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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 劉志文 | zh_TW |
| dc.contributor.advisor | Chih-Wen Liu | en |
| dc.contributor.author | 黃冠霖 | zh_TW |
| dc.contributor.author | KUAN-LIN HUANG | en |
| dc.date.accessioned | 2026-01-27T16:23:38Z | - |
| dc.date.available | 2026-01-28 | - |
| dc.date.copyright | 2026-01-27 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-12-22 | - |
| dc.identifier.citation | "臺灣 2050 淨零排放路徑及策略總說明." 國家發展委員會、行政院環境保護署、經濟部、科技部、交通部、內政部、行政院農業委員會、金融監督管理委員會. https://www.ndc.gov.tw/Default.aspx (accessed Jan 8, 2025).
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Aiyoshi, "A penalty approach to handle inequality constraints in particle swarm optimization," 2010 IEEE International Conference on Systems, Man and Cybernetics, Istanbul, Turkey, 2010, pp. 2520-2525 C. -N. Ko, Y. -P. Chang and C. -J. Wu, "A PSO Method With Nonlinear Time-Varying Evolution for Optimal Design of Harmonic Filters," in IEEE Transactions on Power Systems, vol. 24, no. 1, pp. 437-444, Feb. 2009 B. Xu, A. Oudalov, A. Ulbig, G. Andersson and D. S. Kirschen, "Modeling of Lithium-Ion Battery Degradation for Cell Life Assessment," in IEEE Transactions on Smart Grid, vol. 9, no. 2, pp. 1131-1140, March 2018 D. Natella, S. Onori and F. Vasca, "A Co-Estimation Framework for State of Charge and Parameters of Lithium-Ion Battery With Robustness to Aging and Usage Conditions," in IEEE Transactions on Industrial Electronics, vol. 70, no. 6, pp. 5760-5770, June 2023. K. Dimitriadou, N. Rigogiannis, S. Fountoukidis, F. Kotarela, A. Kyritsis, and N. Papanikolaou, “Current Trends in Electric Vehicle Charging Infrastructure; Opportunities and Challenges in Wireless Charging Integration,” Energies, vol. 16, no. 4, art. no. 2057, 2023. P. Biswas, A. Rashid, A. K. M. A. Habib, M. Mahmud, S. M. A. Motakabber, S. Hossain, et al., “Vehicle to Grid: Technology, Charging Station, Power Transmission, Communication Standards, Techno-Economic Analysis, Challenges, and Recommendations,” World Electr. Veh. J., vol. 16, no. 3, art. no. 142, 2025. Stephen Boyd; Neal Parikh; Eric Chu; Borja Peleato; Jonathan Eckstein, Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , now, 2011. Federal Batteries, “What is Depth of Discharge and why is it so important?,” FederalBatteries,May19,2020.[Online].Available:https://federalbatteries.com.au/news/what-depth-discharge-and-why-it-so-important. [Accessed:may.11, 2025]. Taiwan Power Company, “Residential and Commercial,” n.d. [Online]. Available:https://service.taipower.com.tw/taipowerdsm/residential-and-commercial. [Accessed: Jan. 15, 2025] J. Kennedy and R. Eberhart, "Particle swarm optimization," Proceedings of ICNN'95 - International Conference on Neural Networks, Perth, WA, Australia, 1995, pp. 1942-1948 vol.4. A. Ratnaweera, S. K. Halgamuge and H. C. Watson, "Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients," in IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 240-255, June 2004. T. Takahama and S. Sakai, "Solving Constrained Optimization Problems by the ε Constrained Particle Swarm Optimizer with Adaptive Velocity Limit Control," 2006 IEEE Conference on Cybernetics and Intelligent Systems, Bangkok, Thailand, 2006. Coritech Services,“EV Chargers,” Coritech,2025.[Online]. Available: https://coritech.com/ev-chargers. [Accessed: Apr. 8, 2025]. A. Bills, M. Salazar, D. Zhang and V. Viswanathan, "A Model Predictive Control Scheme for Fast Charging via Accurate Quadratic Battery Models," 2022 American Control Conference (ACC), Atlanta, GA, USA, 2022, pp. 1794-1800. J. Thomas, "Particle swarm optimization based model predictive control for constrained nonlinear systems," 2014 11th International Conference on Informatics in Control, Automation and Robotics (ICINCO), Vienna, Austria, 2014, pp. 397-403. J. Soares, H. Morais and Z. Vale, "Particle Swarm Optimization based approaches to vehicle-to-grid scheduling," 2012 IEEE Power and Energy Society General Meeting, San Diego, CA, USA, 2012, pp. 1-8. 葉昭甫. "臺中市公有路外停車場電動車充電樁研擬增訂管理及使用辦法專案報告." 臺中市政府交通局. https://www.rdec.taichung.gov.tw/ (accessed Jan 30, 2025). A. Dogan and M. Alci, "Heuristic optimization of EV charging schedule considering battery degradation cost," Elektronika ir Elektrotechnika, vol. 24, no. 6, pp. 15-20, 2018. Q. Qin, S. Cheng, X. Chu, X. Lei, and Y. Shi, "Solving non-convex/non-smooth economic load dispatch problems via an enhanced particle swarm optimization," Applied Soft Computing, vol. 59, pp. 229-242, 2017. N. Erdogan, F. Erden and M. Kisacikoglu, "A fast and efficient coordinated vehicle-to-grid discharging control scheme for peak shaving in power distribution system," in Journal of Modern Power Systems and Clean Energy, vol. 6, no. 3, pp. 555-566, May 2018. A. Batista, D. Pozo and J. Vera, "Stochastic Time-of-Use-Type Constraints for Uninterruptible Services," in IEEE Transactions on Smart Grid, vol. 11, no. 1, pp. 229-232, Jan. 2020, H. Turker and S. Bacha, "Optimal Minimization of Plug-In Electric Vehicle Charging Cost With Vehicle-to-Home and Vehicle-to-Grid Concepts," in IEEE Transactions on Vehicular Technology, vol. 67, no. 11, pp. 10281-10292, Nov. 2018, doi: 10.1109/TVT.2018.2867428. Directorate General of Highways, Ministry of Transportation and Communications,“機動車輛登記數[Statistics Query:Motor Vehicle Registrations],n.d.[Online].Available: https://stat.thb.gov.tw/hb01/webMain.aspx?sys=100&funid=11100. [Accessed: Jul. 11, 2025]. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/101382 | - |
| dc.description.abstract | 在全球淨零排放的趨勢下，電動車已逐漸成為電網中重要的一環。透過車輛對電網(Vehicle-to-Grid, V2G)及電網對車輛(Grid-to-Vehicle, G2V)技術，電動車不再僅是單向的電力負載，而是轉變為分散式儲能單元。V2G技術能藉由輔助服務提供電網靈活性、可靠性及穩定性；然而，頻繁的充放電過程亦加速了電動車電池的老化與損耗。在本研究中，我們將使用者總成本，包含能源交易淨成本與電池退化成本之最小化設定為目標，並在即時排程的框架下，探討不同演算法在求解此一複雜最佳化問題上的性能。
為解決此一非線性、多維度且具備即時運算需求的最佳化問題，本研究比較了三種不同性質的演算法。除了做為精確解標竿的混合整數非線性規(Mixed-Integer Nonlinear Programming, MINLP)外，亦探討了粒子群演算法(Particle Swarm Optimization, PSO)與阿基米德浮力演算法(Archimedes Optimization Algorithm, AOA)兩種啟發式演算法。考量到原始啟發式演算法在尋找全域最佳解的過程中，可能面臨效率低落或早熟收斂(Premature Convergence)的問題，本研究進一步針對 PSO與AOA進行改良，分別設計出退火型PSO與結合維度學習的改良式AOA (Modified AOA, MDAOA)，以增強其在V2G即時排程問題上的求解效能。 | zh_TW |
| dc.description.abstract | Amid the global trend toward net-zero emissions, electric vehicles (EVs) have evolved from unidirectional electrical loads into distributed energy storage units through Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) technologies. V2G services enhance grid flexibility, reliability, and stability ; however, frequent charging and discharging cycles accelerate the aging and degradation of EV batteries. This thesis aims to minimize the total user cost, comprising the net cost of energy transactions and battery degradation expenses. Specifically, it investigates the performance of various algorithms in solving this complex optimization problem within a real-time scheduling framework.
To address this non-linear, multi-dimensional optimization problem characterized by real-time computational constraints, this study evaluates three distinct algorithms. In addition to Mixed-Integer Nonlinear Programming (MINLP), which serves as a benchmark for the exact solution, two heuristic algorithms are investigated: Particle Swarm Optimization (PSO) and the Archimedes Optimization Algorithm (AOA). Recognizing that standard heuristic algorithms may suffer from inefficiency or premature convergence, this research proposes enhancements to both methods. Specifically, an Annealing-based PSO and a Modified AOA with Dimension Learning (MDAOA) are developed to improve performance in solving the real-time V2G scheduling problem. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2026-01-27T16:23:38Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2026-01-27T16:23:38Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 目次
論文口試委員審定書 i 摘要 ii ABSTRACT iii 目次 iv 圖次 viii 表次 xi 1 第一章 緒論 1 1.1 研究背景 1 1.2 研究目標 3 1.3 文獻回顧 4 1.4 章節摘要 5 2 第二章 V2G模擬架構與電池技術基礎 7 2.1 V2G技術概述 7 2.1.1 V2G 基本架構 8 2.1.2 集中式控制 (Centralized Control) 8 2.1.3 分散式控制 (Decentralized Control) 10 2.1.4 集中式控制與分散式控制差異 12 2.2 電池狀態與測量 16 2.2.1 充電狀態(State of Charge) 16 2.2.2 健康狀態(State of Health) 17 2.2.3 放電深度 (Depth of Discharge) 17 2.2.4 電池SoC測量方法 19 2.3 時間電價 19 2.3.1 小時制時間電價 20 2.3.2 兩段式時間電價 21 2.3.3 三段式時間電價 22 3 第三章 問題建模與目標函數設計 23 3.1 問題說明 23 3.2 目標函數 24 3.3 電池退化模型 25 3.3.1 日曆老化(Calendar Aging) 25 3.3.2 循環老化(Cycle Aging) 25 3.3.3 電池退化模型建模 26 3.4 限制條件 27 3.5 模型預測控制（Model predictive control） 29 3.5.1 MPC基礎 30 4 第四章 演算法 32 4.1 前言 32 4.2 混合整數非線性規劃 32 4.2.1 混合整數非線性規劃之數學模型 33 4.2.2 MINLP計算複雜度探討 35 4.2.3 MINLP與LP、MILP及NLP的比較分析 36 4.2.4 MINLP問題的分類 38 4.2.5 常見的 MINLP 求解演算法 39 4.2.6 MINLP 在實際的應用 41 4.2.7 小結 42 4.3 粒子群演算法 43 4.3.1 粒子群演算法原理與數學模型 45 4.3.2 PSO演算法流程 48 4.3.3 PSO參數設定與收斂特性 51 4.3.4 PSO約束處理策略 54 4.3.5 PSO計算複雜度與時間效能評估 58 4.3.6 PSO在實際的應用 59 4.3.7 小結 60 4.4 阿基米德浮力演算法 61 4.4.1 阿基米德浮力演算法原理與數學模型 63 4.4.2 AOA演算法流程 67 4.4.3 AOA優化演算法與參數設定 71 4.4.4 AOA時間複雜度 76 4.4.5 AOA實際應用 77 4.4.6 小結 77 5 第五章 研究案例與實驗配置 78 5.1 停車場環境設置 78 5.2 電動車資料隨機化 79 5.3 實驗設計與演算法設置 83 5.4 實驗結果 87 5.4.1 小型停車場V2G模擬實驗 87 5.4.2 中型停車場V2G模擬實驗 95 6 第六章 結論與未來方向 107 6.1 結論 107 6.2 未來研究方向 108 7 參考文獻 110 圖次 圖1 1 台灣電動車新車註冊牌照數量[32] 2 圖1 2 台灣各縣市充電站數量 2 圖2 1 集中式控制架構圖[15] 8 圖2 2 分散式控制架構圖[14] 10 圖2 3 DoD與循環次數造成的最大電荷量關係圖[17] 18 圖2 4 V2G買賣電價模型 20 圖3 1 時間點為t的滾動時域示意圖 31 圖3 2 時間點為t+1的滾動時域示意圖 31 圖4 1 PSO示意圖 43 圖4 2 PSO流程圖 50 圖4 3 PSO的族群迭代比較圖 53 圖4 4 PSO加入修復演算法流程圖 57 圖4 5 阿基米德浮力演算法原理示意圖 61 圖4 6 多個物體在流體中運動與平衡示意圖 62 圖4 7 原始AOA流程圖 70 圖4 8 RL-IAOA流程圖 72 圖4 9 MDAOA流程圖 75 圖5 1 退火型PSO結合MPC流程圖 85 圖5 2 結合PSO優化參數與維度學習的AOA並使用MPC滾動優化流程圖 86 圖5 3 EV0在五個小時區間不同演算法充放電曲線比較 87 圖5 4 EV1在五個小時區間不同演算法充放電曲線比較 88 圖5 5 EV2在五個小時區間不同演算法充放電曲線比較 88 圖5 6 EV3在五個小時區間不同演算法充放電曲線比較 89 圖5 7 EV4在五個小時區間不同演算法充放電曲線比較 89 圖5 8 EV5在五個小時區間不同演算法充放電曲線比較 90 圖5 9 EV6在五個小時區間不同演算法充放電曲線比較 90 圖5 10 EV7在五個小時區間不同演算法充放電曲線比較 91 圖5 11 EV8在五個小時區間不同演算法充放電曲線比較 91 圖5 12 EV9在五個小時區間不同演算法充放電曲線比較 92 圖5 13 EV0在十個小時區間不同演算法充放電曲線比較 95 圖5 14 EV1在十個小時區間不同演算法充放電曲線比較 96 圖5 15 EV2在十個小時區間不同演算法充放電曲線比較 96 圖5 16 EV3在十個小時區間不同演算法充放電曲線比較 97 圖5 17 EV4在十個小時區間不同演算法充放電曲線比較 97 圖5 18 EV5在十個小時區間不同演算法充放電曲線比較 98 圖5 19 EV6在十個小時區間不同演算法充放電曲線比較 98 圖5 20 EV7在十個小時區間不同演算法充放電曲線比較 99 圖5 21 EV8在十個小時區間不同演算法充放電曲線比較 99 圖5 22 EV9在十個小時區間不同演算法充放電曲線比較 100 圖5 23 EV10在十個小時區間不同演算法充放電曲線比較 100 圖5 24 EV11在十個小時區間不同演算法充放電曲線比較 101 圖5 25 EV12在十個小時區間不同演算法充放電曲線比較 101 圖5 26 EV13在十個小時區間不同演算法充放電曲線比較 102 圖5 27 EV14在十個小時區間不同演算法充放電曲線比較 102 圖5 28 EV15在十個小時區間不同演算法充放電曲線比較 103 圖5 29 EV16在十個小時區間不同演算法充放電曲線比較 103 圖5 30 EV17在十個小時區間不同演算法充放電曲線比較 104 圖5 31 EV18在十個小時區間不同演算法充放電曲線比較 104 圖5 32 EV19在十個小時區間不同演算法充放電曲線比較 105 表次 表 2 1集中式控制與分散式控制比較表格 15 表2 2 簡易型時間電價二段式[18] 21 表2 3 簡易型時間電價三段式[18] 22 表4 1 MINLP、LP、MILP和NLP比較表格 37 表5 1 V2G小型停車場設置 78 表5 2 V2G中型停車場設置 79 表5 3 電動車電池容量參考資料 80 表5 4 小型停車場電動車生成參數 81 表5 5中型停車場電動車生成參數 82 表5 6 小型停車場不同演算法之成本 94 表5 7 不同演算法的平均迭代次數與總平均時間比較 94 表5 8 中型停車場的演算法成本 106 表5 9 MDAOA與PSO在中型停車場中的平均迭代數 106 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 粒子群演算法 | - |
| dc.subject | 阿基米德浮力演算法 | - |
| dc.subject | 混合整數非線性規劃 | - |
| dc.subject | 模型預測控制 | - |
| dc.subject | 車輛對電網(V2G) | - |
| dc.subject | Particle Swarm Optimization | - |
| dc.subject | Archimedes Optimization Algorithm | - |
| dc.subject | Mixed-Integer Nonlinear Programming | - |
| dc.subject | Model Predictive Control | - |
| dc.subject | V2G | - |
| dc.title | PSO、AOA與MINLP演算法應用於 V2G/G2V即時充放電規劃中的性能比較與分析研究 | zh_TW |
| dc.title | Performance Comparison and Analysis of PSO, AOA, and MINLP Algorithms for Real-Time V2G/G2V Charging–Discharging Scheduling | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 114-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 蘇恆毅;林子喬 | zh_TW |
| dc.contributor.oralexamcommittee | Heng-Yi Su;Tzu-Chiao Lin | en |
| dc.subject.keyword | 粒子群演算法,阿基米德浮力演算法混合整數非線性規劃模型預測控制車輛對電網(V2G) | zh_TW |
| dc.subject.keyword | Particle Swarm Optimization,Archimedes Optimization AlgorithmMixed-Integer Nonlinear ProgrammingModel Predictive ControlV2G | en |
| dc.relation.page | 113 | - |
| dc.identifier.doi | 10.6342/NTU202504776 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2025-12-22 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 電機工程學系 | - |
| dc.date.embargo-lift | N/A | - |
| 顯示於系所單位： | 電機工程學系 | |
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