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Sell or store? An ADP approach to marketing renewable energy

Author

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  • Jochen Gönsch

    (University of Duisburg-Essen)

  • Michael Hassler

    (University of Augsburg)

Abstract

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.

Suggested Citation

  • Jochen Gönsch & Michael Hassler, 2016. "Sell or store? An ADP approach to marketing renewable energy," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 38(3), pages 633-660, July.
    • Handle: RePEc:spr:orspec:v:38:y:2016:i:3:d:10.1007_s00291-016-0439-x
    DOI: 10.1007/s00291-016-0439-x
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    References listed on IDEAS

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    1. Nils Löhndorf & David Wozabal & Stefan Minner, 2013. "Optimizing Trading Decisions for Hydro Storage Systems Using Approximate Dual Dynamic Programming," Operations Research, INFORMS, vol. 61(4), pages 810-823, August.
    2. Juliana M. Nascimento & Warren B. Powell, 2009. "An Optimal Approximate Dynamic Programming Algorithm for the Lagged Asset Acquisition Problem," Mathematics of Operations Research, INFORMS, vol. 34(1), pages 210-237, February.
    3. Daniel R. Jiang & Warren B. Powell, 2015. "Optimal Hour-Ahead Bidding in the Real-Time Electricity Market with Battery Storage Using Approximate Dynamic Programming," INFORMS Journal on Computing, INFORMS, vol. 27(3), pages 525-543, August.
    4. Möst, Dominik & Keles, Dogan, 2010. "A survey of stochastic modelling approaches for liberalised electricity markets," European Journal of Operational Research, Elsevier, vol. 207(2), pages 543-556, December.
    5. Jae Ho Kim & Warren B. Powell, 2011. "Optimal Energy Commitments with Storage and Intermittent Supply," Operations Research, INFORMS, vol. 59(6), pages 1347-1360, December.
    6. Strbac, Goran, 2008. "Demand side management: Benefits and challenges," Energy Policy, Elsevier, vol. 36(12), pages 4419-4426, December.
    7. Yangfang (Helen) Zhou & Alan Scheller-Wolf & Nicola Secomandi & Stephen Smith, 2016. "Electricity Trading and Negative Prices: Storage vs. Disposal," Management Science, INFORMS, vol. 62(3), pages 880-898, March.
    8. Guoming Lai & François Margot & Nicola Secomandi, 2010. "An Approximate Dynamic Programming Approach to Benchmark Practice-Based Heuristics for Natural Gas Storage Valuation," Operations Research, INFORMS, vol. 58(3), pages 564-582, June.
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    Cited by:

    1. Finnah, Benedikt & Gönsch, Jochen & Ziel, Florian, 2022. "Integrated day-ahead and intraday self-schedule bidding for energy storage systems using approximate dynamic programming," European Journal of Operational Research, Elsevier, vol. 301(2), pages 726-746.
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    3. Benedikt Finnah, 2022. "Optimal bidding functions for renewable energies in sequential electricity markets," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(1), pages 1-27, March.
    4. Finnah, Benedikt & Gönsch, Jochen, 2021. "Optimizing trading decisions of wind power plants with hybrid energy storage systems using backwards approximate dynamic programming," International Journal of Production Economics, Elsevier, vol. 238(C).
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    6. Karakoyun, Ece Cigdem & Avci, Harun & Kocaman, Ayse Selin & Nadar, Emre, 2023. "Deviations from commitments: Markov decision process formulations for the role of energy storage," International Journal of Production Economics, Elsevier, vol. 255(C).
    7. Thijs Klauw & Marco E. T. Gerards & Johann L. Hurink, 2017. "Resource allocation problems in decentralized energy management," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(3), pages 749-773, July.
    8. Fokkema, Jan Eise & uit het Broek, Michiel A.J. & Schrotenboer, Albert H. & Land, Martin J. & Van Foreest, Nicky D., 2022. "Seasonal hydrogen storage decisions under constrained electricity distribution capacity," Renewable Energy, Elsevier, vol. 195(C), pages 76-91.
    9. Johann Hurink & Rüdiger Schultz & David Wozabal, 2016. "Quantitative solutions for future energy systems and markets," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 38(3), pages 541-543, July.
    10. Ioannis Boukas & Damien Ernst & Thibaut Th'eate & Adrien Bolland & Alexandre Huynen & Martin Buchwald & Christelle Wynants & Bertrand Corn'elusse, 2020. "A Deep Reinforcement Learning Framework for Continuous Intraday Market Bidding," Papers 2004.05940, arXiv.org.
    11. Keles, Dogan & Dehler-Holland, Joris, 2022. "Evaluation of photovoltaic storage systems on energy markets under uncertainty using stochastic dynamic programming," Energy Economics, Elsevier, vol. 106(C).
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    13. Rainer Baule & Michael Naumann, 2022. "Flexible Short-Term Electricity Certificates—An Analysis of Trading Strategies on the Continuous Intraday Market," Energies, MDPI, vol. 15(17), pages 1-28, August.

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