• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Mediterranean Conference on Control and Automation >Stochastic Model Predictive Control of Community Energy Storage under High Renewable Penetration
【24h】

Stochastic Model Predictive Control of Community Energy Storage under High Renewable Penetration

机译：高再生渗透下社区储能的随机模型预测控制

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper focuses on the robust optimal on-line scheduling of a grid-connected energy community, where users are equipped with non-controllable (NCLs) and controllable loads (CLs) and share renewable energy sources (RESs) and a community energy storage system (CESS). Leveraging on the pricing signals gathered from the power grid and the predicted values for local production and demand, the energy activities inside the community are decided by a community energy manager. Differently from literature contributions commonly focused on deterministic optimal control schemes, to cope with the uncertainty that affects the forecast of the inflexible demand profile and the renewable production curve, we propose a Stochastic Model Predictive Control (MPC) approach aimed at minimizing the community energy costs. The effectiveness of the method is validated through numerical experiments on the marina of Ballen, Samsø (Denmark). The comparison with a standard deterministic optimal control approach shows that the proposed stochastic MPC achieves higher performance in terms of minimized energy cost and maximized self-consumption of on-site production.
机译：本文重点介绍了网格连接能量社区的强大最佳的在线调度，其中用户配备了不可控制的（NCLS）和可控负载（CLS）并共享可再生能源（RESS）和社区能源存储系统（CESS）。利用从电网收集的定价信号和局部生产和需求的预测值，社区内的能源活动由社区能源经理决定。与通常集中在确定性最佳控制方案的文学贡献不同，以应对影响不灵活的需求配置文件和可再生生产曲线的预测的不确定性，我们提出了一种随机模型预测控制（MPC）方法，旨在最大限度地减少社区能源成本。该方法的有效性通过兰肯，Samsø（丹麦）的Marina上的数值实验进行了验证。与标准确定性最优控制方法的比较表明，该提出的随机MPC在最小化能量成本和最大化现场生产的自耗方面实现了更高的性能。

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