This paper deals with the short-term scheduling problem of hydropower systems. The objective is to meet the daily energy demand in an economic and safe way. The individuality of the generating units and the nonlinearity of their efficiency curves are taken into account. The mathematical model is formulated as a dynamic, mixed integer, nonlinear, nonconvex, combinatorial, and multiobjective optimization problem. We propose two solution methods using metaheuristic approaches. They combine Genetic Algorithm with Strength Pareto Evolutionary Algorithm and Ant Colony Optimization. Both approaches are divided into two phases. In the first one, to maximize the plant’s net generation, the problem is solved for each hour of the day (static dispatch). In the second phase, to minimize the units’ switching on-off, the day is considered as a whole (dynamic dispatch). The proposed methodology is applied to two Brazilian hydroelectric plants, in cascade, that belong to the national interconnected system. The nondominated solutions from both approaches are presented. All of them meet demand respecting the physical, electrical, and hydraulic constraints.
This paper presents an optimization model of the power generation potential for either new or repowered hydroelectric plants. It is based on curves that represent the unit efficiency as a function of the nominal output. The objective is to choose the combination of efficiency curve types that maximizes the power generation for certain load levels. The mathematical formulation results in a mixed integer, nonlinear programming problem. Genetic Algorithm is employed to solve this. The operators and parameters of the model are chosen by simulation using the objective function values as a selection method. A case study is carried out for two Brazilian hydroelectric plants: Sobradinho and Ilha Solteira. The results show the importance of the turbines model choice in order to get the maximum benefit of a plant.
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