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

Author

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  • 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
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    References listed on IDEAS

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    Citations

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    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. 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).
    4. 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.
    5. 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.
    6. 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.
    7. 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).
    8. 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.
    9. 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.
    10. 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.
    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.

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