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Optimization of the size of a solar thermal electricity plant by means of genetic algorithms

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  • Cabello, J.M.
  • Cejudo, J.M.
  • Luque, M.
  • Ruiz, F.
  • Deb, K.
  • Tewari, R.

Abstract

Solar thermal electricity technologies are attractive alternatives to produce electricity by means of a renewable source. One of these technologies is parabolic trough collectors. In Spain, the sale of solar electricity to the national grid is primed. There are three main parameters that affect the behaviour of these plants: area of solar collector field, capacity of thermal storage tanks and power of the auxiliary system. In this paper, a simplified model of the plant is used to optimize the size of its components that produces the maximum yearly profit. The use of traditional methods of optimization is not possible and genetic algorithms have been used. An important feature of the model is that the minimum level of the electricity production of the block of power can be fixed. Once the optimization has been performed, the traditional parameters that characterize the dimension of the plant are analysed (the solar multiple and the capacity factor). For a gross power of 50 MW, the optimum collector area varies between 583,000 m2 and 749,860 m2 with a thermal storage between 6.55 h and 13.46 h respectively. The economic benefit is always higher than 19.30 M€ per year and the cost of the electricity produced is about 18.5 c€/kWh.

Suggested Citation

  • Cabello, J.M. & Cejudo, J.M. & Luque, M. & Ruiz, F. & Deb, K. & Tewari, R., 2011. "Optimization of the size of a solar thermal electricity plant by means of genetic algorithms," Renewable Energy, Elsevier, vol. 36(11), pages 3146-3153.
    • Handle: RePEc:eee:renene:v:36:y:2011:i:11:p:3146-3153
    DOI: 10.1016/j.renene.2011.03.018
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    References listed on IDEAS

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    1. Kalogirou, Soteris A., 2004. "Optimization of solar systems using artificial neural-networks and genetic algorithms," Applied Energy, Elsevier, vol. 77(4), pages 383-405, April.
    2. Fernández-García, A. & Zarza, E. & Valenzuela, L. & Pérez, M., 2010. "Parabolic-trough solar collectors and their applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(7), pages 1695-1721, September.
    3. Varun & Siddhartha, 2010. "Thermal performance optimization of a flat plate solar air heater using genetic algorithm," Applied Energy, Elsevier, vol. 87(5), pages 1793-1799, May.
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    2. Yan, Jian & Peng, You-duo & Cheng, Zi-ran, 2018. "Optimization of a discrete dish concentrator for uniform flux distribution on the cavity receiver of solar concentrator system," Renewable Energy, Elsevier, vol. 129(PA), pages 431-445.
    3. Guo, Su & Liu, Deyou & Chu, Yinghao & Chen, Xingying & Xu, Chang & Liu, Qunming & Guo, Tiezheng, 2017. "Dynamic behavior and transfer function of collector field in once-through DSG solar trough power plants," Energy, Elsevier, vol. 121(C), pages 513-523.
    4. Valencia-Ortega, G. & Levario-Medina, S. & Angulo-Brown, F. & Barranco-Jiménez, M.A., 2023. "Energetic optimization and local stability of heliothermal plant models under three thermo-economic performance regimes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 613(C).
    5. Corral, Nicolás & Anrique, Nicolás & Fernandes, Dalila & Parrado, Cristóbal & Cáceres, Gustavo, 2012. "Power, placement and LEC evaluation to install CSP plants in northern Chile," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(9), pages 6678-6685.
    6. Kicsiny, Richárd, 2018. "Black-box model for solar storage tanks based on multiple linear regression," Renewable Energy, Elsevier, vol. 125(C), pages 857-865.
    7. Fadaee, M. & Radzi, M.A.M., 2012. "Multi-objective optimization of a stand-alone hybrid renewable energy system by using evolutionary algorithms: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 3364-3369.
    8. Adarsh Vaderobli & Dev Parikh & Urmila Diwekar, 2020. "Optimization under Uncertainty to Reduce the Cost of Energy for Parabolic Trough Solar Power Plants for Different Weather Conditions," Energies, MDPI, vol. 13(12), pages 1-17, June.
    9. Zhai, Rongrong & Peng, Pan & Yang, Yongping & Zhao, Miaomiao, 2014. "Optimization study of integration strategies in solar aided coal-fired power generation system," Renewable Energy, Elsevier, vol. 68(C), pages 80-86.
    10. Boukelia, T.E. & Arslan, O. & Mecibah, M.S., 2017. "Potential assessment of a parabolic trough solar thermal power plant considering hourly analysis: ANN-based approach," Renewable Energy, Elsevier, vol. 105(C), pages 324-333.

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