Stochastic weather generator for the design and reliability evaluation of desalination systems with Renewable Energy Sources. (October 2020)

• Record Type: Journal Article
• Authors: Ailliot, Pierre; Boutigny, Marie; Koutroulis, Eftichis; Malisovas, Athanasios; Monbet, Valérie
• Is Part Of: Renewable energy. Volume 158(2020)
• Page Start: 541
• Page End: 553
• Publication Date: 2020-10
• Subjects: Renewable energy sources -- Desalination -- Stochastic weather generators -- Markov-switching autoregressive models -- Non-parametric resampling -- Design optimization; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2020.05.076
• Languages: English
• ISSNs: 0960-1481

Abstract

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. Highlights: Renewable Energy Sources (RES) systems are highly sensitive to weather conditions. Stochastic weather generators (SWGENs) generate realistic weather conditions and enrich available meteorological datasets. SWGENs enable to estimate the reliability of RES systems and design RES systems which are robust to climate variability.