Investigating and Optimizing the Operation of Microgrids with Intelligent Algorithms

Abstract

Microgrids need optimization to reduce economic problems and human losses. Scattered resources in power systems and microgrids have led to many environmental, economic and human, and animal losses. The most important part of these problems is related to voltage and frequency fluctuations when possible occurrences such as extreme load changes or errors in microgrids. These problems lead to microgrid collapse. Therefore, providing optimal solutions that can solve these challenges is essential. For this purpose, the present study has tried to provide a high-performance control structure in the time of internal and external disturbances based on short-term planning. The proposed approach is the use of an evolutionary neuro-fuzzy network. Perhaps the main reason for using this approach can be due to uncertainty in the distribution and distribution of loads in microgrids and power systems. Simulation has been performed in MATLAB and Simulink environments, and the results show that the optimal load distribution has been done evolution in microgrids.