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The DC/DC converters are commonly used in DC microgrids to achieve the desired performance of a system under different operating conditions. The dynamics of an isolated DC microgrid with solar photovoltaic panels, battery, and constant power load is a fractional nonlinear model; thus, the complex nonlinear method should be applied for controller design and stabilizing the system. In multi-input multi-output systems with non-linear elements, non-linear control methods should be used, but this paper presents a straightforward technique for designing decentralized controllers for the buck, boost, and buck-boost converters in an isolated DC microgrid to stabilize the system and implement Maximum Power Point Tracking (MPPT) control of the solar photovoltaic panels. The presented methods consider the nonlinear decentralized feedback linearizing control for the nonlinear system. Depending on the performance of the converters in the system, proper dynamic models are introduced for the converters, and decentralized controllers are determined. Various scenarios are simulated and compared to conventional methods. The results indicate that the settling time of the output variables and the overvoltage of the DC-link voltage are reduced by less than 30% compared to other techniques. [ABSTRACT FROM AUTHOR]

In this study, a novel method called the Continuous–Discrete Student Psychology-Based Optimization (CD-SPBO) is introduced. The technique is designed to address the simultaneous allocation of renewable distributed generators (DGs) and distribution static compensators (D-STATCOMs). A novel multi-objective function is formed by combining indices concerning the active power loss, voltage profile, voltage stability and cost consisting of investment, penalty for violation of environmental limits, and energy loss, which are combined through analytic hierarchical process. The CD-SPBO algorithm is used for solving the multi-objective allocation problem for non-dispatchable solar PV-DGs, dispatchable biomass DGs, D-STATCOMs, and their combinations. The superiority of the CD-SPBO algorithm was both numerically and statistically established by comparing its results with other state of the art methods such as Gorilla Troop Optimizer, Artificial Humming Bird Optimizer, and Harris Hawk Optimizer for simultaneous allocation of one, two, and three pairs of DGs and D-STATCOMs on 33-bus and 118-bus test systems. Further, the simulation findings involving seven distinct planning scenarios for the allocation of DGs and D-STATCOMs supported the CD-SPBO algorithm's effective execution for simultaneous optimal allocation of dispatchable DGs, non-dispatchable DGs, and D-STATCOMS. The suitability of different planning scenarios for improving the overall performance of the distribution system are analyzed in detail. The research insights may prove beneficial for network planners in determining the best combination of devices to meet their needs. [ABSTRACT FROM AUTHOR]