Professional Experience

Associate at the Bulk Grid group

E3, starting from 07/2021

Intern at Research Center for Energy Transition and Social Development

Tsinghua University, 08/2020 to present

  1. Research on worldwide best-practice showcases of tri-networks integration (energy, transportation, and information networks) to explore scenarios of reaching net-zero in future power and transportation industry.
  2. Lead a group of 20 students in Germany, China and the U.S.A to research on policy and market outlook of global solar industry. Published 5 editorials (in Chinese) introducing Germany’s recent solar subsidy policy, Slovenia’s Net Metering schema, Vienna’s citizen solar plants and blockchain, Biden’s renewable energy policy, Germany’s power grid with high penetration of renewables, etc.

Teaching Assistant for “Markets for Electric Power”

Duke University Nicholas School of the Environment, 01/2021-05/2020

  1. Grade assignments, holding office hours to answer questions related to basic economic and engineering concepts during electric power system operation.
  2. Guide students conducting quantitative analysis to examine the effect of different policies and market designs on building a resilient, reliable, economic, safe, and environmentally-sustainable electricity system.

Teaching Assistant for “Modeling for Energy Systems”

Duke University Nicholas School of the Environment, 08/2020-12/2020

  1. Create tutorials for helping students to install Python, optimization package Pyomo and solver cbc.
  2. Instruct students on Python debugging to create multiple linear programming models in lab sessions.
  3. Grading assignments, posting solutions, and holding office hour to answer student questions in a timely manner.

Mentorship program

Supervised by Daniel Munoz-Alvarez, research analyst at Wood Mackenzie Power & Renewables, 05/2020-08/2020

Global wind power generation algorithm: To integrate wind power into system dispatch models, having access to wind turbine outputs at specific location, under various heights, or from different turbine models during 24 hours in a day is essential. Under the guide of Daniel, I created an algorithm using Python and MySQL database to calculate hourly wind power generation with given technical parameters. The algorithm is organized as follows:

  1. Extract and mawind speed data at 2m, 10m and 50m height from NASA MERRA-2 dataset;
  2. Develop wind profile at each (latitude, longitude) point and store vertically interpolation coefficients in a remote database;
  3. Extrace wind profile coefficients and calculate wind power generation at a particular site based on user-specified turbine model, height and timing.

The algorithm can also help analysts to:

  • Compare daily /monthly / annual average potential wind generation at different sites for choosing prespective location to build new wind farms.
  • Estimate needs for storage system in a certain area based on aggregate wind power availability.

Contact me if you are interested in the code or detailed algorithm discription!

Intern at QA Department

Boston Scientific Corporation (Shanghai), 07/2017-08/2017

Data analysis can support QA activities and operations. In the summer of 2017, I interned at Boston Scientific (Shanghai) Company, Department of Quality & Assurance. My work includes analyzing monthly import and export of the company’s medical devices using Pivot Table, completing weekly reports detailing the key sale figures of twenty medical devices, to help colleagues keep track of sales data and prepare for stocks in advance.