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

• Published in: European journal of operational research Vol. 229; no. 1; pp. 212 - 222
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 16-08-2013; Elsevier Sequoia S.A
• Subjects: Algorithms; Convex Hull; Dynamic programming; Hydroelectric plants; Hydrothermal power system; Long term; Long term operation planning; Operations management; Parallel processing; Parallel programming; Stochastic models; Stochastic programming; Studies; Brazil; Hydrothermal power system; Parallel programming; Convex Hull; Dynamic programming; Long term operation planning; Stochastic programming
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2013.02.024

► We analyze to parallelization process of the Stochastic Dynamic Programming (SDP). ► This is applied to the long term hydrothermal system operation planning. ► We examine this approach applied to the Brazilian Power System. ► The parallel processing strategy adopted reduces significantly the computing time. ► This can be use by utilities/government to determine the optimal operation planning.
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.