An Adaptive Hydropower Management Approach for Downstream Ecosystem Preservation
This work addresses ecosystem preservation for downstream environments affected by hydropower operations, presenting an incremental improvement by integrating neural networks into existing optimization frameworks.
The paper tackles the problem of hydropower plants disrupting ecosystems by proposing an adaptive management approach that uses neural networks to predict minimum ecological discharges, resulting in potential increases in electricity production while protecting ecosystems from climate change.
Hydropower plants play a pivotal role in advancing clean and sustainable energy production, contributing significantly to the global transition towards renewable energy sources. However, hydropower plants are currently perceived both positively as sources of renewable energy and negatively as disruptors of ecosystems. In this work, we highlight the overlooked potential of using hydropower plant as protectors of ecosystems by using adaptive ecological discharges. To advocate for this perspective, we propose using a neural network to predict the minimum ecological discharge value at each desired time. Additionally, we present a novel framework that seamlessly integrates it into hydropower management software, taking advantage of the well-established approach of using traditional constrained optimisation algorithms. This novel approach not only protects the ecosystems from climate change but also contributes to potentially increase the electricity production.