The optimal operation of water reservoir systems is a challenging task involving multiple conflicting objectives. The main source of complexity is the presence of the water inflow, which acts as an exogenous, highly uncertain disturbance on the system. When model predictive control (MPC) is employed, the optimal water release is usually computed based on the (predicted) trajectory of the inflow. This choice may jeopardize the closed-loop performance when the actual inflow differs from its forecast. In this work, we consider - for the first time - a stochastic MPC approach for water reservoirs, in which the control is optimized based on a set of plausible future inflows directly generated from past data. Such a scenario-based MPC strategy allows the controller to be more cautious, counteracting droughty periods (e.g., the lake level going below the dry limit) while at the same time guaranteeing that the agricultural water demand is satisfied. The method's effectiveness is validated through extensive Monte Carlo tests using actual inflow data from Lake Como, Italy.

翻译：水库系统的优化运行是一项涉及多个相互冲突目标的挑战性任务。其主要复杂性源于入流作为系统外生且高度不确定的扰动因素。当采用模型预测控制（MPC）时，最优放水量通常基于入流（预测）轨迹进行计算。当实际入流与预测值存在偏差时，这种选择可能会损害闭环性能。本研究首次针对水库系统提出了一种随机MPC方法，其控制策略基于直接从历史数据生成的若干合理未来入流场景进行优化。这种基于场景的MPC策略使控制器更具谨慎性，既能抵御干旱期（如湖泊水位低于干旱限值），同时保证农业用水需求得到满足。通过意大利科莫湖实际入流数据的大规模蒙特卡洛测试，验证了该方法的有效性。