IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v229y2013i1p212-222.html
   My bibliography  Save this article

Parallel computing applied to the stochastic dynamic programming for long term operation planning of hydrothermal power systems

Author

Listed:
  • Dias, Bruno Henriques
  • Tomim, Marcelo Aroca
  • Marcato, André Luís Marques
  • Ramos, Tales Pulinho
  • Brandi, Rafael Bruno S.
  • Junior, Ivo Chaves da Silva
  • Filho, João Alberto Passos

Abstract

In this paper, parallel processing techniques are employed to improve the performance of the stochastic dynamic programming applied to the long term operation planning of electrical power system. The hydroelectric plants are grouped into energy equivalent reservoirs and the expected cost functions are modeled by a piecewise linear approximation, by means of the Convex Hull algorithm. In order to validate the proposed methodology, data from the Brazilian electrical power system is utilized.

Suggested Citation

  • Dias, Bruno Henriques & Tomim, Marcelo Aroca & Marcato, André Luís Marques & Ramos, Tales Pulinho & Brandi, Rafael Bruno S. & Junior, Ivo Chaves da Silva & Filho, João Alberto Passos, 2013. "Parallel computing applied to the stochastic dynamic programming for long term operation planning of hydrothermal power systems," European Journal of Operational Research, Elsevier, vol. 229(1), pages 212-222.
    • Handle: RePEc:eee:ejores:v:229:y:2013:i:1:p:212-222
    DOI: 10.1016/j.ejor.2013.02.024
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221713001574
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2013.02.024?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. Shapiro, Alexander & Tekaya, Wajdi & da Costa, Joari Paulo & Soares, Murilo Pereira, 2013. "Risk neutral and risk averse Stochastic Dual Dynamic Programming method," European Journal of Operational Research, Elsevier, vol. 224(2), pages 375-391.
    2. Shapiro, Alexander, 2011. "Analysis of stochastic dual dynamic programming method," European Journal of Operational Research, Elsevier, vol. 209(1), pages 63-72, February.
    3. 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.
    4. Cervellera, Cristiano & Chen, Victoria C.P. & Wen, Aihong, 2006. "Optimization of a large-scale water reservoir network by stochastic dynamic programming with efficient state space discretization," European Journal of Operational Research, Elsevier, vol. 171(3), pages 1139-1151, June.
    5. Philpott, A.B. & de Matos, V.L., 2012. "Dynamic sampling algorithms for multi-stage stochastic programs with risk aversion," European Journal of Operational Research, Elsevier, vol. 218(2), pages 470-483.
    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. Zhong-Kai Feng & Wen-Jing Niu & Jian-Zhong Zhou & Chun-Tian Cheng & Hui Qin & Zhi-Qiang Jiang, 2017. "Parallel Multi-Objective Genetic Algorithm for Short-Term Economic Environmental Hydrothermal Scheduling," Energies, MDPI, vol. 10(2), pages 1-22, January.
    2. Unai Aldasoro & Laureano Escudero & María Merino & Juan Monge & Gloria Pérez, 2015. "On parallelization of a stochastic dynamic programming algorithm for solving large-scale mixed 0–1 problems under uncertainty," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 703-742, October.
    3. Zheng, Hao & Feng, Suzhen & Chen, Cheng & Wang, Jinwen, 2022. "A new three-triangle based method to linearly concave hydropower output in long-term reservoir operation," Energy, Elsevier, vol. 250(C).
    4. Feng, Zhong-kai & Niu, Wen-jing & Cheng, Chun-tian, 2018. "Optimization of hydropower reservoirs operation balancing generation benefit and ecological requirement with parallel multi-objective genetic algorithm," Energy, Elsevier, vol. 153(C), pages 706-718.
    5. Schulte Beerbühl, S. & Fröhling, M. & Schultmann, F., 2015. "Combined scheduling and capacity planning of electricity-based ammonia production to integrate renewable energies," European Journal of Operational Research, Elsevier, vol. 241(3), pages 851-862.
    6. Ping Sun & Zhi-qiang Jiang & Ting-ting Wang & Yan-ke Zhang, 2016. "Research and Application of Parallel Normal Cloud Mutation Shuffled Frog Leaping Algorithm in Cascade Reservoirs Optimal Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(3), pages 1019-1035, February.
    7. Bao, Minglei & Hui, Hengyu & Ding, Yi & Sun, Xiaocong & Zheng, Chenghang & Gao, Xiang, 2023. "An efficient framework for exploiting operational flexibility of load energy hubs in risk management of integrated electricity-gas systems," Applied Energy, Elsevier, vol. 338(C).
    8. Luckny Zephyr & Bernard F. Lamond & Pascal Lang, 2024. "Hybrid simplicial-randomized approximate stochastic dynamic programming for multireservoir optimization," Computational Management Science, Springer, vol. 21(1), pages 1-44, June.
    9. Aldasoro, Unai & Escudero, Laureano F. & Merino, María & Pérez, Gloria, 2017. "A parallel Branch-and-Fix Coordination based matheuristic algorithm for solving large sized multistage stochastic mixed 0–1 problems," European Journal of Operational Research, Elsevier, vol. 258(2), pages 590-606.
    10. Yong Peng & Anbang Peng & Xiaoli Zhang & Huicheng Zhou & Lin Zhang & Wenzhong Wang & Zixin Zhang, 2017. "Multi-Core Parallel Particle Swarm Optimization for the Operation of Inter-Basin Water Transfer-Supply Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 27-41, January.
    11. Schryen, Guido, 2020. "Parallel computational optimization in operations research: A new integrative framework, literature review and research directions," European Journal of Operational Research, Elsevier, vol. 287(1), pages 1-18.
    12. Andre Luiz Diniz & Maria Elvira P. Maceira & Cesar Luis V. Vasconcellos & Debora Dias J. Penna, 2020. "A combined SDDP/Benders decomposition approach with a risk-averse surface concept for reservoir operation in long term power generation planning," Annals of Operations Research, Springer, vol. 292(2), pages 649-681, September.

    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. de Queiroz, Anderson Rodrigo, 2016. "Stochastic hydro-thermal scheduling optimization: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 382-395.
    2. Lohmann, Timo & Hering, Amanda S. & Rebennack, Steffen, 2016. "Spatio-temporal hydro forecasting of multireservoir inflows for hydro-thermal scheduling," European Journal of Operational Research, Elsevier, vol. 255(1), pages 243-258.
    3. Davi Valladão & Thuener Silva & Marcus Poggi, 2019. "Time-consistent risk-constrained dynamic portfolio optimization with transactional costs and time-dependent returns," Annals of Operations Research, Springer, vol. 282(1), pages 379-405, November.
    4. W. Ackooij & X. Warin, 2020. "On conditional cuts for stochastic dual dynamic programming," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 8(2), pages 173-199, June.
    5. 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.
    6. Andy Philpott & Vitor de Matos & Erlon Finardi, 2013. "On Solving Multistage Stochastic Programs with Coherent Risk Measures," Operations Research, INFORMS, vol. 61(4), pages 957-970, August.
    7. Vincent Guigues, 2014. "SDDP for some interstage dependent risk-averse problems and application to hydro-thermal planning," Computational Optimization and Applications, Springer, vol. 57(1), pages 167-203, January.
    8. Löhndorf, Nils & Shapiro, Alexander, 2019. "Modeling time-dependent randomness in stochastic dual dynamic programming," European Journal of Operational Research, Elsevier, vol. 273(2), pages 650-661.
    9. Gauvin, Charles & Delage, Erick & Gendreau, Michel, 2018. "A stochastic program with time series and affine decision rules for the reservoir management problem," European Journal of Operational Research, Elsevier, vol. 267(2), pages 716-732.
    10. Powell, Warren B., 2019. "A unified framework for stochastic optimization," European Journal of Operational Research, Elsevier, vol. 275(3), pages 795-821.
    11. Weini Zhang & Hamed Rahimian & Güzin Bayraksan, 2016. "Decomposition Algorithms for Risk-Averse Multistage Stochastic Programs with Application to Water Allocation under Uncertainty," INFORMS Journal on Computing, INFORMS, vol. 28(3), pages 385-404, August.
    12. D. Ávila & A. Papavasiliou & N. Löhndorf, 2022. "Parallel and distributed computing for stochastic dual dynamic programming," Computational Management Science, Springer, vol. 19(2), pages 199-226, June.
    13. Rudloff, Birgit & Street, Alexandre & Valladão, Davi M., 2014. "Time consistency and risk averse dynamic decision models: Definition, interpretation and practical consequences," European Journal of Operational Research, Elsevier, vol. 234(3), pages 743-750.
    14. Schur, Rouven & Gönsch, Jochen & Hassler, Michael, 2019. "Time-consistent, risk-averse dynamic pricing," European Journal of Operational Research, Elsevier, vol. 277(2), pages 587-603.
    15. Zhou, Shaorui & Zhang, Hui & Shi, Ning & Xu, Zhou & Wang, Fan, 2020. "A new convergent hybrid learning algorithm for two-stage stochastic programs," European Journal of Operational Research, Elsevier, vol. 283(1), pages 33-46.
    16. Wim Ackooij & Welington Oliveira & Yongjia Song, 2019. "On level regularization with normal solutions in decomposition methods for multistage stochastic programming problems," Computational Optimization and Applications, Springer, vol. 74(1), pages 1-42, September.
    17. Lorenzo Reus & Rodolfo Prado, 2022. "Need to Meet Investment Goals? Track Synthetic Indexes with the SDDP Method," Computational Economics, Springer;Society for Computational Economics, vol. 60(1), pages 47-69, June.
    18. Liu, Rui Peng & Shapiro, Alexander, 2020. "Risk neutral reformulation approach to risk averse stochastic programming," European Journal of Operational Research, Elsevier, vol. 286(1), pages 21-31.
    19. Bakker, Hannah & Dunke, Fabian & Nickel, Stefan, 2020. "A structuring review on multi-stage optimization under uncertainty: Aligning concepts from theory and practice," Omega, Elsevier, vol. 96(C).
    20. Alexander Shapiro & Wajdi Tekaya & Murilo Pereira Soares & Joari Paulo da Costa, 2013. "Worst-Case-Expectation Approach to Optimization Under Uncertainty," Operations Research, INFORMS, vol. 61(6), pages 1435-1449, December.

    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:ejores:v:229:y:2013:i:1:p:212-222. 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.elsevier.com/locate/eor .

    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.