addition, the optimal use of renewable and sustainable energy sources (RSESs) has become crucial for meeting the increase in demand for electricity and reducing greenhouse gas emissions. This requires the development of techno-economic planning models that can measure to what extent modern power systems can host RSESs.
This thesis focuses on maximizing the hosting capacity of modern distribution systems in the presence of renewables and energy storage units. The objectives include the optimal allocation of distributed generation units, shunt capacitor banks, and voltage regulators in the Tala distribution radial feeder (TDRF). The study introduces two novel optimization algorithms: the Archimedes optimization algorithm (AOA) and the RUN optimization technique. In the AOA approach, a multi-objective optimization framework is employed to minimize the costs of different compensators, maximize the benefits of reducing the active power loss and the purchased complex power from the grid, ensure voltage stability, and optimize loading capacity. The RUN optimization technique utilizes the Runge-Kutta method to identify optimal locations and sizes of DGs, CBs, and VRs, considering fixed and operating costs and benefits from reducing power purchased from the grid and losses.
The Snake optimization algorithm is also applied to increase the hosting capacity at each hour and season in the TDRF in the presence of energy storage units. It optimally allocates DGs, CBs, VRs, and energy storage units to improve the voltage stability index of system, and the loading capacity of the system, maximize the benefits obtained from reducing the system’s active power loss and the apparent power purchased from the utility, and finally improve the rural network’s HC while meeting the current requirements of the Egyptian electricity distribution companies. Moreover, The main load demand clusters are obtained using the soft fuzzy C-means clustering approach according to load consumption patterns in this rural area.
Simulation results validate the effectiveness of the proposed methodologies in achieving high hosting capacity, improving voltage stability, reducing power losses, and meeting the requirements of Egyptian electricity distribution companies.