SmartEnergy

SmartEnergy: Integration erneuerbarer Energien in die Elektrizitätsversorgung

Fossile Energieträger wie Stein- oder Braunkohle sind nicht unbegrenzt verfügbar. Atomkraftwerke produzieren radioaktiven Abfall, dessen Endlagerung umstritten und ungeklärt ist. Eine umweltfreundliche, klimaschonende und dauerhafte Stromversorgung ist nur durch erneuerbare Energien möglich. Windkraft-, Solar oder Photovoltaikanlagen sind allerdings wetterabhängig. Ihre Integration in die Stromversorgung stellt eine Herausforderung für die Energiebranche dar, weil Strom nur schwer speicherbar ist und so immer sofort verbraucht werden muss. Vor diesem Hintergrund erforschen wir innovative Möglichkeiten zum wirtschaftlichen Ausgleich von Angebot und Nachfrage.

Management von Stromspeichern

Fossile Energieträger wie Stein- oder Braunkohle sind nicht unbegrenzt verfügbar. Atomkraftwerke produzieren radioaktiven Abfall, dessen Endlagerung umstritten und ungeklärt ist. Eine umweltfreundliche, klimaschonende und dauerhafte Stromversorgung ist nur durch erneuerbare Energien möglich. Windkraft-, Solar oder Photovoltaikanlagen sind allerdings wetterabhängig. Ihre Integration in die Stromversorgung stellt eine Herausforderung für die Energiebranche dar, weil Strom nur schwer speicherbar ist und so immer sofort verbraucht werden muss. Dies spiegelt sich auch in der Struktur der Elektrizitätsmärkte wieder: Meist müssen Kraftwerksbetreiber sich im Voraus auf eine zu liefernde Strommenge festlegen. Kann diese etwa von einem Windkraftbetreiber bei Flaute nicht eingehalten werden, drohen Strafzahlungen. Während erneuerbare Energien in der Vergangenheit – beispielsweise durch feste Einspeisevergütungen – meist vom Markt abgeschirmt wurden, sollen sie zunehmend in diesen integriert werden, um den notwendigen Ausgleich von Angebot und Nachfrage auch beim weiteren Zubau erneuerbarer Erzeugung zu gewährleisten.

Vor diesem Hintergrund wird am Lehrstuhl erforscht, wie Energiespeicher zur Marktintegration erneuerbarer Energien beitragen können. Hierzu wird zunächst ihr operativer Einsatz im Zusammenspiel mit Erzeugung und Vermarktung mit Hilfe innovativer Verfahren des Approximate Dynamic Programming (ADP) optimiert. Auf dieser Basis können dann verschiedene Speichertechnologien mit ihren jeweiligen Charakteristika evaluiert werden.

Bei der Bearbeitung des Themas kooperieren wir mit der BMBF-Nachwuchsgruppe ENREKON an der Universität Augsburg.

Lesen Sie hierzu auch das Interview mit forschung-energiespeicher.info.

Literatur

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  • Finnah, Benedikt: 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 DownloadDetails

    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.; 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 DownloadDetails

    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, Benedikt: Essays on Renewable Energies, Energy Storages and Energy Trading (1). 2020. BIB DownloadDetails
  • Berger, M.; Matt, C.; Gönsch, J.; Hess, T.: Is the Time Ripe? How the Value of Waiting and Incentives Affect Users’ Switching Behaviors for Smart Home Devices. In: Schmalenbach Business Review, Jg. 71 (2019) Nr. 2, S. 91-123. PDFVolltextBIB DownloadDetails

    Product-related and market-related uncertainties often cause users to defer from switching to new IT devices. There is a value of waiting (VoW) for users because waiting allows them to collect more information. At the same time, many IT switching decisions are increasingly complex due to increased connectivity and the resulting interdependencies between jointly used devices. Therefore, switching decisions for connected devices not only need to consider the new device in isolation, but must also account for the potential benefits from internally or externally connecting the device with other devices. Although crucial for users and providers alike, existing models cannot explain whether and when users switch in such connected

    environments.

    We focus on connected Smart Home Devices (SHDs) and simulate users’ actual switching timing based on a real options model which combines switching and deferral concepts in a context-specific setting. We examine how Smart Home Network (SHN) density influences switching and how providers can use incentives to accelerate switching to foster product diffusion. The findings show an accelerating effect of connectivity and a deferring effect of uncertainty on actual switching timing. We also learn that SHD providers should focus more on immediate than on delayed incentives to promote product diffusion, since the latter can also have undesired effects. Interestingly, external connectivity has almost no influence on decision timing in scenarios with highly dense SHNs, leading to further key implications for SHD providers.

  • Gönsch, J.; Hassler, M.: Sell or Store? — An ADP Approach to Marketing Renewable Energy. In: OR Spectrum, Jg. 38 (2016) Nr. 3, S. 633-660. PDFVolltextBIB DownloadDetails

    In deregulated markets, electricity is usually traded in advance, and the advance commitments have a time lag of several periods. For example, in the German intraday market, the seller commits to providing electricity 45 min before the 15-min interval in which delivery has to be made. We consider the problem of a producer that generates energy from stochastic, renewable sources, such as solar or wind and uses a storage device with conversion losses. We model the problem as a Markov Decision Process and consider lagged commitments for the first time in the literature. The problem is solved using an innovative approximate dynamic programming approach. Its key elements are the analytical derivation of the optimal action based on the value function approximation and a new combination of approximate policy iteration with classical backward induction. The new approach is quite general with regard to the stochastic processes describing the energy production and price evolution. We demonstrate the application of our approach by considering a wind farm/storage combination. A numerical study using real-world data shows the applicability and performance of the new approach and investigates how the storage device’s parameters influence profit.

  • Hassler, M.; Gönsch, J.; Krohns, S.: Optimierte Vermarktung von Energie aus stochastischen erneuerbaren Quellen – Be- oder Entlastung des Netzes?. In: Tagungsband Wissenschaftsdialog. Bundesnetzagentur, 2014, S. 129-145. BIB DownloadDetails