因應環保意識抬頭，各國政府紛紛推出電動車計劃，希望藉由市場高滲透率達到節能減碳，未來電動車可望取代現有的燃料汽車，屆時，使用者在回家後將對電動車進行充電，現有家庭基本用電負載加上電動車負載將超出電網限制範圍。本篇論文主要以AIMD演算法(additive increase and multiplicative decrease)的概念完成分散式負載管理，以AIMD演算法為基礎提出三種方法：(1)利用時間電價的概念，調整電動車充電的可用功率；(2)採用網路中壅塞收費調整流量的策略，調整電動車充電功率；(3)引入電動車權重概念，決定電動車充電的優先順序，達到負載管理的效果。我們進一步提出，因電動車權重大小關係著充電的先後順序，權重主要與電動車的停留時間有關，使用者可能謊報停車時間以達到短時間內獲得較多的電量，為確保其他用戶充電的公平性，將電動車權重結合支付參數作充電優先權的選擇，使得謊報用戶反而得不到好處。

Because of the rising awareness of environmental protection, the worldwide governments have proposed policies for enhancing the penetration of the plug-in electric vehicles (PEVs) to reduce the carbon emissions. PEVs are expected to replace the fuel vehicles in the future. PEVs can be charged at home as users finish their daily job. However, the residential power load plus the PEVs charging load would go beyond the limitation of power grid. In this thesis, we apply the concept of additive increase and multiplicative decrease (AIMD) to develop three load management approaches in decentralized ways: First, we modulate the available power according to the varying electricity price. Second, we adjust the charging rate by using the congestion prices. Third, we introduce weight factor to decide charging priority for the load management. The weight factor of the PEV is function of the remaining charging time and the stopping time of the PEV. In order to get high priority in charging, users may submit the less stopping time against their consciences. For fairness purpose, we combine the PEV weight factor with a pricing policy so that those users who submit the false report cannot get any benefit.

論文審定書 i
致謝 ii
摘要 iii
Abstract iv
目錄 v
圖次 vii
表次 viii
第一章 緒論 1
1.1 背景以及動機 1
第二章 相關技術的概念 4
2.1 AIMD 演算法(additive increase and multiplicative decrease) 4
2.2 時間電價 8
2.3 均衡價格 9
2.3.1 需求和需求量 9
2.3.2 供給與供給量 10
2.3.3 均衡狀態 10
2.4 選擇支付價格作流量控制 13
第三章 電動車家庭式管理系統 14
3.1 電池特性 14
3.2 分散式充電管理系統 15
3.2.1 AIMD 演算法 15
3.2.2 時間電價調整可用功率 18
3.2.3 結合壅塞收費與權重概念 20
3.2.4 結合壅塞收費及使用權重值做機率選擇 27
第四章 使用者充電的狀況 29
第五章 模擬結果討論 31
5.1 模擬環境 31
5.1.1 家庭基本用電負載 31
5.1.2 電動車的規格與數量 32
5.1.3 使用者的行為模式 33
5.2 未使用智能管理的負載狀態 34
5.3 集中式智能管理方法的結果 35
5.4 基本AIMD 智能管理方法的結果 36
5.5 利用時間電價調整AIMD 智能充電管理 37
5.6 結合壅塞收費與權重 39
5.7 結合壅塞收費及使用權重值做機率選擇 40
5.8 使用者自行充電的情況 41
5.8.1 模擬情境一 41
5.8.2 模擬情境二 42
5.8.3 模擬情境三 43
5.8.4 模擬情境四 43
5.8.5 模擬情境五 44
5.9 各種智能充電方法之比較 45
第六章 結論 48
參考文獻 49

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