Abstract: The rapid increase of renewable generation and its anticipated continued growth requires greater operational flexibility in modern power systems. One of the methods to increase flexibility in the generation side is to enhance existing generators such as combined-cycle natural gas power plants which, are widely used. Potential flexibility upgrades to existing thermal plants include modifications that enable faster ramp rates, increase maximum load levels, decrease minimum load levels, and provide faster and lower cost startup operations. This research analyzes the effect that specific flexibility upgrades have on the operation of existing natural gas power plants and the value associated with these enhancements. In this research, we apply a unit commitment model to analyze the impact that each specific upgrade has on their operational dynamics and profitability from both the system perspective and from the asset owner perspective. The research publication, The Impacts of Distinct Flexibility Enhancements on the Value and Dynamics of Natural Gas Power Plant Operations, is available on (https://www.linkedin.com/company/ieee-xplore/) as an Early Access paper, (https://ieeexplore.ieee.org/abstract/document/9442870?casa_token=StBxY3MHGGoAAAAA:jd9KpVJbEtEZKh25rezXv99IaES4Q0BGr7u2rB5rEAm1mxxcBCsQtK_UGchZ6kLb185qR9EM1Q). Key takeaways: - Enhancing combined cycle plant flexibility DOES reduce system operator and consumer cost while maintaining system reliability - Enhancing flexibility DOES increase unit owner net revenue (profit) - Benefits of flexibility DEPEND on: type of flexibility enhancement, current unit capacity factor, and unit historical usage pattern - Implementing multiple types of flexibility simultaneously on a given power plant is more valuable than the sum of individual enhancements - The greater the share of non-dispatchable wind and solar capacity on the grid, the greater the value of combined cycle plant flexibility - Market designers should consider additional market mechanisms and incentives for existing generators to invest in increased flexibility Speaker(s): Arupam Narula, Bob Bellis Agenda: 11:45 Check-in and Presentation Technology Check 12:00 Presentation 12:50 Q&A Atlanta, Georgia, United States, Virtual: https://events.vtools.ieee.org/m/278042

. In recent years, advances in machine learning, parallelization and the speed of graphics processing units (GPUs), combined with the availability of open, easily accessible implementations, have brought deep neural networks (DNNs) to the forefront of research in many fields. Likewise, deep learning has offered significant performance gains in the classification of radar micro-Doppler signatures, paving the way for new civilian applications of RF technologies that require a greater ability to recognize a larger number of classes that are similar in nature. However, RF data has a fundamentally different phenomenology from other application areas of deep learning, such as computer vision or natural language processing. The signal received from radar is a complex time-series, whose amplitude and phase are related to the physics of electromagnetic scattering and kinematics of target motion. Radar data oftentimes undergoes a number of filtering and analysis stages before being presented to a DNN as two- or three-dimensional data. Thus, DNN approaches, popular in other fields, do not necessarily translate to the RF domain and their benefits must be separately reassessed. A related challenge, which further necessitates the exploitation of a physics-based, knowledge-aided signal processing approach to DNN design and training, is the small amount of measured data that is available in RF applications. This talk will discuss the unique aspects of application of deep learning to radar micro-Doppler classification, focusing on the problem of DNN training under low sample support. Generative Adversarial Networks (GANs) have achieved great success in synthetic data generation; however, we show how in the case of radar micro-Doppler signatures, misleading signatures with poor kinematic fidelity can also be generated and degrade classification performance. Methods for improving the quality of synthetic signatures generated through adversarial learning are discussed. Results and benefits for two case studies are considered: biomedical gait analysis and recognition of American Sign Language. Linguistic and kinematic insights into the application of deep learning as well as ways in which machine learning and radar signal processing can illuminate linguistics study via RF sensing are presented. Speaker(s): Dr. Gurbuz, Atlanta, Georgia, United States, Virtual: https://events.vtools.ieee.org/m/279263