Improvements to the water management of a run-of-river HPP reservoir: methodology and case study

The operational planning for run-of-river hydroelectric power plants with small storage capacities is directly linked to the natural inflow of the reservoir water. An ability to accurately forecast the natural inflow can result in the increased production of electric energy as a result of an enhanced flexibility in the management of the stored water. In this article a novel methodology for building an efficient short-term water-inflow forecaster using neural network techniques is presented. The emphasis is given to a precise definition of the problem and the tailoring of the problem-solving requirements to specific case conditions. The proposed methodology was applied to the River Soca cascade hydroelectric system. The efficiency of the algorithm was evaluated in terms of the reduction in the amount of water spillage and the enhanced production-planning accuracy. Finally, some results that justify the development of the short-term water-inflow forecasting algorithm are presented.