English  |  正體中文  |  简体中文  |  Items with full text/Total items : 26988/38789
Visitors : 2351949      Online Users : 30
RC Version 4.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Adv. Search
LoginUploadHelpAboutAdminister

Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/48372

Title: 大規模電動車及風力能源併入電力系統之分層分區調度新模式之探討
Study on the hierarchical scheduling framwork of large-scale electric vehicles and wind generations integrated
Authors: Shu,Chia-Sheng
許家陞
Contributors: 國立臺灣海洋大學:輪機工程學系
Keywords: 智慧電網;電動車;風力能源;備轉容量;隨機型直接搜尋法
smart grid;electric vehicles;wind energy;spinning reserve;stochastic direct search method
Date: 2017
Issue Date: 2018-08-22T03:40:21Z
Abstract: 環保意識的逐漸高漲與傳統石化能源的即將耗竭,促使電力系統有極高的興趣倂入風力能源與智慧電動車,然而當孤立電力系統併入大量的風力能源與電動車時,由於風力能源發電輸出的不確定性以及電動車充放電控制的隨意性,將增加系統操作者在電力調度上的負擔,其中一項相當重要且極具挑戰性的問題即是如何有效地管理風力能源輸出發電量的變動性,另一項重要議題則是如何評估大量電動車倂入電網對於發電端、輸電端及配電端所帶來的衝擊。本論文之目的擬提出分層分區之能源管理系統架構,探討大規模電動車與電網雙向互動之多區域動態經濟調度問題,期能一併解決各區域備轉容量分配、區域間壅塞管理及風力發電不確定性的問題,進而探討各區域電動車儲能系統與風火力發電系統之間的協調問題,以改善孤立電力系統運轉的效率與可靠。 為了處理電動車與電網雙向互動的問題,本研究擬開發一套革新的最佳化技術,隨機型直接搜尋法(Stochastic Direct Search Method;SDSM),的電腦程式系統,用以分析大規模電動車儲能系統、風力發電系統及火力發電系統在電力系統中的最佳運轉策略,SDSM演算法主要是以韋伯機率分佈來決定其搜尋跨步量,再透過多點直接隨機搜尋之技術,促使本演算法能有極高的機率求得全域之最佳解。本文將以所開發之軟體分析工具來探討電動車充放電運作模式及電動車滲透率對電力系統電能調度優化所造成的影響,進而規劃各區域電動車代理人之最適充放電策略及評估電網併入電動車的可行性及經濟效益。研究結果可以作為電力公司及時間電價能源用戶設置電動車的參考,對於推動智慧電網及電動車會有正面的助益。
The rise of environmental protection and the progressive exhaustion of traditional fossil energy sources have increased the interests in integrating wind energy sources and electric vehicles (EVs) into existing power systems. However, it is widely believed that large wind and EV penetrations would put an increased burden on system operations due to the uncertain nature of wind power and uncontrolled charging/discharging procedure of EVs. One of the most important future challenges seems to be the management of the integration of fluctuations in the electricity production from wind energy sources. Another important issue regarding the integration of EVs into an isolated system is to assess the impact on generation, transmission and distribution grid side. A new conceptual framework based on a three-level hierarchy for energy management system (EMS) is developed for achieving optimal utilization of wind energy sources and electric vehicles energy storage system. Several key issues of EV-wind-thermal coordination dispatch in a power system are also investigated and discussed in this research to ensure the security and reliability of the isolated system. The goal in this research is to develop a new algorithm, named stochastic direct search method (SDSM), to solve the operating schedule of a hybrid power system with large EV penetrations and wind energy system. Using the parallel stochastic searching mechanism with Weibull distribution strategy about the random calculation step, the proposed SDSM algorithm can give a good direction to elevate the global searching capability. The developed SDSM software is a useful tool to evaluate the effects of electric vehicle charging/discharging strategy, operation mode and penetration level on the operating schedule of a hybrid power system. The computer program developed in this research can also be a power tool for EV aggregators to evaluate the charging/discharging systems design and economic benefits of EVs. The results may serve as a tool for Taiwan power company and TOU users to assess and set up EVs and may serve as a reference for domestic promotion of smart grid, thereby contributing positively to the promotion of smart grid and EV.
URI: http://ethesys.lib.ntou.edu.tw/cgi-bin/gs32/gsweb.cgi?o=dstdcdr&s=G0010566007.id
http://ntour.ntou.edu.tw:8080/ir/handle/987654321/48372
Appears in Collections:[輪機工程學系] 博碩士論文

Files in This Item:

There are no files associated with this item.



All items in NTOUR are protected by copyright, with all rights reserved.

 


著作權政策宣告: 本網站之內容為國立臺灣海洋大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,請合理使用本網站之內容,以尊重著作權人之權益。
網站維護: 海大圖資處 圖書系統組
DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback