- 學位論文
粒子族群演算法應用於儲冰空調系統負載分配最佳化
Optimization of an Ice-Storage Air Conditioning Load by Particle Swarm Algorithm for Reducing Energy Consumption and CO2 Emission
摘要
本研究在於利用儲冰式空調系統藉著夜間儲冷、日間釋冷的特性，將尖峰電力消耗轉移至離峰時段，以減少日間尖峰負載的特性，在能源日漸不足及的今日更形重要。但由於不良的設計，並未考慮主機、儲冰槽及其它附件等操作特性，以致無法有效地提供所需要的冷能並進行電力的節約。 本研究考慮空調負荷大小與儲冰槽及主機之匹配關係。藉由儲冰槽之最高融冰效率，使得主機能用較佳的運轉模式去減少運轉的耗電，然後將儲冰空調系統的操作建立其最佳化數學模式，以能源運轉成本作為目標函數，以主機及儲冰槽的性能作為限制條件。利用粒子族群演算法，以其程式架構簡單，適用於求解最佳化問題的特性，在固定的主機容量下，求出最佳的製冰量。本研究發現當我們固定主機容量為300RT時，儲製冰量由400RT每增加200RT就計算一次結果，結果顯示出，當製冰量為1000RT時，其10年週期成本為最低，故系統運轉若以10年為限，即可選擇1000製冰量，而其CO2排放量擇會隨著製冰量之增加而增加。另外在PSO演算法權重及學習因子探討方面，程式執行結果顯示，當權重w選擇0.6，學習因子皆選擇1，則其標準差、誤差值是較佳的，程式也能正確的找到最解佳，我們建議使用該組參數去做模擬分析。
並列摘要
Utilizing the feature of charge-at-night and discharge-in-daytime to transfer peak power consumption to off-peak hours for reducing the peak load during the day, ice-storage air conditioning system has become increasingly important as the problem of energy shortage keeps aggravating. However, poor design and failure to consider the operational features of the main chiller, ice storage tank and related auxiliary equipment can prevent an ice-storage air-conditioning system from generating the needed cold energy and facilitating the desired power saving.The study accordingly aims at developing and optimal mathematical model for the operation of ice-storage air-conditioning system adopting energy operating cost as the target function and the performance of the main chiller and ice-storage tank as the limitation. A Particle Swarm optimization algorithm is used to measure the optimal ice melting capacity during the daytime and the ice storage capacity . The study further analyzes and compares the system’s life-cycle cost .