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Various approaches in optimization of a typical pressurized water reactor power plant

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  • Sayyaadi, Hoseyn
  • Sabzaligol, Tooraj

Abstract

A typical 1000Â MW pressurized water reactor (PWR) nuclear power plant is considered for optimization. The thermodynamic modeling is performed based on the energy and exergy analysis, while an economic model is developed according to the total revenue requirement (TRR) method. The objective functions based on the thermodynamic and thermoeconomic analysis are obtained. The optimization process with ten degrees of freedom for plant under consideration is performed using the genetic algorithm (GA). Three levels of optimization including thermodynamic single-objective, thermoeconomic single-objective and multi-objective optimizations are performed. In multi-objective optimization, both thermodynamics and thermoeconomic objectives are considered, simultaneously. A series of optimum solutions namely as Pareto frontier are obtained. In the case of multi-objective optimization, an example of decision-making process for selection of the final optimal solution from the Pareto frontier is introduced. The results obtained using the various optimization approaches are compared and discussed.

Suggested Citation

  • Sayyaadi, Hoseyn & Sabzaligol, Tooraj, 2009. "Various approaches in optimization of a typical pressurized water reactor power plant," Applied Energy, Elsevier, vol. 86(7-8), pages 1301-1310, July.
    • Handle: RePEc:eee:appene:v:86:y:2009:i:7-8:p:1301-1310
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    References listed on IDEAS

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    1. Sayyaadi, Hoseyn, 2009. "Multi-objective approach in thermoenvironomic optimization of a benchmark cogeneration system," Applied Energy, Elsevier, vol. 86(6), pages 867-879, June.
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    Cited by:

    1. Wang, Jiang-Jiang & Jing, You-Yin & Zhang, Chun-Fa, 2010. "Optimization of capacity and operation for CCHP system by genetic algorithm," Applied Energy, Elsevier, vol. 87(4), pages 1325-1335, April.
    2. Sayyaadi, Hoseyn & Sabzaligol, Tooraj, 2010. "Comprehensive exergetic and economic comparison of PWR and hybrid fossil fuel-PWR power plants," Energy, Elsevier, vol. 35(7), pages 2953-2964.
    3. Sayyaadi, Hoseyn & Baghsheikhi, Mostafa, 2019. "Retrofit of a steam power plant using the adaptive neuro-fuzzy inference system in response to the load variation," Energy, Elsevier, vol. 175(C), pages 1164-1173.
    4. Baghsheikhi, Mostafa & Sayyaadi, Hoseyn, 2016. "Real-time exergoeconomic optimization of a steam power plant using a soft computing-fuzzy inference system," Energy, Elsevier, vol. 114(C), pages 868-884.
    5. Abdollahipour, Armin & Sayyaadi, Hoseyn, 2022. "A novel electrochemical refrigeration system based on the combined proton exchange membrane fuel cell-electrolyzer," Applied Energy, Elsevier, vol. 316(C).
    6. Sayyaadi, Hoseyn & Babaie, Meisam & Farmani, Mohammad Reza, 2011. "Implementing of the multi-objective particle swarm optimizer and fuzzy decision-maker in exergetic, exergoeconomic and environmental optimization of a benchmark cogeneration system," Energy, Elsevier, vol. 36(8), pages 4777-4789.
    7. Zakaria Triki & Rabah Menasri & Mohamed Najib Bouaziz & Hichem Tahraoui & Mohammed Kebir & Abdeltif Amrane & Jie Zhang & Lotfi Mouni, 2023. "Energy and Exergy Analyses of a PWR-Type Nuclear Power Plant Coupled with an ME-TVC-MED Desalination System," Sustainability, MDPI, vol. 15(10), pages 1-20, May.
    8. Wang, Ligang & Yang, Yongping & Dong, Changqing & Morosuk, Tatiana & Tsatsaronis, George, 2014. "Multi-objective optimization of coal-fired power plants using differential evolution," Applied Energy, Elsevier, vol. 115(C), pages 254-264.
    9. Sayyaadi, Hoseyn & Baghsheikhi, Mostafa, 2018. "Developing a novel methodology based on the adaptive neuro-fuzzy interference system for the exergoeconomic optimization of energy systems," Energy, Elsevier, vol. 164(C), pages 218-235.
    10. Keshtkar, Mohammad Mehdi & Talebizadeh, Pouyan, 2017. "Multi-objective optimization of cooling water package based on 3E analysis: A case study," Energy, Elsevier, vol. 134(C), pages 840-849.

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