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Stochastic weather generator for the design and reliability evaluation of desalination systems with Renewable Energy Sources

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  • Ailliot, Pierre
  • Boutigny, Marie
  • Koutroulis, Eftichis
  • Malisovas, Athanasios
  • Monbet, Valérie

Abstract

The operation of Renewable Energy Sources (RES) systems is highly affected by the continuously changing meteorological conditions and the design of a RES system has to be robust to the unknown weather conditions that it will encounter during its lifetime. In this paper, the use of Stochastic Weather Generators (SWGENs) is introduced for the optimal design and reliability evaluation of hybrid Photovoltaic/Wind-Generator systems providing energy to desalination plants. A SWGEN is proposed, which is based on parametric Markov-Switching Auto-Regressive (MSAR) models and is capable to simulate realistic hourly multivariate time series of solar irradiance, temperature and wind speed of the target installation site. Numerical results are presented, demonstrating that: (i) SWGENs enable to evaluate the reliability of RES-based desalination plants during their operation over a 20 years lifetime period and (ii) using an appropriate time series simulated with a SWGEN as input to the design optimization process results in a RES-based desalination plant configuration with higher reliability compared to the configurations derived when the other types of meteorological datasets are used as input to the design optimization process.

Suggested Citation

  • Ailliot, Pierre & Boutigny, Marie & Koutroulis, Eftichis & Malisovas, Athanasios & Monbet, Valérie, 2020. "Stochastic weather generator for the design and reliability evaluation of desalination systems with Renewable Energy Sources," Renewable Energy, Elsevier, vol. 158(C), pages 541-553.
    • Handle: RePEc:eee:renene:v:158:y:2020:i:c:p:541-553
    DOI: 10.1016/j.renene.2020.05.076
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    References listed on IDEAS

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    1. Amanda S. Hering & Karen Kazor & William Kleiber, 2015. "A Markov-Switching Vector Autoregressive Stochastic Wind Generator for Multiple Spatial and Temporal Scales," Resources, MDPI, vol. 4(1), pages 1-23, February.
    2. Sean D. Campbell & Francis X. Diebold, 2005. "Weather Forecasting for Weather Derivatives," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 6-16, March.
    3. Eriksson, E.L.V. & Gray, E.MacA., 2019. "Optimization of renewable hybrid energy systems – A multi-objective approach," Renewable Energy, Elsevier, vol. 133(C), pages 971-999.
    4. Chan, A.L.S., 2016. "Generation of typical meteorological years using genetic algorithm for different energy systems," Renewable Energy, Elsevier, vol. 90(C), pages 1-13.
    5. Molinos-Senante, María & González, Diego, 2019. "Evaluation of the economics of desalination by integrating greenhouse gas emission costs: An empirical application for Chile," Renewable Energy, Elsevier, vol. 133(C), pages 1327-1337.
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    Cited by:

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