Optimal power flow (OPF) is considered one of the most critical challenges that can substantially impact the sustainable performance of power systems. Solving the OPF problem reduces three essential items: operation costs, transmission losses, and voltage drops. An intelligent controller is needed to adjust the power system’s control parameters to solve this problem optimally. However, many constraints must be considered that make the design process of the OPF algorithm exceedingly tricky due to the increased number of limitations and control variables. This paper proposes a multi-objective intelligent control technique based on three different meta-heuristic optimization algorithms: multi-verse optimization (MVO), grasshopper optimization (GOA), and Harris hawks optimization (HHO) to solve the OPF problem. The proposed control techniques were validated by applying them to the IEEE-30 bus system under different operating conditions through MATLAB simulations. The proposed techniques were then compared with the particle swarm optimization (PSO) algorithm, which is very popular in the literature studying how to solving the OPF problem. The obtained results show that the proposed methods are more effective in solving the OPF problem when compared to the commonly used PSO algorithm. The proposed HHO, in particular, shows that it can form a reliable candidate in solving power systems’ optimization problems.
A Multi-Terminal High Voltage Direct Current (MT-HVDC) network is being considered for utilising the full potential of offshore wind power whereas its realisation is currently being hampered by protection issues. In this paper, a protection strategy for future DC grids based on Modular Multilevel Converter (MMC) based HVDC system is presented. Firstly, a fault detection technique based on initial / measurement is presented and thereafter protection strategies for future DC grids are presented. The fault detection technique presented is based on estimating the initial rate of rise of the current, (/) at fault inception using measured data and thereafter calculating the line inductance. The calculated line inductance is compared with a setting value to determine whether or not a fault has occurred, thus paving the way for a distance protection strategy. Simulations were carried out using Matlab/SIMULINK for varying fault distances. The results obtained show the validity of the technique in detecting and locating DC side short circuits. An advantage of this technique is that it relies only on information from the local end terminal and as such, no communication channel is required, hence satisfying the protection requirement of fast fault detection and location technique for MT-HVDC systems.
A microgrid is a group of distributed energy resources and interconnected loads that may be operated either in isolated mode or connected mode with the main utility within electrical boundaries. Microgrids may consist of different types of renewable energy resources such as photovoltaic panels, wind turbines, fuel cells, micro turbines, and storage units. It is highly recommended to manage the dependency on these resources by implementing an energy management unit to optimize the energy exchange so that the minimum cost is achieved. In this paper, an energy management system based on the grasshopper optimization algorithm (GOA) is proposed to determine the optimal power generated by the distributed generators in the microgrid which is required to minimize the total generation cost. The proposed unit is applied to a microgrid that consists of five generating units feeding residential, commercial, and industrial loads, and results are compared to other available research in literature to validate the proposed algorithm.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.