IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i5p826-d210325.html
   My bibliography  Save this article

Including Wind Power Generation in Brazil’s Long-Term Optimization Model for Energy Planning

Author

Listed:
  • Paula Medina Maçaira

    (Industrial Engineering Department, PUC-Rio, Rio de Janeiro-RJ 22451-900, Brazil)

  • Yasmin Monteiro Cyrillo

    (Electrical Engineering Department, PUC-Rio, Rio de Janeiro-RJ 22451-900, Brazil)

  • Fernando Luiz Cyrino Oliveira

    (Industrial Engineering Department, PUC-Rio, Rio de Janeiro-RJ 22451-900, Brazil)

  • Reinaldo Castro Souza

    (Industrial Engineering Department, PUC-Rio, Rio de Janeiro-RJ 22451-900, Brazil)

Abstract

In the past two decades, wind power’s share of the energy mix has grown significantly in Brazil. However, nowadays planning electricity operation in Brazil basically involves evaluating the future conditions of energy supply from hydro and thermal sources over the planning horizon. In this context, wind power sources are not stochastically treated. This work applies an innovative approach that incorporates wind power generation in the Brazilian hydro-thermal dispatch using the analytical method of Frequency & Duration. The proposed approach is applied to Brazil’s Northeast region, covering the planning period from July 2017 to December 2021, using the Markov chain Monte Carlo method to simulate wind power scenarios. The obtained results are more conservative than the one currently used by the National Electric System Operator, since the proposed approach forecasts 1.8% less wind generation, especially during peak periods, and 0.67% more thermal generation. This conservatism can reduce the chance of water reservoir depletion and, also an ineffective dispatch.

Suggested Citation

  • Paula Medina Maçaira & Yasmin Monteiro Cyrillo & Fernando Luiz Cyrino Oliveira & Reinaldo Castro Souza, 2019. "Including Wind Power Generation in Brazil’s Long-Term Optimization Model for Energy Planning," Energies, MDPI, vol. 12(5), pages 1-20, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:5:p:826-:d:210325
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/5/826/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/5/826/
    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. Zhang, Yao & Wang, Jianxue & Wang, Xifan, 2014. "Review on probabilistic forecasting of wind power generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 255-270.
    3. Lilin Cheng & Haixiang Zang & Tao Ding & Rong Sun & Miaomiao Wang & Zhinong Wei & Guoqiang Sun, 2018. "Ensemble Recurrent Neural Network Based Probabilistic Wind Speed Forecasting Approach," Energies, MDPI, vol. 11(8), pages 1-23, July.
    4. de Jong, P. & Sánchez, A.S. & Esquerre, K. & Kalid, R.A. & Torres, E.A., 2013. "Solar and wind energy production in relation to the electricity load curve and hydroelectricity in the northeast region of Brazil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 526-535.
    5. Anthony Papavasiliou & Yuting Mou & Léopold Cambier & Damien Scieur, 2018. "Application of stochastic dual dynamic programming to the real-time dispatch of storage under renewable supply uncertainty," LIDAM Reprints CORE 2943, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    6. Jussi Ekström & Matti Koivisto & Ilkka Mellin & Robert John Millar & Matti Lehtonen, 2018. "A Statistical Modeling Methodology for Long-Term Wind Generation and Power Ramp Simulations in New Generation Locations," Energies, MDPI, vol. 11(9), pages 1-18, September.
    7. 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.
    8. Anthony Papavasiliou & Yuting Mou & Léopold Cambier & Damien Scieur, 2018. "Application of stochastic dual dynamic programming to the real-time dispatch of storage under renewable supply uncertainty," LIDAM Reprints CORE 3044, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    9. Mendes, Carlos André B. & Beluco, Alexandre & Canales, Fausto Alfredo, 2017. "Some important uncertainties related to climate change in projections for the Brazilian hydropower expansion in the Amazon," Energy, Elsevier, vol. 141(C), pages 123-138.
    10. Landry, Mark & Erlinger, Thomas P. & Patschke, David & Varrichio, Craig, 2016. "Probabilistic gradient boosting machines for GEFCom2014 wind forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1061-1066.
    11. Kusiak, Andrew & Zheng, Haiyang & Song, Zhe, 2009. "On-line monitoring of power curves," Renewable Energy, Elsevier, vol. 34(6), pages 1487-1493.
    12. Soares, Murilo Pereira & Street, Alexandre & Valladão, Davi Michel, 2017. "On the solution variability reduction of Stochastic Dual Dynamic Programming applied to energy planning," European Journal of Operational Research, Elsevier, vol. 258(2), pages 743-760.
    13. Huang, Xu & Maçaira, Paula Medina & Hassani, Hossein & Cyrino Oliveira, Fernando Luiz & Dhesi, Gurjeet, 2019. "Hydrological natural inflow and climate variables: Time and frequency causality analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 480-495.
    14. Suomalainen, Kiti & Pritchard, Geoffrey & Sharp, Basil & Yuan, Ziqi & Zakeri, Golbon, 2015. "Correlation analysis on wind and hydro resources with electricity demand and prices in New Zealand," Applied Energy, Elsevier, vol. 137(C), pages 445-462.
    15. 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.
    16. Iversen, Emil B. & Morales, Juan M. & Møller, Jan K. & Madsen, Henrik, 2016. "Short-term probabilistic forecasting of wind speed using stochastic differential equations," International Journal of Forecasting, Elsevier, vol. 32(3), pages 981-990.
    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. Cardoso de Mendonça, Mário Jorge & Moreira Pessanha, José Francisco & Andrade de Almeida, Victor & Toscano Medrano, Luiz Alberto & Hunt, Julian David & Pereira Junior, Amaro Olímpio & Nogueira, Erika , 2024. "Synthetic wind speed time series generation by dynamic factor model," Renewable Energy, Elsevier, vol. 228(C).
    2. Duca, Victor E.L.A. & Fonseca, Thaís C.O. & Cyrino Oliveira, Fernando L., 2021. "A generalized dynamical model for wind speed forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 136(C).
    3. Luzia, Ruan & Rubio, Lihki & Velasquez, Carlos E., 2023. "Sensitivity analysis for forecasting Brazilian electricity demand using artificial neural networks and hybrid models based on Autoregressive Integrated Moving Average," Energy, Elsevier, vol. 274(C).

    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. 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.
    2. He, Yaoyao & Wang, Yun & Wang, Shuo & Yao, Xin, 2022. "A cooperative ensemble method for multistep wind speed probabilistic forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    3. 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.
    4. Duca, Victor E.L.A. & Fonseca, Thaís C.O. & Cyrino Oliveira, Fernando L., 2021. "A generalized dynamical model for wind speed forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 136(C).
    5. 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).
    6. De Vivero-Serrano, Gustavo & Bruninx, Kenneth & Delarue, Erik, 2019. "Implications of bid structures on the offering strategies of merchant energy storage systems," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    7. Erfan Mohagheghi & Mansour Alramlawi & Aouss Gabash & Pu Li, 2018. "A Survey of Real-Time Optimal Power Flow," Energies, MDPI, vol. 11(11), pages 1-20, November.
    8. Wasilewski, J. & Baczynski, D., 2017. "Short-term electric energy production forecasting at wind power plants in pareto-optimality context," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 177-187.
    9. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
    10. Street, Alexandre & Valladão, Davi & Lawson, André & Velloso, Alexandre, 2020. "Assessing the cost of the Hazard-Decision simplification in multistage stochastic hydrothermal scheduling," Applied Energy, Elsevier, vol. 280(C).
    11. Àlex Alonso & Jordi de la Hoz & Helena Martín & Sergio Coronas & Pep Salas & José Matas, 2020. "A Comprehensive Model for the Design of a Microgrid under Regulatory Constraints Using Synthetical Data Generation and Stochastic Optimization," Energies, MDPI, vol. 13(21), pages 1-26, October.
    12. Psarros, Georgios N. & Papathanassiou, Stavros A., 2023. "Generation scheduling in island systems with variable renewable energy sources: A literature review," Renewable Energy, Elsevier, vol. 205(C), pages 1105-1124.
    13. Alejandra Tabares & Pablo Cortés, 2024. "Using Stochastic Dual Dynamic Programming to Solve the Multi-Stage Energy Management Problem in Microgrids," Energies, MDPI, vol. 17(11), pages 1-24, May.
    14. 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.
    15. 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.
    16. Shahriari, M. & Cervone, G. & Clemente-Harding, L. & Delle Monache, L., 2020. "Using the analog ensemble method as a proxy measurement for wind power predictability," Renewable Energy, Elsevier, vol. 146(C), pages 789-801.
    17. Yin, S. & Wang, J. & Li, Z. & Fang, X., 2021. "State-of-the-art short-term electricity market operation with solar generation: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    18. Herrera, Milton M. & Dyner, Isaac & Cosenz, Federico, 2019. "Assessing the effect of transmission constraints on wind power expansion in northeast Brazil," Utilities Policy, Elsevier, vol. 59(C), pages 1-1.
    19. Duca, Victor E.L.A. & Fonseca, Thaís C.O. & Cyrino Oliveira, Fernando Luiz, 2023. "An overview of non-Gaussian state-space models for wind speed data," Energy, Elsevier, vol. 266(C).
    20. Luyu Wang & Houbo Xiong & Yunhui Shi & Chuangxin Guo, 2023. "Rolling Horizon Robust Real-Time Economic Dispatch with Multi-Stage Dynamic Modeling," Mathematics, MDPI, vol. 11(11), pages 1-20, June.

    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:12:y:2019:i:5:p:826-:d:210325. 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.