AI-Based Long-Term Hydro Power Scheduling

Long-Term hydro scheduling plays a crucial role in optimizing the utilization of hydropower resources while meeting energy demand and minimizing operational costs. This thesis work focuses on replicating the objective function of an Implicit Stochastic Optimization model for long-term hydro scheduling using Artificial Neural Networks (ANN). The objective is to satisfy energy demand solely through hydropower production, considering various scenarios of annual inflow, with the aim of minimizing operational costs. The thesis begins by providing an overview of the importance of long-term hydro scheduling in a sustainable energy generation. The challenges associated with managing hydroelectric systems will be explored and the significance of optimizing the scheduling process to maximize cost-effectiveness will be highlighted. The research methodology involves the application of neural networks to replicate the objective function of the Implicit Stochastic Optimization model. Neural networks offer a powerful tool for capturing complex relationships and patterns within the hydrological data, allowing for accurate prediction and optimization of hydropower production. The thesis presents a detailed analysis of the neural network architecture and training process used to replicate the objective function. It discusses the selection of input variables, network structure, activation functions and training algorithms to achieve optimal performance in replicating the optimization model's objective.Furthermore, the thesis examines the performance and accuracy of the neural network-based approach through experimentation. The results demonstrate the capability of neural networks to replicate the objective function of the Implicit Stochastic Optimization model for long-term hydro scheduling. The neural network-based approach offers a computationally efficient and scalable solution that can provide valuable insights for decision-making in hydropower operations. In conclusion, this thesis contributes to the field of long-term hydro scheduling by showcasing the effectiveness of neural networks in replicating the objective function of an optimization model. The findings highlight the potential of neural network-based approaches in optimizing hydropower production, reducing operational costs and reducing computational time, facilitating sustainable energy generation.

Long-term hydro scheduling plays a crucial role in optimizing the utilization of hydropower resources while meeting energy demand and minimizing operational costs. This thesis work focuses on replicating the objective function of a two-stage stochastic dual dynamic programming (SDDP) model for long-term hydro scheduling using Artificial Neural Networks (ANN). The objective is to satisfy energy demand solely through hydropower production, considering various scenarios of annual inflow, with the aim of minimizing operational costs. The thesis begins by providing an overview of the importance of long-term hydro scheduling in a sustainable energy generation. The challenges associated with managing hydroelectric systems will be explored and the significance of optimizing the scheduling process to maximize cost-effectiveness will be highlighted. The research methodology involves the application of neural networks to replicate the objective function of the stochastic model. Neural networks offer a powerful tool for capturing complex relationships and patterns within the hydrological data, allowing for accurate prediction and optimization of hydropower production. The thesis presents a detailed analysis of the neural network architecture and training process used to replicate the objective function. It discusses the selection of input variables, network structure, activation functions and training algorithms to achieve optimal performance in replicating the optimization model's objective.Furthermore, the thesis examines the performance and accuracy of the neural network-based approach through experimentation. The results demonstrate the capability of neural networks to replicate the objective function of the SDDP model for long-term hydro scheduling. The neural network-based approach offers a computationally efficient and scalable solution that can provide valuable insights for decision-making in hydropower operations. In conclusion, this thesis contributes to the field of long-term hydro scheduling by showcasing the effectiveness of neural networks in replicating the objective function of an optimization model. The findings highlight the potential of neural network-based approaches in optimizing hydropower production, reducing operational costs and reducing computational time, facilitating sustainable energy generation.