Power Optimization in DC Micro-grid with Monetary Perspective

Pressing environmental issues and the rising costs of combustion-based generation have forced utilities worldwide to explore alternate power generation technologies. Distributed generation, as an alternative, involves power generation at/vicinity of the loads. To this end, governments across the globe have promoted renewable distributed energy resources (DERs). Further, hybridization of the DERs enhances the reliability and resiliency of the power networks. DC micro-grid implementation using the hybrid DERs and energy storing devices has gained popularity due to the shift of load demand from predominantly native AC to DC. The prices of the equipment employed in a DC micro-grid significantly influence the implementation feasibility of the latter. Thus, this paper focuses on the DC micro-grid's power optimization through a meta-heuristic optimizer named social spider optimization (SSPO), including a comparative monetary analysis of the considered hybrid DERs. The proficiency of the SSPO has been verified through the MATLAB investigations.