Christian Kaps

Christian Kaps
  • Doctoral Candidate

Contact Information

Research Interests: Renewable electricity, energy storage, sustainable operations.

Links: Personal Website

Research

  • Bert M. Balk, M.B.M. (Rene) De Koster, Christian Kaps, Jose L. Zofio (2021), An evaluation of cross-efficiency methods: With an application to warehouse performance, Applied Mathematics and Computation, 406. https://doi.org/10.1016/j.amc.2021.126261

  • Christian Kaps, Simone Marinesi, Serguei Netessine (2021), When Should the Off-grid Sun Shine at Night? Optimum Renewable Generation and Energy Storage Investments, Major Revision at Management Science.

    Abstract: Renewables have become the cheapest energy source in most of the world, but their generation remains variable and difficult to predict. Recent technological advances have rendered large-scale electricity storage economically viable, thus mitigating the renewable intermittency issue and enabling combinations of renewable generation plus storage, e.g., wind farms and batteries, to be potentially viable candidates to replace fossil fuel power plants. However, it is not yet well-understood how to jointly determine optimal capacity for their generation and storage. Our work aims to shed light on this question by developing a two-product newsvendor model of a utility's strategic capacity investment in renewable generation and storage to match demand with supply, while using fossil-fuel backup, if needed. We establish optimal capacity guidelines contingent on market and technology parameters. We find that renewable generation and storage are strategic complements, except in cases with very high penetration of either technology, when they surprisingly turn into strategic substitutes. We also develop sufficient and necessary conditions, as well as a simple heuristic, to determine which of the many storage technologies can turn a profit in the broadest set of market conditions, and thus is likely to be adopted first (for multi-hour storage). In particular, we show that low-efficiency, cheap technologies, such as thermal, can turn a profit easier than high-efficiency, expensive ones, such as lithium-ion batteries. Using real-life data from Europe and the US, we calibrate our model to provide insight on the role that large-scale storage plays in both the short- and long-term, while technology improves, emission taxes are levied, and renewables become cheaper. Finally, we show that, when generation and storage investment decisions are made by different players, investment levels decrease relative to the monopoly outcome.

Teaching

All Courses

  • OIDD0001 - Prescriptive Analytics

    In this course, we will explore the subject of quantitative business decision making. Specifically, we will study optimization and simulation tools and provide you with a set of key skills in the area of prescriptive analytics. We will illustrate the use of these tools in a variety of business applications, including manufacturing, logistics, inventory management, capital budgeting, insurance, and revenue management.

Activity

Latest Research

Bert M. Balk, M.B.M. (Rene) De Koster, Christian Kaps, Jose L. Zofio (2021), An evaluation of cross-efficiency methods: With an application to warehouse performance, Applied Mathematics and Computation, 406. https://doi.org/10.1016/j.amc.2021.126261
All Research