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A Two-Stage Stochastic Optimisation Methodology for the Operation of a Chlor-Alkali Electrolyser under Variable DAM and FCR Market Prices

Author

Listed:
  • Jens Baetens

    (Electrical Energy Laboratory (EELAB), Department of Electromechanical, Systems and Metal Engineering, Ghent University, Tech Lane Ghent Science Park—Campus Ardoyen, Technologiepark-Zwijnaarde 131, 9052 Ghent, Belgium)

  • Jeroen D. M. De Kooning

    (Electrical Energy Laboratory (EELAB), Department of Electromechanical, Systems and Metal Engineering, Ghent University, Tech Lane Ghent Science Park—Campus Ardoyen, Technologiepark-Zwijnaarde 131, 9052 Ghent, Belgium
    FlandersMake@UGent.be—Corelab EEDT-MP, Flanders Make, 9052 Ghent, Belgium)

  • Greet Van Eetvelde

    (Energy & Cluster Management, Department of Electromechanical, Systems and Metal Engineering, Ghent University, Tech Lane Ghent Science Park—Campus Ardoyen, Technologiepark-Zwijnaarde 131, 9052 Ghent, Belgium
    INEOS Group, 1180 Rolle, Switzerland)

  • Lieven Vandevelde

    (Electrical Energy Laboratory (EELAB), Department of Electromechanical, Systems and Metal Engineering, Ghent University, Tech Lane Ghent Science Park—Campus Ardoyen, Technologiepark-Zwijnaarde 131, 9052 Ghent, Belgium
    FlandersMake@UGent.be—Corelab EEDT-DC, Flanders Make, 9052 Ghent, Belgium)

Abstract

The increased penetration of renewable energy sources in the electrical grid raises the need for more power system flexibility. One of the high potential groups to provide such flexibility is the industry. Incentives to do so are provided by variable pricing and remuneration of supplied ancillary services. The operational flexibility of a chlor-alkali electrolysis process shows opportunities in the current energy and ancillary services markets. A co-optimisation of operating the chlor-alkali process under an hourly variable priced electricity sourcing strategy and the delivery of Frequency Containment Reserve (FCR) is the core of this work. A short term price prediction for the Day-Ahead Market (DAM) and FCR market as input for a deterministic optimisation shows good results under standard DAM price patterns, but leaves room for improvement in case of price fluctuations, e.g., as caused by Renewable Energy Sources (RES). A two-stage stochastic optimisation is considered to cope with the uncertainties introduced by the exogenous parameters. An improvement of the stochastic solution over the deterministic Expected Value (EV) solution is shown.

Suggested Citation

  • Jens Baetens & Jeroen D. M. De Kooning & Greet Van Eetvelde & Lieven Vandevelde, 2020. "A Two-Stage Stochastic Optimisation Methodology for the Operation of a Chlor-Alkali Electrolyser under Variable DAM and FCR Market Prices," Energies, MDPI, vol. 13(21), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:21:p:5675-:d:437386
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    References listed on IDEAS

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    1. Crespo Cuaresma, Jesús & Hlouskova, Jaroslava & Kossmeier, Stephan & Obersteiner, Michael, 2004. "Forecasting electricity spot-prices using linear univariate time-series models," Applied Energy, Elsevier, vol. 77(1), pages 87-106, January.
    2. Lars Ivar Hagfors & Hilde Hørthe Kamperud & Florentina Paraschiv & Marcel Prokopczuk & Alma Sator & Sjur Westgaard, 2016. "Prediction of extreme price occurrences in the German day-ahead electricity market," Quantitative Finance, Taylor & Francis Journals, vol. 16(12), pages 1929-1948, December.
    3. Boroumand, Raphaël Homayoun & Goutte, Stéphane & Porcher, Simon & Porcher, Thomas, 2015. "Hedging strategies in energy markets: The case of electricity retailers," Energy Economics, Elsevier, vol. 51(C), pages 503-509.
    4. Alipour, Manijeh & Mohammadi-Ivatloo, Behnam & Moradi-Dalvand, Mohammad & Zare, Kazem, 2017. "Stochastic scheduling of aggregators of plug-in electric vehicles for participation in energy and ancillary service markets," Energy, Elsevier, vol. 118(C), pages 1168-1179.
    5. Roh, Kosan & Brée, Luisa C. & Perrey, Karen & Bulan, Andreas & Mitsos, Alexander, 2019. "Flexible operation of switchable chlor-alkali electrolysis for demand side management," Applied Energy, Elsevier, vol. 255(C).
    6. William E. Hart & Carl D. Laird & Jean-Paul Watson & David L. Woodruff & Gabriel A. Hackebeil & Bethany L. Nicholson & John D. Siirola, 2017. "Pyomo — Optimization Modeling in Python," Springer Optimization and Its Applications, Springer, edition 2, number 978-3-319-58821-6, December.
    7. Xiao, Liye & Shao, Wei & Yu, Mengxia & Ma, Jing & Jin, Congjun, 2017. "Research and application of a hybrid wavelet neural network model with the improved cuckoo search algorithm for electrical power system forecasting," Applied Energy, Elsevier, vol. 198(C), pages 203-222.
    8. Lago, Jesus & De Ridder, Fjo & De Schutter, Bart, 2018. "Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms," Applied Energy, Elsevier, vol. 221(C), pages 386-405.
    9. Otashu, Joannah I. & Baldea, Michael, 2020. "Scheduling chemical processes for frequency regulation," Applied Energy, Elsevier, vol. 260(C).
    10. Wang, Deyun & Luo, Hongyuan & Grunder, Olivier & Lin, Yanbing & Guo, Haixiang, 2017. "Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm," Applied Energy, Elsevier, vol. 190(C), pages 390-407.
    11. Yang, Zhang & Ce, Li & Lian, Li, 2017. "Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods," Applied Energy, Elsevier, vol. 190(C), pages 291-305.
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