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Summary Microgrid implementation requires effective and efficient strategies for controlling the grid parameters. Various problems are encountered with the deployment of distributed generation in terms of reverse power, an imbalance between power generation and nonlinear load. This paper is focused on the existing controllers in terms of their merits and limitations. Furthermore, the state of the art of the local power distribution system especially on renewable energy resources along with energy storage methods is explored. Reliability and stability of power flow between sources and consumers via voltage source inverter are also considered. The emerging microgrid concept in islanding and grid‐connected mode using different controller along with soft computing algorithm are presented. This paper gives the reader fast insight into the context of the controller and its application.

A blackout is usually the result of load increasing beyond the transmission capacity of the power system. A collapsing system enters a contingency state before the blackout. This contingency state is characterized by a decline in the bus voltage magnitudes. To avoid blackouts, power systems may start shedding load when a contingency state occurs called under voltage load shedding (UVLS). The success of a UVLS scheme in arresting the contingency state depends on shedding the optimum amount of load at the optimum time and location. This paper proposes a hybrid algorithm based on genetic algorithms (GA) and particle swarm optimization (PSO). The proposed algorithm can be used to find the optimal amount of load shed for systems under stress (overloaded) in smart grids. The proposed algorithm uses the fast voltage stability index (FVSI) to determine the weak buses in the system and then calculates the optimal amount of load shed to recover a collapsing system. The performance analysis shows that the proposed algorithm can improve the voltage profile by 0.022 per units with up to 75% less load shedding and a convergence time that is 53% faster than GA.