首页> 外文会议>International High Performance Buildings Conference at Purdue >Model Predictive Control for Central Plant Optimization with Thermal Energy Storage
【24h】

Model Predictive Control for Central Plant Optimization with Thermal Energy Storage

机译:热能储存中央植物优化模型预测控制

获取原文

摘要

Linear Programming is used in order to determine how to distribute both hot and cold water loads across a central energy plant including heat pump chillers, conventional chillers, water heaters, and hot and cold water (thermal energy) storage. The objective of the optimization framework is to minimize cost in response to both real-time energy prices and demand charges. A planning tool that allows for the user to approximate a year's load distribution, and thus cost, in a few minutes is demonstrated. The optimization framework can also be used in real-time plant operation as a model predictive control (MPC) problem. In simulation, the system has demonstrated more than 10% savings over other schedule based control trajectories even when the sub-plants are assumed to be running optimally in both cases (i.e., optimal chiller staging, etc.) For large plants this can mean savings of more than US $1 million per year.
机译:使用线性编程来确定如何在中央能量厂分发热和冷水载荷,包括热泵冷却器,传统的冷却器,热水器和冷水和冷水(热能)储存。 优化框架的目的是最大限度地减少成本,以响应实时能源价格和需求收费。 在几分钟内允许用户允许用户近似一年的负载分配,从而在几分钟内进行规划工具。 优化框架也可用于实时工厂操作作为模型预测控制(MPC)问题。 在模拟中,即使在大型植物的两种情况下(即,最佳冷却器分段等)上,该系统也表现出基于其他时间表的控制轨迹的10%以上的基于时间表的控制轨迹。这可能意味着节省 每年超过100万美元。

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