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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 傅立成(Li-Chen Fu) | |
| dc.contributor.author | Tzu-Han Huang | en |
| dc.contributor.author | 黃咨翰 | zh_TW |
| dc.date.accessioned | 2021-07-11T15:39:56Z | - |
| dc.date.available | 2023-08-21 | |
| dc.date.copyright | 2018-08-21 | |
| dc.date.issued | 2018 | |
| dc.date.submitted | 2018-08-13 | |
| dc.identifier.citation | REFERENCE
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Lee, 'Bidirectional energy trading and residential load scheduling with electric vehicles in the smart grid,' IEEE Journal on Selected Areas in Communications, vol. 31, pp. 1219-1234, 2013. [25] C. Hachem, 'Design of a base case mixed-use community and its energy performance,' Energy procedia, vol. 78, pp. 663-668, 2015. [26] C. D. Korkas, S. Baldi, I. Michailidis, and E. B. Kosmatopoulos, 'Multi-objective control strategy for energy management of grid-connected heterogeneous microgrids,' in American Control Conference (ACC), 2015, 2015, pp. 5515-5520. [27] R. Jafari-Marandi, M. Hu, and O. A. Omitaomu, 'A distributed decision framework for building clusters with different heterogeneity settings,' Applied Energy, vol. 165, pp. 393-404, 2016. [28] C. W. Gellings, 'The concept of demand-side management for electric utilities,' Proceedings of the IEEE, vol. 73, pp. 1468-1470, 1985. [29] H.-H. Kuo, S. K. Pradhan, C.-L. Wu, P.-H. Cheng, Y. Xie, and L.-C. Fu, 'Dynamic demand-side management with user's privacy concern in residential community,' 2016 IEEE International Conference on Automation Science and Engineering (CASE), 2016, pp. 1094-1099. [30] M. Richards and D. Ventura, 'Choosing a starting configuration for particle swarm optimization,' in IEEE Int. Joint. Conf. Neural, 2004, pp. 2309-2312. [31] Q. Du, V. Faber, and M. Gunzburger, 'Centroidal Voronoi tessellations: Applications and algorithms,' SIAM review, vol. 41, pp. 637-676, 1999. [32] C. Clifton, M. Kantarcioglu, J. Vaidya, X. Lin, and M. Y. Zhu, 'Tools for privacy preserving distributed data mining,' ACM Sigkdd Explorations Newsletter, vol. 4, pp. 28-34, 2002. [33] R. Huang, T. Huang, R. Gadh, and N. Li, 'Solar generation prediction using the ARMA model in a laboratory-level micro-grid,' 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm), 2012, pp. 528-533. [34] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, 'Dropout: A simple way to prevent neural networks from overfitting,' The Journal of Machine Learning Research, vol. 15, pp. 1929-1958, 2014. [35] F. A. Gers, J. Schmidhuber, and F. Cummins, 'Learning to forget: Continual prediction with LSTM,' 1999. [36] E. Wilson, 'Commercial and Residential Hourly Load Profiles for all TMY3 Locations in the United States,' O. o. EE a. R. Energy, Ed., ed. US Department of Energy Open Data Catalog: US Department of Energy, 2014. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/79052 | - |
| dc.description.abstract | 近年來,能源議題受到的關注。在這個能源缺乏的年代,如何有效運用能源成為大家的首要目標。需求端管理(Demand-side management)的技術成為一個熱門的研究方向。在時間電價(Real-time pricing)的機制下,透過需求端管理能使用戶可以更加有效的使用能源。而電動車以及小型的住宅型發電裝置蓬勃發展也讓住宅社區可運用的能源選項更加多元。為了能夠更有效的使用能源,本論文提出一個整合住宅區與商業區的使用者的電力管理系統。透過整合用電習慣較為彈性的住宅區使用者以及用電習慣較為固定的商業區使用者,整體社區的用電品質及用電的效率可以得到顯著的提升。
另一方面,在混和型住商社區系統中,住宅型的用戶的生活需要被考量。本論文提出了一個演算法用來評估使用者的類型。並在住商用戶交易能源時,優先考量住宅區用戶的用電需求。 為驗證本論文之系統效能,本論文運行了兩種情境的模擬實驗。實驗結果驗證了系統確實提升了能源使用的效率且不同類型的使用者在本系統中也得到了電費支出的節省,達到雙贏的局面。 | zh_TW |
| dc.description.abstract | Demand-side management (DSM) is a very important topic in recent years thanks to the growth of Electric Vehicle (EV) and the renewable energy nowadays. DSM ability in a residential area has been improved a lot under the enforcement of real-time pricing (RTP) mechanism. In order to utilize the energy efficiency in a residential area and improve the energy quality in whole community. In this work, we aim to integrate the residential users and commercial users into one DSM system. By integrating them, we can utilize the energy more efficiency by sharing the surplus energy in a residential area to the commercial area.
On the other hand, user comfort in residential user is important and need to be concerned in a DSM system. In this work, we propose a method to evaluate the user status in residential area. To make more that, the energy request in the residential area enjoys a high priority through the sharing process to guarantee the quality life of residential user. At the end, we evaluate our system by conducting experiments through evaluation in two scenarios. The experimental results reveal that the proposed DSM system has improved the energy efficiency in the entire community, and at the same time, the energy costs paid by both residential and commercial users are lowered. | en |
| dc.description.provenance | Made available in DSpace on 2021-07-11T15:39:56Z (GMT). No. of bitstreams: 1 ntu-107-R05922104-1.pdf: 2590838 bytes, checksum: f295b3bbee4d4bd061a296ba3de34014 (MD5) Previous issue date: 2018 | en |
| dc.description.tableofcontents | CONTENTS
口試委員會審定書 I 誌謝 II 中文摘要 III ABSTRACT IV CONTENTS V LIST OF FIGURES VIII LIST OF TABLES X Chapter 1 Introduction 1 1.1 Background 1 1.2 Challenge 3 1.2.1 Challenge of Identifying User Preference 3 1.2.2 Challenge of Energy Management in Mixed-use Community 4 1.3 Related work 5 1.4 Objective 8 1.4.1 User Comfort Identification Engine 9 1.4.2 DSM System in Mixed-use Community 9 1.5 Thesis Organization 10 Chapter 2 Preliminary 12 2.1 Demand-side Management 12 2.2 Particle Swarm Optimization 14 2.3 M-CHESS 17 2.4 Privacy Protection 20 Chapter 3 DSM System in Residential and Commercial Community 22 3.1 Smart Home Environment 22 3.2 Single Home Scenario 24 3.2.1 Systems Architecture 25 3.2.2 Input Data 26 3.2.3 User Comfort Identification Engine (UCIE) 30 3.2.4 Problem Formulation 32 3.2.5 Optimization Flow 36 3.3 Mixed-use Community Scenario 37 3.3.1 Systems Architecture 38 3.3.2 Problem Formulation in Residential User 39 3.3.3 Problem Formulation in Commercial User 42 3.3.4 Sharing Mechanism 44 3.3.5 Optimization Flow 47 3.4 Improved Particle Swarm Optimization 49 3.4.1 Algorithm Overview 49 3.4.2 Improve Strategy 50 Chapter 4 Experiment Result 53 4.1 Experiment Setting 53 4.1.1 Experiment Setting in Residential User 53 4.1.2 Experiment Setting in Commercial User 57 4.2 Algorithm Comparison in Single Home Scenario 59 4.3 Evaluation in Schedule Flexible Level 61 4.4 Evaluation in Dynamic Sharing Price Mechanism 62 4.5 Evaluation in Mixed-use Community Scenario 63 4.5.1 Evaluation in Small-scale Mixed-use Community 64 4.5.2 Evaluation in Large-scale Mixed-use Community 68 Chapter 5 Conclusions 70 5.1 Summary 70 5.2 Future Work 71 REFERENCE 72 | |
| dc.language.iso | en | |
| dc.subject | 粒子群聚演算法 | zh_TW |
| dc.subject | 需求端管理 | zh_TW |
| dc.subject | 住商整合社區 | zh_TW |
| dc.subject | 能源共享 | zh_TW |
| dc.subject | 智慧電網 | zh_TW |
| dc.subject | smart grid | en |
| dc.subject | Demand-side management | en |
| dc.subject | energy sharing | en |
| dc.subject | Mixed-use community | en |
| dc.subject | particle swarm optimization | en |
| dc.title | 使用者導向於動態共享價格機制之住商混和型社區需量反應電力管理系統 | zh_TW |
| dc.title | User-driven Demand-side Management in Mixed-use Community under Dynamic Sharing Price Mechanism using Particle Swarm Optimization | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 106-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 周俊廷(Chun-Ting Chou),廖峻鋒(Chun-Feng Liao),蔣宗哲(Tsung-Che Chiang),于天立(Tian-Li Yu) | |
| dc.subject.keyword | 需求端管理,住商整合社區,能源共享,粒子群聚演算法,智慧電網, | zh_TW |
| dc.subject.keyword | Demand-side management,energy sharing,Mixed-use community,particle swarm optimization,smart grid, | en |
| dc.relation.page | 76 | |
| dc.identifier.doi | 10.6342/NTU201802951 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2018-08-13 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| dc.date.embargo-lift | 2023-08-21 | - |
| 顯示於系所單位: | 資訊工程學系 | |
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