Version 1
: Received: 13 September 2017 / Approved: 14 September 2017 / Online: 14 September 2017 (08:41:07 CEST)
How to cite:
Narayan, A.; Ponnambalam, K.; Pagsuyoin, S.A. Spatial Dependence Modeling of Wind Resource under Uncertainty Using C-Vine Copulas and Its Impact on Solar-Wind Energy Co-Generation. Preprints2017, 2017090053. https://doi.org/10.20944/preprints201709.0053.v1
Narayan, A.; Ponnambalam, K.; Pagsuyoin, S.A. Spatial Dependence Modeling of Wind Resource under Uncertainty Using C-Vine Copulas and Its Impact on Solar-Wind Energy Co-Generation. Preprints 2017, 2017090053. https://doi.org/10.20944/preprints201709.0053.v1
Narayan, A.; Ponnambalam, K.; Pagsuyoin, S.A. Spatial Dependence Modeling of Wind Resource under Uncertainty Using C-Vine Copulas and Its Impact on Solar-Wind Energy Co-Generation. Preprints2017, 2017090053. https://doi.org/10.20944/preprints201709.0053.v1
APA Style
Narayan, A., Ponnambalam, K., & Pagsuyoin, S.A. (2017). Spatial Dependence Modeling of Wind Resource under Uncertainty Using C-Vine Copulas and Its Impact on Solar-Wind Energy Co-Generation. Preprints. https://doi.org/10.20944/preprints201709.0053.v1
Chicago/Turabian Style
Narayan, A., Kumaraswamy Ponnambalam and Sheree A. Pagsuyoin. 2017 "Spatial Dependence Modeling of Wind Resource under Uncertainty Using C-Vine Copulas and Its Impact on Solar-Wind Energy Co-Generation" Preprints. https://doi.org/10.20944/preprints201709.0053.v1
Abstract
Investments in wind and solar power are driven by the aim to maximize the utilization of renewable energy (RE). This results in an increased concentration of wind farms at locations with higher average wind speeds and of solar panel installations at sites with higher average solar insolation. This is unfavourable for energy suppliers and for the overall economy when large power output fluctuations occur. Thus, when evaluating investment options for spatially distributed RE systems, it is necessary to model resource fluctuations and power output correlations between locations. In this paper, we propose a methodology for analyzing the spatial dependence, accurate modeling, and forecasting of wind power systems with special consideration to spatial dispersion of installation sites. We combine vine-copulas with the Kumaraswamy distribution to improve accuracy in forecasting wind power from spatially dispersed wind turbines and to model solar power generated at each location. We then integrate these methods to formulate an optimization model for allocating wind turbines and solar panels spatially, with an end goal of maximizing overall power generation while minimizing the variability in power output. A case study of wind and solar power systems in Central Ontario, Canada is also presented.
Keywords
renewable energy; wind and solar power; Kumaraswamy distribution; C-Vine copula
Subject
Engineering, Energy and Fuel Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
