The optimal dispatch of hydropower plants consists of the challenge of taking advantage of both available head and river flows. Despite the objective of delivering the maximum power to the grid, some variables are uncertain, dynamic, non-linear, and non-parametric. Nevertheless, some models may help hydropower generating players with computer science evolution, thus maximizing the hydropower plants’ power production. Over the years, several studies have explored Machine Learning (ML) techniques to optimize hydropower plants’ dispatch, being applied in the pre-operation, real-time and post-operation phases. Hence, this work consists of a systematic review to analyze how ML models are being used to optimize energy production from hydropower plants. The analysis focused on criteria that interfere with energy generation forecasts, operating policies, and performance evaluation. Our discussions aimed at ML techniques, schedule forecasts, river systems, and ML applications for hydropower optimization. The results showed that ML techniques have been more applied for river flow forecast and reservoir operation optimization. The long-term scheduling horizon is the most common application in the analyzed studies. Therefore, supervised learning was more applied as ML technique segment. Despite being a widely explored theme, new areas present opportunities for disruptive research, such as real-time schedule forecast, run-of-river system optimization and low-head hydropower plant operation.

The operation of hydroelectric power plants is directly dependent on data from various systems. These systems are classified as Operational Technologies (OT) when focused on fieldlevel control and Information Technologies (IT) when aimed at enterprise-level applications. The efficient integration of these systems is a challenge for industries since each system type has different perspectives and objectives. However, this integration is crucial to achieving the best possible operational performance. In the hydroelectric power generation market, there is often a lack of applications that can successfully integrate and analyze data from IT and OT systems, transforming it into useful information for operation. This often results in manual activities to collect and analyze data, reducing their effectiveness. This article presents an architecture for the integration of IT/OT systems, focusing on the hydroelectric power generation market, and discusses its implementation and results at the Jirau Hydroelectric Plant.