IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i13p3427-d379731.html
   My bibliography  Save this article

Wind Power Long-Term Scenario Generation Considering Spatial-Temporal Dependencies in Coupled Electricity Markets

Author

Listed:
  • Geovanny Marulanda

    (Institute for Research in Technology (IIT), ICAI School of Engineering, Comillas Pontifical University, 28015 Madrid, Spain)

  • Antonio Bello

    (Institute for Research in Technology (IIT), ICAI School of Engineering, Comillas Pontifical University, 28015 Madrid, Spain)

  • Jenny Cifuentes

    (Santander Big Data Institute, Universidad Carlos III de Madrid, 28903 Getafe, Spain)

  • Javier Reneses

    (Institute for Research in Technology (IIT), ICAI School of Engineering, Comillas Pontifical University, 28015 Madrid, Spain)

Abstract

Wind power has been increasing its participation in electricity markets in many countries around the world. Due to its economical and environmental benefits, wind power generation is one of the most powerful technologies to deal with global warming and climate change. However, as wind power grows, uncertainty in power supply increases due to wind intermittence. In this context, accurate wind power scenarios are needed to guide decision-making in power systems. In this paper, a novel methodology to generate realistic wind power scenarios for the long term is proposed. Unlike most of the literature that tackles this problem, this paper is focused on the generation of realistic wind power production scenarios in the long term. Moreover, spatial-temporal dependencies in multi-area markets have been considered. The results show that capturing the dependencies at the monthly level could improve the quality of scenarios at different time scales. In addition, an evaluation at different time scales is needed to select the best approach in terms of the distribution functions of the generated scenarios. To evaluate the proposed methodology, several tests have been made using real data of wind power generation for Spain, Portugal and France.

Suggested Citation

  • Geovanny Marulanda & Antonio Bello & Jenny Cifuentes & Javier Reneses, 2020. "Wind Power Long-Term Scenario Generation Considering Spatial-Temporal Dependencies in Coupled Electricity Markets," Energies, MDPI, vol. 13(13), pages 1-19, July.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:13:p:3427-:d:379731
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/13/3427/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/13/3427/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jung, Jaesung & Broadwater, Robert P., 2014. "Current status and future advances for wind speed and power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 762-777.
    2. 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.
    3. Hong, Tao & Pinson, Pierre & Fan, Shu & Zareipour, Hamidreza & Troccoli, Alberto & Hyndman, Rob J., 2016. "Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond," International Journal of Forecasting, Elsevier, vol. 32(3), pages 896-913.
    4. Yeojin Kim & Jin Hur, 2020. "An Ensemble Forecasting Model of Wind Power Outputs Based on Improved Statistical Approaches," Energies, MDPI, vol. 13(5), pages 1-11, March.
    5. Alberto Orgaz & Antonio Bello & Javier Reneses, 2019. "A New Model to Simulate Local Market Power in a Multi-Area Electricity Market: Application to the European Case," Energies, MDPI, vol. 12(11), pages 1-15, May.
    6. Bello, Antonio & Reneses, Javier & Muñoz, Antonio & Delgadillo, Andrés, 2016. "Probabilistic forecasting of hourly electricity prices in the medium-term using spatial interpolation techniques," International Journal of Forecasting, Elsevier, vol. 32(3), pages 966-980.
    7. Eising, Manuel & Hobbie, Hannes & Möst, Dominik, 2020. "Future wind and solar power market values in Germany — Evidence of spatial and technological dependencies?," Energy Economics, Elsevier, vol. 86(C).
    8. Christensen, Troels Sønderby & Pircalabu, Anca & Høg, Esben, 2019. "A seasonal copula mixture for hedging the clean spark spread with wind power futures," Energy Economics, Elsevier, vol. 78(C), pages 64-80.
    9. Laura Casula & Guglielmo D'Amico & Giovanni Masala & Filippo Petroni, 2020. "Performance estimation of a wind farm with a dependence structure between electricity price and wind speed," The World Economy, Wiley Blackwell, vol. 43(10), pages 2803-2822, October.
    10. Aasim, & Singh, S.N. & Mohapatra, Abheejeet, 2019. "Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting," Renewable Energy, Elsevier, vol. 136(C), pages 758-768.
    11. Jurate Saltyte Benth & Fred Espen Benth, 2010. "Analysis and modelling of wind speed in New York," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(6), pages 893-909.
    12. Wang, Gang & Jia, Ru & Liu, Jinhai & Zhang, Huaguang, 2020. "A hybrid wind power forecasting approach based on Bayesian model averaging and ensemble learning," Renewable Energy, Elsevier, vol. 145(C), pages 2426-2434.
    13. Tsai, Arthur C. & Liou, Michelle & Simak, Maria & Cheng, Philip E., 2017. "On hyperbolic transformations to normality," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 250-266.
    14. Guidolin, Mariangela & Guseo, Renato, 2014. "Modelling seasonality in innovation diffusion," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 33-40.
    15. Tilmann Gneiting & Fadoua Balabdaoui & Adrian E. Raftery, 2007. "Probabilistic forecasts, calibration and sharpness," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 243-268, April.
    16. Erasmo Cadenas & Wilfrido Rivera & Rafael Campos-Amezcua & Christopher Heard, 2016. "Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model," Energies, MDPI, vol. 9(2), pages 1-15, February.
    17. Ren, Guorui & Liu, Jinfu & Wan, Jie & Guo, Yufeng & Yu, Daren, 2017. "Overview of wind power intermittency: Impacts, measurements, and mitigation solutions," Applied Energy, Elsevier, vol. 204(C), pages 47-65.
    18. Hering, Amanda S. & Genton, Marc G., 2010. "Powering Up With Space-Time Wind Forecasting," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 92-104.
    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. Gómez-Pérez, Jesús D. & Latorre-Canteli, Jesus M. & Ramos, Andres & Perea, Alejandro & Sanz, Pablo & Hernández, Francisco, 2024. "Improving operating policies in stochastic optimization: An application to the medium-term hydrothermal scheduling problem," Applied Energy, Elsevier, vol. 359(C).
    2. Ewa Chodakowska & Joanicjusz Nazarko & Łukasz Nazarko, 2021. "ARIMA Models in Electrical Load Forecasting and Their Robustness to Noise," Energies, MDPI, vol. 14(23), pages 1-22, November.
    3. Plaga, Leonie Sara & Bertsch, Valentin, 2023. "Methods for assessing climate uncertainty in energy system models — A systematic literature review," Applied Energy, Elsevier, vol. 331(C).
    4. Dana-Mihaela Petroșanu & Alexandru Pîrjan, 2020. "Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network," Sustainability, MDPI, vol. 13(1), pages 1-31, December.
    5. Elianne Mora & Jenny Cifuentes & Geovanny Marulanda, 2021. "Short-Term Forecasting of Wind Energy: A Comparison of Deep Learning Frameworks," Energies, MDPI, vol. 14(23), pages 1-26, November.
    6. Dhaval Dalal & Muhammad Bilal & Hritik Shah & Anwarul Islam Sifat & Anamitra Pal & Philip Augustin, 2023. "Cross-Correlated Scenario Generation for Renewable-Rich Power Systems Using Implicit Generative Models," Energies, MDPI, vol. 16(4), pages 1-20, February.

    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. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    2. Ambach, Daniel & Schmid, Wolfgang, 2017. "A new high-dimensional time series approach for wind speed, wind direction and air pressure forecasting," Energy, Elsevier, vol. 135(C), pages 833-850.
    3. Liu, Hui & Duan, Zhu & Chen, Chao, 2020. "Wind speed big data forecasting using time-variant multi-resolution ensemble model with clustering auto-encoder," Applied Energy, Elsevier, vol. 280(C).
    4. Daniel Ambach & Robert Garthoff, 2016. "Vorhersagen der Windgeschwindigkeit und Windenergie in Deutschland," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 10(1), pages 15-36, February.
    5. Alonso-Suárez, R. & David, M. & Branco, V. & Lauret, P., 2020. "Intra-day solar probabilistic forecasts including local short-term variability and satellite information," Renewable Energy, Elsevier, vol. 158(C), pages 554-573.
    6. Gensler, André & Sick, Bernhard & Vogt, Stephan, 2018. "A review of uncertainty representations and metaverification of uncertainty assessment techniques for renewable energies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 352-379.
    7. Liu, Hui & Duan, Zhu, 2020. "A vanishing moment ensemble model for wind speed multi-step prediction with multi-objective base model selection," Applied Energy, Elsevier, vol. 261(C).
    8. Ma, Long & Huang, Ling & Shi, Huifeng, 2023. "Multi-node wind speed forecasting based on a novel dynamic spatial–temporal graph network," Energy, Elsevier, vol. 285(C).
    9. Daniel Ambach & Robert Garthoff, 2016. "Vorhersagen der Windgeschwindigkeit und Windenergie in Deutschland [Predictions of wind speed and wind energy in Germany]," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 10(1), pages 15-36, February.
    10. Luis Mazorra-Aguiar & Philippe Lauret & Mathieu David & Albert Oliver & Gustavo Montero, 2021. "Comparison of Two Solar Probabilistic Forecasting Methodologies for Microgrids Energy Efficiency," Energies, MDPI, vol. 14(6), pages 1-26, March.
    11. Tawn, R. & Browell, J., 2022. "A review of very short-term wind and solar power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    12. Yiqi Chu & Chengcai Li & Yefang Wang & Jing Li & Jian Li, 2016. "A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction," Energies, MDPI, vol. 9(11), pages 1-20, October.
    13. Cheng-Yu Ho & Ke-Sheng Cheng & Chi-Hang Ang, 2023. "Utilizing the Random Forest Method for Short-Term Wind Speed Forecasting in the Coastal Area of Central Taiwan," Energies, MDPI, vol. 16(3), pages 1-18, January.
    14. Bartosz Uniejewski & Rafal Weron & Florian Ziel, 2017. "Variance stabilizing transformations for electricity spot price forecasting," HSC Research Reports HSC/17/01, Hugo Steinhaus Center, Wroclaw University of Technology.
    15. Daniela Castro-Camilo & Raphaël Huser & Håvard Rue, 2019. "A Spliced Gamma-Generalized Pareto Model for Short-Term Extreme Wind Speed Probabilistic Forecasting," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(3), pages 517-534, September.
    16. Wen, Honglin & Pinson, Pierre & Gu, Jie & Jin, Zhijian, 2024. "Wind energy forecasting with missing values within a fully conditional specification framework," International Journal of Forecasting, Elsevier, vol. 40(1), pages 77-95.
    17. Yang, Dazhi & van der Meer, Dennis, 2021. "Post-processing in solar forecasting: Ten overarching thinking tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    18. Rodrigo A. de Marcos & Derek W. Bunn & Antonio Bello & Javier Reneses, 2020. "Short-Term Electricity Price Forecasting with Recurrent Regimes and Structural Breaks," Energies, MDPI, vol. 13(20), pages 1-14, October.
    19. Yitian Xing & Fue-Sang Lien & William Melek & Eugene Yee, 2022. "A Multi-Hour Ahead Wind Power Forecasting System Based on a WRF-TOPSIS-ANFIS Model," Energies, MDPI, vol. 15(15), pages 1-35, July.
    20. Taylor, James W., 2017. "Probabilistic forecasting of wind power ramp events using autoregressive logit models," European Journal of Operational Research, Elsevier, vol. 259(2), pages 703-712.

    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:13:y:2020:i:13:p:3427-:d:379731. 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.