粒子群演算法於火力機組排程問題之研究

A Study on Particle Swarm Optimization for Thermal Unit Commitment Problems

冼祐臣(Yu-Chen Hsiem)

指導教授 ： 李俊耀

中原大學/工學院/電機工程研究所/碩士(2009年)

https://doi.org/10.6840/cycu200901242

摘要

目前電力系統大多以火力機組為發電主力，對此數量龐大的運轉機組而言，適當的機組發電排程調度，所節省之發電成本不容小覷；所謂機組發電排程係指在指定的時間內，以總發電成本為考量，在滿足系統需求及限制條件下，決定機組開、關狀態及其發電量問題。
為尋求最小之總發電成本，本文提出一種編碼方式，以建構有效解之搜尋模式，同時縮短搜尋時間。研究工作首先，利用各機組之最大及最小發電量限制值，透過加總、排序、編碼及解碼程序，定義各機組之開關狀態；其次，利用粒子群最佳化法則(Particle swarm optimization)於多點搜尋問題上之強健性，在經濟調度(Economic dispatch)法則下，求解機組排程及其發電量最佳化問題；最後，以四部火力機組、十部火力機組及二十部火力機組之案例，分別比較應用粒子群最佳化搜尋法則，與具突變機制之粒子群最佳化(Mutated particle swarm optimization)，兩者在總發電成本及搜尋時間上之差異。模擬結果顯示，應用本文所提之編碼方式，配合具突變機制之粒子群最佳化法則，於機組排程最佳化及搜尋時間問題上具有其優越性。

關鍵字

經濟調度 ； 火力機組排程 ； 粒子群最佳化

並列摘要

Nowadays, power system is mainly generated by thermal units. For the huge operation units, appropriate economic dispatch and unit commitment of thermal units, which save lots of generation cost, cannot be neglected. Unit commitment refers to the total generation cost in an specific period of time. The ON/OFF state and generating outputs problem are determined under the conditions of system demand and constraints.
In order to find the minimum cost of the total generation, this study presents a coding method to construct a searching model of the effective solution, and it can shorten the searching time meanwhile. First, this study applies maximum/minimum generation limits of each unit through the process of sort, sum, coding and decoding to define the ON/OFF state of each unit. Second, this study applies particle swarm optimization (PSO) to strengthen more searching points and solves unit commitment and optimum generation outputs problem by using economic dispatch. Finally, based on the cases of four thermal units, ten thermal units and twenty thermal units, this study compares the differences between PSO and mutated particle swarm optimization (MPSO) to both total generation costs and CPU time respectively. The simulated result revealed that the application of coding method mentioned in this study and MPSO to the unit commitment and searching time problems reaches to its superiority.

並列關鍵字

particle swarm optimization ； thermal unit commitment ； economic dispatch

參考文獻

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