Abstract
•Stochastic optimization of the design parameters in distributed control of microgrids.•Advancement of the gradient-based power flow solution method for islanded microgrids.•Analysis of cooperatively operating microgrids with droop-controlled generation units.•Modeling of sequential sampling-based optimization procedure for nonlinear systems.
Microgrids can operate in a grid-connected or islanded mode and host renewable energy sources. However, there are significant challenges associated with enabling and securing the islanded microgrids’ operational control, with the problem of optimal selection of droop coefficients taking the lead. The problem of optimal selection of droop coefficients is further exacerbated due to the nonlinear nature of the system and stochasticity stemming from renewable energy sources and demand loads. We present a new solution approach to this problem by integrating a Newton-Raphson (NR) algorithm into a sequential sampling-based particle swarm optimization (PSO). The scalability and fast quadratic convergence of the NR algorithm make it ideal for solving the power flow equations of islanded microgrids. The ability of PSO to run without any assumptions on the structure of objective functions or constraints renders it a versatile option to carry out the optimization process. Simulation experiments show that the proposed approach improves the stability of a microgrid while minimizing active/reactive distribution losses within the network. Results further report significant improvements in the voltage profiles of the buses while maintaining a stable frequency during the islanded mode of operation within the considered microgrids in comparison to their conventional counterparts.
