No abstract

It is widely known that hydroelectric power plants benefit from optimized operation schedules, since the latter prevent water and, therefore, monetary wastes, contributing to significant environmental and economic gains. The level of detail on the representation of such systems is related to how far ahead the planning horizon is extended. Aiming at the very short-term optimization of hydroelectric power plants, which usually requires the most detailed models, this paper addresses an undesired effect that, despite being already mentioned in the literature, has not been properly explored and explained yet. This effect is given by the indication of spillage by the optimizer, even when the reservoir does not reach its maximum capacity. Simulations implemented in Julia language using real power plant data expose this phenomenon. Possible ways to circumvent it are presented. Results showed that, in specific cases, spillage allows the achieving of more efficient operating points by reducing the gross head and increasing the amount of water that flows through turbines. Furthermore, it was verified that applying water outflow-based objective functions prevents undesired spillage indications, despite causing machines to operate at lower efficiency levels, compared with the utilization of power losses-based objective functions.

No abstract

Hydroelectric power plants’ operational decisions are associated with several factors, such as generation planning, water availability and dam safety. One major challenge is to control the water spillage from the reservoir. Although this action represents a loss of energy production, it is a powerful strategy to regulate the reservoir level, ensuring the dam’s safety. The decision to use this strategy must be made in advance based on level and demand predictions. The present work applies supervised machine learning techniques to predict the operating condition of spillage in a hydroelectric plant for 5 h ahead. The use of this method, in real time, aims to assist the operator so that he can make more assertive and safer decisions, avoiding waste of energy resources and increasing the safety of dams. The Random Forest and Multilayer Perceptron methods were used to define the architecture compared to the forecasting capacity. The proposed methodology was applied to a 902.5 MW Hydroelectric Power Plant located on the Tocantins River, Brazil. The results demonstrate effective assistance to operators in the decision-making, presenting accuracy of up to 99.15% for the spill decision.

No abstract