G. Tsekouras, and D. Koutsoyiannis, Stochastic analysis and simulation of hydrometeorological processes associated with wind and solar energy, *Renewable Energy*, 63, 624–633, doi:10.1016/j.renene.2013.10.018, 2014.

[doc_id=1404]

[English]

The current model for energy production, based on the intense use of fossil fuels, is both unsustainable and environmentally harmful and consequently, a shift is needed in the direction of integrating the renewable energy sources into the energy balance. However, these energy sources are unpredictable and uncontrollable as they strongly depend on time varying and uncertain hydrometeorological variables such as wind speed, sunshine duration and solar radiation. To study the design and management of renewable energy systems we investigate both the properties of marginal distributions and the dependence properties of these natural processes, including possible long-term persistence by estimating and analyzing the Hurst coefficient. To this aim we use time series of wind speed and sunshine duration retrieved from European databases of daily records. We also study a stochastic simulation framework for both wind and solar systems using the software system Castalia, which performs multivariate and multi-time-scale stochastic simulation, in order to conduct simultaneous generation of synthetic time series of wind speed and sunshine duration, on yearly, monthly and daily scale.

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**See also:**
http://dx.doi.org/10.1016/j.renene.2013.10.018

**Our works referenced by this work:**

1. | D. Koutsoyiannis, Statistical Hydrology, Edition 4, 312 pages, doi:10.13140/RG.2.1.5118.2325, National Technical University of Athens, Athens, 1997. |

2. | D. Koutsoyiannis, and Th. Xanthopoulos, Engineering Hydrology, Edition 3, 418 pages, doi:10.13140/RG.2.1.4856.0888, National Technical University of Athens, Athens, 1999. |

3. | D. Koutsoyiannis, A generalized mathematical framework for stochastic simulation and forecast of hydrologic time series, Water Resources Research, 36 (6), 1519–1533, doi:10.1029/2000WR900044, 2000. |

4. | D. Koutsoyiannis, Coupling stochastic models of different time scales, Water Resources Research, 37 (2), 379–391, doi:10.1029/2000WR900200, 2001. |

5. | D. Koutsoyiannis, The Hurst phenomenon and fractional Gaussian noise made easy, Hydrological Sciences Journal, 47 (4), 573–595, doi:10.1080/02626660209492961, 2002. |

6. | D. Koutsoyiannis, Climate change, the Hurst phenomenon, and hydrological statistics, Hydrological Sciences Journal, 48 (1), 3–24, doi:10.1623/hysj.48.1.3.43481, 2003. |

7. | D. Koutsoyiannis, C. Onof, and H. S. Wheater, Multivariate rainfall disaggregation at a fine timescale, Water Resources Research, 39 (7), 1173, doi:10.1029/2002WR001600, 2003. |

8. | D. Koutsoyiannis, Reliability concepts in reservoir design, Water Encyclopedia, Vol. 4, Surface and Agricultural Water, edited by J. H. Lehr and J. Keeley, 259–265, doi:10.1002/047147844X.sw776, Wiley, New York, 2005. |

9. | D. Koutsoyiannis, Nonstationarity versus scaling in hydrology, Journal of Hydrology, 324, 239–254, doi:10.1016/j.jhydrol.2005.09.022, 2006. |

10. | D. Koutsoyiannis, On the quest for chaotic attractors in hydrological processes, Hydrological Sciences Journal, 51 (6), 1065–1091, doi:10.1623/hysj.51.6.1065, 2006. |

11. | D. Koutsoyiannis, C. Makropoulos, A. Langousis, S. Baki, A. Efstratiadis, A. Christofides, G. Karavokiros, and N. Mamassis, Climate, hydrology, energy, water: recognizing uncertainty and seeking sustainability, Hydrology and Earth System Sciences, 13, 247–257, doi:10.5194/hess-13-247-2009, 2009. |

12. | D. Koutsoyiannis, A random walk on water, Hydrology and Earth System Sciences, 14, 585–601, doi:10.5194/hess-14-585-2010, 2010. |

13. | H. Tyralis, and D. Koutsoyiannis, Simultaneous estimation of the parameters of the Hurst-Kolmogorov stochastic process, Stochastic Environmental Research & Risk Assessment, 25 (1), 21–33, 2011. |

14. | D. Koutsoyiannis, Hurst-Kolmogorov dynamics and uncertainty, Journal of the American Water Resources Association, 47 (3), 481–495, doi:10.1111/j.1752-1688.2011.00543.x, 2011. |

15. | Y. Markonis, and D. Koutsoyiannis, Climatic variability over time scales spanning nine orders of magnitude: Connecting Milankovitch cycles with Hurst–Kolmogorov dynamics, Surveys in Geophysics, 34 (2), 181–207, doi:10.1007/s10712-012-9208-9, 2013. |

16. | G. Tsekouras, C. Ioannou, A. Efstratiadis, and D. Koutsoyiannis, Stochastic analysis and simulation of hydrometeorological processes for optimizing hybrid renewable energy systems, European Geosciences Union General Assembly 2013, Geophysical Research Abstracts, Vol. 15, Vienna, EGU2013-11660, doi:10.13140/RG.2.2.30250.62404, European Geosciences Union, 2013. |

**Our works that reference this work:**

1. | A. Efstratiadis, Y. Dialynas, S. Kozanis, and D. Koutsoyiannis, A multivariate stochastic model for the generation of synthetic time series at multiple time scales reproducing long-term persistence, Environmental Modelling and Software, 62, 139–152, doi:10.1016/j.envsoft.2014.08.017, 2014. |

2. | Y. Markonis, S. C. Batelis, Y. Dimakos, E. C. Moschou, and D. Koutsoyiannis, Temporal and spatial variability of rainfall over Greece, Theoretical and Applied Climatology, doi:10.1007/s00704-016-1878-7, 2016. |

3. | P. Kossieris, C. Makropoulos, C. Onof, and D. Koutsoyiannis, A rainfall disaggregation scheme for sub-hourly time scales: Coupling a Bartlett-Lewis based model with adjusting procedures, Journal of Hydrology, 556, 980–992, doi:10.1016/j.jhydrol.2016.07.015, 2018. |

4. | P. Dimitriadis, and D. Koutsoyiannis, Stochastic synthesis approximating any process dependence and distribution, Stochastic Environmental Research & Risk Assessment, 32 (6), 1493–1515, doi:10.1007/s00477-018-1540-2, 2018. |

5. | G. Koudouris, P. Dimitriadis, T. Iliopoulou, N. Mamassis, and D. Koutsoyiannis, A stochastic model for the hourly solar radiation process for application in renewable resources management, Advances in Geosciences, 45, 139–145, doi:10.5194/adgeo-45-139-2018, 2018. |

6. | P. Dimitriadis, D. Koutsoyiannis, T. Iliopoulou, and P. Papanicolaou, A global-scale investigation of stochastic similarities in marginal distribution and dependence structure of key hydrological-cycle processes, Hydrology, 8 (2), 59, doi:10.3390/hydrology8020059, 2021. |

7. | D. Koutsoyiannis, Stochastics of Hydroclimatic Extremes - A Cool Look at Risk, Edition 2, ISBN: 978-618-85370-0-2, 346 pages, doi:10.57713/kallipos-1, Kallipos Open Academic Editions, Athens, 2022. |

8. | G.-K. Sakki, I. Tsoukalas, P. Kossieris, C. Makropoulos, and A. Efstratiadis, Stochastic simulation-optimisation framework for the design and assessment of renewable energy systems under uncertainty, Renewable and Sustainable Energy Reviews, 168, 112886, doi:10.1016/j.rser.2022.112886, 2022. |

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2. | Abdelaziz, A. Y., Y. G. Hegazy, W. El-Khattam and M.M. Othman, Optimal allocation of stochastically dependent renewable energy based distributed generators in unbalanced distribution networks, Electric Power Systems Research, 119, 34-44, 2015. |

3. | #Fortuna, L., S. Nunnari and A. Gallo, A typical day based approach to detrend solar radiation time series, MAED '14 Proceedings of the 3rd ACM International Workshop on Multimedia Analysis for Ecological Data, 25-30, ACM New York, NY, USA, 2014. |

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**Tagged under:**
Hurst-Kolmogorov dynamics,
Hydrosystems,
Stochastics,
Students' works,
Water and energy