Integrated Artificial Intelligence and Physics-Based Modeling for Long-Term Cascaded Hydropower Scheduling Under Extreme Heat Events

The operation of hydropower plants are significantly challenged by extreme heatwave events. This paper integrates artificial intelligence and a physics-based model to introduce an efficient long-term scheduling framework aimed at maximizing hydropower generation during extreme heat events. The water values derived from the proposed long-term scheduling model can be used in developing effective strategies for short-term hydropower scheduling. A physics-based evaporation model (PEM), which captures key land-atmosphere interactions, is developed to account for significant variations in reservoir evaporation rates during extreme heat events. A multivariate long short-term memory (M-LSTM) forecasting model is also utilized to predict the key input parameters required for both the PEM and the long-term scheduling problem. A regression-based machine learning algorithm is also utilized to estimate the hydropower production function, which enables linear integration of the nonlinear and nonconvex behavior of hydropower plant in the mixed-integer linear formulation of the scheduling problem. The proposed model is applied to a case study of eleven cascaded hydropower units located on the Columbia river. The numerical results demonstrate that the proposed long-term scheduling model effectively manages reservoir operations, mitigating the adverse impacts of extreme heat events on hydropower generation and operator profitability.