Editor's Note: This is the third in a series of profiles provided by the Hydro Research Foundation that highlight potential future members of the hydroelectric power industry and their accomplishments.
The Hydro Research Foundation is actively supporting graduate students to conduct research related to conventional and pumped storage hydropower. These students are funded through the Department of Energy’s Water Power Program and industry partners.
Sue Nee Tan is graduating this summer from Cornell University, earning her doctoral degree in Environmental and Water Resources Systems Engineering. Sue Nee Tan grew up in the suburbs of Kuala Lumpur, Malaysia and first stepped foot in the US as a high school exchange student in New Jersey. She enjoyed her exchange year immensely, and decided to attend Lehigh University after completion of her exchange program in the fall of 2004. Sue Nee graduated in 2009 with Highest Honors with dual Bachelor of Science degrees in Civil Engineering and Earth & Environmental Science. Through this experience has learned about the complex nature of the power system in the Northwest. Upon graduation Sue Nee will take a full time position with Pacific Gas and Electric.
Sue Nee’s research focused on the Stochastic Dynamic Programming Approach to Balancing wind Intermittency with Hydropower. The goal of the research project was to construct a portfolio of systems analysis methods to analyze hydropower and its increased value in power grids involving intermittent renewable energy sources. Of particular interest is the symbiotic interaction between hydropower and wind operation. Specifically, she wishes to develop statistical methods for analyzing daily trends in hydro- and wind power generation and pricing, and use optimization and simulation methods to optimize hydropower operations when there is a high penetration of wind in the grid.
The research explored the interaction between the statistical variability of both wind and inflow to hydropower reservoirs and the price structure for supplying hydropower almost immediately when wind unexpectedly dies down. This indicates which kinds of policies are most effective for an efficient, economical and reliable power supply.
To improve upon existing methods, the research proposed using more stochastic optimization algorithms that have been applied previously in both reservoir management and unit commitment for power generation but have not previously been used with wind or other renewables. By doing so, it can provide more realistic operation schedules and estimates of expected revenue from generating hydropower when a high penetration of intermittent wind sources is in the power grid.