Increasing renewable energy usage puts an extra pressure on decision-making in river hydropower systems. Decision support tools are used for near-future forecasting of the water available. Model-driven forecasting used for river state estimation often provides bad results due to numerous uncertainties. False inflows and poor initialization are some of the uncertainty sources. To overcome this, standard data assimilation (DA) techniques (e.g., ensemble Kalman filter) are used, which are not always applicable in real systems. This paper presents further insight into the novel, tailor-made model update algorithm based on control theory. According to water-level measurements over the system, the model is controlled and continuously updated using proportional–integrative–derivative (PID) controller(s). Implementation of the PID controllers requires the controllers’ parameters estimation (tuning). This research deals with this task by presenting sequential, multi-metric procedure, applicable for controllers’ initial tuning. The proposed tuning method is tested on the Iron Gate hydropower system in Serbia, showing satisfying results.
Uncertainty of the boundary and initial conditions affects model-driven forecasting.
Data Assimilation is used to overcome these problems.
Research presents potential of using novel, tailor-made, PID controllers-based data assimilation method for river hydraulic models update.
Method could be used as a decision-support tool for hydropower systems control.
Sequential, multi-metric tuning procedure has been introduced.