Load frequency regulation of a Standalone Microgrid using Firefly algorithm based Model Predictive Control

This paper proposes a load frequency regulation approach of a standalone microgrid which is based on an optimized model predictive controller. The microgrid under investigation includes photovoltaic power generation, wind, diesel, fuel cells, and batteries as sources of energy. The ever-present imbalance between the generated electrical power and the load demand is responsible for the deviation of the frequency from its fixed values. This difference in generated electrical power and demand of electrical power is largely due to the infrequent nature of wind and solar power generation and load disruption in a standalone microgrid. It becomes important to control the frequency under the intermittent nature of power generation. The model predictive control technique is an advanced method that gives an improved frequency regulation in a standalone microgrid over the conventional frequency regulation methods. In this paper, the diesel generator unit and fuel cell unit present in the standalone microgrid control the load frequency, the inputs of which are controlled by a Model Predictive Controller whose parameter is optimized by the Firefly algorithm.