Optimization of Renewable Energy Sources in a Microgrid Using Artificial Fish Swarm Algorithm

Abstract

Abstract Advances in microgrid enabling technologies and utilization of Renewable Energy Sources are prompting more and more number of smaller investors to invest in Renewable energy generation and distribution at microgrid level. The increased competition requires the energy producers to offer energy at minimum possible cost to gain the confidence of consumers, which needs efficient methods to schedule the energy generation among the available Renewable Energy Sources. Optimal scheduling of generation is one of the methods used to reduce the cost of generation. Out of many types of algorithms used effectively to solve the problem, evolutionary program techniques are proven and time tested to be one of the best solutions. A stochastic based search algorithm, called Artificial Fish Swarm Algorithm is used in this article to solve the problem of optimal scheduling of energy generation among the available Renewable Energy Sources. The effectiveness of the algorithm is validated by implementing to schedule generation in a microgrid scenario. The results are validated by comparing to an already tested Additive Increase Multiplicative Decrease algorithm.