應用粒子族群演算法於空調冰水系統最佳化運轉之研究

Application of Particle Swarm Algorithm on Optimal Chilled Water System Operation of HVAC

黃郁勝(Yu-Sheng Huang)

指導教授 ： 張永宗

國立臺北科技大學/機電學院/能源與冷凍空調工程系碩士班/碩士(2010年)

摘要

工業革命後經濟的起飛，造成用電量逐年攀升，地球暖化的現象也日益嚴重，節能減碳不再只是口號而是要真正去實行。在用電量方面工業部門佔最大有53%，而科技廠空調系統用電量又佔最高40%，空調系統中又以冰水主機60%耗電量為最高，因此過去著眼於冰水主機的研究相當多，而忽略了部件相互間的影響，如能將整體作最佳化，其節省效能將更加明顯。
本研究引用外國學者所推導出的熱平衡方程式，針對空調系統中冰水主機、空調箱、區域泵浦相互影響的關係，將冰水側部分施以最佳化，並使用粒子族群演算法在滿足空間負載的條件之下，搜尋出冰水出水溫度、空調箱送風量和冰水水量的最佳設定點，完成空調系統冰水側的最小耗電量，達到節能之目的。結果顯示在三天中各別可省下24.68%、15.05%、15.56%用電量。
與其它演算法做比較，粒子族群演算法不只在收斂速度上有較好的優勢外，其原理簡單、程式容易撰寫，雖較晚期推出，但現今應用的範圍卻很廣泛，對於解決最佳化問題時是一個不錯的選擇。

關鍵字

最佳化運轉 ； 粒子族群演算法 ； 空調系統 ； 熱平衡方程式

並列摘要

The boom of the global economy after the Industrial Revolution has not only led to the increase in power consumption over the years, but also the escalation of global warming. And now, energy conservation and carbon reduction are no longer mere slogans, but goals that mankind has to achieve. When it comes to power consumption, industrial departments use approximately 53% of the total power consumption; out of various categories of power consumption at high-tech plants, HVAC systems constitute the largest portion of power consumption at 40%. Among various HVAC equipments, chillers consume the most power, at 60%. As such, a significant number of the researches conducted in the past focused on chillers. However, most of these studies have overlooked the impact of components on HVAC performance. It is safe to assume that the energy saving benefits of HVAC systems would be more apparent if one could optimize the entire system.
In this study, the author has adopted heat exchange equations proposed by scholars abroad to optimize chiller water according to the correlation between the chiller, air handling unit and zone pump in an HVAC system. In addition, the author has also applied particle swarm algorithm to determine the ideal configuration for chiller water temperature, air handling unit load and chiller water volume with cooling load conditions satisfied in order to achieve minimum power consumption for the chiller component in an air-conditioning system to accomplish the goal of energy conservation. Results showed that the configurations saved 24.68%, 15.05% and 15.56% of power over the course of three days.
Compared to other algorithms, a particle swarm algorithm not only offers better convergence speed but also involves simpler principles and programs that are easier to write. Although the algorithm was not available until much later, it has witnessed extensive scope of application today. The algorithm is certainly an ideal choice for the solution of optimization related problems.

並列關鍵字

Optimal Operation ； Particle Swarm Algorithm ； HVAC system ； Heat balance equation

參考文獻

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