Recently, a special attention has been attributed to the renewable energy in Egypt. Optimal sizing of small renewable energy system has a very important role in the use of renewable energy effectively and economically. Particle swarm optimisation (PSO) is a popular stochastic optimisation method that has found in wide applications. Conventional PSO suffers from high computational complexity and slow convergence speed. This study presents a modified PSO (MPSO) technique to optimise the capacity sizes of different components of hybrid PV/wind/battery power generation system for supplying communication and information technology centre in Mansoura University‐Egypt. A feasibility study for two options is investigated; stand‐alone system composed of PV/wind/battery combination and a grid connected PV/wind system. The proposed MPSO technique proves faster convergence speed and shorter computational time as compared with conventional techniques.

This paper proposes a protection scheme for multi-terminal high voltage direct current (MT-HVDC) transmission grids. The proposed scheme is based on three protection levels; di/dt overcurrent protection as the main protection, dv/dt undervoltage protection as a backup one protection. If the main and backup one fails to detect and clear the fault current, then the fault tripping and clearing is accomplished using the overcurrent protection by AC circuit breakers (ACCBs) in the AC grid side after a coordinated time. The tripping in the main and backup one protection levels are based on using fault isolation devices (FIDs) tripping. Support vector machine (SVM) is proposed for the fault detection and discrimination in the MT-HVDC using the backup two protection system. The Internet of things (IoT) has been used for monitoring and coordination between the AC grid and MT-HVDC grid. The proposed protection scheme is applied and verified on the MT-HVDC network under different transmission system faults using the MATLAB/Simulink ^® environment. The results show that the proposed scheme's effectiveness as a fast and reliable scheme in fault detection and discrimination for the MT-HVDC grids.

The main objective of this paper is to develop a complete model that fully simulate a biogas-fueled power plant which can be used to supply a rural farm with sufficient electricity. The reactor is fed with animal manure of the farm. The proposed model consists of three main parts; a biogas reactor, a microturbine (MT) coupled to a permanent magnet synchronous generator, and a storage system. The model describes the dynamics of an MT and it is suitable for both steady state and transient simulation and analysis. The volume of biogas output delivered from the Anaerobic Digester depends on the reactor volume, reactor temperature, and animal manure type. The storage system is used to store the excess value of biogas if any. It is composed of two parts: a comparator and a storage tank. The comparator compares the volume of biogas produced by the reactor with that needed to supply the load. An adaptive controller is developed to withstand the system against any transient condition such as suddenly load increase/decrease. The proposed model is implemented for chemical and physical behaviors of the biogas production process, as well as for different variables of MT-generator operations. The model is implemented in Matlab/Simulink environment and tested under different operating conditions in both steady state and transient status to study the impacts of different variables on the system output. The output results prove its applicability and effectiveness under different operating conditions.

Ensuring reliable and easily accessible charging infrastructure becomes crucial as more people adopt electric vehicles. This study introduces a recommendation system designed to assist electric vehicle users in finding convenient charging stations, enhancing the charging experience, and reducing range anxiety. The system employs advanced data analysis techniques to offer personalized suggestions based on users’ preferences. Real-time data on factors like charging station availability, individual preferences, and past usage patterns are collected and processed using a restricted Boltzmann machine-learning algorithm. The waterwheel plant algorithm, known for its effectiveness in solving complex optimization problems, is utilized to optimize the parameters of the restricted Boltzmann machine. The recommendation system considers various user preferences, including charging speed, cost, network compatibility, amenities, and proximity to the user’s current location. The system aims to minimize user frustration, improve charging performance, and enhance customer satisfaction by addressing these aspects. Results indicate the system’s efficiency in suggesting convenient charging locations. The study explores the statistical significance of the optimized waterwheel plant algorithm and restricted Boltzmann machine model through Wilcoxon rank-sum and Analysis of Variance tests.