Microgrids are essential for improving resilience and reliability of the distribution networks, however, diverse and often incompatible microgrid control technologies has impeded rapid scalability and deployment. Microgrid Building Blocks (MBBs) present a potential solution to address these interoperability challenges in control and communication systems. This paper proposes a comprehensive testing and validation framework for MBBs, utilizing advanced National Renewable Energy Laboratory (NREL) physical assets and emulators in conjunction with Hardware-in-the-Loop (HIL) and Power Hardware-in-the-Loop (PHIL) simulations, as a part of the ARIES platform. ARIES platform is well-equipped for an analysis of widely-used industrial protocols including Modbus, IEC 61850, and DNP3, as well as emerging communication technologies being used in the energy sector. The framework incorporates a performance metric for system evaluation, focusing on voltage and frequency stability, as well as communication latencies originating from MBB modules. The proposed framework aims to ensure rigorous validation of MBBs prior to their industrial deployment, thereby facilitating standardization and interoperability in microgrid control systems. A test case was performed with practical microgrid to verify the communication impacts.

This paper focuses on enhancing the performance of hydro-emulation platforms by investigating the impact of inertial and machine losses on dynamic characterization. Utilizing power-hardware-in-the-loop experiments, a compensating strategy is proposed to minimize these effects. Experimental validation demonstrates the significant influence of inertia and machine losses on emulation platform dynamics. Signal manipulation techniques are employed to achieve optimal performance, facilitating accurate characterization of system dynamics. The results demonstrate that the compensated scenarios, especially with torque dynamics characterization, improve normalized root mean square error by greater than 30% compared to the uncompensated scenario.

This paper proposes a novel integrated multiport bidirectional back-to-back power converter for a type-4 wind turbine that accommodates a battery and supercapacitor for energy storage. The circuit topology reduces 4 switches compared to the traditional configuration. Moreover, owing to the dual-buck structure embedded in the phase leg, the circuitry has no short-circuit path, therefore it withstands short-circuited events for a much longer time than the normal phase-leg and prevents the reverse current in turn-off recovery. The use of a hybrid energy storage system with battery and supercapacitor helps smooth out the power output under wind gusts and stabilizes the DC-link voltage under grid fault conditions. The case studies are carried out with a 1.5 MW wind turbine system. Simulation results are provided for the theoretical validation.

Hydrogen electrolyzers, with their operational flexibility, can be configured as smart dynamic loads which can provide grid services and facilitate the integration of more renewable energy sources into the electrical grid. However, to enable this ability, the electrolyzer system should be able to control both active and reactive power in coordination with the low-level controller of the electrolyzer via the power electronics system interface between the utility grid and electrolyzer. This paper discusses power converter topologies and the control scheme of this power electronics interface for electrolyzer applications to enable electricity grid services. For the sake of unity, in this paper, we consider the power converter system interfacing the utility grid at the line-to-line root mean square (RMS) value of 480 VAC-60 Hz and supplying to the 3500 A-750 kW PEM electrolyzer stack.

In this paper, we present a novel distributed state estimation approach in networked DC microgrids to detect the false data injection in the microgrid control network. Each microgrid monitored by a distributed state estimator will detect if there is manipulated data received from their neighboring microgrids for control purposes. A dynamic model supporting the dynamic state estimation will be constructed for networked microgrids. The optimal distributed state estimation, which is robust to load disturbances but sensitive to false data injected from neighboring microgrids will be presented. To demonstrate the effectiveness of the proposed approach, we simulate a 12kV three-bus networked DC microgrids in MATLAB/Simulink. Residual information corresponding to the false data injected from neighbors validates the efficacy of the proposed approach in detecting compromised agents of neighboring microgrids.