Automatic generation control of microgrid using artificial intelligence techniques

Microgrid is a small scale independent power system consisting of renewable energy sources: solar and wind power generation and backup by controllable sources: diesel generator, fuel cell, aqua electrolyzer and battery. In the microgrid, the ramp rate limit in power change in controllable sources has been implemented by means of generation rate constraint (GRC) and power frequency (P-f) droop characteristics (R) is also included for the parallel operation of generating sources participating in automatic generation control (AGC). These GRC and P-f droop make the system non linear and we have used artificial intelligence techniques (AI) like bacterial foraging optimization (BFO), particle swarm optimization (PSO), genetic algorithm (GA) to tune the important parameters simultaneously in AGC of microgrid. Simulation results show the superiority of BFO for optimal calculation of multiple parameters in microgrid over PSO, GA and classical methods.