A New Control Strategy For Controlling Isolated Microgrid

Microgrid control in the isolated mode is a highly important subject area. in the present paper, a new method is used for controlling isolated microgrids. this method was used based on the classification of the microgrids into two groups, namely fastdynamic (battery and flywheel) and slowdynamic (diesel generator, electrolyzer, and fuel cell). for the microgrid components with fast dynamics, a separate controller has been used. also, another separate controller has been deployed for those components of the microgrid that are characterized by slow dynamics. this method was simulated in matlab software. a fractionalorder proportionalintegraldifferential (fopid) controller optimized by the grey wolf optimizer (gwo) algorithm was used as the proposed controller in the new control strategy. the proposed method was compared with the fopid controller that has been optimized by particle swarm optimization (pso) algorithm and genetic algorithm (ga). besides, it was compared with the common proportionalintegraldifferential (pid) controller, the coefficients of which have been obtained using the zieglernichols method, and proportionalintegral (pi) controller, the coefficients of which have been obtained using the neural network (nn) method. the obtained results indicated the improved response speed, improved transientstate performance, and an improved steadystate performance of the proposed method compared with those mentioned.

A New Control Strategy for Controlling Isolated Microgrid

Microgrid control in the isolated mode is a highly important subject area. In the present paper, a new method is used for controlling isolated microgrids. This method was used based on the classification of the microgrids into two groups, namely fastdynamic (battery and flywheel) and slowdynamic (diesel generator, electrolyzer, and fuel cell). For the microgrid components with fast dynamics, a separate controller has been used. Also, another separate controller has been deployed for those components of the microgrid that are characterized by slow dynamics. This method was simulated in MATLAB software. A fractionalorder proportionalintegraldifferential (FOPID) controller optimized by the grey wolf optimizer (GWO) algorithm was used as the proposed controller in the new control strategy. The proposed method was compared with the FOPID controller that has been optimized by particle swarm optimization (PSO) algorithm and genetic algorithm (GA). Besides, it was compared with the common ProportionalIntegralDifferential (PID) controller, the coefficients of which have been obtained using the ZieglerNichols method, and proportionalintegral (PI) controller, the coefficients of which have been obtained using the neural network (NN) method. The obtained results indicated the improved response speed, improved transientstate performance, and an improved steadystate performance of the proposed method compared with those mentioned.