An Adaptive Hydropower Management Approach for Downstream Ecosystem Preservation

• URL: http://arxiv.org/abs/2403.02821v2
• Date: Thu, 4 Apr 2024 12:47:28 GMT
• Title: An Adaptive Hydropower Management Approach for Downstream Ecosystem Preservation
• Abstract summary: We highlight the overlooked potential of using hydropower plant as protectors of ecosystems by using adaptive ecological discharges. We propose using a neural network to predict the minimum ecological discharge value at each desired time. We present a novel framework that seamlessly integrates it into hydropower management software.
• Score: 2.621434923709917
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

Related papers

• Bringing GRACE to Recommendation: Fine-Tuning for Sustainable and Accurate Personalization [57.58261252242669]
  Growing concern about environmental sustainability has motivated green'' recommender systems that steer users toward more eco-friendly and healthier choices.<n>Many existing green recommendation approaches require training new models from scratch, incurring substantial computational and energy costs.<n>We propose GRACE (Green Recommendation via Adaptive Conflict-rEsolution), a fine-tuning framework that integrates item-level sustainability signals into pretrained recommendation models.
  arXiv  Detail & Related papers  (2026-07-24T14:17:37Z)
• Reinforcement learning-based dynamic cleaning scheduling framework for solar energy system [0.4061135251278187]
  This study presents a reinforcement learning-based framework to autonomously optimize the cleaning schedules of PV panels in arid regions.<n>It achieves up to 13% cost savings by dynamically responding to weather uncertainties.<n>This work underscores the potential of RL-driven autonomous decision-making to optimize maintenance operations in renewable energy systems.
  arXiv  Detail & Related papers  (2026-03-08T08:13:36Z)
• A Reinforcement Learning Approach for Optimal Control in Microgrids [43.122212629962235]
  Microgrids provide a promising solution by enabling localized control over energy generation, storage, and distribution.<n>This paper presents a novel reinforcement learning (RL)-based methodology for optimizing microgrid energy management.
  arXiv  Detail & Related papers  (2025-06-28T20:10:00Z)
• Deep Learning Innovations for Energy Efficiency: Advances in Non-Intrusive Load Monitoring and EV Charging Optimization for a Sustainable Grid [0.0]
  The global energy landscape is undergoing a profound transformation, driven by the urgent need to mitigate climate change, reduce greenhouse gas emissions, and ensure sustainable energy supplies.<n>New investments in renewables, as well as the phase out of high CO2-emission energy sources, hampers the pace of the energy transition.<n>This dissertation investigates the development of novel Deep Learning techniques to create tools which solve limitations in these two key energy domains.
  arXiv  Detail & Related papers  (2025-05-07T12:36:19Z)
• AI-Driven approach for sustainable extraction of earth's subsurface renewable energy while minimizing seismic activity [44.99833362998488]
  Injection of fluids into the Earth's crust can induce or trigger earthquakes. We propose a new approach based on Reinforcement Learning for the control of human-induced seismicity. We show that the reinforcement learning algorithm can interact efficiently with a robust controller.
  arXiv  Detail & Related papers  (2024-08-07T10:06:04Z)
• Optimal Power Flow in Highly Renewable Power System Based on Attention Neural Networks [43.19619268243832]
  The integration of renewable energy sources, like wind and solar, poses challenges due to their inherent variability. This variability, driven largely by changing weather conditions, demands frequent recalibrations of power settings. We present a cutting-edge, physics-informed machine learning methodology, trained using imitation learning and historical European weather datasets.
  arXiv  Detail & Related papers  (2023-11-23T12:02:58Z)
• The Influence of Neural Networks on Hydropower Plant Management in Agriculture: Addressing Challenges and Exploring Untapped Opportunities [3.072340427031969]
  Hydropower plants serve as vital water sources for agriculture sustainable. A key concern is the potential conflict between electricity generation and agricultural water needs. We propose the adoption of the Agriculture Conscious Hydropower Plant Management framework.
  arXiv  Detail & Related papers  (2023-11-22T10:22:59Z)
• A Novel Hybrid Algorithm for Optimized Solutions in Ocean Renewable Energy Industry: Enhancing Power Take-Off Parameters and Site Selection Procedure of Wave Energy Converters [0.0]
  Ocean renewable energy, particularly wave energy, has emerged as a pivotal component for diversifying the global energy portfolio. This study delves into the optimization of power take-off (PTO) parameters and the site selection process for an offshore oscillating surge wave energy converter (OSWEC) By employing the HC-EGWO method, we achieved an upswing of up to 3.31% in power output compared to other methods.
  arXiv  Detail & Related papers  (2023-09-19T13:30:17Z)
• Long-Term Hourly Scenario Generation for Correlated Wind and Solar Power combining Variational Autoencoders with Radial Basis Function Kernels [0.0]
  We propose an innovative method for generating long-term hourly scenarios for wind and solar power generation. By incorporating the Radial Basis Function (RBF) kernel in our artificial neural network architecture, we aim to obtain a latent space with improved regularization properties.
  arXiv  Detail & Related papers  (2023-06-27T14:02:10Z)
• Battery and Hydrogen Energy Storage Control in a Smart Energy Network with Flexible Energy Demand using Deep Reinforcement Learning [2.5666730153464465]
  We introduce a hybrid energy storage system composed of battery and hydrogen energy storage. We propose a deep reinforcement learning-based control strategy to optimise the scheduling of the hybrid energy storage system and energy demand in real-time.
  arXiv  Detail & Related papers  (2022-08-26T16:47:48Z)
• Data-Driven Stochastic AC-OPF using Gaussian Processes [54.94701604030199]
  Integrating a significant amount of renewables into a power grid is probably the most a way to reduce carbon emissions from power grids slow down climate change. This paper presents an alternative data-driven approach based on the AC power flow equations that can incorporate uncertainty inputs. The GP approach learns a simple yet non-constrained data-driven approach to close this gap to the AC power flow equations.
  arXiv  Detail & Related papers  (2022-07-21T23:02:35Z)
• An Energy and Carbon Footprint Analysis of Distributed and Federated Learning [42.37180749113699]
  Classical and centralized Artificial Intelligence (AI) methods require moving data from producers (sensors, machines) to energy hungry data centers. Emerging alternatives to mitigate such high energy costs propose to efficiently distribute, or federate, the learning tasks across devices. This paper proposes a novel framework for the analysis of energy and carbon footprints in distributed and federated learning.
  arXiv  Detail & Related papers  (2022-06-21T13:28:49Z)
• Enforcing Policy Feasibility Constraints through Differentiable Projection for Energy Optimization [57.88118988775461]
  We propose PROjected Feasibility (PROF) to enforce convex operational constraints within neural policies. We demonstrate PROF on two applications: energy-efficient building operation and inverter control.
  arXiv  Detail & Related papers  (2021-05-19T01:58:10Z)
• Leveraging traditional ecological knowledge in ecosystem restoration projects utilizing machine learning [77.34726150561087]
  Community engagement throughout the stages of ecosystem restoration projects could contribute to improved community well-being. We suggest that adaptive and scalable practices could incentivize interdisciplinary collaboration during all stages of ecosystemic ML restoration projects.
  arXiv  Detail & Related papers  (2020-06-22T16:17:48Z)
• Towards a Peer-to-Peer Energy Market: an Overview [68.8204255655161]
  This work focuses on the electric power market, comparing the status quo with the recent trend towards the increase in distributed self-generation capabilities by prosumers. We introduce a potential multi-layered architecture for a Peer-to-Peer (P2P) energy market, discussing the fundamental aspects of local production and local consumption as part of a microgrid. To give a full picture to the reader, we also scrutinise relevant elements of energy trading, such as Smart Contract and grid stability.
  arXiv  Detail & Related papers  (2020-03-02T20:32:10Z)
• Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning [68.37641996188133]
  We introduce a framework for tracking realtime energy consumption and carbon emissions. We create a leaderboard for energy efficient reinforcement learning algorithms. We propose strategies for mitigation of carbon emissions and reduction of energy consumption.
  arXiv  Detail & Related papers  (2020-01-31T05:12:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.