IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v35y2010i9p1991-2000.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148110000480
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2010.01.031?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. Edward Frees & Ping Wang, 2005. "Credibility Using Copulas," North American Actuarial Journal, Taylor & Francis Journals, vol. 9(2), pages 31-48.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Anderson Mitterhofer Iung & Fernando Luiz Cyrino Oliveira & André Luís Marques Marcato, 2023. "A Review on Modeling Variable Renewable Energy: Complementarity and Spatial–Temporal Dependence," Energies, MDPI, vol. 16(3), pages 1-24, January.
    2. 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.
    3. 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.
    4. Asadi, Meysam & Pourhossein, Kazem, 2021. "Wind farm site selection considering turbulence intensity," Energy, Elsevier, vol. 236(C).
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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).
    10. 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).
    11. Zhou, Wei & Chen, Yan & Chen, Jin, 2022. "Risk spread in multiple energy markets: Extreme volatility spillover network analysis before and during the COVID-19 pandemic," Energy, Elsevier, vol. 256(C).
    12. Hakimi, Seyed Mehdi & Hasankhani, Arezoo & Shafie-khah, Miadreza & Catalão, João P.S., 2021. "Stochastic planning of a multi-microgrid considering integration of renewable energy resources and real-time electricity market," Applied Energy, Elsevier, vol. 298(C).
    13. 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.
    14. Feijóo, Andrés & Villanueva, Daniel, 2016. "Assessing wind speed simulation methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 473-483.
    15. 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.
    16. 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.
    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. 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.
    19. 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.
    20. 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.
    21. Jianwei Gao & Yu Yang & Fangjie Gao & Pengcheng Liang, 2021. "Optimization of Electric Vehicles Based on Frank-Copula- GlueCVaR Combined Wind and Photovoltaic Output Scheduling Research," Energies, MDPI, vol. 14(19), pages 1-15, September.
    22. 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.
    23. 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.
    24. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. M.L. Nores & M.P. Díaz, 2016. "Bootstrap hypothesis testing in generalized additive models for comparing curves of treatments in longitudinal studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(5), pages 810-826, April.
    2. Miao, Ruiqing & Hennessy, David A. & Feng, Hongli, 2016. "The Effects of Crop Insurance Subsidies and Sodsaver on Land-Use Change," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 41(2), May.
    3. Lu Yang & Claudia Czado, 2022. "Two‐part D‐vine copula models for longitudinal insurance claim data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(4), pages 1534-1561, December.
    4. Pisit Leeahtam & Chukiat Chaiboonsri & Kanchana Chokethaworn & Prasert Chaitip & Songsak Sriboonchitta, 2011. "The Appropriate Model and Dependence Measures of Thailand’s Exchange Rate and Malaysia’s Exchange Rate: Linear, Nonlinear and Copulas Approach," Journal of Knowledge Management, Economics and Information Technology, ScientificPapers.org, vol. 1(6), pages 1-14, October.
    5. Majeed, Fahd & Khanna, Madhu & Miao, Ruiqing & Betes, Elena Blanc & Hudiburg, Tara & DeLucia, Evan, 2022. "Payment for carbon mitigation reduces riskiness of bioenergy crop production," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322277, Agricultural and Applied Economics Association.
    6. Ozonder, Gozde & Miller, Eric J., 2021. "Longitudinal investigation of skeletal activity episode timing decisions – A copula approach," Journal of choice modelling, Elsevier, vol. 40(C).
    7. Junker, Robert R. & Griessenberger, Florian & Trutschnig, Wolfgang, 2021. "Estimating scale-invariant directed dependence of bivariate distributions," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).
    8. Mohit Anand & Ruiqing Miao & Madhu Khanna, 2019. "Adopting bioenergy crops: Does farmers’ attitude toward loss matter?," Agricultural Economics, International Association of Agricultural Economists, vol. 50(4), pages 435-450, July.
    9. Jiří Dvořák & Tomáš Mrkvička, 2022. "Graphical tests of independence for general distributions," Computational Statistics, Springer, vol. 37(2), pages 671-699, April.
    10. Erlend Bø & Peter Lambert & Thor Thoresen, 2012. "Horizontal inequity under a dual income tax system: principles and measurement," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 19(5), pages 625-640, October.
    11. McHenry, Mark P. & Doepel, David, 2015. "The ‘low power’ revolution: Rural off-grid consumer technologies and portable micropower systems in non-industrialised regions," Renewable Energy, Elsevier, vol. 78(C), pages 679-684.
    12. Ji, Xiangfeng & Chu, Yanyu, 2020. "A target-oriented bi-attribute user equilibrium model with travelers’ perception errors on the tolled traffic network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 144(C).
    13. Fantazzini, Dean, 2011. "Analysis of multidimensional probability distributions with copula functions," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 22(2), pages 98-134.
    14. Göran Kauermann & Christian Schellhase & David Ruppert, 2013. "Flexible Copula Density Estimation with Penalized Hierarchical B-splines," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(4), pages 685-705, December.
    15. Cole, Matthew A. & Elliott, Robert J.R. & Occhiali, Giovanni & Strobl, Eric, 2018. "Power outages and firm performance in Sub-Saharan Africa," Journal of Development Economics, Elsevier, vol. 134(C), pages 150-159.
    16. Dongdong Li & X. Joan Hu & Mary L. McBride & John J. Spinelli, 2020. "Multiple event times in the presence of informative censoring: modeling and analysis by copulas," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(3), pages 573-602, July.
    17. Ross, S.J. & McHenry, M.P. & Whale, J., 2012. "The impact of state feed-in tariffs and federal tradable quota support policies on grid-connected small wind turbine installed capacity in Australia," Renewable Energy, Elsevier, vol. 46(C), pages 141-147.
    18. Michael Schomaker & Christian Heumann, 2020. "When and when not to use optimal model averaging," Statistical Papers, Springer, vol. 61(5), pages 2221-2240, October.
    19. Panagiota Galiatsatou & Christos Makris & Panayotis Prinos & Dimitrios Kokkinos, 2019. "Nonstationary joint probability analysis of extreme marine variables to assess design water levels at the shoreline in a changing climate," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 98(3), pages 1051-1089, September.
    20. Arbel, Julyan & Crispino, Marta & Girard, Stéphane, 2019. "Dependence properties and Bayesian inference for asymmetric multivariate copulas," Journal of Multivariate Analysis, Elsevier, vol. 174(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:35:y:2010:i:9:p:1991-2000. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.