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491 records · Page 28

Real-Time Optimization and Control of Next-Generation Distribution Infrastructure

This presentation highlights the key developments of the ARAP-e NODES project, including the innovative real-time optimal power flow (RT-OPF) algorithm for distributed energy resource (DER) management, trip planning for T+D coordination, and extensive validation of the RT-OPF algorithm in NREL ESIF laboratory with 100+ physical hardware devices, controller-hardware-in-the-loop test at Southern California Edison (SCE), and two field demonstrations.

hardware-in-the-loop↗

Evaluation of Centralized Model-Based FLISR in a Lab Setup: Preprint

Utilities are installing advanced distribution management systems (ADMS) around the globe to improve the sensing and control of distribution systems. ADMS is becoming a critical component to improve the resiliency and reliability of distribution systems. These management systems host a multitude of applications that can be used to sense, control, and operate distribution systems. Fault location, isolation, and service restoration (FLISR) is one of the ADMS applications that is critical to improving resilience during fault conditions. FLISR applications use smart, controllable devices that are installed in the distribution system for FLISR operation. These controllable devices include distributed energy resources (DER) and reclosers. Evaluating such ADMS applications before installation in the field can help de-risk the field implementation and avoid costly failures in the field. This paper presents a background on experimental setup that are typically used to evaluate ADMS applications. This is followed by briefly presenting the setup used to evaluate the FLISR application in an off-the-shelf ADMS tool. Finally, results from the evaluation experiments are presented.

94 GMLC - Grid Modernization Laboratory Consortium↗

Power Hardware-in-the-Loop Interfaces for Inverter-Based Microgrid Experiments Including Transitions: Preprint

Microgrids continue to proliferate, particularly those that rely on inverter-based resources as the microgrid's voltage and frequency leader. Hardware-in-the-loop (HIL) simulations can de-risk microgrid deployments, and therefore we need power-hardware-in-the-loop (PHIL) interfaces for grid-forming (GFM) inverters to enable simulations of seamless transitions between islanded and grid-connected operation. Such microgrid interfaces are particularly challenging to model when the inverters need to switch modes, i.e., between grid-following (GFL) and GFM as the microgrid transitions between grid-connected and islanded operation. This paper presents two PHIL interfaces for such microgrids and the preliminary experimental results: One interface is suitable for those who have access to a power amplifier that can operate as a controllable current source, and the other is for those who need a solution that uses a voltage-mode power amplifier.

battery energy storage system↗

A CHIL Validation of Machine Learning-Assisted Methods for Real-Time Controls of Solar PV for Grid Services: Preprint

Recent research has highlighted the potential for solar to act as a zero-marginal-cost and zero-emission flexibility resource on the bulk power system when operated with advanced control systems. To increase the performance of these systems, leading technologies, including machine learning (ML) and hierarchical inverter set point allocation, have been proposed; however, these technologies lack comprehensive validation under real-world application scenarios. This paper addresses this gap by designing and developing a controller-hardware-in-the-loop framework to evaluate the performance of different flexible solar technologies in responding to automatic generation control signals in a closed-loop fashion. Simulation results indicate the superior performance of an ML-based approach compared to the conventional reference-control grouping-based approach, showcasing its potential to support grid stability and operational efficiency.

closed-loop validation↗

Alfalfa

Alfalfa-based testbeds enable building equipment, control products, and workforce development tools to interact with dynamic building simulations representing the desired building, system, weather, and grid configuration. Alfalfa is used to de-risk implementation of load flexibility prior to field deployment, reducing the costs and timelines associated with adoption of decarbonization technology at the grid edge.

building energy modeling↗