一個運行於智慧電網中可靠且誠實的能源管理及監測系統

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研究生：	邱聖元 Chiu, Sheng-Yuan
論文名稱：	一個運行於智慧電網中可靠且誠實的能源管理及監測系統 Robust and Truthful Power Management: A Back to Front Framework for Energy Auditing and Scheduling
指導教授：	韓永楷 Hon, Wing-Kai
口試委員:	李哲榮廖崇碩吳尚鴻謝孫源彭勝龍
學位類別：	博士 Doctor
系所名稱：	電機資訊學院 - 資訊工程學系 Computer Science
論文出版年：	2016
畢業學年度：	104
語文別：	英文
論文頁數：	107
中文關鍵詞：	智慧電網、能源監測網路、能源排程、誠實機制、壓縮感知
外文關鍵詞：	Smart Grid, Energy Auditing Network, Energy Scheduling, Truthful Mechanism, Compressive Sensing
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隨著智慧電網的演進，一般使用者得以透過嵌入在一般電網裡的網路與電力公司溝通，這項跨時代的改變使能源觀測以及能源排程變得可行。有了這兩項功能，電力公司可以利用能源觀測深入分析電力的使用量；而透過能源排程，更可以讓能源的使用效率提高。儘管如此，隨著能獲取的資訊量增加，如果沒有一套完善的措施來消除潛在的資安威脅，智慧電網將會是一個漏洞百出的系統。在這篇論文中，我們提出一套整合前後端的系統，不但可以消除可能的安全疑慮，同時也可以讓能源使用更加有效率。

這篇論文分成兩部分。第一個部分我們會先介紹一個基於壓縮感測技術的能源觀測網路。此能源觀測網路不僅可以增加資料的完整性，同時還可以保護使用者的資料安全。至於在第二個部分，我們會介紹一個可以用於能源排程的誠實機制。

With the advent of smart grids, there can be two-way communications between the users and the electricity company through the power grids. This
allows two promising features, namely energy auditing and energy scheduling. These features enable better energy efficiency as well as pervasive energy
analysis. However, the grid can become vulnerable if an adequate system,
which eliminates potential threats arising from the massive data exchange,
is not present. In this dissertation, we propose a back-to-front framework
which jointly secures user privacy as well as improves the energy consumption distribution.
This dissertation is divided into two parts. In the first part, we will show
a compressive sensing (CS) based approach which enhances data fidelity and
security in energy auditing network. Then, in the second part, a truthful
mechanism for energy scheduling will be given.

Introduction 4
1 Wireless Energy Auditing Networks . . . . . . . . . . . . . . . 6
2 Power Management in Smart Grids . . . . . . . . . . . . . . . 11
3 Dissertation Organization . . . . . . . . . . . . . . . . . . . . 15
Preliminaries 16
1 Compressive Sensing . . . . . . . . . . . . . . . . . . . . . . . 16
2 Truthful Mechanism . . . . . . . . . . . . . . . . . . . . . . . 18
2.1 Vickrey-Clarke-Groves Auction . . . . . . . . . . . . . 19
3 Energy Consumption Model and System Model in TRUMA . . 20
3.1 Energy Consumption Model . . . . . . . . . . . . . . . 21
3.2 System Model . . . . . . . . . . . . . . . . . . . . . . . 23
4 GridLAB-D Simulator . . . . . . . . . . . . . . . . . . . . . . 24
Problem Formulation 25
1 Compression and Encryption in Wireless Energy Auditing Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
1
2 Truthful Mechanism Design for Unified Energy Allocation . . 27
JICE: Joint Data Compression and Encryption using Compressive Sensing 30
1 An Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2 Design Details . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
2.1 Approximate Sparsity . . . . . . . . . . . . . . . . . . 33
2.2 Design of Representation Basis Ψ . . . . . . . . . . . . 37
2.3 Design of Measurement Matrix Φ . . . . . . . . . . . . 40
2.4 Autonomous and Dynamic Configuration for Ψ . . . . 42
3 Data Secrecy of Compressive Sensing . . . . . . . . . . . . . . 48
3.1 Basic Secrecy Property Achieved by CS . . . . . . . . . 48
3.2 Leak of Statistics under CS . . . . . . . . . . . . . . . 49
3.3 Noisy-Plaintext Attack . . . . . . . . . . . . . . . . . . 52
4 Testbed and Benchmarking . . . . . . . . . . . . . . . . . . . 56
4.1 Implementation Details . . . . . . . . . . . . . . . . . . 57
4.2 Benchmarking . . . . . . . . . . . . . . . . . . . . . . . 59
5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
5.1 Experiment Methodology . . . . . . . . . . . . . . . . . 61
5.2 Experimental Results . . . . . . . . . . . . . . . . . . . 62
TRUMA: Truthful Mechanism for Unified Allocation 66
1 An Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
2 Design Details . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
2
2.1 Residential Demand . . . . . . . . . . . . . . . . . . . 67
2.2 Shiftable Demand . . . . . . . . . . . . . . . . . . . . . 68
2.3 Discardable Demand . . . . . . . . . . . . . . . . . . . 75
2.4 Eliminating Interrelation . . . . . . . . . . . . . . . . . 78
3 Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . 82
4 Truthfulness . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
4.1 Definition of Untruthful Reporting . . . . . . . . . . . 83
4.2 Truthfulness of TRUMA . . . . . . . . . . . . . . . . . 84
5 Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
5.1 Scheduling Efficiency with Controlled Demands . . . . 91
5.2 Performance Comparison with IPM Approach . . . . . 92
5.3 GridLAB-D Simulation . . . . . . . . . . . . . . . . . . 95
Conclusion and Open Problems 98
1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
2 Open Problems . . . . . . . . . . . . . . . . . . . . . . . . . . 100
                                

[1] Waheed Bajwa, Jarvis Haupt, Akbar Sayeed, and Robert Nowak. Compressive Wireless Sensing. In Proceedings of ACM International Conference on Information Processing in Sensor Networks (IPSN), pages
134–142, 2006.
[2] BBC. Smart Meter Project is Delayed. http://bbc.in/1ptjSoq.
[3] Radu Berinde, Anna C Gilbert, Piotr Indyk, Howard Karloff, and Martin J Strauss. Combining Geometry and Combinatorics: A Unified
Approach to Sparse Signal Recovery. In Proceedings of IEEE Annual
Allerton Conference on Communication, Control, and Computing, pages
798–805, 2008.
[4] Severin Borenstein. The Long-Run Efficiency of Real-Time Electricity
Pricing. The Energy Journal, pages 93–116, 2005.
[5] Emmanuel Candes and Justin Romberg. Sparsity and Incoherence in
Compressive Sampling. Inverse Problems, 23(3):969, 2007.
[6] Emmanuel J Cand`es. Compressive Sampling. In International Congress
of Mathematicians, 2006.
[7] Emmanuel J Cand`es and Michael B Wakin. An Introduction to Compressive Sampling. IEEE Signal Processing Magazine, 25(2):21–30, 2008.
[8] St´ephane Caron and George Kesidis. Incentive-Based Energy Consumption Scheduling Algorithms for the Smart Grid. In Proceedings of IEEE
International Conference on Smart Grid Communications (SmartGridComm), pages 391–396, 2010.
[9] Sheng-Yuan Chiu, Hoang Hai Nguyen, Rui Tan, Deokwoo Jung,
and David K. Y. Yau, 2013. http://code.google.com/p/
compress-sensing-nesc/.
[10] Shane S Clark, Hossen Mustafa, Benjamin Ransford, Jacob Sorber,
Kevin Fu, and Wenyuan Xu. Current Events: Identifying Webpages
by Tapping the Electrical Outlet. In Proceedings of European Symposium on Research in Computer Security (ESORICS), pages 700–717,
2013.
[11] Stephen Dawson-Haggerty, Steven Lanzisera, Jay Taneja, Richard
Brown, and David Culler. @scale: Insights from a Large, Long-Lived
Appliance Energy WSN. In Proceedings of ACM International Conference on Information Processing in Sensor Networks (IPSN), pages
37–48, 2012.
[12] Eaton. Next-Generation Power Quality Meters. http://bit.ly/
1B9QOst.
[13] Miro Enev, Sidhant Gupta, Tadayoshi Kohno, and Shwetak N Patel.
Televisions, Video Privacy, and Powerline Electromagnetic Interference.
In Proceedings of ACM Conference on Computer and Communications
Security (CCS), pages 537–550, 2011.
[14] GridLAB-D. GridLAB-D Simulation Software. http://www.gridlabd.
org/.
[15] Christian Ibars, Monica Navarro, and Lorenza Giupponi. Distributed
Demand Management in Smart Grid with a Congestion Game. In Proceedings of IEEE International Conference on Smart Grid Communications (SmartGridComm), pages 495–500, 2010.
[16] Xiaofan Jiang, Stephen Dawson-Haggerty, Prabal Dutta, and David
Culler. Design and Implementation of a High-Fidelity AC Metering Network. In Proceedings of ACM International Conference on Information
Processing in Sensor Networks (IPSN), pages 253–264, 2009.
[17] Xiaofan Jiang, Minh Van Ly, Jay Taneja, Prabal Dutta, and David
Culler. Experiences with a High-Fidelity Wireless Building Energy Auditing Network. In Proceedings of ACM Conference on Embedded Networked Sensor Systems (SenSys), pages 113–126, 2009.
[18] Nate Lawson. Reverse-Engineering a Smart Meter. http://bit.ly/
9v4nY8.
[19] An Liu and Peng Ning. TinyECC: A Configurable Library for Elliptic Curve Cryptography in Wireless Sensor Networks. In Proceedings
of IEEE International Conference on Information Processing in Sensor
Networks (IPSN), pages 245–256, 2008.
[20] Benny PL Lo, Surapa Thiemjarus, Rachel King, and Guang-Zhong
Yang. Body Sensor Network–A Wireless Sensor Platform for Pervasive
Healthcare Monitoring. In Adjunct Proceedings of IEEE International
Conference on Pervasive Computing (PerCom), pages 77–80, 2005.
[21] Xin Lou, David KY Yau, Hoang Hai Nguyen, and Binbin Chen. ProfitOptimal and Stability-Aware Load Curtailment in Smart Grids. IEEE
Transactions on Smart Grid, 4(3):1411–1420, 2013.
[22] Chong Luo, Feng Wu, Jun Sun, and Chang Wen Chen. Compressiv e
Data Gathering for Large-Scale Wireless Sensor Networks. In Proceedings of ACM Annual International Conference on Mobile Computing
and Networking (MobiCom), pages 145–156, 2009.
[23] MathWorks. Discrete Cosine Transform Matrix. http://bit.ly/
11yqSW1.
[24] MathWorks. Haar Wavelet Transform Matrix. http://bit.ly/
171YHXx.
[25] Amir-Hamed Mohsenian-Rad, Vincent WS Wong, Juri Jatskevich, and
Robert Schober. Optimal and Autonomous Incentive-Based Energy
Consumption Scheduling Algorithm for Smart Grid. In Proceedings of
IEEE Conference on Innovative Smart Grid Technologies (ISGT), pages
1–6, 2010.
[26] Amir-Hamed Mohsenian-Rad, Vincent WS Wong, Juri Jatskevich,
Robert Schober, and Alberto 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.
[27] Adem Orsdemir, H Oktay Altun, Gaurav Sharma, and Mark F Bock o.
On the Security and Robustness of Encryption via Compressed Sensing. In Proceedings of IEEE Military Communications Conference (MILCOM), pages 1–7, 2008.
[28] Sylvain Pelissier. Cryptography Algorithms for TinyOS. http://bit.
ly/rwzDJT.
[29] Dennis E Phillips, Rui Tan, Mohammad-Mahdi Moazzami, Guoliang
Xing, Jinzhu Chen, and David KY Yau. Supero: A Sensor System for
Unsupervised Residential Power Usage Monitoring. In Proceedings of
IEEE International Conference on Pervasive Computing and Communications (PerCom), pages 66–75, 2013
[30] Yaron Rachlin and Dror Baron. The Secrecy of Compressed Sensing
Measurements. In Proceedings of IEEE Annual Allerton Conference on
Communication, Control, and Computing, pages 813–817, 2008.
[31] Mardavij Roozbehani, Ali Faghih, Mesrob I Ohannessian, and
Munther A Dahleh. The Intertemporal Utility of Demand and Price
Elasticity of Consumption in Power Grids with Shiftable Loads. In Proceedings of IEEE Conference on Decision and Control and European
Control Conference (CDC-ECC), pages 1539–1544, 2011.
[32] Ishtiaq Rouf, Hossen Mustafa, Miao Xu, Wenyuan Xu, Rob Miller, and
Marco Gruteser. Neighborhood Watch: Security and Privacy Analysis
of Automatic Meter Reading Systems. In Proceedings of ACM Conference on Computer and Communications Security (CCS), pages 462–473,
2012.
[33] Christopher M Sadler and Margaret Martonosi. Data Compression Algorithms for Energy-Constrained Devices in Delay Tolerant Networks. In
Proceedings of ACM International Conference on Embedded Networked
Sensor Systems (SenSys), pages 265–278, 2006.
[34] Pedram Samadi, Hamed Mohsenian-Rad, Robert Schober, and Vincent WS Wong. Advanced Demand Side Management for the Future
Smart Grid using Mechanism Design. IEEE Transactions on Smart
Grid, 3(3):1170–1180, 2012.
[35] Tuomas Sandholm. Issues in Computational Vickrey Auctions. International Journal of Electronic Commerce, 4(3):107–129, 2000.
[36] Sonnonet. http://www.sonnonet.com.
[37] Goran Strbac. Demand Side Management: Benefits and Challenges.
Energy Policy, 36(12):4419–4426, 2008.
[38] Dharmpal Takhar, Jason N Laska, Michael B Wakin, Marco F Duarte,
Dror Baron, Shriram Sarvotham, Kevin F Kelly, and Richard G Baraniuk. A New Compressive Imaging Camera Architecture using OpticalDomain Compression. In Proceedings of IS&T/SPIE Symposium on
Electronic Imaging, pages 43–52, 2006.
[39] TinyOS. Packet Link Layer. http://bit.ly/11W2aPL.
[40] TinyOS. Security and Cryptography in TinyOS. http://bit.ly/
11FqFkk.
[41] Eric W. Weisstein. Binet’s Fibonacci Number Formula, 1986.
[42] Ami Wiesel, Yonina C Eldar, and Arie Yeredor. Linear Regression with
Gaussian Model Uncertainty: Algorithms and Bounds. IEEE Transactions on Signal Processing, 56(6):2194–2205, 2008.
[43] Wikipedia. Auction. https://en.wikipedia.org/wiki/Auction.
[44] Wikipedia. Continuous Knapsack Problem. http://goo.gl/Pxo508.
[45] Wikipedia. Prefix Sum. https://en.wikipedia.org/wiki/Prefix_
sum.
[46] Frank A Wolak. Residential Customer Response to Real-Time Pricing:
The Anaheim Critical Peak Pricing Experiment. Center for the Study
of Energy Markets, 2007.
[47] Xiaopei Wu and Mingyan Liu. In-Situ Soil Moisture Sensing: Measurement Scheduling and Estimation using Compressive Sensing. In Proceedings of ACM International Conference on Information Pr ocessing
in Sensor Networks (IPSN), pages 1–12, 2012.

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