WORKSHOP 2025

Thursday – November 13, 2025 (Online)

TOPICS

Modern DC Transmission System Technologies
Dr. Gregory Kish, Associate Professor ECE

High-voltage DC (HVDC) transmission systems using classical line-commutated converter (LCC) technology have enjoyed great success over several decades for delivering gigawatt-scale power over long distances with very low losses. LCC-HVDC is also widely used for interfacing
asynchronous AC systems or AC grids with different frequencies. However, advancements in voltage-sourced converter (VSC) technology over the past 20+ years, in particular the development of modular multilevel converters (MMCs), have made newer VSC-HVDC systems
the state-of-the-art solution for modern applications that have long been challenging for classical LCC-HVDC to accommodate, primarily the grid integration of offshore wind and the development of multiterminal systems including DC grids. Hybrid LCC/VSC-HVDC systems have also started to emerge, where the idea is to combine the best traits of each technology. Regardless, both LCC-HVDC and VSC-HVDC systems are currently experiencing rapid levels of global growth. Related to the surging global demand in HVDC technologies, Flexible AC Transmission System (FACTS) controllers are also being increasingly utilized. FACTS controllers are a collection of power electronic devices that provide rapid and precise control of the interrelated parameters that govern the operation of transmission systems (Z, I, V). They often (but not always) accompany HVDC installations as they can substantially improve power transfer capacity and voltage regulation capabilities, through the control of line active and reactive power flows. HVDC and FACTS are therefore key technologies shaping the future power system landscape.

The talk will be delivered in three parts. The first part will an overview of LCC-HVDC and VSC- HVDC technologies and discuss their operating principles and benefits and tradeoffs. The secon part will discuss several example HVDC systems inspired by real-world projects as different case
studies, covering different applications such as point-to-point and back-to-back links, grid integration of offshore wind power, multi-terminal systems and DC grids. The third part will give an overview of FACTS technologies and discuss the core fundamentals and operating principles
of several commonly utilized FACTS controllers.

Next-Generation Power Systems – From Flexible Demand to AI-Driven Grids
Dr. Yize Chen, Assistant Professor, ECE

Untapping Demand-Side Flexibility: Current States, Mechanisms, and New Methods
As power systems transition toward high penetrations of renewable generation and widespread electrification, demand-side flexibility is emerging as a critical resource for maintaining grid reliability, mitigating congestion, and reducing wholesale price volatility. At the same time, new sources of flexible demand, including electric vehicles, behind-the-meter solar-plus-storage, and
smart building systems, are rapidly expanding and reshaping the potential of demand-side participation.
This talk will survey the current state of demand-side flexibility, reviewing both the mechanisms currently deployed in North America and the barriers that have limited broader adoption. We will then explore recent research advances in three areas:

Parameter estimation and characterization of flexible loads,

2. Modeling and optimization frameworks for coordinating and aggregating diverse flexibility programs, and

3. Planning approaches for integrating flexible demand into future low-carbon grids. Finally, we will discuss open challenges—such as
uncertainty, customer engagement, and market design—that must be addressed to fully unlock the value of demand-side flexibility in the energy transition.

Grid Modeling and Visualization with Open Source Data
Power grids are experiencing fast transformations nowadays, with exciting and heterogenous projects on generation, transmission, and demand sides. To guide through this ever-changing landscape and get better prepared for emerging energy resources and unseen
load conditions, this talk summarizes our recent explorations of methods and practices for power grid modeling and visualization through a data science and machine learning perspective. By utilizing ubiquitous geographical, electrical, and socioeconomic data, we are able to reverse
engineer realistic power networks with learned line parameters. We validate such a model using market clearing data and power systems’ emission data, suggesting the promises of uncovering unknown patterns in our grids. A visualization paradigm will be also discussed based
on a working Alberta grid case study.
Power for AI, AI for Power
The rapid rise of artificial intelligence (AI) is creating unprecedented electricity demand driven by data centres and high-performance computing facilities. Meeting this demand requires new strategies for planning and operating power systems that are more reliable, resilient, and sustainable. This “Power for AI” challenge spans efficient siting of data centres, enhancing grid interconnections and ride-through capabilities, managing cooling and power density requirements, and integrating renewable energy to support carbon-aware AI workloads. At the same time, AI is becoming a powerful tool for transforming the way we design and operate power systems – “AI for Power.” Recent advances in machine learning have enabled more accurate short-term load forecasting, robust parameter estimation for power system models, and intelligent control for integrating variable renewables and flexible demand. Moreover, decision- focused learning and reinforcement learning are opening new directions for system optimization and real-time operations.
This talk will explore both sides of this synergy: how the growth of AI reshapes the needs of modern power systems, and how AI methods can help solve long-standing challenges in forecasting, control, and planning. We will also discuss opportunities and risks, including data
privacy, explainability, and ensuring reliable performance of AI-driven decision-making in critical energy infrastructure.

AGENDA

Time	Topic
8:30am – 8:40am	Introduction and opening remarks.
8:40am – 9:40am	LCC-HVDC and VSC-HVDC Technologies
9:40am – 9:50am	Break
9:50am – 10:50am	HVDC System Case Studies
10:50am – 11:00am	Break
11:00am – 12:00pm	FACTS Controllers
12:00pm – 12:10pm	Wrap up and closing remarks.
12:10pm – 1:00 pm	Lunch
1:00pm – 1:50pm	Untapping Demand-Side Flexibility: Current States, Mechanisms, and New Methods
1:50pm – 2:00pm	Break
2:00pm – 2:50pm	Grid Modeling and Visualization with Open Source Data
2:50pm – 3:00pm	Break
3:00pm – 3:55pm	Power for AI, AI for Power
3:55pm – 4:00pm	Wrap up and closing remarks.

SPEAKERS

Dr. Gregory Kish received the BESc degree in electrical engineering from the University of Western Ontario, where he was awarded the Governor General’s Silver Academic Medal, and received both his MASc and PhD degrees from the University of Toronto. From 2002 to 2005, Dr. Kish worked in industry where he was involved in many projects related to electrical energy systems, ranging from PLCs, electric drives and rotating machines to high-voltage electrical substations. He joined the University of Alberta in 2015.

Dr. Kish is a senior member of the IEEE, and a member of the IEEE Power and Energy Society (PES), IEEE Power Electronics Society (PELS) and IEEE Industrial Electronics Society (IES). He is also a member of CIGRE. From 2017-2021, he was involved with CIGRE international Working Group B4.76 “DC-DC converters in HVDC Grids and for connections to HVDC systems”. Dr. Kish is a Professional Engineer in Alberta (APEGA) and Ontario (PEO).

Yize Chen is an assistant professor with ECE Department at the University of Alberta. He got his Ph.D. degree in Electrical and Computer Engineering from University of Washington in 2021; and his undergraduate degree from Chu Kochen College at Zhejiang University in 2016. He was a postdoctoral researcher at the Computing Sciences Area of Lawrence Berkeley National Lab. He has also held multiple research positions at ISO New England, Microsoft Research, Los Alamos National Laboratory, and Harvard Medical School, working on a set of novel projects related to data centers, power grid infrastructures, biological dynamics and the built environment. Previously he was an assistant professor at Artificial Intelligence Thrust of Hong Kong University of Science and Technology, also affiliated with the Department of Computer Science and Engineering at HKUST.

Yize’s research focuses on the intersection between control, optimization and machine learning, and he is interested in designing cyber-physical systems, especially power systems with performance guarantees. He is also committed to achieving sustainable and autonomous clean energy systems. He is also a recipient of several best paper and prize paper awards at IEEE PES General Meeting (2024, 2022), Power Systems Computation Conference (PSCC) (2020), and ACM e-Energy (2019).