Management of water resources has become more complex in recent years as a result of changing attitudes towards sustainability and the attribution of greater attention to environmental issues, especially under a scenario of water scarcity risk introduced by climate changes and anthropogenic pressures. This study addresses the optimal short-term operation of a multi-purpose hydropower system under an environment where objectives are conflicting. New optimization models using mixed integer nonlinear programming (MINLP) with binary variables adopted for incorporating unit commitment constraints and adaptive real-time operations are developed and applied to a real life hydropower reservoir in Brazil, utilizing evolutionary algorithms. These formulations address water quality concerns downstream of the reservoir and optimal operations for power generation in an integrated manner and deal with uncertain future flows due to climate change. Results obtained using genetic algorithm (GA) solvers were superior to gradient based methods, converging to superior optimal solutions especially due to computational intractability problems associated with combinatorial domain of integer variables in the unit commitment formulation. The adaptive operation formulation in conjunction with the solution of turbine unit commitment problem yielded more reliable solutions, reducing forecasting uncertainty and providing more flexible operational rules.
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