Collaborative Research: Information Geometry for Model Verification in Energy Systems with Renewables

Brigham Young University, Provo UT

Investigators

Abstract

Emerging communication and computation capabilities have the potential to profoundly change and improve infrastructures such as electric power systems. The architecture and composition of modern power systems have been undergoing significant changes recently. These include new sources, such as gas-fired plants and co-generation facilities, and from new loads connected through power electronic converters and tightly controlled through local communication networks. The programmable nature of new sources and loads offers new capabilities, but at the same time necessitates frequently repeated model verification. Models preferred by energy engineers are often motivated by the physical properties of components and sub-systems. These models are typically nonlinear in terms of parameters. However, reliable identification of parameters from measurements is a challenging problem that is largely unsolved for the case of nonlinear models. This project aims to deploy new model verification tools that combine profound mathematical foundations (differential geometry and information theory) with modern computational algorithms. This project will have direct implications on other branches of engineering that use similar types of models. Within energy systems, this project has the potential to result in economic, environmental, and resilience benefits by enabling more precise operation of future electricity markets and control in actual power plants and customer sites. This project builds on computational advances in differential geometry, and offers a new, global characterization of challenges frequently encountered in system identification and model reduction of energy systems. The premise of this approach is that a model with many parameters is a mapping from a parameter space into a data or prediction space. A key difficulty in dealing with models of complex systems is the highly anisotropic nature of the mapping between the parameters and data spaces, meaning that small variations in parameter space may lead to dramatic changes in the measurement (data) space while other variations in parameters can lead to no discernable change in the in the model behavior. This project will use event recordings from daily operation (e.g., from phasor measurement units following line switchings and load variations) to motivate new model validation and selection algorithms. The long-term vision is to develop global and semi-global identification procedures for nonlinearly-parametrized energy components and systems, to establish limits of performance with phasor measurement unit sensors, to develop novel model reduction procedures, and to lay the groundwork for identification of large-scale energy systems. Specific goals include: 1) parameter identification for wind and solar plants, including more detailed manifold maps; 2) parameter identification for conventional sources (synchronous generators) and loads; and 3) re-parametrization and reduction for models that are typically employed in dynamic studies. Simulations will use industry-standard and custom software and recordings of hardware experiments to quantify progress. Anticipated results will be relevant for microgrids, virtual entities (virtual utilities, energy hubs) that are often considered essential in the long-term evolution of smart grids, and future electricity markets that will likely operate on shorter time-scales and thus depend on model fidelity of system dynamics.