進階搜尋


   電子論文尚未授權公開,紙本請查館藏目錄
(※如查詢不到或館藏狀況顯示「閉架不公開」,表示該本論文不在書庫,無法取用。)
系統識別號 U0026-1308201813410400
論文名稱(中文) 智慧型微電網下基於Lyapunov Optimization之即時需量反應控制
論文名稱(英文) Real-Time Demand Response Control for Microgrids Based on Lyapunov Optimization
校院名稱 成功大學
系所名稱(中) 工業與資訊管理學系
系所名稱(英) Department of Industrial and Information Management
學年度 106
學期 2
出版年 107
研究生(中文) 陳力瑋
研究生(英文) Li-Wei Chen
學號 R36051238
學位類別 碩士
語文別 中文
論文頁數 44頁
口試委員 指導教授-劉任修
口試委員-張裕清
口試委員-胡政宏
中文關鍵字 智慧電網  Lyapunov最佳化  即時電價  微電網 
英文關鍵字 Keywords:Smart Grid  Lyapunov Optimization  Real-Time Pricing  Microgrids 
學科別分類
中文摘要 摘要
為防止全球氣候變遷現象持續惡化,減少碳排放已成為人類的共識,如何
更有效率地使用能源成為近來重要的課題。受惠於資訊與通訊科技的進步,智
慧電網得以應用在我們生活當中。雙向通訊讓電力事業及使用者互相交流資訊
並快速做出反應,分散式發電及電力儲存裝置也為整體用電提供更大的彈性以
及穩定性。由於用電資訊的透明化,電力公司得以監控整體用電狀況,提早對
用電高峰或是緊急狀況進行防範。另一方面,用戶可即時得知電價或是其他系
統資訊,讓前述電力公司之措施能快速得到效果,降低尖峰用電並分散用電量
至其他時間。除降低尖峰用電量外,更提高整體用電效率,降低不必要的發電
量。
本研究站在消費者之觀點,研究如何善用再生能源系統及電池,使用
戶們的平均用電成本能達到最低。我們將此問題建成隨機最佳化問題,利
用Lyapunov最佳化方法,將電力儲存系統視為一個佇列(Queue),求得不超過
O(1=V) 之近似解,而其中控制變數V 可以幫助我們在電池容量與平均成本之間
做權衡。此方法除計算複雜度不高外,執行時不需要未來資訊,只需要觀察系
統當下狀態做出控制決策,亦不需要變數的統計資訊。在最後我們以實際資料
進行實驗,並驗證演算法的有效性。
英文摘要 In order to prevent the global climate change from deteriorating, reducing carbon emissions has become a consensus of human beings, how to exploit energy more efficiently has become an important topic recently. Benefiting from advances in information and communication technology, smart grids can be applied to our daily lives. Two-way communication allows utilities and users to exchange information and respond quickly. Distributed power generation and energy storage also provide greater flexibility and stability for overall power usage. Due to the transparency of electricity consumption information, utilities can monitor the overall power consumption situation and prevent power peaks or emergencies early. On the other hand, users can instantly know the price of electricity or other system information, so that they can quickly respond to the measures of the aforementioned
utilities, reduce the peak load and disperse the power consumption to other time. In addition to reducing the peak power consumption, the efficiency in power usage is
enhanced, both reduce unnecessary power generation. We put ourselves in consumers’ position, studies how to make good use of renewable energy systems and batteries so that the average cost of electricity for users can be minimized. We build this problem into a stochastic optimization problem and exploit the Lyapunov optimization method, considering the power storage system
as a queue and find an approximate solution that does not exceed O(1/V). The control variable V can help us to make a trade-off between battery capacity and average cost. Apart from low computational complexity, this method does not require future information when it is executed. It only needs to observe the current state of the system to make control decisions and does not need statistical information of variables. Finally, we experiment with actual data and verified the effectiveness of our proposed algorithm.
論文目次 目錄
摘要i
EXTENDED ABSTRACT ii
誌謝x
目錄xii
表目錄xiv
圖目錄xv
1 緒論1
1.1 研究背景與動機. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 貢獻. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 論文架構. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2 文獻探討7
2.1 需量反應(Demand Response,DR) . . . . . . . . . . . . . . . . . . . . 7
2.2 成本最小化問題相關文獻. . . . . . . . . . . . . . . . . . . . . . . . 9
2.3 利用Lyapunov最佳化方法相關文獻. . . . . . . . . . . . . . . . . . 11
2.4 小結. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3 研究方法13
3.1 問題描述及模型建立. . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.2 Lyapunov最佳化方法. . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.3 分散式貪婪演算法. . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.4 演算法性質分析. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
4 實驗及結果探討34
4.1 實驗環境建置與參數設定. . . . . . . . . . . . . . . . . . . . . . . . 34
4.1.1 實驗環境建置. . . . . . . . . . . . . . . . . . . . . . . . . . 34
4.1.2 實驗參數設定. . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.2 實驗結果比較與分析. . . . . . . . . . . . . . . . . . . . . . . . . . 37
4.2.1 與最佳解表現比較. . . . . . . . . . . . . . . . . . . . . . . 37
4.2.2 與其他演算法表現比較. . . . . . . . . . . . . . . . . . . . . 38
5 結論及未來發展40
參考文獻41
參考文獻 Abushnaf, J., Rassau, A., & G´ornisiewicz, W. (2016). Impact on electricity use of introducing time-of-use pricing to a multi-user home energy management system.
International Transactions on Electrical Energy Systems, 26(5), 993–1005.
Alamdari, S., Biedl, T., Chan, T. M., Grant, E., Jampani, K. R., Keshav, S., . . . Pathak, V. (2013). Smart-grid electricity allocation via strip packing with slicing. In Workshop of algorithms and data structures (pp. 25–36).
Danandeh, A., Zhao, L., & Zeng, B. (2014). Job scheduling with uncertain local generation in smart buildings: Two-stage robust approach. IEEE Transactions on Smart Grid, 5(5), 2273–2282.
Du, P., & Lu, N. (2011). Appliance commitment for household load scheduling. IEEE transactions on Smart Grid, 2(2), 411–419.
Fang, X., Misra, S., Xue, G., & Yang, D. (2012). Smart grid—the new and improved power grid: A survey. IEEE communications surveys & tutorials, 14(4), 944–980.
Farhangi, H. (2010). The path of the smart grid. IEEE power and energy magazine,8(1).
Gholian, A., Mohsenian-Rad, H., & Hua, Y. (2016). Optimal industrial load control in smart grid. IEEE Transactions on Smart Grid, 7(5), 2305–2316.
Gholian, A., Mohsenian-Rad, H., Hua, Y., & Qin, J. (2013). Optimal industrial load control in smart grid: A case study for oil refineries. In Power and energy society general meeting (pes), 2013 ieee (pp. 1–5).
Guo, Y., Pan, M., & Fang, Y. (2012). Optimal power management of residential customers in the smart grid. IEEE Transactions on Parallel and Distributed Systems,
23(9), 1593–1606.
Jia, L., & Tong, L. (2012). Optimal pricing for residential demand response: A stochastic
optimization approach. In Communication, control, and computing (allerton), 2012 50th annual allerton conference on (pp. 1879–1884).
Joe-Wong, C., Sen, S., Ha, S., & Chiang, M. (2012). Optimized day-ahead pricing for smart grids with device-specific scheduling flexibility. IEEE Journal on Selected
Areas in Communications, 30(6), 1075–1085.
Kamyab, F., Amini, M., Sheykhha, S., Hasanpour, M., & Jalali, M. M. (2016). Demand response program in smart grid using supply function bidding mechanism. IEEE Transactions on Smart Grid, 7(3), 1277–1284.
Liu, R.-S., & Hsu, Y.-F. (2018). A scalable and robust approach to demand side management for smart grids with uncertain renewable power generation and bi-directional
energy trading. International Journal of Electrical Power & Energy Systems, 97, 396–407.
Neely, M. J. (2010). Stochastic network optimization with application to communication and queueing systems. Morgan & Claypool Publishers. Retrieved from http://portal.igpublish.com/iglibrary/search/MCPB0000138.html?0
Neely, M. J., Tehrani, A. S., & Dimakis, A. G. (2010). Efficient algorithms for renewable energy allocation to delay tolerant consumers. In Smart grid communications
(smartgridcomm), 2010 first ieee international conference on (pp. 549–554).
New York Independent System Operator. (1997). Market and operational data. Retrieved from http://www.nyiso.com/public/index.jsp
Siano, P. (2014). Demand response and smart grids—a survey. Renewable and sustainable energy reviews, 30, 461–478.
Song, L., Xiao, Y., & Van Der Schaar, M. (2014). Demand side management in smart grids using a repeated game framework. IEEE Journal on Selected Areas in Communications, 32(7), 1412–1424.
Stoffel, T., & Andreas, A. (1981). NREL Solar Radiation Research Laboratory (SRRL): Baseline Measurement System (BMS); Golden, Colorado (Data). (DA-5500-56488).
Retrieved from http://www.osti.gov/scitech/servlets/purl/1052221
Tang, S., Huang, Q., Li, X.-Y., & Wu, D. (2013). Smoothing the energy consumption: Peak demand reduction in smart grid. In Infocom, 2013 proceedings ieee (pp. 1133–1141).
Urgaonkar, R., Urgaonkar, B., Neely, M. J., & Sivasubramaniam, A. (2011). Optimal power cost management using stored energy in data centers. In Proceedings of
the acm sigmetrics joint international conference on measurement and modeling of computer systems (pp. 221–232).
U.S. Energy Information Administration. (1977). Frequently asked questions. Retrieved from https://www.eia.gov/tools/faqs/faq.php?id=97&t=3
Yoon, J. H., Bladick, R., & Novoselac, A. (2014). Demand response for residential buildings based on dynamic price of electricity. Energy and Buildings, 80, 531–541.
Zeng, B., & Zhao, L. (2013). Solving two-stage robust optimization problems using a column-and-constraint generation method. Operations Research Letters, 41(5), 457–461.
Zhou, K., Pan, J., & Cai, L. (2014). Optimal combined heat and power system scheduling in smart grid. In Infocom, 2014 proceedings ieee (pp. 2831–2839).
論文全文使用權限
  • 同意授權校內瀏覽/列印電子全文服務,於2020-06-29起公開。


  • 如您有疑問,請聯絡圖書館
    聯絡電話:(06)2757575#65773
    聯絡E-mail:etds@email.ncku.edu.tw