A Short Review on the Optimization Methods Using for Distributed Generation Planning

Abstract

Industrial and social developments increase the need for energy. Due to the fact that fossil fuels will run out to meet energy needs, alternative technologies have been sought. Interest in renewable energy sources has increased. However, since renewable energy sources vary according to geographical conditions, their continuity must be ensured. Therefore, the focus has been on distributed generation systems. The components of distributed generation systems are divided into renewable, non-renewable and storage systems. Technical difficulties may arise in the connection of distributed generation systems with renewable resources. Therefore, it is very important to optimize distributed generation so that the distributed grid provides the expected power. Optimization systems are emphasized so as to decrease efficiency, reduce costs and cut down power fluctuations in distributed generation systems. In this study, distributed generation system, renewable energy source and energy storage system are mentioned. Metaheuristic methods for the efficiency of an energy system are studied. Evolution based algorithms can only keep the search space information in the iteration found, while swarm based algorithms can store the search space information throughout the iteration. Most metaheuristic algorithms use the biological cycle process, swarm behaviour, and physical laws. Evolution based and swarm-based optimization methods are examined in metaheuristic methods. As evolution based metaheuristic methods, genetic algorithm and swarm based optimization methods particle swarm optimization, ant colony optimization, grey wolf optimization, bat algorithm, whale optimization algorithm, cuckoo search algorithm are discussed. A general comparison of the investigated optimization methods is presented. It was concluded that the swarm optimization methods examined were able to make fast convergence and avoid local minima and computational efficiency evaluations.

Solar Irradiance Forecasting Based on Deep Learning for Sustainable Electrical Energy in Cameroon

“…The depletion of fossil fuels and the phenomenon of global warming has prompted a surge in the utilization and advancement of Renewable Energy Sources (RES) in recent times. Renewable energy sources, namely solar, wind, hydro, and geothermal power, have been acknowledged as innovative solutions to the aforementioned issues and are indicative of the future of energy progress [1]. Solar and wind power have gained widespread popularity as alternative sources of energy, and are being increasingly adopted in numerous countries worldwide, surpassing other conventional sources.…”