This paper investigates integration of distributed energy resources (DERs) in microgrids (MGs) through two-stage power conversion structures consisting of DC-DC boost converter and DC-AC voltage source converter (VSC) subsystems. In contrast to existing investigations that treated DC-link voltage as an ideal constant voltage, this paper considers the non-ideal dynamic coupling between both subsystems for completeness and higher accuracy, which introduces additional DC-side dynamics to the VSC. The analysis shows parameters of the boost converter’s power model that impact stability through the DC-link. Carefully selecting these parameters can mitigate this effect on stability and improve dynamic performance across the DC-link. Hence, an optimization framework is developed to facilitate in selecting adequate boost converter parameters in designing a stable voltage source converter-based microgrid (VSC-MG). The developed optimization framework, based on particle swarm optimization, considers dynamic coupling between both subsystems and is also effective in avoiding inadequate boost converter parameters capable of propagating instability through the DC-link to the VSC. Simulations are performed with MATLAB/Simulink to validate theoretical analyses.

This paper investigates a novel engineering problem, i.e., security-constrained multi-period operation of micro energy-water nexuses. This problem is computationally challenging because of its high nonlinearity, nonconvexity, and large dimension. We propose a two-stage iterative algorithm employing a hybrid physics and data-driven contingency filtering (CF) method and convexification to solve it. The convexified master problem is solved in the first stage by considering the base case operation and binding contingencies set (BCS). The second stage updates BCS using physics-based data-driven methods, which include dynamic and filtered data sets. This method is faster than existing CF methods because it relies on offline optimization problems and contains a limited number of online optimization problems. We validate effectiveness of the proposed method using two different case studies: the IEEE 13-bus power system with the EPANET 8-node water system and the IEEE 33-bus power system with the Otsfeld 13-node water system.