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Ant Colony Optimization for Multi-Purpose Reservoir Operation

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  • D. Kumar
  • M. Reddy

Abstract

In this paper a metaheuristic technique called Ant Colony Optimization (ACO) is proposed to derive operating policies for a multi-purpose reservoir system. Most of the real world problems often involve non-linear optimization in their solution with high dimensionality and large number of equality and inequality constraints. Often the conventional techniques fail to yield global optimal solutions. The recently proposed evolutionary algorithms are also facing problems, while solving large-scale problems. In this study, it is intended to test the usefulness of ACO in solving such type of problems. To formulate the ACO model for reservoir operation, the problem is approached by considering a finite time series of inflows, classifying the reservoir volume into several class intervals, and determining the reservoir release for each period with respect to a predefined optimality criterion. The ACO technique is applied to a case study of Hirakud reservoir, which is a multi-purpose reservoir system located in India. The multiple objectives comprise of minimizing flood risks, minimizing irrigation deficits and maximizing hydropower production in that order of priority. The developed model is applied for monthly operation, and consists of two models viz., for short-time horizon operation and for long-time horizon operation. To evaluate the performance of ACO, the developed models are also solved using real coded Genetic Algorithm (GA). The results of the two models indicate that ACO model performs better, in terms of higher annual power production, while satisfying irrigation demands and flood control restrictions, compared to those obtained by GA. Finally it is found that ACO model outperforms GA model, especially in the case of long-time horizon reservoir operation. Copyright Springer Science+Business Media, Inc. 2006

Suggested Citation

  • D. Kumar & M. Reddy, 2006. "Ant Colony Optimization for Multi-Purpose Reservoir Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 20(6), pages 879-898, December.
    • Handle: RePEc:spr:waterr:v:20:y:2006:i:6:p:879-898
    DOI: 10.1007/s11269-005-9012-0
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    References listed on IDEAS

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    1. Fi-John Chang & Li Chen, 1998. "Real-Coded Genetic Algorithm for Rule-Based Flood Control Reservoir Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 12(3), pages 185-198, June.
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