Lebenslauf:

01/2016 - 10/2021

Wissenschaftlicher Mitarbeiter am Lehrstuhl für Betriebswirtschaftslehre, insb. Service Operations

04/2014 - 01/2016:

Master-Studium der Mathematik an der FernUniversität Hagen:

Master of Science mit der Vertiefung Numerische Mathematik und Analysis

Thema der Masterarbeit:

Lineare Tschebyscheff-Approximation auf diskreten Mengen

09/2010 - 02/2014:

Bachelor-Studium der angewandten Mathematik an der Fachhochschule Bielefeld:

Bachelor of Science

Thema der Bachelorarbeit:

Quaternionen und hyperkomplexe Zahlensysteme zur Darstellung von Drehungen

09/2013 - 12/2013:

Praktikum bei Minda Industrieanlagen GmbH in Minden
Bereich: Softwareentwicklung

Publikationen:

• Finnah, B.; Otto, A.; Gönsch, J.: Overcoming Poor Data Quality: Optimizing Validation of Precedence Relation Data. In: European Journal of Operational Research, Jg. 322 (2024) Nr. 3, S. 740-752. doi:10.1016/j.ejor.2024.11.009VolltextBIB DownloadKurzfassungDetails

  Insufficient data quality prevents data usage by decision support systems (DSS) in many areas of business. This is the case for data on precedence relations between tasks, which is relevant, for instance, in project scheduling and assembly line balancing. Inaccurate data on unnecessary precedence relations cannot be used, otherwise the recommendations of DSS may turn
  infeasible. So, unnecessary relations must be satisfied, diminishing the baseline problem’s solution space and the business result. Experts can validate the data, but their time is limited.
  We apply an optimization lens and formulate the data validation problem (DVP). Restricted by the available time budget, an expert dynamically receives queries about specific data entries and corrects or validates them. The DVP searches for an interview policy that states queries to the expert, each using up some of the time budget, in a way that maximizes the (weighted) number of removed precedence relations. We model the DVP as a dynamic program, derive optimal policies for several important special cases and design a heuristic interview policy LSTD. In a case study of an automobile manufacturer, this policy substantially reduces the stations’ idle time after selectively addressing about 8% of the data entries.
  We prove theoretically and numerically that data validation by experts can lead to significant savings. The number of queries required to validate the data exhaustively is much less than naive estimates. Additionally, the probability to remove an unnecessary precedence relation per query in a series of queries is high, even for simple interview policies.

• Finnah, B.; Ziel, F.; Gönsch, J.: Integrated Day-Ahead and Intraday Self-Schedule Bidding for Energy Storages Using Approximate Dynamic Programming. In: European Journal of Operational Research, Jg. 301 (2022) Nr. 2, S. 726-746. doi:10.1016/j.ejor.2021.11.010PDFBIB DownloadKurzfassungDetails

  Most modern energy markets trade electricity in advance for technical reasons. Thus, market participants must commit to delivering or consuming a certain amount of energy before the actual delivery. In Germany, two markets with daily auctions coexist. In the day-ahead auction market, the energy is traded in 60-minute time slots, which are further partitioned into 15-minute time slots for the intraday auction market. Because of the slow ramp-ups of nuclear and fossil power plants, these price-makers trade mostly in the day-ahead market. Only the residual energy is traded in the intraday market, where the market prices fluctuate substantially more. These fluctuations as well as the expected price difference between these markets can be exploited by fast ramping energy storage systems. We address the decision problem of an owner of an energy storage who trades on both markets, taking ramping times into account. Because the state variable of our dynamic programming formulation includes all features of our high-dimensional electricity price forecast, this problem cannot be solved to optimality. Instead, we use approximate dynamic programming. In a numerical study based on real-world data, we benchmark the algorithm against an adapted state-of-the-art approach from literature and an expectation model with a receding horizon. Furthermore, we investigate the influence of the price forecast on expected profit and demonstrate that it is essential for the dynamic program to capture the high dimensionality of the price forecast to compete with the expectation model, which does not suffer from the curses of dimensionality.

• Finnah, B.: Optimal Bidding Functions for Renewable Energies in Sequential Electricity Markets. In: OR Spectrum, Jg. 44 (2021). doi:10.1007/s00291-021-00646-9VolltextBIB DownloadDetails
• Finnah, B.; Gönsch, J.: Optimizing Trading Decisions of Wind Power Plants with Hybrid Energy Storage Systems Using Backwards Approximate Dynamic Programming. In: International Journal of Production Economics, Jg. 238 (2021), S. 108-155. doi:10.1016/j.ijpe.2021.108155PDFVolltextBIB DownloadKurzfassungDetails

  On most modern energy markets, electricity is traded in advance and a power producer has to commit to deliver a certain amount of electricity some time before the actual delivery. This is especially difficult for power producers with renewable energy sources that are stochastic (like wind and solar). Thus, short term electricity storages like batteries are used to increase flexibility.
  By contrast, long term storages allow to exploit price fluctuations over time, but have a comparably bad efficiency over short periods of time.
  In this paper, we consider the decision problem of a power producer who sells electricity from wind turbines on the continuous intraday market and possesses two storage devices: a battery and a hydrogen based storage system. The problem is solved with a backwards approximate dynamic programming algorithm with optimal computing budget allocation. Numerical results show the algorithm’s high solution quality. Furthermore, tests on real-world data demonstrate the value of using both storage types and investigate the effect of the storage parameters on profit.

• Finnah, B.: Essays on Renewable Energies, Energy Storages and Energy Trading (1). 2020. BIB DownloadDetails

Vorträge:

• Jochen Gönsch, Benedikt Finnah: Approximative Dynamische Optimierung zur Steuerung von Energiespeichern - Workshop MaXFab 2020, , 10.12.2020, Online. Details
• Finnah, Benedikt; Gönsch, Jochen; Ziel, Florian: Integrated day-ahead and intraday self-schedule bidding for energy storages using approximate dynamic programming, Workshop der GOR-Arbeitsguppe "OR im Umweltschutz", 08.10.2020, Online. Details
• Finnah, Benedikt: Gebotskurven für erneuerbare Energien an den spanischen Strommärkten, Intensiv-Workshop Operations Research 2019, 08.10.2019, Würzburg. Details
• Finnah, Benedikt; Gönsch, Jochen; Ziel, Florian: Integrated day-ahead and intraday self-schedule bidding for energy storages using approximate dynamic programming, OR 2019, 05.09.2019, Dresden. Details
• Finnah, Benedikt: Approximate dynamic programming for energy storages in the continuous intraday market, Workshop on Intraday Electricity Markets, 02.04.2019, Cambridge. Details
• Finnah, Benedikt: Optimale Betriebsführung von Pumpspeicherkraftwerken an den deutschen Strommärkten, 4. Pricing-Workshop, 06.12.2018, Obergurgl. Details
• Benedikt Finnah, Jochen Gönsch: Backwards approximate dynamic programming for wind power plants with hybrid energy storage systems, OR 2018, 13.09.2018, Brüssel. Details
• Benedikt Finnah, Jochen Gönsch: Optimal control of a wind power plant with a hybrid energy storage system, Intensiv-Workshop Operations Research 2017, 04.10.2017, Würzburg. Details
• Benedikt Finnah, Jochen Gönsch: Optimal control of a wind power plant with a hybrid energy storage system, BKM Doctoral Workshop 2017, 25.07.2017, Duisburg. Details
• Benedikt Finnah, Jochen Gönsch: Optimale Betriebsführung von Windenergieanlagen mit hybriden Energiespeichersystemen, 27. Workshop der Quantitativen Betriebswirtschaftslehre, 13.03.2017, Sylt. Details