...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Energy >Coordinated energy management for a cluster of buildings through deep reinforcement learning
【24h】

Coordinated energy management for a cluster of buildings through deep reinforcement learning

机译：通过深度加强学习为一群建筑物协调能源管理

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Advanced control strategies can enable energy flexibility in buildings by enhancing on-site renewable energy exploitation and storage operation, significantly reducing both energy costs and emissions. However, when the energy management is faced shifting from a single building to a cluster of buildings, uncoordinated strategies may have negative effects on the grid reliability, causing undesirable new peaks. To overcome these limitations, the paper explores the opportunity to enhance energy flexibility of a cluster of buildings, taking advantage from the mutual collaboration between single buildings by pursuing a coordinated approach in energy management. This is achieved using Deep Reinforcement Learning (DRL), an adaptive model-free control algorithm, employed to manage the thermal storages of a cluster of four buildings equipped with different energy systems. The controller was designed to flatten the cluster load profile while optimizing energy consumption of each building. The coordinated energy management controller is tested and compared against a manually optimised rule-based one. Results shows a reduction of operational costs of about 4%, together with a decrease of peak demand up to 12%. Furthermore, the control strategy allows to reduce the average daily peak and average peak-to average ratio by 10 and 6% respectively, highlighting the benefits of a coordinated approach. (c) 2021 Elsevier Ltd. All rights reserved.
机译：通过增强现场可再生能源开采和存储运行，高级控制策略可以在建筑物中实现能量灵活性，从而降低能源成本和排放。然而，当能量管理面临从一个建筑物到一组建筑物的转变时，未开补的策略可能对网格可靠性产生负面影响，导致不良的新峰。为了克服这些限制，该文件探讨了提高建筑物集群能量灵活性的机会，利用单一建筑物之间的相互协作，通过追求能源管理的协调方法。这是使用深度加强学习（DRL），自适应模型控制算法实现，该算法用于管理配备有不同能量系统的四个建筑物集群的热存储器。控制器旨在平衡群集负载曲线，同时优化每个建筑物的能量消耗。测试协调能量管理控制器并与手动优化的规则的基于规则进行比较。结果表明，峰值需求降低约4％的运营成本约为4％，高达12％。此外，控制策略允许分别将平均每日峰值和平均峰值平均比例降低10％和6％，突出了协调方法的益处。 （c）2021 elestvier有限公司保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号