Bio


Holger Teichgraeber is a Ph.D. candidate in the Department of Energy Resources Engineering at Stanford University. He is a Wells Family Stanford Graduate Fellow and is advised by Prof. Adam Brandt.

In his research, he focuses on applying state-of-the-art computational tools at the intersection of optimization and machine learning to energy systems problems. As an example, he has worked extensively on the development of new algorithms and applications of time-series aggregation for infrastructure planning and operations.
Out of his research, two open-source software packages have emerged: TimeSeriesClustering implements unsupervised learning methods for time-series data (git.io/TimeSeriesClustering), and CapacityExpansion provides an extensible, data-driven infrastructure planning tool for energy systems (git.io/CapacityExpansion).

Holger has previously interned at the battery software company Doosan GridTech; in the renewable energy forecasting division of Vaisala (formerly 3Tier); in the market optimization group at RWE Power, one of Europe's largest utility companies; and at ThyssenKrupp, one of the world's largest steel producers.

Honors & Awards


  • Outstanding Achievement in Mentoring Award, Stanford University (06/04/2019)
  • 2nd place winner, SunCode 2019 (largest US cleantech hackathon), Powerhouse (05/18/2019)
  • Grid Sciences Winter School & Conference Scholarship, Los Alamos National Laboratory (01/07/2019)
  • Centennial Teaching Assistant Award, Stanford University (06/17/2017)
  • Stanford Graduate Fellowship in Science and Engineering (SGF), VPGE Stanford University (09/01/2014)
  • Scholarship, Member, Studienstiftung des deutschen Volkes/ German National Academic Foundation (01/01/2011)

Professional Affiliations and Activities


  • Member, The Association of German Engineers (VDI - Verband Deutscher Ingenieure) (2011 - Present)

Education & Certifications


  • Ignite Certificate, Stanford Graduate School of Business, Ignite Program in Entrepreneurship and Innovation (2018)
  • MS, Stanford University, Energy Resources Engineering (2016)
  • BS, RWTH Aachen University, Mechanical Engineering (2014)
  • Exch. Student, Research Scholar, University of California, Davis, Chemical Engineering (2013)

Stanford Advisors


Work Experience


  • Analytics Intern, Doosan GridTech (7/2019 - 9/2019)

    Demand charge management: Built new software product using forecasting and optimization

    Location

    Seattle, WA

  • Research Intern, Vaisala (6/2015 - 9/2015)

    Determined the value of improved wind power forecasting in global electricity markets.

    Location

    Seattle, WA

  • Power Plant Engineering Intern, RWE Power AG (10/2013 - 1/2014)

    Location

    Germany

  • Undergraduate Researcher, UC Davis, Chemical Engineering (1/2013 - 6/2013)

    Modeling and Optimization of Hybrid Renewable Energy Systems, working with Ahmed Palazoglu & Nael El-Farra, UC Davis Process Systems Engineering

    Location

    Davis, CA

  • Technical Intern, Siempelkamp Giesserei (3/2011 - 3/2011)

    Location

    Germany

  • Engineering Intern, ThyssenKrupp Nirosta GmbH (5/2010 - 7/2010)

    Location

    Germany

All Publications


  • Clustering methods to find representative periods for the optimization of energy systems: An initial framework and comparison APPLIED ENERGY Teichgraeber, H., Brandt, A. R. 2019; 239: 1283–93
  • Design and operations optimization of membrane-based flexible carbon capture International Journal of Greenhouse Gas Control Yuan, M. 2019; 84: 154-163
  • Optimal design and operations of a flexible oxyfuel natural gas plant Energy Teichgraeber, H., Brodrick, P. G., Brandt, A. R. 2017; 141: 506-518
  • Identifying and Evaluating New Market Opportunities with Capacity Expansion Models Stanford Clean Energy Finance Forum Teichgraeber, H., Brandt, A. R. 2017: 1–12
  • CO2 vs Biomass: Identification of Environmentally Beneficial Processes for Platform Chemicals from Renewable Carbon Sources 12th International Symposium on Process Systems Engineering & 25th European Symposium on Computer Aided Process Engineering (PSE2015/ESCAPE25) Sternberg, A., Teichgräber, H., Voll, P., Bardow, A. 2015: 1361–66
  • An economic receding horizon optimization approach for energy management in the chlor-alkali process with hybrid renewable energy generation JOURNAL OF PROCESS CONTROL Wang, X., Teichgraeber, H., Palazoglu, A., El-Farra, N. H. 2014; 24 (8): 1318-1327