Engineering topics

Glass, Jim

Publications and source records attributed to Glass, Jim.

4 records

DOE OSTI2023

Flexible Boundary Design for a Chattanooga Microgrid Powered by Landfill Solar Photovoltaic and Battery Storage

Landfill based microgrids powered by renewable energy aid reliability and resiliency while promoting environmental and energy justice. This paper aims to design a flexible boundary algorithm for a proposed Chattanooga landfill microgrid with the ability to shrink or expand its boundaries based on the available power from the solar PV and battery storage. This helps to improve resiliency unlike the conventional fixed boundary microgrids. The flexible boundary algorithm determines the combination and switching status of the intellirupter ® which changes the microgrid boundaries to achieve power balance by dropping or energizing specific load sections. Finally, the microgrid with flexible boundary was simulated in MATLAB/Simulink and performed satisfactorily when tested under various scenarios.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Citadels Final Report (GMLC 2.2.1: Citadels)

This is the final project report for the Grid Modernization Laboratory Consortium (GMLC) Resilient Distribution System (RDS) Citadels project. The primary goal of this GMLC project was to increase the operational flexibly of power systems by engaging microgrids distributedly, coordinated using consensus algorithms. The primary goal was successfully achieved. The primary goal was divided into three areas: Implement peer-to-peer control between microgrid controllers using the Open Field Message Bus (OpenFMB) approach; Develop and implement consensus algorithms on commercially available hardware that allows a group of microgrids to distributedly implement operational controls; Develop the architectures and controls to enable groups of microgrids to coordinate their operations to support the bulk power system during abnormal events, and end-use loads in the event the bulk power systems fail.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

IMoFi (Intelligent Model Fidelity): Physics-Based Data-Driven Grid Modeling to Accelerate Accurate PV Integration Updated Accomplishments

This report summarizes the work performed under a project funded by U.S. DOE Solar Energy Technologies Office (SETO), including some updates from the previous report SAND2022-0215, to use grid edge measurements to calibrate distribution system models for improved planning and grid integration of solar PV. Several physics-based data-driven algorithms are developed to identify inaccuracies in models and to bring increased visibility into distribution system planning. This includes phase identification, secondary system topology and parameter estimation, meter-to-transformer pairing, medium-voltage reconfiguration detection, determination of regulator and capacitor settings, PV system detection, PV parameter and setting estimation, PV dynamic models, and improved load modeling. Each of the algorithms is tested using simulation data and demonstrated on real feeders with our utility partners. The final algorithms demonstrate the potential for future planning and operations of the electric power grid to be more automated and data-driven, with more granularity, higher accuracy, and more comprehensive visibility into the system.

14 SOLAR ENERGY↗

DOE OSTI2022

IMoFi - Intelligent Model Fidelity: Physics-Based Data-Driven Grid Modeling to Accelerate Accurate PV Integration (Final Report)

This report summarizes the work performed under a project funded by U.S. DOE Solar Energy Technologies Office (SETO) to use grid edge measurements to calibrate distribution system models for improved planning and grid integration of solar PV. Several physics-based data-driven algorithms are developed to identify inaccuracies in models and to bring increased visibility into distribution system planning. This includes phase identification, secondary system topology and parameter estimation, meter-to-transformer pairing, medium-voltage reconfiguration detection, determination of regulator and capacitor settings, PV system detection, PV parameter and setting estimation, PV dynamic models, and improved load modeling. Each of the algorithms is tested using simulation data and demonstrated on real feeders with our utility partners. The final algorithms demonstrate the potential for future planning and operations of the electric power grid to be more automated and data-driven, with more granularity, higher accuracy, and more comprehensive visibility into the system.

14 SOLAR ENERGY↗