Reservoir system optimisation using a penalty approach and a multi-population genetic algorithm

Abstract

A multi-population genetic algorithm (GA) was used to optimise a system of two reservoirs that supplies monthly varying demands and environmental flow requirements. Optimisation aimed at minimising the penalty resulting from non-supply of water and the occurrence of low reservoir storage states that would limit non-consumptive utilisation of water in the reservoirs. Four cases were analysed viz. Case I: Reservoir capacities and demands were fixed and the operating rules were optimised; Case II: Demands were fixed and the reservoir capacities and operating rules were optimised; Case III: Reservoir capacities were fixed and the demands and operating rules were optimised; and Case IV: Reservoir capacities, demands and operating rules were optimised. The genetic algorithm obtained reasonable solutions for all cases. A detailed analysis of Case IV obtained several high-performance solutions of varied sizes and supply capabilities. This analysis revealed specific limitations of supply reliability and the expected storage states of one of the reservoirs.

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