Abstract The paper presents a new control method to optimize energy flows of a micro-scale concentrated solar power (MicroCSP) system in order to minimize the electrical energy consumption of a building heating, ventilation, and air conditioning (HVAC) system integrated with a MicroCSP system. A new real-time optimal control method is proposed using exergy-based model predictive control (XMPC) techniques. To achieve this, the first law of thermodynamics (FLT) and the second law of thermodynamics (SLT) based mathematical models of MicroCSP are developed and integrated into FLT and SLT based models of an office building located at Michigan Technological University. Then, an XMPC framework is designed to optimize MicroCSP operation in accordance with the building HVAC energy demand. The new controller shows 45% grid electrical energy saving, compared to a common rule-based controller. Furthermore, a probability analysis using Monte-Carlo simulations shows energy saving ranges from 44% to 46.5% in the presence of prediction uncertainties and 35% to 57.5% energy savings considering seasonal variations of the weather.

中文翻译：

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用于建筑 HVAC 应用的 MicroCSP 系统最佳性能的火用预测控制框架

摘要 本文提出了一种优化微型聚光太阳能 (MicroCSP) 系统能量流的新控制方法，以最大限度地减少与 MicroCSP 集成的建筑供暖、通风和空调 (HVAC) 系统的电能消耗。系统。使用基于火用的模型预测控制 (XMPC) 技术提出了一种新的实时优化控制方法。为实现这一目标，开发了基于热力学第一定律 (FLT) 和热力学第二定律 (SLT) 的 MicroCSP 数学模型，并将其集成到位于密歇根理工大学的办公楼的基于 FLT 和 SLT 的模型中。然后，设计 XMPC 框架以根据建筑 HVAC 能源需求优化 MicroCSP 运行。新控制器显示45%的电网电能节省，与常见的基于规则的控制器相比。此外，使用蒙特卡罗模拟的概率分析表明，在存在预测不确定性的情况下，节能范围为 44% 至 46.5%，考虑到天气的季节性变化，节能范围为 35% 至 57.5%。