(3.238.186.43) 您好!臺灣時間:2021/03/05 22:49
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:李書帆
研究生(外文):Shu-Fan Lee
論文名稱:利用粒子群最佳化演算法於智慧電網社區電力需求管理
論文名稱(外文):Power Demand Side Management Using Particle Swarm Optimization in Smart Grid Community
指導教授:傅立成傅立成引用關係
指導教授(外文):Li-Chen Fu
口試委員:鍾炳利馮明惠許永真陸敬互
口試日期:2014-07-30
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:資訊工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
語文別:英文
論文頁數:79
中文關鍵詞:智慧電網需求端管理再生能源電力負載波動
外文關鍵詞:Smart GridDemand Side ManagementRenewable EnergyPeak-to-Average Ratio
相關次數:
  • 被引用被引用:0
  • 點閱點閱:209
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
近年來,由於能源過度使用造成的環境問題,讓節能減碳成為世界各國最為關切的焦點議題之一。隨著世界的快速發展,傳統電力網正面臨用電需求大幅提升的挑戰。因應用電需求的改變,能夠提供需求端管理的次世代電網,智慧電網,是相當重要的發展。例如:能夠讓家庭進行家中能源管理。透過智慧電網與需求端管理,不但可以有效地減少家中電費還可以同時降低電力負載的波動性。在智慧電網的架構下,同時考慮再生能源、儲能設備與動態電價進行〝日常活動排程的最佳化〞,如此可以達到節省電費的最佳化。另一方面,透過社區中多戶家庭互相合作進行電能管理,則可以最小化電力負載的波動性,如此不但可以有效地降低契約容量還可以使整體的電力系統更穩定。然而過去對於這方面的研究都專注於家電本身的資訊來進行分別的排程,卻忽略了家電狀態與家中日常活動之間的關係,事實上人在家中特定的空間使用某些家電會形成活動,例如:有人在客廳且大燈與電視皆為開啟的狀態,則很高的機率此人正在從事的活動為〝看電視〞。
本研究的主要貢獻有以下三點: 第一,本研究透過建立家中活動與電器之間的關係,先了解家中的日常生活習慣,再以活動進行排程,如此能更親近使用者的直覺。第二,當了解每戶家庭不同的活動的電器偏好,我們將日常活動的排程問題轉換成一種最佳化排程問題,對於單一家庭來說,我們將流動電費的節省達到最佳化,對於社區中多戶家庭來說,我們同時進行最佳化電費的節省與最小化電力負載的波動性,且不犧牲家庭使用者的偏好。第三,我們導入解決最佳化問題的方法,並將本研究提出的最佳化排程問題利用該法找出近似最佳之活動排程。

The issues of energy conservation and carbon reduction have been discussed for years. Because the world evolves rapidly, conventional power grid suffers from the increasingly high power demand. For the next generation power grid, a.k.a. Smart Grid, being able to perform Demand Side Management (DMS) is crucial, i.e. capable of managing the energy demand at residences. With both DSM and Smart Grid, the residents not only can minimize their electricity cost but also can alleviate Peak-to-Average Ratio (PAR) of their total power consumption distribution. Here, minimization of electricity cost is achieved through optimal scheduling of daily activities, which simultaneously takes into account the distributed generation from Renewable Energy (RE) sources, the energy storage devices, and the dynamic pricing. In addition, an optimal PAR that makes the power system stabler can be achieved via cooperation among multiple homes in a community. However, most of the prior works focus on exchanges of appliance-level information but ignore the potential of context-awareness, e.g. disregarding the relation between activity and associated power consumption.
There are three major contributions in this thesis. Firstly, the context related to an activity is considered while optimization of the power demand is being performed. Secondly, we formulate the power demand side management into an optimization problem. For individual home power, the electricity cost is minimized. For multiple homes, electricity cost and PAR are minimized simultaneously without compromising the preference of residents. Thirdly, a method for scheduling daily activity is proposed for both individual home and the environment with multiple homes, i.e. community.

誌謝 i
中文摘要 ii
ABSTRACT iii
Table of Contents iv
List of Figures vii
List of Tables ix
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Challenges 2
1.2.1 Challenges of Complex Problem Formulation 3
1.2.2 Challenges of Multi-objective Optimization in Community 3
1.2.3 Challenge of Optimization Constraint Handling 4
1.3 Related Work 5
1.3.1 Smart Grid 5
1.3.2 Demand Response 7
1.3.3 Demand Side Management 8
1.4 Objective 11
1.5 System Overview 13
1.6 Thesis Organization 14
Chapter 2 Preliminaries 15
2.1 Particle Swarm Optimization 15
2.1.1 PSO Initialization 15
2.1.2 PSO Particle Evaluation 16
2.1.3 PSO Velocity Update 17
2.1.4 PSO Particle Update 18
2.1.5 Multi-Objective Particle Swarm Optimization 20
2.2 M-CHESS Overview 21
2.2.1 Energy Responsive Context Inference Engine 24
2.2.2 User Comfort Evaluation Engine 26
2.2.3 Energy Saving Decision Support Engine 26
Chapter 3 Individual Home Power Demand Optimization 28
3.1 Smart Energy Saving Home 28
3.2 Schedule Optimization Engine (SOE) 31
3.2.1 Optimization Flow 31
3.2.2 Data Preprocessing 32
3.2.3 PSO-based Schedule Optimization 38
Chapter 4 Residential Community Power Demand Optimization 46
4.1 Smart Grid Community 46
4.2 Community Schedule Optimizer 49
4.2.1 Optimization Flow 49
4.2.2 MOPSO-based Schedule Optimization 51
Chapter 5 System Evaluation 57
5.1 Simulated Environment 57
5.2 Individual Home Electricity Cost 63
5.3 Residential Community Electricity Cost &; PAR 69
Chapter 6 Conclusion 74
6.1 Summary 74
6.2 Future Work 75
REFERENCE 76

[1]M. Ullah, A. Mahmood, S. Razzaq, M. Ilahi, R. Khan, and N. Javaid, "A Survey of Different Residential Energy Consumption Controlling Techniques for Autonomous DSM in Future Smart Grid Communications," arXiv preprint arXiv:1306.1134, 2013.
[2]Aravind Kailas, Valentina Cecchi, and A. Mukherjee, "A Survey of Communications and Networking Technologies for Energy Management in Buildings and Home Automation," Journal of Computer Networks and Communications, p. 12, 2012.
[3]G. T. Costanzo, Z. Guchuan, M. F. Anjos, and G. Savard, "A System Architecture for Autonomous Demand Side Load Management in Smart Buildings," IEEE Transactions on Smart Grid, vol. 3, pp. 2157-2165, 2012.
[4]C. W. Gellings, "The concept of demand-side management for electric utilities," IEEE Proceedings, vol. 73, pp. 1468-1470, 1985.
[5]H. Farhangi, "The path of the smart grid," IEEE Power and Energy Magazine, vol. 8, pp. 18-28, 2010.
[6]M. H. Albadi and E. F. El-Saadany, "A summary of demand response in electricity markets," Electric Power Systems Research, vol. 78, pp. 1989-1996, 11 2008.
[7]A. Ipakchi and F. Albuyeh, "Grid of the future," IEEE Power and Energy Magazine, vol. 7, pp. 52-62, 2009.
[8]M. Erol-Kantarci and H. T. Mouftah, "Wireless Sensor Networks for Cost-Efficient Residential Energy Management in the Smart Grid," IEEE Transactions on Smart Grid, vol. 2, pp. 314-325, 2011.
[9]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, vol. 1, pp. 320-331, 2010.
[10]Z. Ziming, T. Jie, S. Lambotharan, C. Woon Hau, and F. Zhong, "An integer linear programming based optimization for home demand-side management in smart grid," in Innovative Smart Grid Technologies (ISGT), IEEE PES, 2012, pp. 1-5.
[11]A. Barbato, A. Capone, G. Carello, M. Delfanti, M. Merlo, and A. Zaminga, "House energy demand optimization in single and multi-user scenarios," in IEEE International Conference on Smart Grid Communications (SmartGridComm),, 2011, pp. 345-350.
[12]C. Chen, S. Kishore, and L. V. Snyder, "An innovative RTP-based residential power scheduling scheme for smart grids," in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011, pp. 5956-5959.
[13]Z. Zhuang, L. Won Cheol, S. Yoan, and S. Kyung-Bin, "An Optimal Power Scheduling Method for Demand Response in Home Energy Management System," IEEE Transactions on Smart Grid, vol. 4, pp. 1391-1400, 2013.
[14]S. A. Azad, A. M. T. Oo, and M. F. Islam, "A low complexity residential demand response strategy using binary particle swarm optimization," in Universities Power Engineering Conference 2012, pp. 1-6.
[15]I. Georgievski, V. Degeler, G. A. Pagani, N. Tuan Anh, A. Lazovik, and M. Aiello, "Optimizing Energy Costs for Offices Connected to the Smart Grid," IEEE Transactions on Smart Grid, vol. 3, pp. 2273-2285, 2012.
[16]D. S. Markovic, D. Zivkovic, I. Branovic, R. Popovic, and D. Cvetkovic, "Smart power grid and cloud computing," Renewable and Sustainable Energy Reviews, vol. 24, pp. 566-577, 2013.
[17]C. Alcaraz and J. Lopez, "Wide-Area Situational Awareness for Critical Infrastructure Protection," Computer, vol. 46, pp. 30-37, 2013.
[18]J. Bryson and P. Gallagher, "NIST framework and roadmap for smart grid interoperability standards, release 2.0," National Institute of Standards and Technology (NIST), Tech. Rep. NIST Special Publication 1108R2, pp. 2-0, 2012.
[19]http://www.nist.gov/.
[20]P. Faria, Z. Vale, J. Soares, and J. Ferreira, "Demand Response Management in Power Systems Using Particle Swarm Optimization," Intelligent Systems, IEEE, vol. 28, pp. 43-51, 2013.
[21]C. H. Lu, C. L. Wu, T. H. Yang, H. W. Yeh, M. Y. Weng, L. C. Fu, et al., "Energy-Responsive Aggregate Context for Energy Saving in a Multi-Resident Environment," IEEE Transactions on Automation Science and Engineering, vol. PP, pp. 1-15, 2013.
[22]J. Kennedy and R. Eberhart, "Particle swarm optimization," in IEEE International Conference on Neural Networks, Proceedings, 1995, pp. 1942-1948 vol.4.
[23]C. A. Coello Coello and M. S. Lechuga, "MOPSO: a proposal for multiple objective particle swarm optimization," in Proceedings of the Congress on Evolutionary Computation, 2002, pp. 1051-1056.
[24]E. Zitzler, Evolutionary algorithms for multiobjective optimization: Methods and applications vol. 63: Shaker Ithaca, 1999.
[25]K. Seul-Ki, J. Jin-Hong, C. Chang-Hee, A. Jong-Bo, and K. Sae-Hyuk, "Dynamic Modeling and Control of a Grid-Connected Hybrid Generation System With Versatile Power Transfer," IEEE Transactions on Industrial Electronics, vol. 55, pp. 1677-1688, 2008.
[26]H. Rui, H. Tiana, R. Gadh, and L. Na, "Solar generation prediction using the ARMA model in a laboratory-level micro-grid," in IEEE Third International Conference on Smart Grid Communications (SmartGridComm), 2012, pp. 528-533.
[27]https://solaranywhere.com/.
[28]P. Whitle, Hypothesis testing in time series analysis vol. 4: Almqvist &; Wiksells, 1951.
[29]R. Mancini and B. Carter, Op Amps for everyone: IET, 2009.
[30]D. Thanh and K. Ringland, "Optimal load scheduling for residential renewable energy integration," in IEEE Third International Conference on Smart Grid Communications (SmartGridComm), 2012, pp. 516-521.
[31]M. E. V. Team, "A Guide to Understanding Battery Specifications," 2008.
[32]D. Jeyakumar, T. Jayabarathi, and T. Raghunathan, "Particle swarm optimization for various types of economic dispatch problems," International Journal of Electrical Power &; Energy Systems, vol. 28, pp. 36-42, 2006.
[33]M. Jaccard, L. Failing, and T. Berry, "From equipment to infrastructure: community energy management and greenhouse gas emission reduction," Energy Policy, vol. 25, pp. 1065-1074, 1997.

QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關期刊
 
系統版面圖檔 系統版面圖檔