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Scaling Performance Parameters of Reciprocating Engines for Sustainable Energy System Optimization Modelling

Author

Listed:
  • Ward Suijs

    (Department of Electromechanical, Systems and Metal Engineering, Ghent University, Sint-Pietersnieuwstraat 41, 9000 Ghent, Belgium)

  • Sebastian Verhelst

    (Department of Electromechanical, Systems and Metal Engineering, Ghent University, Sint-Pietersnieuwstraat 41, 9000 Ghent, Belgium
    Department of Energy Sciences, Lund University, Ole Römers väg 1, Box 118, SE-221 00 Lund, Sweden)

Abstract

The increased share of variable renewable energy sources such as wind and solar power poses constraints on the stability of the grid and the security of supply due to the imbalance between electricity production and demand. Chemical storage or power-to-X technologies can provide the flexibility that is needed to overcome this issue. To quantify the needs of such storage systems, energy system optimization models (ESOMs) are used, guiding policy makers in nationwide energy planning. The key input parameters for such models are the capacity and efficiency values of the conversion devices. Gas turbines, reciprocating engines, fuel cells and Rankine engines are often mentioned here as cogeneration technologies. Their performance parameters will however need to be revised when switching from fossil to renewable fuels. This study therefore investigates the possibility of using size-based scaling laws to predict the efficiency and power values of one type of conversion technology: the reciprocating engine. The most straightforward scaling laws are the ones based on the fundamental engine performance parameters and are constructed by fitting an arithmetic function for a large set of representative engine data. Their accuracy was tested with a case study, consisting of thirty large-bore, spark-ignited gas engines. Two alternative methods were also investigated: scaling laws based on the Willans line method and scaling laws based on the similarity theory. Their use is deemed impractical for the current research problem.

Suggested Citation

  • Ward Suijs & Sebastian Verhelst, 2023. "Scaling Performance Parameters of Reciprocating Engines for Sustainable Energy System Optimization Modelling," Energies, MDPI, vol. 16(22), pages 1-28, November.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:22:p:7497-:d:1276521
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    References listed on IDEAS

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    1. Ducduy Nguyen & Tanmay Kar & James W. G. Turner, 2023. "Performance, Emissions, and Combustion Characteristics of a Hydrogen-Fueled Spark-Ignited Engine at Different Compression Ratios: Experimental and Numerical Investigation," Energies, MDPI, vol. 16(15), pages 1-19, July.
    2. Stefania Falfari & Giulio Cazzoli & Valerio Mariani & Gian Marco Bianchi, 2023. "Hydrogen Application as a Fuel in Internal Combustion Engines," Energies, MDPI, vol. 16(6), pages 1-13, March.
    3. Cephas Samende & Zhong Fan & Jun Cao & Renzo Fabián & Gregory N. Baltas & Pedro Rodriguez, 2023. "Battery and Hydrogen Energy Storage Control in a Smart Energy Network with Flexible Energy Demand Using Deep Reinforcement Learning," Energies, MDPI, vol. 16(19), pages 1-20, September.
    4. Yun, Kyung Tae & Cho, Heejin & Luck, Rogelio & Mago, Pedro J., 2013. "Modeling of reciprocating internal combustion engines for power generation and heat recovery," Applied Energy, Elsevier, vol. 102(C), pages 327-335.
    5. Moret, Stefano & Codina Gironès, Víctor & Bierlaire, Michel & Maréchal, François, 2017. "Characterization of input uncertainties in strategic energy planning models," Applied Energy, Elsevier, vol. 202(C), pages 597-617.
    6. George Safonov & Vladimir Potashnikov & Oleg Lugovoy & Mikhail Safonov & Alexandra Dorina & Andrei Bolotov, 2020. "The low carbon development options for Russia," Climatic Change, Springer, vol. 162(4), pages 1929-1945, October.
    7. D'Errico, G. & Cerri, T. & Pertusi, G., 2011. "Multi-objective optimization of internal combustion engine by means of 1D fluid-dynamic models," Applied Energy, Elsevier, vol. 88(3), pages 767-777, March.
    8. Dagoumas, Athanasios S. & Koltsaklis, Nikolaos E., 2019. "Review of models for integrating renewable energy in the generation expansion planning," Applied Energy, Elsevier, vol. 242(C), pages 1573-1587.
    9. Plazas-Niño, F.A. & Ortiz-Pimiento, N.R. & Montes-Páez, E.G., 2022. "National energy system optimization modelling for decarbonization pathways analysis: A systematic literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
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