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  1. NTU Theses and Dissertations Repository
  2. 電機資訊學院
  3. 資訊網路與多媒體研究所
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/60978
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor逄愛君
dc.contributor.authorChe-Wei Paien
dc.contributor.author白哲瑋zh_TW
dc.date.accessioned2021-06-16T10:39:32Z-
dc.date.available2015-08-23
dc.date.copyright2013-08-23
dc.date.issued2013
dc.date.submitted2013-08-13
dc.identifier.citation[1] U. D. of Energy, “”the smart grid: An introduction”,” 2009.
[2] A. Vojdani, “”smart integration”,” Power and Energy Magazine, IEEE, vol. 6,
no. 6, pp. 71–79, 2008.
[3] P. Samadi, A. Mohsenian-Rad, R. Schober, V. Wong, and J. Jatskevich,
“”optimal real-time pricing algorithm based on utility maximization for smart
grid”,” 2010 IEEE First International Conference on SmartGridComm, pp. 415–
420, 2010.
[4] C. Joe-Wong, S. S., S. Ha, and M. Chiang, “”optimized day-ahead pricing
for smart grids with device-specific scheduling flexibility”,” IEEE Journal on
Selected Areas in Communications, vol. 30, no. 6, pp. 1075–1085, 2012.
[5] I. E. A. D. S. M. Programme, “”integration of demand side management,
distributed generation, renewable energy sources and energy storages”,” 2008.
[6] A. Kossoy and P. Guigon, “”state and trends of the carbon market 2012”,” The
World Bank, Washington, DC, 2012.
[7] S. Boyd and L. Vandenberghe, “”convex optimization”,” Cambridge University
Press, 2004.
[8] A. Mohsenian-Rad, V. 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, pp. 320–331, 2010.
[9] P. Samadi, H. Mohsenian-Rad, R. Schober, and V. Wong, “”advanced demand
side management for the future smart grid using mechanism design”,” IEEE
Transactions on Smart Grid, pp. 1170–1180, 2012.
[10] N. Li, L. Chen, and S. Low, “”optimal demand response based on utility
maximization in power networks”,” in IEEE Power and Energy Society General
Meeting, 2011.
[11] Y. Yamamoto, “”pricing electricity from residential photovoltaic systems: A
comparison of feed-in tariffs, net metering, and net purchase and sale”,” Solar
Energy, pp. 2678 – 2685, 2012.
[12] P. Yang, P. Chavali, and A. Nehorai, “”parallel autonomous optimization of
demand response with renewable distributed generators”,” in 2012 IEEE Third
International Conference on SmartGridComm, 2012, pp. 55–60.
[13] I. Atzeni, L. Ordonez, G. Scutari, D. Palomar, and J. Fonollosa, “”demandside
management via distributed energy generation and storage optimization”,”
IEEE Transactions on Smart Grid, pp. 866–876, 2013.
[14] T. Couture and Y. Gagnon, “An analysis of feed-in tariff remuneration models:
Implications for renewable energy investment,” Energy Policy, pp. 955 – 965,
2010.
[15] M. Fahrioglu and F. Alvarado, “”using utility information to calibrate customer
demand management behavior models”,” IEEE Transactions on Power Systems,
vol. 16, no. 2, pp. 317–322, 2001.
[16] X. L. B. Stephen and M. Almir, “”subgradient methods”,” Stanford University,
Autumn, 2003.
[17] A. de Miguel and J. Bilbao, “”test reference year generation from meteorological
and simulated solar radiation data ”,” Solar Energy, pp. 695 – 703, 2005.
[18] R. Huang, T. Huang, R. Gadh, and N. Li, “”solar generation prediction
using the arma model in a laboratory-level micro-grid”,” in 2012 IEEE Third
International Conference on SmartGridComm, 2012.
[19] I. Ontario, “”market data”,” http://www.ieso.ca/imoweb/marketdata/
marketData.asp, Sep. 2011.
[20] H. Nguyen, J. song, and Z. Han, “”demand side management to reduce peakto-
average ratio using game theory in smart grid”,” in 2012 IEEE Conference
on INFOCOM WKSHPS, 2012, pp. 91–96.
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/60978-
dc.description.abstract基於最近先進的電力系統技術以及無線通訊技術的發展, 智慧電網的概念開始
逐漸蓬勃發展, 到了現在使用者已經有能力可以在家中裝設再生能源發電的裝置
和儲存電力的設備。另一方面, 由於環境的考量, 電力公司也積極利用再生能源發
電來替代傳統發電的能源。其中一種有效的方法就是電力公司提供一個有吸引力
的買電價來激勵使用者生產更多的再生能源並將自己的再生能源賣回去給電力公
司。跟之前的研究不同的是, 我們考慮一種結合再生能源的買回價格的動態電價方
法。我們將整個電價問題利用凹性最佳化數學模型來解決, 並且提出一個日前動態
計價方法並利用分散式演算法來處理我們的問題, 這樣一來我們可以同時兼顧使
用者的隱私以及整體系統的彈性。我們的目標就是希望可以將整體利益最大化並
且同時對使用者和電力公司都可以帶來利益。同時以我們有限的知識所知,我們
是最早考慮到結合環境利益的研究。實驗的結果也顯示我們的方法確實可以有效
的降低尖峰時刻電力的負擔和平衡整體電力系統的供電曲線。
zh_TW
dc.description.abstractThanks to the recent advance on power engineering and wireless communications,
the smart grid technology has emerged and users are now capable of deploying
renewable energy generators and storage devices at their homes. On the other
side, due to the rise of environmental consciousness, electric companies are eager
to replace traditional generators with renewable energy. One of the most efficient
ways is to provide an electricity buyback scheme for electric companies to encourage
users to generate more renewable energy at their homes. Different from the previous
works, we consider dynamic pricing with renewable energy buyback as our target
scenario. We formulate the dynamic pricing problem as a convex optimization dual
problem and propose a day-ahead time-dependent pricing scheme in a distributed
manner which provides more privacy to users. The goal of our developed framework
is to achieve the maximum benefits for both users and electric companies. To
the best of our knowledge, this is one of the very first works to tackle the timedependent
problem with taking the environmental benefit of renewable energy into
consideration for smart grid. The numerical results show that our framework can
significantly reduce the peak time loading and efficiently balance the system energy
provision.
en
dc.description.provenanceMade available in DSpace on 2021-06-16T10:39:32Z (GMT). No. of bitstreams: 1
ntu-102-R00944006-1.pdf: 2058093 bytes, checksum: 95284207a69b127f2e7d8c950fdc9aac (MD5)
Previous issue date: 2013
en
dc.description.tableofcontents口試委員會審定書i
誌謝ii
摘要iii
Abstract iv
Contents vi
List of Figures vii
List of Tables viii
1 Introduction 1
2 Related Work 4
3 System Model 6
3.1 Customers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
3.1.1 Utility function . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.1.2 Energy storage cost function . . . . . . . . . . . . . . . . . . . 8
3.1.3 Renewable energy cost function . . . . . . . . . . . . . . . . . 9
3.2 Electric company . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.2.1 Cost function . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.2.2 Carbon emission trading function . . . . . . . . . . . . . . . . 10
4 Day-Ahead Pricing Framework 12
4.1 Optimization Problem Formulation . . . . . . . . . . . . . . . . . . . 12
4.2 Lagrangian Dual Decomposition . . . . . . . . . . . . . . . . . . . . . 14
4.3 Distributed Day-Ahead Pricing Algorithms . . . . . . . . . . . . . . . 18
5 Performance Evaluation 22
5.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
5.2 Comparison of different scenarios . . . . . . . . . . . . . . . . . . . . 24
5.3 Comparison of different pricing schemes . . . . . . . . . . . . . . . . 26
5.3.1 Flat Selling with Flat Buyback pricing (FSFB) . . . . . . . . 26
5.3.2 Dynamic Selling with Flat Buyback pricing (DSFB) . . . . . . 26
5.3.3 Our approach, Dynamic Selling with Dynamic Buyback pricing
(DSDB) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
v
6 Conclusion 29
Bibliography 32
dc.language.isoen
dc.subject智慧電網zh_TW
dc.subject動態計價zh_TW
dc.subject再生能源zh_TW
dc.subject凹性最佳化zh_TW
dc.subject碳交易zh_TW
dc.subjectrenewable energyen
dc.subjectconvex optimizationen
dc.subjectcarbon emission tradingen
dc.subjectday-ahead pricingen
dc.subjectSmart griden
dc.title智慧電網結合再生能源之動態計價方法zh_TW
dc.titleOptimal Day-Ahead Pricing with Renewable Energy in Smart Griden
dc.typeThesis
dc.date.schoolyear101-2
dc.description.degree碩士
dc.contributor.oralexamcommittee馮輝文,蔡孟勳,楊舜仁
dc.subject.keyword智慧電網,動態計價,再生能源,凹性最佳化,碳交易,zh_TW
dc.subject.keywordSmart grid,day-ahead pricing,renewable energy,convex optimization,carbon emission trading,en
dc.relation.page32
dc.rights.note有償授權
dc.date.accepted2013-08-13
dc.contributor.author-college電機資訊學院zh_TW
dc.contributor.author-dept資訊網路與多媒體研究所zh_TW
顯示於系所單位:資訊網路與多媒體研究所

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