簡易檢索 / 詳目顯示

回結果列表
研究生： 王景煜
Wang, Jing-Yu
論文名稱： 用戶集成商參與需量競價之抑低負載量預測與收益評估
Load-Reduction Forecasting and Revenue Estimation for an Aggregator in Demand-bidding Program
指導教授： 楊宏澤
Yang, Hong-Tzer
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 電機工程學系
Department of Electrical Engineering
論文出版年： 2017
畢業學年度： 105
語文別： 英文
論文頁數： 82
中文關鍵詞： 需量反應 、需量競價 、抑低負載量預測
外文關鍵詞： demand response, demand-bidding, load-reduction forecasting
相關次數： 點閱：75  下載：8 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報

    • 近年由於電力需求增長，台灣發電機組之備轉容量率偏低反映供電緊急之狀況屢見不鮮。有鑑於此，台電推動需量競價措施，透過強化需求端負載管理，期望解決電力供需失衡之問題。雖然目前符合需量競價參加資格者僅限於高壓電力用戶，但近期推出聯合型需量反應，允許眾多電力用戶集結後透過用戶代表向台電申請參加。考慮未來開放低壓用戶參與需量反應之可能性，本文模擬高壓電力用戶作為集成商聯合低壓電力用戶參與需量競價之情境，並評估其效益。
    本文利用備轉容量率及氣溫資料透過類神經網路預估可能之需量競價得標價格，再由預估之得標價格分別透過類神經網路及模糊邏輯系統評估高壓、低壓用戶之可抑低負載量。文中提出藉模糊推論邏輯以模擬各個低壓用戶配合抑低需量之不確定性，並提出用戶模型之修正方法，未來若取得用戶集成商的實際運轉資料，即可以此法進行修正，進而提高可抑低需量的預測精確度。此外，亦透過最佳化演算法決定與台電簽訂之最佳抑低契約容量。
    本文採用台灣與美國德州之實際負載量測資料，並搭配高、低壓用戶與現有不同需量競價方案，進而分析用戶集成商整體營運收益。同時，本文考慮預測誤差對於不同需量競價方案下之收益比較，同時考量設備成本、年限與參與用戶之獲利，未來可做為需量反應用戶集成商實際參與需量競價時之評估方式。

    In the recent decades, as a result of the increase of demand for electricity, it is getting more and more frequently that the spinning reserve ratio of the generators in Taiwan reaches lower level which reflects the emergency of power supply. Therefore, Taiwan Power Company (TPC) expects to solve the problem through promotion of demand-bidding scheme enhancing the demand-side management. Even though the customers who can participate in the scheme are only high-voltage (HV) electricity customers, there is another new scheme allowing several customers take part in the demand response through an aggregator. Regarding the fact that it is possible for the low-voltage (LV) customers to participate in the demand-bidding scheme, this thesis simulates a HV customer serving as an aggregator to cooperate with LV customers on the bidding scheme and assess the total benefits.
    The thesis employs neural network (NN) to forecast the clearing price of the bidding through spinning reserve rate and temperature data. Subsequently with the forecast clearing price, the load-reduction of HV and LV customers are forecasted through NN and fuzzy logic system, respectively. Fuzzy system is adopted for the forecasting of LV customer to simulate the uncertainties of load-reduction considering different situations during demand response (DR). In order to improve the forecasting accuracy when realistic data of DR is available, another procedure of correcting the customers’ model for forecasting is proposed. Afterwards, the feasible contract capacity of load-reduction signed with TPC is determined through an optimization algorithm.
    To actually assess the benefit, the realistic load data from Taiwan and Texas is used in the simulation. In the simulation, the thesis compares the total incentives for the HV customer cooperates with different types of LV customers on different bidding schemes. In the analyses of results, the total incentives for different bidding schemes considering forecasting error are compared. Another comparison is performed is the cost-benefit analysis which takes the cost and lifespan of hardware into consideration. In summary, the thesis proposes a structure that helps to evaluate the potential for an aggregator to participate in the demand-bidding program in Taiwan.

    摘要 i ABSTRACT ii 誌謝 iv Table of Contents v List of Tables vii List of Figures viii Chapter 1. INTRODUCTION 1 1.1. Background and Motivation 1 1.2. Review of Literature 2 1.3. Research Method and Contributions 4 1.4. Organization of the Thesis 6 Chapter 2. OVERVIEW OF THE DEMAND-BIDDING PROGRAM IN TAIWAN 7 2.1. Introduction 7 2.2. Overall structure 7 2.3. The Rules of Demand-bidding of Taiwan Power Company 9 Chapter 3. PROBLEM FORMULATION AND THE PROPOSED BIDDING STRATEGY OPTIMIZATION 13 3.1. Introduction 13 3.2. Forecasting Models 14 3.2.1. Forecasting of Clearing Price for Demand-Bidding 14 3.2.2. Forecasting of Load-reduction for the High-Voltage Customers 16 3.2.3. Forecasting of Load-reduction for the Low-Voltage Customers 17 3.3. Formulation of Bidding Strategy Optimization 21 3.3.1. Objective Function and Constraints of Demand-biddings 23 3.4. Cost Saving of Load-Scheduling 27 3.5. Forecasting Correction for Low-Voltage Customer 28 3.5.1. Objective Function and Constraints of the Correction Process 29 Chapter 4. SIMULATION RESULTS 30 4.1. Introduction 30 4.2. Simulation Parameters 31 4.2.1. Load Profiles of the Customers 31 4.2.2. Electricity Tariffs 37 4.3. Forecasting of Load-reduction and Incentive 39 4.3.1. Forecasting Result of Clearing Price 39 4.3.2. Scenario 1 (R-A-A) 41 4.3.3. Scenario 2 (R-A-S) 47 4.3.4. Scenario 3 (A-A-A) 50 4.3.5. Scenario 4 (A-A-S) 53 4.3.6. Summary for the simulation of the four scenarios 56 4.4. Cost Savings of Load Scheduling Considering Different Tariffs 57 4.5. Analysis of Simulation Results 58 4.5.1. Sensitivity Analysis for Forecasting Error 58 4.5.2. Cost-Benefit Analysis 60 4.6. Preference Curve Correction for LV Customers 64 Chapter 5. CONCLUSION AND FUTURE PROSPECTS 65 5.1. Conclusion 65 5.2. Future Prospects 66 REFERENCES 67

    [1] Mokhtari, G. Nourbakhsh, F. Zare, and A. Ghosh, “Overvoltage Prevention in LV Smart Grid Using Customer Resources Coordination,” Energy and Buildings, vol. 61, pp. 387–395, Jun. 2013.
    [2] Q. Li, and M. Zhou, “The Future-Oriented Grid-Smart Grid,” Journal of Computers, vol. 6, no. 1, pp. 98-105, 2011.
    [3] C. O. Bjork, and B. G. Karlsson, “Load Management Applications for Industrial Loads,” IEEE Transaction on Power Apparatus and Systems, vol. 104, no. 8, pp. 2058-2063, Aug. 1985.
    [4] J. J. Hietala, and R. E. Riebs, “Load Management at a Medium-size Utility,” IEEE Transaction on Power Systems, vol. 5, no. 2, pp. 513-519, May 1990.
    [5] A. Ashok, and R. Banerjee, “Optimal Operation of Industrial Cogeneration for Load Management,” IEEE Transaction on Power Systems, vol. 18, no. 2, pp. 931-937, vol. 18, no. 2 May 2003.
    [6] T. Logenthiran, D. Srinivasan, and T. Z. Shun, “Demand Side Management in Smart Grid Using Heuristic Optimization,” IEEE Transaction on Smart Grid, vol. 3, no. 3, pp. 1244-1252, Sep. 2012.
    [7] 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 Transaction on Smart Grid, vol. 1, no. 3, pp. 320-331, Dec. 2010.
    [8] T. Samad, E. Koch and P. Stluka, “Automated Demand Response for Smart Buildings and Microgrids: The State of the Practice and Research Challenges,” Proceedings of the IEEE, vol. 104, no. 4, pp. 726-744, Apr. 2016.
    [9] R. Deng, Z. Yang, M. Y. Chow and J. Chen, “A Survey on Demand Response in Smart Grids: Mathematical Models and Approaches,” IEEE Transaction on Industrial Informatics, vol. 11, no. 3, pp. 570-582, Jun. 2015.
    [10] B. Ram, “Tariffs and Load Management: A Post Privatization Study of the UK Electricity Supply Industry,” IEEE Transaction on Power Systems, vol. 10, no. 2, pp. 1111-1117, May 1995.
    [11] C. A. Babu, and S. Ashok, “Peak Load Management in Electrolytic Process Industries,” IEEE Transaction on Power Systems, vol. 23, no. 2, pp. 399-405, May 2008.
    [12] J. N. Sheen, C. S. Chen, and J. K. Yang, “Time-of-use Pricing for Load Management Programs in Taiwan Power Company,” IEEE Transaction on Power Systems, vol. 9, no. 1, pp. 388-396, Feb. 1994.
    [13] G. Song, Y. Zhou, W. Zhang, and A. Song, “A Multi-interface Gateway Architecture for Home Automation Networks,” IEEE Transaction on Customer Electronics, vol. 54, no. 3, pp. 1110-1113, Aug. 2008.
    [14] K. Balasubramanian and A. Cellatoglu, “Improvements in home automation strategies for designing apparatus for efficient smart home,” IEEE Transactions on Customer Electronics, vol. 54, no. 4, pp. 1681-1687, Nov. 2008.
    [15] Y. Zong, D. Kullmann, A. Thavlov, O. Gehrke, and H. W. Bindner, “Application of Model Predictive Control for Active Load Management in a Distributed Power System With High Wind Penetration,” IEEE Transaction on Smart Grid, vol. 3, no. 2, pp. 1055-1062, Jun. 2012.
    [16] D. Huang and R. Billinton, “Effects of Load Sector Demand Side Management Applications in Generating Capacity Adequacy Assessment,” IEEE Transaction on Power Systems, vol. 27, no. 1, pp. 335-343, Feb. 2012.
    [17] M. H. Albadi and E. F. El-Saadany, “A summary of demand response in electricity markets,” Electric Power Systems Research, vol. 78, Issue 11, pp. 1989-1996, 2008.
    [18] M. H. Albadi and E. F. El-Saadany, “Demand Response in Electricity Markets: An Overview,” 2007 IEEE Power Engineering Society General Meeting, Tampa, Florida, pp. 1-5.
    [19] M. Shakeri, M. Shayestegan, H. Abunima, S.M.S. Reza, M. Akhtaruzzaman, A.R.M. Alamoud, K. Sopian, and N. Amin, “An intelligent system architecture in home energy management systems (HEMS) for efficient demand response in smart grid,” Energy and Buildings, vol. 138, pp. 154-164, Mar. 2017.
    [20] H. Saboori, M. Mohammadi and R. Taghe, “Virtual Power Plant (VPP), Definition, Concept, Components and Types,” 2011 Asia-Pacific Power and Energy Engineering Conference, Wuhan, China, pp. 1-4.
    [21] A. Mnatsakanyan and S. W. Kennedy, “A Novel Demand Response Model with an Application for a Virtual Power Plant,” IEEE Transaction on Smart Grid, vol. 6, no. 1, pp. 230-237, Jan. 2015.
    [22] M. Rahimiyan and L. Baringo, “Strategic Bidding for a Virtual Power Plant in the Day-Ahead and Real-Time Markets: A Price-Taker Robust Optimization Approach,” IEEE Transaction on Power Systems, vol. 31, no. 4, pp. 2676-2687, Jul. 2016.
    [23] 台灣電力股份有限公司, 需量反應負載管理措施, online available: http://www.taipower.com.tw/content/q_service/q_service02.aspx?PType=2 [Accessed Jun. 2017.]
    [24] 台灣電力股份有限公司, 需量競價措施, online available: http://www.taipower.com.tw/UpFile/PowerSavFile/3.2%E9%9C%80%E9%87%8F%E7%AB%B6%E5%83%B9%E6%8E%AA%E6%96%BDDM_106.01.10.pdf [Accessed Jun. 2017.]
    [25] C. I. Lin, “Home Energy Management System Considering Forecasting System and Appliance Scheduling,” Tainan, Taiwan, 2012.
    [26] 台灣電力股份有限公司, 台灣詳細電價表, online available: http://www.taipower.com.tw/content/q_service/q_service02.aspx?PType=1 [Accessed Jun. 2017.]
    [27] Electric Reliability Council of Texas, “2015 Day-Ahead Market Hourly Locational Marginal Price Results Reports,” online available: http://www.ercot.com/mktinfo/dam [Accessed Apr. 2016.]
    [28] Pecan Street Inc., “Residential Load Data from Austin, Texas,” online available: https://dataport.cloud [Accessed Apr. 2017.]

    下載圖示 校內：2020-07-01公開
    校外：2022-07-01公開
    QR CODE