Author: |
謝孟霖 Hsieh, Meng-Lin |
---|---|
Thesis Title: |
基於二進制差分演化演算法之加入太陽能電池的家庭能源排程 Using Binary Differential Evolution for Home Energy Scheduling with Solar Energy |
Advisor: |
蔣宗哲
Chiang, Tsung-Che |
Degree: |
碩士 Master |
Department: |
資訊工程學系 Department of Computer Science and Information Engineering |
Thesis Publication Year: | 2015 |
Academic Year: | 103 |
Language: | 中文 |
Number of pages: | 74 |
Keywords (in Chinese): | 家庭能源管理 、二進制差分演化演算法 、限制最佳化 |
Thesis Type: | Academic thesis/ dissertation |
Reference times: | Clicks: 124 Downloads: 8 |
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因應時代變化,電子化產品逐漸增多,用電需求益發增加,每戶家庭所需負擔的電費越來越多,因此能源管理變得越加重要。演化演算法 (evolutionary algorithm) 在解決問題上具有優越的效能,被眾多領域廣泛運用,而其中差分演化演算法 (Differential Evolution, DE) 的效果特別突出。因此本論文提出了一個基於離散型二進制的差分演化演算法的排程演算法,能夠安排每日家庭所需的活動排程,以電費最小化為目標,且為了因應趨勢,加入了具有能夠充放電的可充電之再生能源電池,除了能從太陽獲得能源供給以外,也能決定是否要從電網獲得電力進行儲存,以支援電器用品所需的電力。本論文設計的演算法總共分為兩階段,第一階段為活動排程之演化,主要在於活動的安排規劃。第二階段為電池排程之演化,主要目的是降低活動規劃的花費。並再加入限制處理 (constraint handling)、參數控制 (parameter control) 等機制。實驗部分為自製的8個問題,會驗證本論文所改善的部分具有效果,且會與其餘離散型演化演算法進行比較,討論其優劣的原因。
[1]http://energymonthly.tier.org.tw/report/201403/10303.pdf 經濟部能源報導2014年3月
[2]http://www.smart-grid.org.tw/content/smart_grid/development.aspx 台灣智慧型電網產業協會
[3]S. F. Lee, “Power Demand Side Management Using Particle Swarm Optimization in Smart Grid Community,” National Taiwan University, Taipei, Taiwan, 2014.
[4]R. Storn, K. Price, “Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces,” Journal of Global Optimization, vol. 11, No. 4, pp. 341-359, 1997.
[5]Z. Zhu, J. Tang, S. Lambotharan, W. H. Chin, Z. Fan, “An integer linear programming based optimization for home demand-side management in smart grid,” Innovative Smart Grid Technologies, IEEE PES, pp. 1-5, 2012.
[6]S. A. Azad, A. M. T. Oo, M. F. Islam, “A Low Complexity Residential Demand Response Strategy using Binary Particle Swarm Optimization,” Universities Power Engineering Conference, pp. 1-6, 2012.
[7]Z. Zhao, W. C. Lee , Y. Shin, K. Song, “An Optimal Power Scheduling Method for Demand Response in Home Energy Management System,” IEEE Transactions on Smart Grid, Vol. 4, No. 3, pp. 1391-1400, 2013.
[8]P. Chavali, P. Yang, A. Nehorai, “A Distributed Algorithm of Appliance Scheduling for Home Energy Management System,” IEEE Transactions on Smart Grid, Vol. 5, No. 1, pp. 282-290, 2014.
[9]I. Georgievski, V. Degeler, G. A. Pagani, T. A. Nguyen, A. Lazovik, M. Aiello, “Optimizing Energy Costs for Offices Connected to the Smart Grid,” IEEE Transactions on Smart Grid, Vol. 3, No. 4, pp. 2273-2285, 2012.
[10]J. Brest, S. Greiner, B. Boskovic, M. Mernik, V. Zumer, “Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems,” IEEE Transactions on Evolutionary Computation, Vol. 10, No. 6, pp. 646-657, 2006.
[11]J. Zhang, C. Sanderson, “JADE: Adaptive Differential Evolution with Optional External Archive,” IEEE Transactions on Evolutionary Computation, Vol. 13, No. 5, pp. 945-958, 2009.
[12]A. K. Qin, V. L. Huang, P. N. Suganthan, “Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization,” IEEE Transactions on Evolutionary Computation, Vol. 13, No. 2, pp. 398–417, 2009.
[13]J. Kennedy, R. C. Eberhar, “A Discrete Binary Version of the Particle Swarm Algorithm,” IEEE International Conference on Computational Cybernetics and Simulation, Vol. 5, pp. 4104-4108, 1997.
[14]G. Pampara, A. P. Engelbrecht, N. Franken, “Binary Differential Evolution,” IEEE Congress on Evolutionary Computation, pp. 1873-1879, 2006.
[15]A.P. Engelbrecht, G. Pampara, “Binary Differential Evolution Strategies,” IEEE Congress on Evolutionary Computation, pp. 1942-1947, 2007.
[16]T. Gong, A. L. Tuson, “Differential Evolution for Binary Encoding,” Advances in Soft Computing, Vol. 39, pp. 251-262, 2007.
[17]J. C. Barbosa, A. C.C. Lemonge, “An Adaptive Penalty Scheme in Genetic Algorithms for Constrained Optimization Problems,” Proceedings of the genetic and evolutionary computation conference (GECCO), 2002.
[18]R. Farmani, J. A. Wright, “Self-Adaptive Fitness Formulation for Constrained Optimization,” IEEE Transactions on Evolutionary Computation, Vol. 7, No. 5, pp. 445-455, 2003.
[19] C. Y. Lin and W. H.Wu, “Self-organizing Adaptive Penalty Strategy in Constrained Genetic Search,” Structural and Multidisciplinary Optimization, Vol. 26, No. 6, pp. 417-428, 2004.
[20]B. Tessema, G. G. Yen, “A Self Adaptive Penalty Function Based Algorithm for Constrained Optimization,” IEEE Congress on Evolutionary Computation, pp. 246-253, 2006.
[21]B. Tessema, G. G. Yen, “An Adaptive Penalty Formulation for Constrained Evolutionary Optimization,” IEEE Transactions on Evolutionary Computation, Vol. 39, No. 3, pp. 565-578, 2009.
[22]K. Deb, “An Efficient Constraint Handling Method for Genetic Algorithms,” Computer Methods in Applied Mechanics and Engineering, Vol. 186, No. 2-4, pp. 311-338, 2000.
[23]D. G. Mayer, B. P. Kinghorn, A. A. Archer, “Differential Evolution – An Easy and Efficient Evolutionary Algorithm for Model Optimization,” Agricultural Systems, Vol. 83, No. 3, pp. 315-328, 2005.
[24]Q. Pan, M. F. Tasgetiren, Y. Liang, “A Discrete Differential Evolution Algorithm for the Permutation Flowshop Scheduling Problem,” Computers & Industrial Engineering, Vol. 55, No. 4, pp. 795-816, 2008.
[25]L. Wang, Q. Pan, P. N. Suganthan, W. Wang, Y. Wang, “A Novel Hybrid Discrete Differential Evolution Algorithm for Blocking Flowshop Scheduling Problems,” Computers & Operations Research, Vol. 37, No. 3, pp. 509-520, 2010.
[26]X. Yuan, A. Su, H. Nie, Y. Yuan, L. Wang, “Application of Enhanced Discrete Differential Evolution Approach to Unit Commitment Problem,” Energy Conversion and Management, Vol. 50, No. 9, pp. 2449-2456, 2009.
[27]D. Datta, S. Dutta, “A Binary-real-coded Differential Evolution for Unit Commitment Problem,” Electrical Power & Energy Systems, Vol. 42, No. 1, pp. 517-524, 2012.