Smart hydropower generation and pressure management in water distribution networks using a novel in-pipe turbine

In-pipe turbines have emerged as a promising solution for harnessing surplus energy in water transmission networks for distributed power generation. While much of the previous research has focused on optimizing the turbine itself, practical implementation presents additional challenges. To address these challenges, this study proposes a hydraulic scheme that integrates an in-pipe turbine with accompanying control valves, intended to replace conventional pressure regulation valves. This system not only regulates pressure in water distribution networks but also functions as a power generation unit. The utilized turbine in this study is a modified version of previously introduced drag-based in-pipe turbines, demonstrating significantly higher efficiency. The study proposes a dynamic control method to optimize turbine performance under transient hydraulic conditions of pipelines, based on reinforcement learning. This hydraulic control algorithm successfully adapts to new scenarios, achieving desired power generation while maintaining the pressure constraints of the water distribution network at various flow conditions. When tested on a benchmark network, the trained model can recover up to 40% of the energy that would be otherwise dissipated by a pressure-reducing valve or left unused. The proposed methodology in this study enhances the feasibility and reliability of in-pipe turbines by integrating their prior advancements in the design and optimization with an RL-based framework for their optimal deployment in water transmission networks.