Superconducting magnetic energy storage (SMES) system is one of the commonly used techniques by the end-users to mitigate the voltage sag at their premises from the distribution system. The SMES is a superconductor coil wound on a nonmagnetic core. The paper is aimed at optimizing the dimensions of the coil to achieve minimum coil loss. The Genetic Algorithm (GA) is utilized to minimize the coil loss as a multi-objective function with indices including AC loss and refrigeration loss. The results show that the optimal total loss of SMES coil at capacity energy of 4 MJ is 85.04 W against 114.5 W in a previous work. The corresponding optimal dimensions of the coil which include the inner diameter, outer diameter and coil length are 0.19, 0.2 and 2.17 m, respectively. Different low-temperature superconductivity coil materials at operating temperature of 4.2 oK are considered to select the best material with minimum coil loss. The results conclude that Nb–Ti material achieves minimum coil loss when compared with MgB2 and YBCO. The effect of the twist pitch and circumferential stress on the total losses in the SMES coil is studied.

Superconducting magnetic energy storage (SMES) system is one of the commonly used technique by the end users to mitigate the voltage sag at their premises from the distribution system. The SMES is a superconductor coil wound on a nonmagnetic core. Contribution of refrigeration and AC loss in total SMES coil loss at different output power rating is investigated. Different Low Temperature Superconductivity (LTS) coil materials at operating temperature of 4oK are considered to select the best material with minimum coil loss. The results concluded that Nb–Ti material achieved minimum coil loss when compared with MgB2 and YBCO.

Superconducting magnetic energy storage (SMES) system is one of the commonly used technique by the end users to mitigate the voltage sag at their premises from the distribution system. The SMES is a superconductor coil wound on a nonmagnetic core. Contribution of refrigeration and AC loss in total SMES coil loss at different output power rating is investigated. Different Low Temperature Superconductivity (LTS) coil materials at operating temperature of 4oK are considered to select the best material with minimum coil loss. The results concluded that Nb–Ti material achieved minimum coil loss when compared with MgB2 and YBCO.

Hyper-spherical search algorithm (HSSA) is proposed for optimal allocation and sizing of Photovoltaic Distributed Generation System (PVDGS) in the distribution network. Firstly, Power Loss Index (PLI) technique is presented to get the highest candidate buses for installing PVDGS. Secondly, the proposed HSSA is developed to decide the most optimal locations of PVDGS and their economic sizing at the elected buses by PLI. Herein, the cost objective function is designed to diminish the total cost of the system losses, and subsequently increase the annual net saving. Hourly variation of solar radiation, and temperature is taken into account in cost calculation of the PV system. In addition, the present worth value for the costs of the maintenance and the PV system components is estimated as function of interest and inflation rates. The proposed algorithm is tested on 69- IEEE and 118 IEEE radial distribution systems to ensure the effectiveness of the proposed algorithm in increasing the net saving via precise cost calculation

Due to various faults occur to transmission lines and because it was necessary to find and recover these faults quickly as possible. This paper discussing fault detection, classification and determining fault location as fast as possible via Artificial Neural Network (ANN) algorithm. The software used for modeling the proposed network is a MATLAB/SIMULINK software environment. The training, testing and evaluation of the intelligent locator processes are done based on a multilayer Perceptron feed forward neural network with back propagation algorithm. Mean Square Error (MSE) algorithm is used to evaluate the performance of the detector/classifier as well as fault locator. The results show that the validation performance (MSE) for the fault detector/classifier is 2.36e-9 and for fault locator is 2.179e-5. The system can detect if there is a fault or not, can classify the fault type and determine the fault location very precisely.

The significant way to support the electricity sector and encourage the consumer to use renewable resources in electricity generation in Egypt is investing energy produced from hybrid renewable sources system with the grid. A hybrid PV/wind turbine system is optimized as a core source of energy for households in Egypt. The storage battery bank and diesel unit are used as secondary energy sources. The surplus and shortage of energy are invested with the utility grid. Optimization results at interest rate of 12% indicate that cost of energy (COE) and loss of power supply probability (LPSP) when connecting PV/wind system with the utility grid are 0.024 $/kWh and zero against 0.131 $/kWh and 35.5% for gridindependent PV/wind/battery system, respectively. This corresponding to 81.7% reduction in COE. Thus, it is more economic feasibility to connect PV/wind system with the utility grid than grid-independent PV/wind/battery system.

The significant way to support the electricity sector and encourage the consumer to use renewable resources in electricity generation in Egypt is investing energy produced from hybrid renewable sources system with the grid. A hybrid PV/wind turbine system is optimized as a core source of energy for households in Egypt. The storage battery bank and diesel unit are used as secondary energy sources. The surplus and shortage of energy are invested with the utility grid. Optimization results at interest rate of 12% indicate that cost of energy (COE) and loss of power supply probability (LPSP) when connecting PV/wind system with the utility grid are 0.024 $/kWh and zero against 0.131 $/kWh and 35.5% for gridindependent PV/wind/battery system, respectively. This corresponding to 81.7% reduction in COE. Thus, it is more economic feasibility to connect PV/wind system with the utility grid than grid-independent PV/wind/battery system.

This paper proposed a new application of an efficient Adaptive Sine Cosine Algorithm (ASCA) to determine the optimal settings of the PID controllers in hybrid renewable energy system (HRES). The ASCA is proposed to enhance the searching capabilities of the traditional Sine Cosine Optimization (SCA) and its stagnation to local optima. The ASCA is based on modifying traditional SCA by applying the Levy flight distribution and adaptive operators. The HRES consists of three sources photovoltaic (PV) source, wind turbine and battery storage. These sources are connected to a DC/DC boost converter for converting the DC voltage to an AC voltage through three-phase inverter. The considered objective function is formulated in terms of the current and voltage errors to enable the HRES to participate effectively within the connected micro-grid via optimal gains of the PID controllers. The results verify that the performance of the HRES is enhanced considerably by optimizing the parameters of the HRES controllers using the ASCA under several operating conditions of solar irradiation, temperature and wind speed.

This paper proposed a new application of an efficient Adaptive Sine Cosine Algorithm (ASCA) to determine the optimal settings of the PID controllers in hybrid renewable energy system (HRES). The ASCA is proposed to enhance the searching capabilities of the traditional Sine Cosine Optimization (SCA) and its stagnation to local optima. The ASCA is based on modifying traditional SCA by applying the Levy flight distribution and adaptive operators. The HRES consists of three sources photovoltaic (PV) source, wind turbine and battery storage. These sources are connected to a DC/DC boost converter for converting the DC voltage to an AC voltage through three-phase inverter. The considered objective function is formulated in terms of the current and voltage errors to enable the HRES to participate effectively within the connected micro-grid via optimal gains of the PID controllers. The results verify that the performance of the HRES is enhanced considerably by optimizing the parameters of the HRES controllers using the ASCA under several operating conditions of solar irradiation, temperature and wind speed.

This paper proposed a new application of an efficient Adaptive Sine Cosine Algorithm (ASCA) to determine the optimal settings of the PID controllers in hybrid renewable energy system (HRES). The ASCA is proposed to enhance the searching capabilities of the traditional Sine Cosine Optimization (SCA) and its stagnation to local optima. The ASCA is based on modifying traditional SCA by applying the Levy flight distribution and adaptive operators. The HRES consists of three sources photovoltaic (PV) source, wind turbine and battery storage. These sources are connected to a DC/DC boost converter for converting the DC voltage to an AC voltage through three-phase inverter. The considered objective function is formulated in terms of the current and voltage errors to enable the HRES to participate effectively within the connected micro-grid via optimal gains of the PID controllers. The results verify that the performance of the HRES is enhanced considerably by optimizing the parameters of the HRES controllers using the ASCA under several operating conditions of solar irradiation, temperature and wind speed.