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Multi-Reservoir Operation Planning Using Hybrid Genetic Algorithm and Linear Programming (GA-LP): An Alternative Stochastic Approach

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Abstract

Many models have been suggested to deal with the multi-reservoir operation planning stochastic optimization problem involving decisions on water releases from various reservoirs in different time periods of the year. A new approach using genetic algorithm (GA) and linear programming (LP) is proposed here to determine operational decisions for reservoirs of a hydro system throughout a planning period, with the possibility of considering a variety of equally likely hydrologic sequences representing inflows. This approach permits the evaluation of a reduced number of parameters by GA and operational variables by LP. The proposed algorithm is a stochastic approximation to the hydro system operation problem, with advantages such as simple implementation and the possibility of extracting useful parameters for future operational decisions. Implementation of the method is demonstrated through a small hypothetical hydrothermal system used in literature as an example for stochastic dual dynamic programming (SDDP) method of Pereira and Pinto (Pereira, M. V. F. and Pinto, L. M. V. G.: 1985, Water Res. Res. 21(6), 779–792). The proposed GA-LP approach performed equally well as compared to the SDDP method.

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Authors and Affiliations

  1. São Carlos School of Engineering, University of São Paulo, Avenue Trabalhador São Carlense, 400, São Carlos, São Paulo, Brazil

    L. F. R. Reis & F. H. Chaudhry

  2. Department of Engineering, School of Engineering and Computer Science, University of Exeter, North Park Road, Exeter, EX4 4QF, U.K.

    G. A. Walters & D. Savic

Authors
  1. L. F. R. Reis
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  2. G. A. Walters
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  3. D. Savic
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  4. F. H. Chaudhry
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Correspondence to L. F. R. Reis.

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Reis, L.F.R., Walters, G.A., Savic, D. et al. Multi-Reservoir Operation Planning Using Hybrid Genetic Algorithm and Linear Programming (GA-LP): An Alternative Stochastic Approach. Water Resour Manage 19, 831–848 (2005). https://doi.org/10.1007/s11269-005-6813-0

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  • DOI: https://doi.org/10.1007/s11269-005-6813-0

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