Day-ahead management of energy sources and storage in hybrid microgrid to reduce uncertainty

Abstract

A day ahead management strategy is proposed in this article to schedule energy generators and storage in presence of Renewable Energy Sources under uncertainty conditions with an objective to optimize the cost of energy generation. Artificial Fish Swarm algorithm is used as optimization tool. The optimization problem is framed considering all the practical constraints of energy generators and storage units. The uncertainty of Renewable Energy Sources is treated with a proven uncertainty model and several scenarios are drawn for energy availability and demand. The proposed energy management algorithm is tested numerically on a grid connected microgrid hosting a group of hybrid energy sources and storage battery for day ahead scheduling under dynamic pricing and demand side management in one of the generated uncertainty scenarios. The obtained results show that the performance of Artificial Fish Swarm algorithm as an optimizing tool is validated and the proposed Energy Management System is found to optimize the cost of energy generation while matching the power generated with power required.