Abstract
Among the consequences of climate change are increased temperatures and changes in rainfall patterns that bring longer periods of drought. This creates limitations in the administration of hydroelectric plant reservoirs, restricting, in some cases, the amount of electrical energy generated. The objective of this research is to solve the multi-objective optimization problem that seeks to minimize the production of electrical energy from hydroelectric plants with low inflow and, at the same time, minimize electrical rationing due to this low production. As these objectives conflict with each other, it is necessary to apply multi-objective optimization problem-solving methodologies, among which are genetic algorithms. The mathematical model is built considering the operating conditions of the reservoirs of the hydroelectric plants under study, including their minimum operating levels, which are included in the model restrictions. The non-dominated genetic classification algorithm II is used to obtain the Pareto front, which is composed of a total of 78 non-dominated solutions that are useful to manage the considered reservoirs at the time of maximum demand. It is recommended to use other multi-objective optimization algorithms for comparison purposes, selecting the ideal indicators to evaluate the performance of each algorithm used, in addition to incorporating monetary and environmental cost restrictions into the model
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