Grid Optimization – Private LTE Provides the Optimal Foundation for Utilities

366 Pinnacle Ridge Rd, Rutland, Vermont, United States, 05701, Virtual:

This will be a hybrid meeting. For those who plan on attending in person, food and non-alcoholic drinks will be provided. Agenda: 5:00 pm - Intro 5:15 pm - Presentation 6:45 pm - Q & A 366 Pinnacle Ridge Rd, Rutland, Vermont, United States, 05701, Virtual:

Invited Talk: Dr. Timothy M. Hanses

Room 336 Fairfield Rd, Storrs, CT, United States

Abstract: The trend in electric power systems is the displacement of traditional synchronous generation (e.g., coal, natural gas) with renewable energy resources (e.g., wind, solar photovoltaic) and battery energy storage. These energy resources require power electronic converters (PECs) to interconnect to the grid and have different response characteristics and dynamic voltage and frequency stability issues compared to conventional synchronous generators. As a result, there is a need for next-generation methods to characterize and mitigate PEC-based dynamic stability issues, especially for converter-dominated power systems (e.g., island power systems, remote microgrids). This talk will discuss recent advancements in dynamic state estimation and control of battery energy storage systems. A framework will be introduced to provide fast frequency dynamic voltage support for converter-dominated power systems using both model-based and model-free state estimation and control approaches. Model-based methods will first be introduced using reduced-order power system dynamics equations. Specifically, a moving horizon state and model parameter estimator provides dynamic state inputs to a model-predictive controller. These classic model-based methods are then compared to state-of-the-art model-free methods from machine learning; a neural ordinary differential equations (NODEs)-based framework will be described to infer critical state information of the power system frequency dynamics. The state information is used by a soft-actor-critic (SAC) reinforcement learning-based controller. The model-based and model-free methods are compared for performance and computational efficiency. The topics presented will have broad applicability to both undergraduate and graduate electrical and computer engineering students, including: - How is the global energy transition impacting electric power grid operations? - What is the interaction and future role of power electronics with the electric power system? and - What are the tradeoffs between complexity, accuracy, and computational tractability of traditional model-based and model-free machine learning approaches? Speaker Bio: Timothy M. Hansen (IEEE Senior Member 2020) received the B.S. degree in computer engineering with high honors from the Milwaukee School of Engineering, Milwaukee, WI, USA, in 2011, and the Ph.D. degree in electrical engineering from Colorado State University, Fort Collins, CO, USA, in 2015. In 2014-2015, he held a graduate research position in the Distributed Energy Systems Integration group at the National Renewable Energy Laboratory, Golden, CO, USA. He is currently the Harold C. Hohbach Endowed Associate Professor with the Electrical Engineering and Computer Science Department, South Dakota State University, Brookings, SD, USA. His research interests are in the application of optimization, high-performance/edge/quantum computing, and distributed stochastic control to the areas of sustainable power and energy systems, smart cities, robotics, and cyber-physical-social systems. Speaker(s): Timothy M. Hansen, Room: 336, Bldg: ITE Building, 371 Fairfield Way, Storrs, Connecticut, United States

ExComm November 2023 Meeting

Room: SNO 412, 100 Training Hill Road, Middletown, Connecticut, United States, 06457, Virtual:

Section Activities Agenda: Review Secretary and Treasury reports Discuss Affinity Group Activities - Consultant Networks, WIE, YP and PACE Review technical society activities - AP/MTT/UFFC, ComSoc/SP, Computyer/SMC, PES, PELS Review committee activities - Membership, Entrepreneur Society, Industry Liason, Student Activities Old Business New Business Room: SNO 412, 100 Training Hill Road, Middletown, Connecticut, United States, 06457, Virtual:

Distributed Load Changers on Modern Distribution Systems


This webinar will delve into various aspects of distributed loads, including different load modifiers considering both temporal and spatial aspects. We will look at their fundamental principles and operations to their real-world applications. It will feature presentations and insights from experts in the field, offering participants a comprehensive understanding of the following key areas: - Definition, types, and functions of new Distributed loads - The role of DLCs in modern grid systems – electrification - Benefits and Challenges: - How these loads drive the discussion around resilience - Challenges and potential solutions - Potential to support renewable energy integration and demand response. - Case studies showcasing a planning example. By participating in this webinar, attendees will gain valuable insights into the transformative potential of distributed load changers in modern distribution systems. Whether you are an industry professional, researcher, policymaker, or enthusiast interested in the future of power distribution, this event promises to be a rich source of knowledge. Co-sponsored by: Eversource Energy Center Virtual:


Bldg: CCSU Applied Innovation Hub, 1615 Stanley St, New Britain, Connecticut, United States, Virtual:

11/29 ExCom Bldg: CCSU Applied Innovation Hub, 1615 Stanley St, New Britain, Connecticut, United States, Virtual: