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Duncan Anderson

Senior Research Analyst II: North American Power

Duncan Anderson, Senior Research Analyst at IHS Markit focuses on the development and operation of market simulation, big data, and advanced analytical models for the North American Power Analytics team.

Mr. Anderson has worked in electricity market modeling for almost a decade; developing, calibrating, and tailoring commercial and bespoke simulation products for consulting and retainer work focused on long-term power sector outlooks. At IHS Markit, he focuses primarily on using his broad technical skill set to develop new ways to process large volumes of power market data to produce actionable insights for clients and develop new products for the North American market.

Mr. Anderson is the lead analyst responsible for developing the FastLMP locational price forecasting tool for the North American market. He is also responsible for developing and maintaining the North American Power Market dashboards on Connect.

He holds a Bachelor of Science degree in Applied Economics from Cornell University and is currently completing a thesis in pursuit of a Masters of Science in Engineering degree in Industrial Engineering and Operations Research from UT Austin. His academic research focuses on the efficacy of clustering algorithms for infrastructure placement in simulated networks.

  • Climate and Sustainability
  • Clean Technology & Renewables
  • Data Science
  • Quantitative Research
  • Research and Development (R&D)

Watch a replay or join an upcoming event

EVENT REPLAY

Risky prospects: Forecasting nodal basis risk for wind project siting (AMER/EMEA)

19 Nov 2020 North America

This webinar explores the pitfalls of various (commonly used) heuristic methods for assessing basis risk and showcases FastLMP, a new IHS Markit tool that uses advanced analytics and machine learning to deliver accurate LMP basis forecasts while capturing the timing relationships between nodal basis formation and power production, examining wind project development as a use case. These relationships are key to understanding LMP basis risk.

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