IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i3p1013-d1038118.html
   My bibliography  Save this article

A Review on Modeling Variable Renewable Energy: Complementarity and Spatial–Temporal Dependence

Author

Listed:
  • Anderson Mitterhofer Iung

    (Industrial Engineering Department, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro 22451-040, Brazil)

  • Fernando Luiz Cyrino Oliveira

    (Industrial Engineering Department, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro 22451-040, Brazil)

  • André Luís Marques Marcato

    (Electrical Engineering Department, Universidade Federal de Juiz de Fora (UFJF), Juiz de Fora 36036-900, Brazil)

Abstract

The generation from renewable sources has increased significantly worldwide, mainly driven by the need to reduce the global emissions of greenhouse gases, decelerate climate changes, and meet the environmental, social, and governance agenda (ESG). The main characteristics of variable renewable energy (VRE) are the stochastic nature, its seasonal aspects, spatial and time correlations, and the high variability in a short period, increasing the complexity of modeling, planning, operating, and the commercial aspects of the power systems. The research on the complementarity and dependence aspects of VREs is gaining importance, given the development of hybrid generation systems and an array of VREs generators spread over a large region, which could be compounded by different renewable sources, such as hydro, solar, and wind. This review is based on a systematic literature review, providing a comprehensive overview of studies that investigated applied methodologies and methods to address dependence and complementarity. It is a recent field of interest, as 60% of the articles were published in the last five years, a set of methods that have been employed to address this issue, from conventional statistics methods to artificial intelligence. The copulas technique appears as an important approach to modeling renewable energy interdependence. There is a gap in articles comparing the accuracy of the methods employed and the computational efforts.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1013-:d:1038118
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/3/1013/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/3/1013/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yuan, Ran & Wang, Bo & Mao, Zhixin & Watada, Junzo, 2021. "Multi-objective wind power scenario forecasting based on PG-GAN," Energy, Elsevier, vol. 226(C).
    2. Deng, Jingchuan & Li, Hongru & Hu, Jinxing & Liu, Zhenyu, 2021. "A new wind speed scenario generation method based on spatiotemporal dependency structure," Renewable Energy, Elsevier, vol. 163(C), pages 1951-1962.
    3. Aria, Massimo & Cuccurullo, Corrado, 2017. "bibliometrix: An R-tool for comprehensive science mapping analysis," Journal of Informetrics, Elsevier, vol. 11(4), pages 959-975.
    4. Fu, Xueqian & Guo, Qinglai & Sun, Hongbin & Pan, Zhaoguang & Xiong, Wen & Wang, Li, 2017. "Typical scenario set generation algorithm for an integrated energy system based on the Wasserstein distance metric," Energy, Elsevier, vol. 135(C), pages 153-170.
    5. Viviescas, Cindy & Lima, Lucas & Diuana, Fabio A. & Vasquez, Eveline & Ludovique, Camila & Silva, Gabriela N. & Huback, Vanessa & Magalar, Leticia & Szklo, Alexandre & Lucena, André F.P. & Schaeffer, , 2019. "Contribution of Variable Renewable Energy to increase energy security in Latin America: Complementarity and climate change impacts on wind and solar resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    6. E.C.M. Noyons & H.F. Moed & M. Luwel, 1999. "Combining mapping and citation analysis for evaluative bibliometric purposes: A bibliometric study," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 50(2), pages 115-131.
    7. Pinheiro Neto, Daywes & Domingues, Elder Geraldo & Coimbra, António Paulo & de Almeida, Aníbal Traça & Alves, Aylton José & Calixto, Wesley Pacheco, 2017. "Portfolio optimization of renewable energy assets: Hydro, wind, and photovoltaic energy in the regulated market in Brazil," Energy Economics, Elsevier, vol. 64(C), pages 238-250.
    8. Densing, Martin & Wan, Yi, 2022. "Low-dimensional scenario generation method of solar and wind availability for representative days in energy modeling," Applied Energy, Elsevier, vol. 306(PB).
    9. Hua Zhou & Huahua Wu & Chengjin Ye & Shijie Xiao & Jun Zhang & Xu He & Bo Wang, 2019. "Integration Capability Evaluation of Wind and Photovoltaic Generation in Power Systems Based on Temporal and Spatial Correlations," Energies, MDPI, vol. 12(1), pages 1-12, January.
    10. Das, Utpal Kumar & Tey, Kok Soon & Seyedmahmoudian, Mehdi & Mekhilef, Saad & Idris, Moh Yamani Idna & Van Deventer, Willem & Horan, Bend & Stojcevski, Alex, 2018. "Forecasting of photovoltaic power generation and model optimization: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 912-928.
    11. Yang, Qin & Du, Qiang & Razzaq, Asif & Shang, Yunfeng, 2022. "How volatility in green financing, clean energy, and green economic practices derive sustainable performance through ESG indicators? A sectoral study of G7 countries," Resources Policy, Elsevier, vol. 75(C).
    12. Morales, J.M. & Mínguez, R. & Conejo, A.J., 2010. "A methodology to generate statistically dependent wind speed scenarios," Applied Energy, Elsevier, vol. 87(3), pages 843-855, March.
    13. 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.
    14. Ahmed, Adil & Khalid, Muhammad, 2019. "A review on the selected applications of forecasting models in renewable power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 9-21.
    15. Qin, Zhilong & Li, Wenyuan & Xiong, Xiaofu, 2013. "Incorporating multiple correlations among wind speeds, photovoltaic powers and bus loads in composite system reliability evaluation," Applied Energy, Elsevier, vol. 110(C), pages 285-294.
    16. Camal, S. & Teng, F. & Michiorri, A. & Kariniotakis, G. & Badesa, L., 2019. "Scenario generation of aggregated Wind, Photovoltaics and small Hydro production for power systems applications," Applied Energy, Elsevier, vol. 242(C), pages 1396-1406.
    17. Jacobson, Mark Z. & Delucchi, Mark A., 2011. "Providing all global energy with wind, water, and solar power, Part I: Technologies, energy resources, quantities and areas of infrastructure, and materials," Energy Policy, Elsevier, vol. 39(3), pages 1154-1169, March.
    18. Lucheroni, Carlo & Boland, John & Ragno, Costantino, 2019. "Scenario generation and probabilistic forecasting analysis of spatio-temporal wind speed series with multivariate autoregressive volatility models," Applied Energy, Elsevier, vol. 239(C), pages 1226-1241.
    19. Monforti, F. & Huld, T. & Bódis, K. & Vitali, L. & D'Isidoro, M. & Lacal-Arántegui, R., 2014. "Assessing complementarity of wind and solar resources for energy production in Italy. A Monte Carlo approach," Renewable Energy, Elsevier, vol. 63(C), pages 576-586.
    20. Simian Pang & Zixuan Zheng & Fan Luo & Xianyong Xiao & Lanlan Xu, 2021. "Hybrid Forecasting Methodology for Wind Power-Photovoltaic-Concentrating Solar Power Generation Clustered Renewable Energy Systems," Sustainability, MDPI, vol. 13(12), pages 1-16, June.
    21. E. C. M. Noyons & H. F. Moed & A. F. J. Raan, 1999. "Integrating research performance analysis and science mapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 46(3), pages 591-604, November.
    22. Belanger, Camille & Gagnon, Luc, 2002. "Adding wind energy to hydropower," Energy Policy, Elsevier, vol. 30(14), pages 1279-1284, November.
    23. Delucchi, Mark A. & Jacobson, Mark Z., 2011. "Providing all global energy with wind, water, and solar power, Part II: Reliability, system and transmission costs, and policies," Energy Policy, Elsevier, vol. 39(3), pages 1170-1190, March.
    24. Domínguez, R. & Carrión, M. & Oggioni, G., 2020. "Planning and operating a renewable-dominated European power system under uncertainty," Applied Energy, Elsevier, vol. 258(C).
    25. Tascikaraoglu, Akin & Sanandaji, Borhan M. & Poolla, Kameshwar & Varaiya, Pravin, 2016. "Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform," Applied Energy, Elsevier, vol. 165(C), pages 735-747.
    26. Denault, Michel & Dupuis, Debbie & Couture-Cardinal, Sébastien, 2009. "Complementarity of hydro and wind power: Improving the risk profile of energy inflows," Energy Policy, Elsevier, vol. 37(12), pages 5376-5384, December.
    27. Ávila, Leandro & Mine, Miriam R.M & Kaviski, Eloy & Detzel, Daniel H.M., 2021. "Evaluation of hydro-wind complementarity in the medium-term planning of electrical power systems by joint simulation of periodic streamflow and wind speed time series: A Brazilian case study," Renewable Energy, Elsevier, vol. 167(C), pages 685-699.
    28. Zhang, Yi & Cheng, Chuntian & Cao, Rui & Li, Gang & Shen, Jianjian & Wu, Xinyu, 2021. "Multivariate probabilistic forecasting and its performance’s impacts on long-term dispatch of hydro-wind hybrid systems," Applied Energy, Elsevier, vol. 283(C).
    29. Mayer, Martin János & Gróf, Gyula, 2021. "Extensive comparison of physical models for photovoltaic power forecasting," Applied Energy, Elsevier, vol. 283(C).
    30. Ferreira, Pedro Guilherme Costa & Oliveira, Fernando Luiz Cyrino & Souza, Reinaldo Castro, 2015. "The stochastic effects on the Brazilian Electrical Sector," Energy Economics, Elsevier, vol. 49(C), pages 328-335.
    31. Jaramillo, O.A. & Borja, M.A. & Huacuz, J.M., 2004. "Using hydropower to complement wind energy: a hybrid system to provide firm power," Renewable Energy, Elsevier, vol. 29(11), pages 1887-1909.
    32. Souza, Reinaldo Castro & Marcato, André Luı´s Marques & Dias, Bruno Henriques & Oliveira, Fernando Luiz Cyrino, 2012. "Optimal operation of hydrothermal systems with Hydrological Scenario Generation through Bootstrap and Periodic Autoregressive Models," European Journal of Operational Research, Elsevier, vol. 222(3), pages 606-615.
    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. Amir Ali Safaei Pirooz & Mohammad J. Sanjari & Young-Jin Kim & Stuart Moore & Richard Turner & Wayne W. Weaver & Dipti Srinivasan & Josep M. Guerrero & Mohammad Shahidehpour, 2023. "Adaptation of High Spatio-Temporal Resolution Weather/Load Forecast in Real-World Distributed Energy-System Operation," Energies, MDPI, vol. 16(8), pages 1-16, April.

    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. Engeland, Kolbjørn & Borga, Marco & Creutin, Jean-Dominique & François, Baptiste & Ramos, Maria-Helena & Vidal, Jean-Philippe, 2017. "Space-time variability of climate variables and intermittent renewable electricity production – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 600-617.
    2. Li, Jinghua & Zhou, Jiasheng & Chen, Bo, 2020. "Review of wind power scenario generation methods for optimal operation of renewable energy systems," Applied Energy, Elsevier, vol. 280(C).
    3. Santos-Alamillos, F.J. & Pozo-Vázquez, D. & Ruiz-Arias, J.A. & Lara-Fanego, V. & Tovar-Pescador, J., 2014. "A methodology for evaluating the spatial variability of wind energy resources: Application to assess the potential contribution of wind energy to baseload power," Renewable Energy, Elsevier, vol. 69(C), pages 147-156.
    4. Büşra Ayan & Elif Güner & Semen Son-Turan, 2022. "Blockchain Technology and Sustainability in Supply Chains and a Closer Look at Different Industries: A Mixed Method Approach," Logistics, MDPI, vol. 6(4), pages 1-39, December.
    5. François, B. & Zoccatelli, D. & Borga, M., 2017. "Assessing small hydro/solar power complementarity in ungauged mountainous areas: A crash test study for hydrological prediction methods," Energy, Elsevier, vol. 127(C), pages 716-729.
    6. Jakub Jurasz & Jerzy Mikulik, 2017. "A strategy for the photovoltaic-powered pumped storage hydroelectricity," Energy & Environment, , vol. 28(5-6), pages 544-563, September.
    7. Henao, Felipe & Viteri, Juan P. & Rodríguez, Yeny & Gómez, Juan & Dyner, Isaac, 2020. "Annual and interannual complementarities of renewable energy sources in Colombia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    8. 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.
    9. Markos A. Kousounadis-Knousen & Ioannis K. Bazionis & Athina P. Georgilaki & Francky Catthoor & Pavlos S. Georgilakis, 2023. "A Review of Solar Power Scenario Generation Methods with Focus on Weather Classifications, Temporal Horizons, and Deep Generative Models," Energies, MDPI, vol. 16(15), pages 1-29, July.
    10. Jurasz, Jakub & Dąbek, Paweł B. & Kaźmierczak, Bartosz & Kies, Alexander & Wdowikowski, Marcin, 2018. "Large scale complementary solar and wind energy sources coupled with pumped-storage hydroelectricity for Lower Silesia (Poland)," Energy, Elsevier, vol. 161(C), pages 183-192.
    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. Hu, Jinxing & Li, Hongru, 2022. "A transfer learning-based scenario generation method for stochastic optimal scheduling of microgrid with newly-built wind farm," Renewable Energy, Elsevier, vol. 185(C), pages 1139-1151.
    13. Yao, Ye & Du, Huibin & Zou, Hongyang & Zhou, Peng & Antunes, Carlos Henggeler & Neumann, Anne & Yeh, Sonia, 2023. "Fifty years of Energy Policy: A bibliometric overview," Energy Policy, Elsevier, vol. 183(C).
    14. Zhang, Yi & Cheng, Chuntian & Cao, Rui & Li, Gang & Shen, Jianjian & Wu, Xinyu, 2021. "Multivariate probabilistic forecasting and its performance’s impacts on long-term dispatch of hydro-wind hybrid systems," Applied Energy, Elsevier, vol. 283(C).
    15. Dumas, Jonathan & Wehenkel, Antoine & Lanaspeze, Damien & Cornélusse, Bertrand & Sutera, Antonio, 2022. "A deep generative model for probabilistic energy forecasting in power systems: normalizing flows," Applied Energy, Elsevier, vol. 305(C).
    16. Diana Cantor & Andrés Ochoa & Oscar Mesa, 2022. "Total Variation-Based Metrics for Assessing Complementarity in Energy Resources Time Series," Sustainability, MDPI, vol. 14(14), pages 1-15, July.
    17. Xiaomei Ma & Yongqian Liu & Jie Yan & Han Wang, 2023. "A WGAN-GP-Based Scenarios Generation Method for Wind and Solar Power Complementary Study," Energies, MDPI, vol. 16(7), pages 1-20, March.
    18. David Gattie & Michael Hewitt, 2023. "National Security as a Value-Added Proposition for Advanced Nuclear Reactors: A U.S. Focus," Energies, MDPI, vol. 16(17), pages 1-26, August.
    19. Ronnie D. Lipschutz & Dustin Mulvaney, 2013. "The road not taken, round II: centralized vs. distributed energy strategies and human security," Chapters, in: Hugh Dyer & Maria Julia Trombetta (ed.), International Handbook of Energy Security, chapter 22, pages 483-506, Edward Elgar Publishing.
    20. Peter Lund, 2012. "The European Union challenge: integration of energy, climate, and economic policy," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 1(1), pages 60-68, July.

    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:gam:jeners:v:16:y:2023:i:3:p:1013-:d:1038118. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.