Simulation of Model-based Predictive Control Applied to a Solar-assisted Cold Climate Heat Pump System
Simulation of Model-based Predictive Control Applied to a Solar-assisted Cold Climate Heat Pump System
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基于模型的预测控制应用于太阳能辅助寒冷气候热泵系统的仿真
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
V. R. Dehkordi
中科院分区：
文献类型：
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作者：
J. Candanedo;V. R. Dehkordi
本文对基于模型的预测控制（MPC）应用于双空气源冷气候热泵供暖系统进行了仿真研究。其中一个热泵使用BIPV/T屋顶预热的空气；另一个热泵使用室外空气。BIPV/T屋顶为热泵提供热量，峰值电力输出约为52千瓦（整个屋顶的功率为104千瓦），占地面积为320米。一个20米的水箱用于储存两台热泵提供的热能。本文描述了建筑、热泵、BIPV/T屋顶和TES水箱的建模方法。在Simulink环境下对系统的性能进行了研究。MPC算法的开发是为了选择最优的运行状态顺序的热泵在一个使用时间的电价配置文件。MPC算法计算期望热负荷，并以24小时为间隔用遗传算法求解优化问题。MPC算法与基准的基于规则的控制策略的性能比较表明，节省了约8%。
This paper presents a simulation study of model-based predictive control (MPC) applied to a heating system using two air-source cold-climate heat pumps. One of the heat pumps uses air preheated by a BIPV/T roof; the other heat pump uses outdoor air. The BIPV/T roof supplying heat to the heat pump has a peak electric output of about 52 kW (out of 104 kW for the entire roof) and covers an area of 320 m. A 20m water tank is used for storing thermal energy supplied by both heat pumps. The paper describes the modelling approaches followed for the building, the heat pumps, the BIPV/T roof and the TES tank. The performance of the system was studied in a Simulink environment. The MPC algorithm was developed in order to select the optimal sequence of operation states for both heat pumps under a time-of-use electricity pricing profile. The MPC algorithm calculates the expected heating load and solves the optimization problem with a genetic algorithm at 24 hour intervals. A comparison of the performance of the MPC algorithm with a benchmark, rule-based control strategy, indicates savings of about 8%.