Hydrothermal systems operation planning using a discretization of energy interchange between subsystems

•We present an alternative approach to solve hydrothermal systems operation planning.•This proposed methodology uses stochastic dynamic programming (SDP).•The variables that make up the state space to determine the expected cost-to-go functions (ECF) are the initial stored energy and the energy net...

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Bibliographic Details
Published in:	Electric power systems research Vol. 132; pp. 67 - 77
Main Authors:	Conceição, Wellington C., Marcato, André L.M., Ramos, Tales Pulinho, Filho, João Alberto Passos, Brandi, Rafael B.S., David, Pedro Américo M.S., da Silva Júnior, Ivo Chaves
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01-03-2016
Subjects:	Convex Hull algorithm Energy interchange Equivalent systems of energy Hydrothermal dispatch Operation planning Stochastic dynamic programming Stochastic dynamic programming Hydrothermal dispatch Operation planning Equivalent systems of energy Convex Hull algorithm Energy interchange
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Summary:	•We present an alternative approach to solve hydrothermal systems operation planning.•This proposed methodology uses stochastic dynamic programming (SDP).•The variables that make up the state space to determine the expected cost-to-go functions (ECF) are the initial stored energy and the energy net interchange of subsystem.•The results showed the effectiveness of the proposed methodology when compared with other traditional techniques. This article presents an alternative approach to solve hydrothermal systems operation planning based on stochastic dynamic programming. Under the presented approach, the hydroelectric power plants are grouped into equivalent subsystems of energy and the expected cost functions are modeled by a piecewise linear approximation, by means of the Convex Hull algorithm. Also, under this methodology, the problem is solved independently for each subsystem such that the state variables to be considered are the energy storage and energy net interchange of the subsystem. The presented results have shown that this subsystems separation technique reduces significantly the computation time when compared with the traditional techniques of stochastic dynamic programming, demonstrating the effectiveness of the proposed methodology.
ISSN:	0378-7796 1873-2046
DOI:	10.1016/j.epsr.2015.11.007