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Using Copulas for analysis of large datasets in renewable distributed generation: PV and wind power integration in Iran

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  • Valizadeh Haghi, H.
  • Tavakoli Bina, M.
  • Golkar, M.A.
  • Moghaddas-Tafreshi, S.M.

Abstract

Renewable distributed generation introduced as an environmental friendly alternative energy supply while it provided the power system with ever-growing technical benefits such as loss reduction and feeder voltage improvement. The evaluation of the effects of small residential photovoltaic and wind DG systems on various system operating indices and the system net load is complicated by both the probabilistic nature of their output and the variety of their spatial allocations. The increasing penetration of renewable distributed generation in power systems necessitates the modeling of this stochastic structure in operation and planning studies. An advanced stochastic modeling of the system requires multivariate uncertainty analysis involving non-normal correlated random variables. Such an analysis is to epitomize the aggregate uncertainty corresponding to spatially spread stochastic variables. In this paper, an integration study of photovoltaics and wind turbines, distributed in a distribution network, is investigated based on the stochastic modeling using Archimedean copulas as a new efficient tool. The basic theory concerning the use of copulas for dependence modeling is presented and focus is given on an Archimedean algorithm. A comprehensive case study for Davarzan area in Iran is presented after reviewing Iran's renewable energy status. This study shows an application of the presented technique when large datasets, assuming 10-min interval between data points of PV, wind and load profiles, are involved where a deterministic study is not trivial.

Suggested Citation

  • Valizadeh Haghi, H. & Tavakoli Bina, M. & Golkar, M.A. & Moghaddas-Tafreshi, S.M., 2010. "Using Copulas for analysis of large datasets in renewable distributed generation: PV and wind power integration in Iran," Renewable Energy, Elsevier, vol. 35(9), pages 1991-2000.
    • Handle: RePEc:eee:renene:v:35:y:2010:i:9:p:1991-2000
    DOI: 10.1016/j.renene.2010.01.031
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    References listed on IDEAS

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    1. Yan, Jun, 2007. "Enjoy the Joy of Copulas: With a Package copula," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 21(i04).
    2. Edward Frees & Ping Wang, 2005. "Credibility Using Copulas," North American Actuarial Journal, Taylor & Francis Journals, vol. 9(2), pages 31-48.
    3. McHenry, Mark P., 2009. "Why are remote Western Australians installing renewable energy technologies in stand-alone power supply systems?," Renewable Energy, Elsevier, vol. 34(5), pages 1252-1256.
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    Cited by:

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    5. Di Somma, M. & Graditi, G. & Heydarian-Forushani, E. & Shafie-khah, M. & Siano, P., 2018. "Stochastic optimal scheduling of distributed energy resources with renewables considering economic and environmental aspects," Renewable Energy, Elsevier, vol. 116(PA), pages 272-287.
    6. Hagspiel, Simeon & Papaemannouil, Antonis & Schmid, Matthias & Andersson, Göran, 2012. "Copula-based modeling of stochastic wind power in Europe and implications for the Swiss power grid," Applied Energy, Elsevier, vol. 96(C), pages 33-44.
    7. Nuño Martinez, Edgar & Cutululis, Nicolaos & Sørensen, Poul, 2018. "High dimensional dependence in power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 197-213.
    8. Ciupăgeanu, Dana-Alexandra & Lăzăroiu, Gheorghe & Barelli, Linda, 2019. "Wind energy integration: Variability analysis and power system impact assessment," Energy, Elsevier, vol. 185(C), pages 1183-1196.
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    10. Abdullah, M.A. & Agalgaonkar, A.P. & Muttaqi, K.M., 2013. "Probabilistic load flow incorporating correlation between time-varying electricity demand and renewable power generation," Renewable Energy, Elsevier, vol. 55(C), pages 532-543.
    11. Najafi, G. & Ghobadian, B. & Mamat, R. & Yusaf, T. & Azmi, W.H., 2015. "Solar energy in Iran: Current state and outlook," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 931-942.
    12. Fabrizio Durante & Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2022. "A Multivariate Dependence Analysis for Electricity Prices, Demand and Renewable Energy Sources," Papers 2201.01132, arXiv.org.
    13. Westner, Günther & Madlener, Reinhard, 2012. "Investment in new power generation under uncertainty: Benefits of CHP vs. condensing plants in a copula-based analysis," Energy Economics, Elsevier, vol. 34(1), pages 31-44.
    14. Feijóo, Andrés & Villanueva, Daniel, 2016. "Assessing wind speed simulation methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 473-483.
    15. Fetanat, Abdolvahhab & Shafipour, Gholamreza & Mohtasebi, Seyedeh-Maryam, 2019. "Measuring public acceptance of climate-friendly technologies based on creativity and cognitive approaches: Practical guidelines for reforming risky energy policies in Iran," Renewable Energy, Elsevier, vol. 134(C), pages 1248-1261.
    16. Shiping Geng & Gengqi Wu & Caixia Tan & Dongxiao Niu & Xiaopeng Guo, 2021. "Multi-Objective Optimization of a Microgrid Considering the Uncertainty of Supply and Demand," Sustainability, MDPI, vol. 13(3), pages 1-21, January.
    17. Li, M.S. & Lin, Z.J. & Ji, T.Y. & Wu, Q.H., 2018. "Risk constrained stochastic economic dispatch considering dependence of multiple wind farms using pair-copula," Applied Energy, Elsevier, vol. 226(C), pages 967-978.
    18. Elberg, Christina & Hagspiel, Simeon, 2015. "Spatial dependencies of wind power and interrelations with spot price dynamics," European Journal of Operational Research, Elsevier, vol. 241(1), pages 260-272.
    19. Talari, Saber & Shafie-khah, Miadreza & Osório, Gerardo J. & Aghaei, Jamshid & Catalão, João P.S., 2018. "Stochastic modelling of renewable energy sources from operators' point-of-view: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1953-1965.
    20. McPherson, Madeleine & Harvey, L.D. Danny & Karney, Bryan, 2017. "System design and operation for integrating variable renewable energy resources through a comprehensive characterization framework," Renewable Energy, Elsevier, vol. 113(C), pages 1019-1032.
    21. Shahryari, E. & Shayeghi, H. & Mohammadi-ivatloo, B. & Moradzadeh, M., 2019. "A copula-based method to consider uncertainties for multi-objective energy management of microgrid in presence of demand response," Energy, Elsevier, vol. 175(C), pages 879-890.
    22. Hasankhani, Arezoo & Hakimi, Seyed Mehdi, 2021. "Stochastic energy management of smart microgrid with intermittent renewable energy resources in electricity market," Energy, Elsevier, vol. 219(C).
    23. He, Y.X. & Xia, T. & Liu, Z.Y. & Zhang, T. & Dong, Z., 2013. "Evaluation of the capability of accepting large-scale wind power in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 19(C), pages 509-516.
    24. Sakki, G.K. & Tsoukalas, I. & Kossieris, P. & Makropoulos, C. & Efstratiadis, A., 2022. "Stochastic simulation-optimization framework for the design and assessment of renewable energy systems under uncertainty," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).

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