臺灣博碩士論文加值系統

近年來，節能減碳的議題逐漸引起世人的關注，如何減少能源之消耗成為各國關注的焦點。因此能夠即時因應用電需求做更改、提供需求端管理的智慧電網將成為未來電力網路的趨勢。透過智慧電網與需求端管理，不但可有效減少家中用電支出還可同時降低電力負載的波動性。其中，在智慧電網架構下，一個能夠即時因應環境變化及使用者偏好提供最佳節能策略之系統將扮演非常重要的角色。於本論文中，將於智慧電網架構下，同時考慮再生能源、儲能設備與時間電價進
行〝日常活動排程之最佳化〞以節省居家用電支出。另一方面於住宅型社區中，在不洩漏個別使用者生活隱私之條件下，所有家庭可共同合作進行電力需量管理，在可接受之個別家庭電費增加限度下最小化電力負載的波動性，使最高用電峰值下降低並可使社區與電力公司協議更為適合之契約容量，使個別家庭需繳交之總體電費仍有效地減少，同時提升電力公司對於電能管理之效率。本研究的主要貢獻有以下兩點：第一，為了因應使用者進行活動之不確定性，我們改良了傳統的粒子群最佳化演算法，使其運算時間大幅減低，當使用者臨時不依照決策結果進行活動或是環境資訊發生變化，能在最短的時間內告知使用者未來最適當之活動順序及電池使用方式。第二，在社區節能方面，我們認為保護住戶隱私應優先於社區節能之成效，因此設計一套分散式的系統架構，透過特別之資訊傳遞演算法，使得住戶個人之用電習慣及生活型態不會洩漏，同時整個系統又能掌握所需之資訊進行社區之多目標節能最佳化問題。

In smart grids, demand-side management (DSM) plays an important role betweencustomers and utility company, since it can reduce the total electricity cost of each customer meanwhile alleviating the aggregate peak-to-average ratio (PAR) of utility of a customer community subject to real-time pricing (RTP) policy. Moreover, a good DSM system should also be able to react quickly when users do not take its advice or when the environment state changes. This works aims at making the DSM system dynamic, which means that the proposed DSM can reschedule the order of activities in nearly real-time whenever the original schedule is no longer feasible due to the aforementioned situations. In our problem formulation, each home is assumed to be connected a renewable energy resource, say, photovoltaic (PV) system, and is equipped with an energy storage device so that the scheduling can be made more flexible. Thus, besides the case where the original recommended schedule is not followed by the user, another case where the solar power distribution changes from what was predicted also initiates the rescheduling under the dynamic DSM. In addition, for a residential community composed of multiple homes, keeping the privacy of lifestyle for each home is also an issue which is as important as energy saving, and therefore a “cooperative architecture” is also formulated so as to transfer only the necessary information but hide all the details of power consumption of each home. The simulation results show that the proposed DSM system not only reduces the cost in individual home but also reschedule quickly whenever there is a need, while making a tradeoff between cost and PAR with privacy concern in a residential community.

口試委員會審定書 #
中文摘要 i
ABSTRACT ii
CONTENTS iii
LIST OF FIGURES vi
LIST OF TABLES viii
Chapter 1 Introduction 1
1.1 Background 1
1.2 Challenges 3
1.2.1 Challenges of Utilizing the Renewable Energy 3
1.2.2 Challenges of Dynamic DSM in Single Home 4
1.2.3 Challenges of DSM in a Community with Privacy Concern 5
1.3 Related Work 6
1.3.1 Smart Grid 7
1.3.2 Demand Response 9
1.3.3 Approach of Demand Side Management 10
1.4 Objective 13
1.5 System Overview 15
1.6 Thesis Organization 18
Chapter 2 Preliminaries 19
2.1 Particle Swarm Optimization 19
2.1.1 PSO initialization 20
2.1.2 PSO Particle Evaluation 20
2.1.3 PSO Velocity Update 21
2.1.4 PSO Particle Update 22
2.1.5 Multi-Objective Particle Swarm Optimization 24
2.2 Game Theory 25
2.2.1 Cooperative Game 26
2.2.2 Nash Equilibrium 27
2.3 M-CHESS 28
Chapter 3 Dynamic Demand Side Management in Single Home 31
3.1 Smart Home Environment 31
3.2 System Architecture 33
3.3 Activity Scheduling System 35
3.3.1 Optimization Flow 35
3.3.2 Data Preprocessing 37
3.3.3 Fast Particle Swarm Optimization of Activity Scheduling 38
3.4 Dynamic Monitoring System 44
3.5 Communication System with Energy Storage Device 46
Chapter 4 Dynamic Demand Side Management in Community 48
4.1 Smart Grid Community 48
4.2 Dynamic Activity Scheduling System of Distributed Cooperative Game 50
4.2.1 Fast Particle Swarm Optimization for a Community 50
4.2.2 Cooperative Game of Community with Privacy Concern 53
Chapter 5 System Evaluation 55
5.1 Environment Setting 55
5.2 Evaluation of Single Home 57
5.2.1 Electricity Cost & Computation Time 58
5.2.2 Dynamic Situation: Re-scheduling Process 62
5.3 Evaluation of Community 63
5.3.1 Peak-to-Average Ratio & Cost 64
5.3.2 Computation Time & PAR for Large Scale Community 67
Chapter 6 Conclusion 71
6.1 Summary 71
6.2 Future Work 72
REFERENCE 73

[1]http://www.eia.gov/forecasts/ieo/pdf/0484(2013).pdf
[2]Gellings, C.W., "The Concept of Demand-side Management for Electric Utilities," in Proceedings of the IEEE, vol.73, no.10, pp.1468-1470, Oct. 1985
[3]S. Lee, G. Ryu, Y. Chon, R. Ha, and H. Cha, “Automatic Standby Power Man-agement Using Usage Profiling and Prediction,” IEEE Trans. Human-Machine Syst., vol. 43, no. 6, pp. 535–546, 2013.
[4]A. Ipakchi and F. Albuyeh, "Grid of the Future," IEEE Power and Energy Maga-zine, vol. 7, pp. 52-62, 2009.
[5]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.
[6]A. H. Mohsenian-Rad, V. W. S. Wong, J. Jatskevich, R. Schober, and A. Le-on-Garcia, “Autonomous Demand-side Management based on Game-theoretic En-ergy Consumption Scheduling for the Future Smart Grid,” IEEE Trans. Smart Grid, vol. 1, no. 3, pp. 320–331, 2010.
[7]Z. Ziming, T. Jie, S. Lambotharan, C. Woon Hau, and F. Zhong, "An Integer Line-ar Programming Based Optimization for Home Demand-side Management in Smart Grid," in Innovative Smart Grid Technologies (ISGT), IEEE PES, 2012, pp. 1-5.
[8]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, pp. 345-350, 2011.
[9]K. C. Sou, J. Weimer, H. Sandberg, and K. H. Johansson, “Scheduling Smart Home Appliances Using Mixed Integer Linear Programming,” IEEE Conf. Decis. Control Eur. Control Conf., pp. 5144–5149, 2011.
[10]I. Atzeni, L. G. Ordonez, G. Scutari, D. P. Palomar, and J. R. Fonollosa, “De-mand-Side Management via Distributed Energy Generation and Storage Optimiza-tion,” IEEE Trans. Smart Grid, vol. 4, no. 2, pp. 866–876, 2013.
[11]W. Zhang, G. Chen, Y. Su, Z. Dong, and I. Engineering, “A Dynamic Game Be-havior: Demand Side Management based on Utility Maximization with Renewable Energy and Storage Integration,” Power Eng. Conf. (AUPEC), 2014 Australas. Univ., no. October, pp. 1–5, 2014.
[12]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.
[13]N. Kinhekar, N. P. Padhy, and H. O. Gupta, “Multiobjective Demand Side Man-agement Solutions for Utilities with Peak Demand Deficit,” Int. J. Electr. Power Energy Syst., vol. 55, pp. 612–619, 2014.
[14]T. Logenthiran, D. Srinivasan, and T. Z. Shun, “Demand Side Management in Smart Grid Using Heuristic Optimization,” IEEE Trans. Smart Grid, vol. 3, no. 3, pp. 1244–1252, Sep. 2012.
[15]Y. Liu, C. Yuen, S. Huang, N. Ul Hassan, X. Wang, and S. Xie, “Peak-to-Average Ratio Constrained Demand-Side Management with Residence’s Preference in Res-idential Smart Grid,” IEEE J. Sel. Top. Signal Process., vol. 8, no. 6, pp. 1084–1097, 2014.
[16]C. Ogwumike, M. Short, and M. Denai, “Near-Optimal Scheduling of Residential Smart Home Appliances Using Heuristic Approach,” IEEE Int. Conf. Ind. Technol. (ICIT), 2015, pp. 3128–3133, 2015.
[17]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 Trans-actions on Smart Grid, vol. 3, pp. 2273-2285, 2012.
[18]S. A. Azad, A. M. T. Oo, and M. F. Islam, "A Low Complexity Residential De-mand Response Strategy Using Binary Particle Swarm Optimization," in Universi-ties Power Engineering Conference, pp. 1-6, 2012.
[19]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.
[20]C. Alcaraz and J. Lopez, "Wide-Area Situational Awareness for Critical Infrastruc-ture Protection," Computer, vol. 46, pp. 30-37, 2013.
[21]J. Bryson and P. Gallagher, "NIST Framework and Roadmap for Smart Grid In-teroperability Standards, release 2.0," National Institute of Standards and Technol-ogy (NIST), Tech. Rep. NIST Special Publication 1108R2, pp. 2-0, 2012.
[22]http://www.nist.gov/
[23]H. Farhangi, "The Path of the Smart Grid," IEEE Power and Energy Magazine, vol. 8, pp. 18-28, 2010.
[24]M. H. Albadi and E. F. El-Saadany, "A Summary of Demand Response in Electric-ity Markets," Electric Power Systems Research, vol. 78, pp. 1989-1996, 11 2008.
[25]J. Kennedy and R. Eberhart, "Particle Swarm Optimization," in IEEE International Conference on Neural Networks, Proceedings, 1995, pp. 1942-1948 vol.4.
[26]C. A. Coello and M. S. Lechuga, “MOPSO: A Proposal for Multiple Objective Par-ticle Swarm Optimization,” in Proceedings of the Congress on Evolutionary Com-putation, 2002, pp. 1051-1056.
[27]E.Zitzler, Evolutionary Algorithms for Multi Objective Optimization: Methods and applications vol. 63: Shaker Ithaca, 1999.
[28]C. H. Lu, C. L. Wu, T. H. Yang, H. W. Yeh, M. Y. Weng, L. C. Fu, "Ener-gy-Responsive Aggregate Context for Energy Saving in a Multi-Resident Envi-ronment," IEEE Transactions on Automation Science and Engineering, vol. PP, pp. 1-15, 2013.
[29]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 Versa-tile Power Transfer,” IEEE Transactions on Industrial Electronics, vol. 55, pp. 1677-1688, 2008.
[30]Richards, M.; Ventura, D., "Choosing a Starting Configuration for Particle Swarm Optimization," in Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on, vol.3, no., pp.2309-2312 vol.3, 25-29 July 2004
[31]Qiang Du, Vance Faber, and Max Gunzburger, “Centroidal Voronoi Tessellations: Applications and Algorithms”, SIAM Rev. 41, 4 (December 1999), 637-676.
[32]Z. Lu, Z. Hou and J. Du, “Particle Swarm Optimization with Adaptive Mutation”, Acta Electronica Sinica, vol. 32, no. 3, (2004), pp. 416-420.
[33]C. Clifton, M. Kantarcioglu, J. Vaidya, X. Lin, and M. Y. Zhu, “Tools for Priva-cy-preserving Distributed Data Mining,” SIGKDD Explorations, vol. 4, no. 2, pp. 28–34, Dec. 2002.
[34]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, 2012, pp. 528-533.