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博碩士論文 etd-0604113-141654 詳細資訊
Title page for etd-0604113-141654
論文名稱
Title
基於智能電網下的分散式電動車充/放電管理機制之研究
Study on Decentralized Plug-in Electric Vehicle Charging/Discharging Strategies Based on Smart Grid
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
68
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2013-07-01
繳交日期
Date of Submission
2013-07-11
關鍵字
Keywords
智能電網、分散式管理、交替方向乘子法、最佳電力潮流、充(放)電、電動車
ADMM, optimal power flow, decentralized algorithm, Smart grid, plug-in hybrid electric vehicle, charging(discharging)
統計
Statistics
本論文已被瀏覽 5665 次,被下載 180
The thesis/dissertation has been browsed 5665 times, has been downloaded 180 times.
中文摘要
於本篇論文中, 我們基於智能電網下提出一分散式的充(放) 電管理機制, 改善當電動車的市場滲透率逐漸提升時, 智能電網進行控管的時候會面臨到的問題。我們主要是利用交替方向乘子法(Alternating Direction Method of Multipliers, 以下簡稱ADMM)可平行運算的特性作分散式處理。ADMM若要達到收斂, 需進行非常多次疊代, 為了解決此問題, 我們將原本的大電力系統分成兩個子系統: 使用者層子系統及電網層子系統兩部分, 兩個子系統各自作分散式的運算, 首先於使用者層由電動車各自決定是否要進行充(放) 電, 進而得知平均所需電量; 接著, 電網層再分散式計算各自電壓。我們更進
一步的透過觀察使用者的使用模式及一般實際的電力網路模型, 提出一分散式電動車充(放) 電的管理機制: 各電動車與電網之間不需經過太多次的溝通, 即可各自決定是否要進行充(放) 電, 且此時所需的總電量不會超過電網上的電力限制, 同時, 所造成的電壓變動也在允許範圍內, 最後所得到的結果近似於集中式管理。整體而言, 本論文的主要貢獻如下: (1) 於智能電網及最佳電力潮流系統之下, 建立一個可以滿足系統限制、發電成本最低及使用者能達最大滿意的集中式智能管理系統;(2) 提出的電動車充(放) 電分散式管理機制改善了當電動車數量過於龐大時, 集中式管理無法實現的問題, 同時還能達到與集中式管理相近的結果; (3) 為了達到全分散式的管理, 我們將整個大電力系統分成使用者層及電網層兩個子系統, 並且透過觀察使用者的
使用模式及一般典型的電力網路模型加快ADMM 收斂的速度。
Abstract
The emergence of plug-in electric vehicles (PEVs) shall bring a significant impact on the existing power system. In this thesis, we propose decentralized energy management strategies for PEVs to mitigate the impact of PEV-charging by using the alternating direction method of multipliers (ADMM) base on smart grid. With the proposed strategies, we can handle the high penetration level of PEVs. However,
the decentralized algorithm based on the ADMM method requires large numbers of iterations to achieve their optimal solutions. To improve their convergence rate, we divide the whole system into two sub-systems: the user sub-system and the grid sub-system. The user sub-system coordinates the PEV charging(discharging) and aims at maximizing user convenience levels while meeting predefined power demand
limits. We develop a distributed algorithm to solve the charging(discharging) problem in a decentralized manner. In addition, we exploit driving habit of individual EV user to accelerate the convergence rate. On the other hand, the grid sub-system
aims at minimizing power generation cost while meeting some circuit-level limits. We also develop a distributed algorithm to solve the optimization problem at the grid side. Finally, several simulations are conducted to verify the efficiency of the proposed energy management strategies.
目次 Table of Contents
誌謝 i
摘要 ii
Abstract iii
目錄 iv
圖次 vi
表次 viii
1 緒論 1
1.1 背景以及動機 1
1.2 論文組織介紹 2
2 相關環境與技術概要 3
2.1 最佳電力潮流(Optimal power flow) 3
2.2 交流電模型簡介 6
2.3 ADMM介紹 7
2.3.1 雙升子 (Dual ascent) 8
2.3.2 乘子法 (Method of multipliers) 10
2.3.3 ADMM (Alternating direction method of multipliers) 11
2.3.4 全域一致性問題(Global variable consensus problem) 12
2.3.5 交換式問題(Exchange problem) 14
3 系統模型分析 18
3.1 電池特性 18
3.2 系統模型 19
4 分散式管理 28
4.1 使用者層的分散式管理 28
4.2 電網曾的分散式管理 33
5 減少疊代次數分析 37
5.1 使用者層 37
5.2 電網層 39
6 模擬討論 42
6.1 模擬環境的建立 42
6.1.1 模擬環境–住宅區基本用電負載 42
6.1.2 模擬環境–電動車的規格與數量 44
6.1.3 模擬環境–使用者的行為模式 44
6.2 電網層收斂速度分析比較 45
6.3 不同控管方法之負載曲線圖比較 47
6.4 不同控管方法之離去時的SOC比較 49
6.5 不同控管方法之電壓變動情況 50
6.6 各種控管方法之比較 54
7 結論 55
參考文獻 56
參考文獻 References
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