Increasing renewable energy usage puts 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.

可再生能源使用的增加对河流水电系统的决策施加了额外的压力。决策支持工具用于未来可用水的预测。由于存在许多不确定性，用于河流状态估计的模型驱动的预测通常会提供不好的结果。错误的流入和不良的初始化是不确定性的来源。为了克服这个问题，使用了标准数据同化（DA）技术（例如，集成卡尔曼滤波器），这些技术并不总是适用于实际系统。本文进一步介绍了基于控制理论的新颖量身定制的模型更新算法。根据系统上的水位测量，使用比例积分微分（PID）控制器对模型进行控制和不断更新。PID控制器的实施需要控制器的参数估计（调整）。本研究通过提出适用于控制器初始调整的顺序，多度量程序来处理此任务。在塞尔维亚的铁门水电系统上对所提出的调整方法进行了测试，结果令人满意。