Multiobjective Optimization of Droop-Controlled Distributed Generators in DC Microgrids

DC microgrids (DCMGs) are becoming more popular in modern power systems due to their simplicity, efficiency, and reliability. Autonomous control of DCMG is primarily based on the droop control. Typically, the droop coefficients of each distributed generator (DG) are fixed and assigned based on their capacity. This article introduces a multiobjective optimization (MOO) based intelligent computation approach to derive the optimal droop coefficients for DGs in an islanded DCMG. The proposed approach takes into consideration not only the capacities of the DGs but also the system voltage regulation, system total loss minimization, and enhanced current sharing among the DGs. The Pareto optimal front of the constructed MOO problem is obtained using the elitist nondominated sorting genetic algorithm (NSGA II). A best compromise solution is extracted from the generated Pareto optimal front by employing a fuzzy membership function approach. Moreover, a state feedback linearization based controller is introduced to facilitate the control actions to experimentally validate the effectiveness and the applicability of the generated optimal droop relationships. The proposed approach was tested with a parallel connected dc 9-bus system, IEEE 30-bus system, and experimentally validated on a 5-bus system. However, the same concept can be extended to any other bus system without loss of generality.