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.

翻译：水电站在推动清洁与可持续能源生产方面发挥着关键作用，为全球向可再生能源转型做出了重要贡献。然而，目前水电站既被视为可再生能源的积极来源，也被视为生态系统的破坏者。在本研究中，我们强调了利用水电站通过自适应生态流量来保护生态系统这一被忽视的潜力。为倡导这一观点，我们提出使用神经网络在每个所需时间点预测最小生态流量值。此外，我们提出了一种新颖的框架，将其无缝集成到水电管理软件中，充分利用了传统约束优化算法的成熟方法。这一新方法不仅能保护生态系统免受气候变化的影响，还可能有助于增加电力产量。