IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i3p677-d316528.html
   My bibliography  Save this article

A Framework for the Synthesis of Optimum Operating Profiles Based on Dynamic Simulation and a Micro Genetic Algorithm

Author

Listed:
  • Erik Rosado-Tamariz

    (School of Engineering and Sciences, Tecnologico de Monterrey, Av. Eugenio Garza Sada Sur No. 2501, Col. Tecnologico, Monterrey 64849, Mexico
    Instituto Nacional de Electricidad y Energías Limpias (INEEL), Av. Reforma 113, Col. Palmira, Cuernavaca CP 62490, Mexico)

  • Miguel A. Zuniga-Garcia

    (School of Engineering and Sciences, Tecnologico de Monterrey, Av. Eugenio Garza Sada Sur No. 2501, Col. Tecnologico, Monterrey 64849, Mexico
    Instituto Nacional de Electricidad y Energías Limpias (INEEL), Av. Reforma 113, Col. Palmira, Cuernavaca CP 62490, Mexico)

  • Alfonso Campos-Amezcua

    (Instituto Nacional de Electricidad y Energías Limpias (INEEL), Av. Reforma 113, Col. Palmira, Cuernavaca CP 62490, Mexico)

  • Rafael Batres

    (School of Engineering and Sciences, Tecnologico de Monterrey, Av. Eugenio Garza Sada Sur No. 2501, Col. Tecnologico, Monterrey 64849, Mexico)

Abstract

This paper presents an approach to managing the thermal power plant’s flexible operation based on the steam generation process optimization. A strategy at the process level, as a first step in the operational optimization of the entire power plant, is proposed. The proposed approach focuses on minimizing the drum boiler startup time, since it is considered the most critical element in the steam generation process and in the thermal power plant’s efficient operation. An approach that addresses the problem to find the optimal sequences of control valves that minimize the drum boiler startup time as a dynamic optimization problem is proposed. To solve the optimization problem, a dynamic optimization framework based on a micro genetic algorithm (mGA) coupled with a dynamic simulation model is implemented. The dynamic simulation model is validated against data available in the literature, and the proposed optimization algorithm is characterized by the use of variable length chromosomes and the use of small population sizes. The results show that optimized operating profiles minimize the drum boiler startup time by at least 35 percent and generate control valve operating sequences that must be carried out to achieve the desired profile, while the structural integrity constraints are fulfilled at all times.

Suggested Citation

  • Erik Rosado-Tamariz & Miguel A. Zuniga-Garcia & Alfonso Campos-Amezcua & Rafael Batres, 2020. "A Framework for the Synthesis of Optimum Operating Profiles Based on Dynamic Simulation and a Micro Genetic Algorithm," Energies, MDPI, vol. 13(3), pages 1-23, February.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:3:p:677-:d:316528
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/3/677/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/3/677/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Krüger, Klaus & Franke, Rüdiger & Rode, Manfred, 2004. "Optimization of boiler start-up using a nonlinear boiler model and hard constraints," Energy, Elsevier, vol. 29(12), pages 2239-2251.
    2. Mirandola, A. & Stoppato, A. & Lo Casto, E., 2010. "Evaluation of the effects of the operation strategy of a steam power plant on the residual life of its devices," Energy, Elsevier, vol. 35(2), pages 1024-1032.
    3. Rossi, Iacopo & Sorce, Alessandro & Traverso, Alberto, 2017. "Gas turbine combined cycle start-up and stress evaluation: A simplified dynamic approach," Applied Energy, Elsevier, vol. 190(C), pages 880-890.
    4. Kubik, M.L. & Coker, P.J. & Barlow, J.F., 2015. "Increasing thermal plant flexibility in a high renewables power system," Applied Energy, Elsevier, vol. 154(C), pages 102-111.
    5. Taler, Jan & Dzierwa, Piotr & Taler, Dawid & Harchut, Piotr, 2015. "Optimization of the boiler start-up taking into account thermal stresses," Energy, Elsevier, vol. 92(P1), pages 160-170.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hedrick, Katherine & Omell, Benjamin & Zitney, Stephen E. & Bhattacharyya, Debangsu, 2024. "Development of a health monitoring framework: Application to a supercritical pulverized coal-fired boiler," Energy, Elsevier, vol. 290(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Romero-Anton, N. & Martin-Escudero, K. & Portillo-Valdés, L.A. & Gómez-Elvira, I. & Salazar-Herran, E., 2018. "Improvement of auxiliary BI-DRUM boiler operation by dynamic simulation," Energy, Elsevier, vol. 148(C), pages 676-686.
    2. Neshumayev, Dmitri & Rummel, Leo & Konist, Alar & Ots, Arvo & Parve, Teet, 2018. "Power plant fuel consumption rate during load cycling," Applied Energy, Elsevier, vol. 224(C), pages 124-135.
    3. Zhang, Hengliang & Xie, Danmei & Yu, Yanzhi & Yu, Liangying, 2016. "Online optimal control schemes of inlet steam temperature during startup of steam turbines considering low cycle fatigue," Energy, Elsevier, vol. 117(P1), pages 105-115.
    4. Andrzej Rusin & Martyna Tomala & Henryk Łukowicz & Grzegorz Nowak & Wojciech Kosman, 2021. "On-Line Control of Stresses in the Power Unit Pressure Elements Taking Account of Variable Heat Transfer Conditions," Energies, MDPI, vol. 14(15), pages 1-21, August.
    5. Cheng, Yi & Azizipanah-Abarghooee, Rasoul & Azizi, Sadegh & Ding, Lei & Terzija, Vladimir, 2020. "Smart frequency control in low inertia energy systems based on frequency response techniques: A review," Applied Energy, Elsevier, vol. 279(C).
    6. González-Aparicio, I. & Monforti, F. & Volker, P. & Zucker, A. & Careri, F. & Huld, T. & Badger, J., 2017. "Simulating European wind power generation applying statistical downscaling to reanalysis data," Applied Energy, Elsevier, vol. 199(C), pages 155-168.
    7. Polleux, Louis & Guerassimoff, Gilles & Marmorat, Jean-Paul & Sandoval-Moreno, John & Schuhler, Thierry, 2022. "An overview of the challenges of solar power integration in isolated industrial microgrids with reliability constraints," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    8. Dianfa Wu & Zhiping Yang & Ningling Wang & Chengzhou Li & Yongping Yang, 2018. "An Integrated Multi-Criteria Decision Making Model and AHP Weighting Uncertainty Analysis for Sustainability Assessment of Coal-Fired Power Units," Sustainability, MDPI, vol. 10(6), pages 1-27, May.
    9. Antti Alahäivälä & Juha Kiviluoma & Jyrki Leino & Matti Lehtonen, 2017. "System-Level Value of a Gas Engine Power Plant in Electricity and Reserve Production," Energies, MDPI, vol. 10(7), pages 1-13, July.
    10. Taler, Jan & Dzierwa, Piotr & Taler, Dawid & Harchut, Piotr, 2015. "Optimization of the boiler start-up taking into account thermal stresses," Energy, Elsevier, vol. 92(P1), pages 160-170.
    11. Chen, Jinli & Xiao, Gang & Ferrari, Mario Luigi & Yang, Tianfeng & Ni, Mingjiang & Cen, Kefa, 2020. "Dynamic simulation of a solar-hybrid microturbine system with experimental validation of main parts," Renewable Energy, Elsevier, vol. 154(C), pages 187-200.
    12. Andrychowicz, Mateusz & Olek, Blazej & Przybylski, Jakub, 2017. "Review of the methods for evaluation of renewable energy sources penetration and ramping used in the Scenario Outlook and Adequacy Forecast 2015. Case study for Poland," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 703-714.
    13. Lim, Jin Han & Hu, Eric & Nathan, Graham J., 2016. "Impact of start-up and shut-down losses on the economic benefit of an integrated hybrid solar cavity receiver and combustor," Applied Energy, Elsevier, vol. 164(C), pages 10-20.
    14. Cervone, Guido & Clemente-Harding, Laura & Alessandrini, Stefano & Delle Monache, Luca, 2017. "Short-term photovoltaic power forecasting using Artificial Neural Networks and an Analog Ensemble," Renewable Energy, Elsevier, vol. 108(C), pages 274-286.
    15. Sterkhov, K.V. & Khokhlov, D.A. & Zaichenko, M.N., 2024. "Zero carbon emission CCGT power plant with integrated solid fuel gasification," Energy, Elsevier, vol. 294(C).
    16. Eser, Patrick & Singh, Antriksh & Chokani, Ndaona & Abhari, Reza S., 2016. "Effect of increased renewables generation on operation of thermal power plants," Applied Energy, Elsevier, vol. 164(C), pages 723-732.
    17. Marco Badami & Gabriele Fambri & Salvatore Mancò & Mariapia Martino & Ioannis G. Damousis & Dimitrios Agtzidis & Dimitrios Tzovaras, 2019. "A Decision Support System Tool to Manage the Flexibility in Renewable Energy-Based Power Systems," Energies, MDPI, vol. 13(1), pages 1-16, December.
    18. Stinner, Sebastian & Huchtemann, Kristian & Müller, Dirk, 2016. "Quantifying the operational flexibility of building energy systems with thermal energy storages," Applied Energy, Elsevier, vol. 181(C), pages 140-154.
    19. Hong, Feng & Wang, Rui & Song, Jie & Gao, Mingming & Liu, Jizhen & Long, Dongteng, 2022. "A performance evaluation framework for deep peak shaving of the CFB boiler unit based on the DBN-LSSVM algorithm," Energy, Elsevier, vol. 238(PA).
    20. Villamor, Lila Vázquez & Avagyan, Vitali & Chalmers, Hannah, 2020. "Opportunities for reducing curtailment of wind energy in the future electricity systems: Insights from modelling analysis of Great Britain," Energy, Elsevier, vol. 195(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:3:p:677-:d:316528. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.