Intelligent energy scheduling in a Microgrid using hybrid intelligent Algorithm

Abstract

Power usage in the world is ever increasing and needs new technologies in generation, control and supply. Bulk power generation is economical and requires the transmission, distribution system for the power supply. This conventional power generation is catering to the needs of the ever-increasing electrical power demands. In this case, the distributed enerator has benefits and is required to deliver the surplus power needed by the consumer. The major focus in the distributed generation is the integration with the grid for smooth and reliable operation. Therefore Micro Grid (MG) is examined with the latest loads and distributed generators in this research work. The main objective of the electrical power providers is minimizing operating cost. This is accomplished using intelligent algorithms such as the Firefly algorithm, Spider Monkey Optimization (SMO) and hybrid of the firefly algorithm and SMO. Firefly algorithm mimics the firefly swarm intelligence for optimization. The light emitted by the firefly is rhythmic at a particular frequency while observing the willingness of the brighter firefly to accept its attractiveness. The steps followed in the Firefly algorithm are finding attractiveness, the distance between the brighter firefly and then the firefly s movement towards the brighter firefly. The brighter firefly is the best optimized solution in the search space. SMO uses the intelligence of the spider monkey s social behaviour. The spider monkey lives as a group and this group gets fissioned to search for food during scarcity and fusion during the surplus of food. Fission and fusion are the exploration and exploitation of the search process. SMO provides a more optimal result by balancing exploration and exploitation. SMO uses a balanced exploration and exploitation search, whereas firefly uses a greedy algorithm that produces better neighbourhood optimized output newline