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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 林風 | |
dc.contributor.author | Yen-Ting Hsu | en |
dc.contributor.author | 許彥婷 | zh_TW |
dc.date.accessioned | 2021-06-07T23:59:21Z | - |
dc.date.copyright | 2013-08-20 | |
dc.date.issued | 2013 | |
dc.date.submitted | 2013-08-16 | |
dc.identifier.citation | [1] G.-R. Liu, P. Lin, Y.-T. Hsu, and Y.-B. Lin. Analysis and simulation
for in-home smart grid. Technical report, National Taiwan University, 2013. http://www.csie.ntu.edu.tw/ plin/optimaldecisionpolicy.pdf. [2] X. Fang, S. Misra, G. Xue, and D. Yang. Smart grid - the new and improved power grid: A survey. IEEE Communications Surveys Tutorials, 14(4):944–980, 2012. [3] V. C. Gungor, D. Sahin, T. Kocak, S. Ergut, C. Buccella, C. Cecati, and G. P. Hancke. A survey on smart grid potential applications and communication requirements. IEEE Transactions on Industrial Informatics, 9:28–42, Feb. 2013. [4] Z. Fan, P. Kulkarni, S. Gormus, C. Efthymiou, G. Kalogridis, M. Sooriyabandara, Z. Zhu, S. Lambotharan, andW. H. Chin. Smart grid communications: Overview of research challenges, solutions, and standardization activities. IEEE Communications Surveys and Tutorials, 15:21–38, First Quarter 2013. [5] Y. Guo, M. Pan, and Y. Fang. Optimal power management of residential customers in the smart grid. IEEE Transactions on Parallel and Distributed Systems, 23(9):1593–1606, 2012. [6] K. M. Tsui and S.C. Chan. Demand response optimization for smart home scheduling under real-time pricing. IEEE Transactions on Smart Grid, 3(4):1812–1821, 2012. [7] K. Tanaka, K. Uchida, K. Ogimi, T. Goya, A. Yona, T. Senjyu, T. Funabashi, and C.- H. Kim. Optimal operation by controllable loads based on smart grid topology considering insolation forecasted error. IEEE Transactions on Smart Grid, 2(3):438– 444, 2011. [8] http://big5.nikkeibp.com.cn/eco/news/catcorporatesj/2019 20120119.html. [9] R. Deng, J. Chen, X. Cao, Y. Zhang, S. Maharjan, and S. Gjessing. Sensingperformance tradeoff in cognitive radio enabled smart grid. IEEE Transactions on Smart Grid, 4(1):302–310, 2013. [10] A.-H. Mohsenian-Rad, V. W. S. Wong, J. Jatskevich, R. Schober, and A. Leon- Garcia. Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid. IEEE Transactions on Smart Grid, 1(3):320–331, 2010. [11] D.-H. Kim, D.-M. Kim, and J.-O Kim. Determination of the optimal incentives and amount of load reduction for a retailer to maximize profits considering demand response programs. In Power and Energy Conference, 2008. PECon 2008. IEEE 2nd International, pages 1290–1295, 2008. [12] New Mexico Solar Energy Association (NMSEA) http://www.nmsea.org/Curriculum/7 12/Cost/calculate solar cost.htm. [13] A.-H. Mohsenian-Rad and A. Leon-Garcia. Optimal residential load control with price prediction in real-time electricity pricing environments. Smart Grid, IEEE Transactions on, 1(2):120–133, 2010. [14] J. Byun, I. Hong, and S. Park. Intelligent cloud home energymanagement systemusing household appliance priority based scheduling based on prediction of renewable energy capability. IEEE Transactions on Consumer Electronics, 58(4):1194–1201, 2012. [15] M. He, S. Murugesan, and J. Zhang. Multiple timescale dispatch and scheduling for stochastic reliability in smart grids with wind generation integration. 2011 Proceedings IEEE INFOCOM, pages 461–465, 10-15 April 2011. [16] I. Koutsopoulos and L. Tassiulas. Optimal control policies for power demand scheduling in the smart grid. IEEE Journal on Selected Areas in Communications, 30:1049–1060, July 2012. [17] T. T. Kim and H. V. Poor. Scheduling power consumption with price uncertainty. Smart Grid, IEEE Transactions on, 2(3):519–527, 2011. [18] M. Alizadeh, A. Scaglione, and R. J. Thomas. From packet to power switching: Digital direct load scheduling. IEEE Journal on Selected Areas in Communications, 30:1027–1036, July 2012. [19] A. W. Berger and F. C. Schweppe. Real time pricing to assist in load frequency control. IEEE Transactions on Power Systems, 4(3):920–926, 1989. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/17168 | - |
dc.description.abstract | 在家中智慧電網內,電力可由傳統電力系統或再生能源電力系統供給。
一個有效率的電費管理系統在家中智慧電網內是不可或缺的。 現存的家中智慧電網之研究工作均未考慮再生能源電力系統的充電過程和不同種類的家電,因此使得這些研究工作的應用範圍較為受限。 為了改良這些現存的研究工作,技術文件cite{technical}在考慮再生能源電力系統的充電過程和不同種類的家電之情形下,提出了電力花費的數學分析模型, 並提出了一個「門檻策略」來減少電力的花費。 在此篇碩士論文當中,我們設計並實作了一個模擬家中智慧電網在cite{technical}提出的「門檻策略」以及「基本策略」下的行為之模擬模型。 我們的效能分析結果顯示了「門檻策略」不僅能節省電力花費,與「基本策略」相比,「門檻策略」能將較多的傳統電力系統之電力負載量移轉至再生能源電力系統上。 | zh_TW |
dc.description.abstract | In an In-home Smart Grid (SG), the power can be supplied by either Conventional Power System (CPS) or Renewable Power System (RPS). The cost efficient management is required in an In-home SG. Since the existing works of In-home SG do not consider the charging process of RPS and multiple types of household appliances, the applications of these works are limited. To improve the existing works, cite{technical} proposed analytical models for the electricity cost of a household with the considerations of the charging process of RPS and multiple types of household appliances. A extit{threshold} policy is proposed in cite{technical} to reduce the electricity cost for a household.
In this thesis, we design and implement the simulation model to simulate the behavior of an In-home SG under both the extit{threshold} and the extit{default} policies proposed in cite{technical} and investigate the performance of the two policies. Our performance study show that the extit{threshold} policy can not only save the electricity cost but also has a better capability to shift the service load from CPS to RPS. | en |
dc.description.provenance | Made available in DSpace on 2021-06-07T23:59:21Z (GMT). No. of bitstreams: 1 ntu-102-R00922093-1.pdf: 2425664 bytes, checksum: 786b590d21323196531c3245db75b8ea (MD5) Previous issue date: 2013 | en |
dc.description.tableofcontents | Contents
Acknowledgement i English Abstract v 1 Introduction 1 2 Related Works 5 3 Renewable Power System Cost 9 4 Problem Formulation 13 4.1 Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 4.2 The Default Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 4.3 The Threshold Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 5 Simulation Model 19 6 Performance Evaluation 25 6.1 Simulation Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 6.2 Cost Saving Ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 6.3 The Effect of Power Demand Arrival Rate J . . . . . . . . . . . . . . . 28 6.4 Load Shifting Capability . . . . . . . . . . . . . . . . . . . . . . . . . . 28 7 Conclusion 33 Bibliography 35 | |
dc.language.iso | en | |
dc.title | 家中智慧電網模擬平台之設計與實作 | zh_TW |
dc.title | Design and Implementation of a Simulation Platform for In-home Smart Grid System | en |
dc.type | Thesis | |
dc.date.schoolyear | 101-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 黃怡碩,洪樂文,幸多,蘇淑茵 | |
dc.subject.keyword | 即時電價,再生能源,模擬平台,智慧電網, | zh_TW |
dc.subject.keyword | Realtime Pricing (RTP),Renewable Energy,Simulation Platform,Smart Grid, | en |
dc.relation.page | 37 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2013-08-16 | |
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 |
顯示於系所單位: | 資訊工程學系 |
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