Interconnected multi-area microgrids are the future of sustainable and reliable power systems. Microgrids require effective load frequency control for stable operation. This paper presents a proportional–integral–derivative (PID)based load frequency controller(LFC) for a stochastic microgrid power system having multiple power sources viz. 6 solar, wind, diesel engine generator, and electrical battery. The gain parameters of the proposed microgrid PID LFC 7 regulator were optimized by using a genetic algorithm (GA), teaching learning-based optimization (TLBO), and cohort 8 intelligence algorithms. The integral time-multiplied absolute error (ITAE) and integral time-squared error(ITSE) were 9 used as the cost functions for all algorithms. The performances of all optimized microgrid PID LFC architectures 10 were examined by applying random step load disruption conditions. Primary results show that the cohort intelligence 11 optimized PID LFC controller minimizes the computation time and attains superior robust response characteristics. The 12 cohort intelligence algorithm also consumes a lesser number of iterations and improves the power supply quality in the 13 multi-power micro-grid electrical power framework with regard to effective load frequency control characteristics.
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