請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/83932完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 洪一薰(I-Hsuan Hong) | |
| dc.contributor.author | Yu-Hsuan Ho | en |
| dc.contributor.author | 何郁暄 | zh_TW |
| dc.date.accessioned | 2023-03-19T21:24:04Z | - |
| dc.date.copyright | 2022-07-07 | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-06-30 | |
| dc.identifier.citation | Abdi, H. (2007). The eigen-decomposition: Eigenvalues and eigenvectors. Encyclopedia of measurement and statistics, 304-308. Albadi, M. H., & El-Saadany, E. F. (2008). A summary of demand response in electricity markets. Electric power systems research, 78(11), 1989-1996. Alves, M. J., & Antunes, C. H. (2018). A semivectorial bilevel programming approach to optimize electricity dynamic time-of-use retail pricing. Computers & Operations Research, 92, 130-144. Bureau of Energy. (2018). Energy White Paper. Retrieved from https://energywhitepaper.tw/#/ Capgemini, C. (2012). Enerdata, 2008. Demand Response-a decisive breakthrough for Europe. Study report. Carreiro, A. M., Jorge, H. M., & Antunes, C. H. (2017). Energy management systems aggregators: A literature survey. Renewable and Sustainable Energy Reviews, 73, 1160–1172. Caves, D. W., Glyer, J. D., Electric Power Research Institute., & Christensen Associates. (1992). Designing an integrated menu of electric service options: Modeling customer demand for priority service using C-VALU, the Niagara Mohawk application: final report. Palo Alto, Calif: Prepared for Electric Power Research Institute. Chen, J., & Zhu, Q. (2017). A stackelberg game approach for two-level distributed energy management in smart grids. IEEE Transactions on Smart Grid, 9(6), 6554–6565. Di Somma, M., Graditi, G., & Siano, P. (2018). Optimal bidding strategy for a der aggregator in the day-ahead market in the presence of demand flexibility. IEEE Transactions on Industrial Electronics, 66(2), 1509–1519. Dorn, W. S. (1960). Duality in quadratic programming. Quarterly of Applied Mathematics, 18(2), 155–162. Executive Yuan, R.O.C. (2019). Amendments to the Electricity Act. Retrieved from https://english.ey.gov.tw/News3/9E5540D592A5FECD/216f6992-1527-4258-8be0-3fe3d21a6e6a Fahrioglu, M., & Alvarado, F. L. (1999, January). Designing cost effective demand management contracts using game theory. In IEEE Power Engineering Society. 1999 Winter Meeting (Cat. No. 99CH36233) (Vol. 1, pp. 427-432). IEEE. Forti, M. (1994). On global asymptotic stability of a class of nonlinear systems arising in neural network theory. Journal of differential equations, 113(1), 246-264. Fortuny-Amat, J., & McCarl, B. (1981). A representation and economic interpretation of a two-level programming problem. Journal of the Operational Research Society, 32(9), 783–792. Hobbs, B. F., Metzler, C. B., & Pang, J.-S. (2000). Strategic gaming analysis for electric power systems: An MPEC approach. IEEE Transactions on Power Systems, 15(2), 638–645. Horn, R. A., & Johnson, C. R. (2012). Matrix analysis. Cambridge university press. Horst, R., & Pardalos, P. M. (2013). Handbook of global optimization (Vol. 2). Springer Science & Business Media. IEA. (2021). The IEA at COP26. Retrieved from https://www.iea.org/commentaries/the-iea-at-cop26 Iso, C. (2013). Demand Response and Energy Efficency Roadmap: Maximizing preferred resources. Jordehi, A. R. (2019). Optimisation of demand response in electric power systems, a review. Renewable and sustainable energy reviews, 103, 308-319. Kenward, A., & Raja, U. (2014). Blackout: Extreme weather climate change and power outages. Climate central, 10, 1-23. Lau, L. J. (1978). Testing and imposing monoticity, convexity and quasi-convexity constraints. Production economics: A dual approach to theory and applications, 1, 409-453. Li, J., Ma, G., Li, T., Chen, W., & Gu, Y. (2019). A Stackelberg game approach for demand response management of multi-microgrids with overlapping sales areas. Science China Information Sciences, 62(11), 1-13. Lu, Q., Lu?, S., & Leng, Y. (2019). A nash-stackelberg game approach in regional energy market considering users’integrated demand response. Energy, 175, 456–470. Mahmoudi, N., Saha, T. K., & Eghbal, M. (2014). Modelling demand response aggregator behavior in wind power offering strategies. Applied Energy, 133, 347–355. Mahmoudi, N., Heydarian-Forushani, E., Shafie-khah, M., Saha, T. K., Golshan, M., & Siano, P. (2017). A bottom-up approach for demand response aggregators’ participation in electricity markets. Electric Power Systems Research, 143, 121–129. Mohsenian-Rad, A. H., Wong, V. W., Jatskevich, J., Schober, R., & Leon-Garcia, A. (2010). 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. Narimani, A., Nourbakhsh, G., Ledwich, G., & Walker, G. (2018). Optimum electricity purchase scheduling for aggregator storage in a reliability framework for rural distribution networks. International Journal of Electrical Power & Energy Systems, 94, 363–373. National Development Council. (2022). Real-time information on the power generation of each unit of Taipower system (including purchased power). Retrieved from https://data.nat.gov.tw/en/datasets/8931 Niromandfam, A., Yazdankhah, A. S., & Kazemzadeh, R. (2020). Modeling demand response based on utility function considering wind profit maximization in the day ahead market. Journal of Cleaner Production, 251, 119317. Okur, ?., Voulis, N., Heijnen, P., & Lukszo, Z. (2019). Aggregator-mediated demand response: Minimizing imbalances caused by uncertainty of solar generation. Applied Energy, 247, 426–437. Queiroz, M., J?dice, J., & Humes Jr, C. (2004). The symmetric eigenvalue complementarity problem. Mathematics of Computation, 73(248), 1849-1863. Panteli, M., & Mancarella, P. (2015). Influence of extreme weather and climate change on the resilience of power systems: Impacts and possible mitigation strategies. Electric Power Systems Research, 127, 259-270. Parvania, M., Fotuhi-Firuzabad, M., & Shahidehpour, M. (2013). Optimal demand response aggregation in wholesale electricity markets. IEEE Transactions on Smart Grid, 4(4), 1957–1965. Rashidizadeh-Kermani, H., Vahedipour-Dahraie, M., Shafie-khah, M., & Catal?o, J. P. (2019). Stochastic programming model for scheduling demand response aggregators considering uncertain market prices and demands. International Journal of Electrical Power & Energy Systems, 113, 528-538. Riaz, S., Marzooghi, H., Verbi?, G., Chapman, A. C., & Hill, D. J. (2017). Generic demand model considering the impact of prosumers for future grid scenario analysis. IEEE Transactions on Smart Grid, 10(1), 819–829. Samadi, P., Mohsenian-Rad, H., Schober, R., & Wong, V. W. (2012). Advanced demand side management for the future smart grid using mechanism design. IEEE Transactions on Smart Grid, 3(3), 1170-1180. Shakrina, Y., & Margossian, H. (2021). A Stackelberg game?inspired model of real?time economic dispatch with demand response. International Transactions on Electrical Energy Systems, 31(11), e13076. Srinivasan, D., Rajgarhia, S., Radhakrishnan, B. M., Sharma, A., & Khincha, H. (2017). Game-theory based dynamic pricing strategies for demand side management in smart grids. Energy, 126, 132–143. Taipower. (2021b). Understanding Tariffs. Retrieved from https://www.taipower.com.tw/en/page.aspx?mid=4499&cid=2888&cchk=eb122fec-3d38-495f-b848-2ad5a5823dd5 Taipower. (2021c). Demand-Based Bidding Platform. Retrieved from https://www.taipower.com.tw/en/page.aspx?mid=4483 Torriti, J., Hassan, M. G., & Leach, M. (2010). Demand response experience in Europe: Policies, programmes and implementation. Energy, 35(4), 1575-1583. United Nations. (2021). COP26: Together for our planet. Retrieved from https://www.un.org/en/climatechange/cop26 Wang, K., Ouyang, Z., Krishnan, R., Shu, L., & He, L. (2015). A game theory-based energy management system using price elasticity for smart grids. IEEE Transactions on Industrial Informatics, 11(6), 1607–1616. Wang, Y. P. (2020). The application of bilevel programming model on demand response with aggregators (Unpublished master’s thesis). National Taiwan University, Taipei. Wood, A. J., & Bruce, F. (1996). Wollenberg. “Power Generation, Operation and Control”. John Wily & Sons, Inc, USA. Young, W. H. (1909). IX.—On the Conditions for the Reversibility of the Order of Partial Differentiation. Proceedings of the Royal Society of Edinburgh, 29, 136-164. Zhou, K., & Yang, S. (2018). Smart energy management. Comprehensive Energy Systems, 5, 423-456. Zugno, M., Morales, J. M., Pinson, P., & Madsen, H. (2013). A bilevel model for electricity retailers' participation in a demand response market environment. Energy Economics, 36, 182-197. 台灣電力公司. (2021a). AMI智慧電表布建資訊網. Retrieved from https://ami-meter.taipower.com.tw/views/home.php 台灣電力公司. (2021d). 購入電力概況. Retrieved from https://www.taipower.com.tw/tc/page.aspx?mid=207&cid=166&cchk=e3adb9f4-f40a-4971-ab11-e90a75e69863 台灣電力公司. (2022). 再生能源發電概況. Retrieved from https://www.taipower.com.tw/tc/page.aspx?mid=204 台灣安奈諾克股份有限公司. (2017). TPC DR program 2年合約調度200mw/小時. Retrieved from http://www.tsa.org.tw/file/15652_170503093923.pdf | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/83932 | - |
| dc.description.abstract | 近年來，由於能源轉型及氣候變遷影響，造成電力供需失衡。為了解決電力短缺的瓶頸，政府推動電力市場自由化並將民間電力資源引入電力市場。其中，以電力公司所推動的用戶群代表制度與需量反應管理措施為供給與需求端平衡下，供電穩定的解方。因此，本研究將王怡萍 (2020) 針對用戶群代表制度所提出的雙層最佳化模型，利用Karush-Kuhn-Tucker (KKT) 條件及強對偶理論 (Strong duality theorem) 整理為單層二次規劃模型後，收集台灣電力公司再生能源發電量等相關數據資料，並針對特定參數的變動進行抑低電量結果的敏感度分析，探討用戶群代表制度於在兩種需量反應市場情況下的成效。 | zh_TW |
| dc.description.abstract | Facing energy transition and global climate change, improving grid reliability has become an important issue nowadays. In order to minimize the imbalance caused by the uncertain generation of renewable energy sources and extreme climate, developed countries are devoting efforts to electricity market deregulation. Demand Response programs (DR) and aggregators are seen as contributing solutions to this trend. Our research aims to propose the bilevel programming models to evaluate the effectiveness of aggregators in a DR program. We convert the bilevel programming models into single-level quadratic programming models with Karush-Kuhn-Tucker (KKT) conditions, linearize techniques, and strong duality theorem. As a case study, we implement realistic data in Taiwan’ s electricity market to demonstrate the robustness of our models. We find that the aggregators play a crucial role in a DR program. | en |
| dc.description.provenance | Made available in DSpace on 2023-03-19T21:24:04Z (GMT). No. of bitstreams: 1 U0001-2806202201342200.pdf: 2809066 bytes, checksum: dee95c5a658e4deec86b729a8cf63303 (MD5) Previous issue date: 2022 | en |
| dc.description.tableofcontents | Table of Contents Acknowledgements i 摘要 ii Abstract iii Table of Contents iv List of Figures vi List of Tables vii Chapter 1 Introduction 1 Chapter 2 Literature Review 4 2.1 Research on DR with Stackelberg Game Model 4 2.2 Research on Application of Aggregators 5 Chapter 3 Bilevel Model for Demand Response 7 3.1 Problem Description and Notation 7 3.2 Consumer Utility Function 10 3.3 Demand Response Market 11 3.3.1 Model for DSO (Leader) in the DR Market 11 3.3.2 Model for Aggregator (Follower) in the DR Market 12 3.4 Demand Response Market with RES 13 3.4.1 Model for DSO (Leader) in the DR Market with RES 14 3.4.2 Model for Renewable Energy Aggregators (Follower) in the DR Market with RES 15 Chapter 4 Solution Approach 17 4.1 Model Reformulation 17 4.1.1 Reformulation of Bilevel Model in the DR Market 17 4.1.2 Reformulation of Bilevel Model in the DR Market with RES 20 4.2 Linearization of KKT Conditions 23 4.3 Strong Duality Theorem 24 Chapter 5 Case Study 26 5.1 Parameter Settings 26 5.2 Study Results of the DR Market 30 5.3 Study Results of the DR Market with RES 32 5.3.1 The DR Market with RES in Summer 33 5.3.2 The DR Market with RES in Winter 36 5.3.3 Comparison of Seasons 39 5.4 Sensitivity Analysis 40 Chapter 6 Conclusion and Future Research 43 References 45 List of Figures Fig. 1. DR Event Sequence of Interaction 8 Fig. 2. The Framework of the DR Market with RES 14 Fig. 3. Price and Reward in the DR Market 31 Fig. 4. Total Load Curtailment in the DR Market 32 Fig. 5. Price and Reward in 2021/06/21 DR Market with RES 34 Fig. 6. Total Load Curtailment in 2021/06/21 DR Market with RES 35 Fig. 7. Profit in 2021/06/21 DR Market with RES 35 Fig. 8. The Capacity of Energy Storage in 2021/06/21 DR Market with RES 36 Fig. 9. Power Flow in 2021/06/21 DR Market with RES 36 Fig. 10. Price and Reward in 2021/12/21 DR Market with RES 37 Fig. 11. Total Load Curtailment in 2021/12/21 DR Market with RES 38 Fig. 12. Profit in 2021/12/21 DR Market with RES 38 Fig. 13. The Capacity of Energy Storage in 2021/12/21 DR Market with RES 39 Fig. 14. Power Flow in 2021/12/21 DR Market with RES 39 Fig. 15. Relationship Between Z and Aggregator’s DR Quantity in 2021/06/21 42 Fig. 16. Relationship Between Z and Aggregator’s DR Quantity in 2021/12/21 42 List of Tables Table. 1. Setting of Parameters Used in the Case Study 28 Table. 2. Block Definitions for Emergency Generator – The DR Market 28 Table. 3. Period for Emergency Generator – The DR Market with RES in Summer 28 Table. 4. Period for Emergency Generator – The DR Market with RES in Winter 29 Table. 5. Emergency Generation Cost at time h – The DR Market 29 Table. 6. Emergency Generation Cost at time h – The DR Market with RES in Summer 29 Table. 7. Emergency Generation Cost at time h – The DR Market with RES in Winter 30 Table. 8. Profit and Unit Price for Different Decision Makers in the DR Market 32 Table. 9. Profit in the DR Market with RES 39 Table. 10. Average Unit Price / Financial Incentive in the DR Market with RES 40 Table. 11. Sensitivity Analysis Information Table in 2021/06/21 41 Table. 12. Sensitivity Analysis Information Table in 2021/12/21 42 | |
| dc.language.iso | en | |
| dc.subject | 電力市場自由化 | zh_TW |
| dc.subject | 雙層模型 | zh_TW |
| dc.subject | 需量反應 | zh_TW |
| dc.subject | 用戶群代表 | zh_TW |
| dc.subject | Electricity Market Deregulation | en |
| dc.subject | Aggregator | en |
| dc.subject | Bilevel Programming Model | en |
| dc.subject | Demand Response | en |
| dc.title | 用戶群代表雙層最佳化模型於需量反應市場成效探討 | zh_TW |
| dc.title | A Bilevel Programming Model with Aggregators in a Demand Response Market | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 110-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 吳政鴻(Cheng-Hung Wu),黃奎隆(Kwei-Long Huang),藍俊宏(Jackey Blue) | |
| dc.subject.keyword | 電力市場自由化,需量反應,用戶群代表,雙層模型, | zh_TW |
| dc.subject.keyword | Electricity Market Deregulation,Demand Response,Aggregator,Bilevel Programming Model, | en |
| dc.relation.page | 50 | |
| dc.identifier.doi | 10.6342/NTU202201167 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2022-07-03 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 工業工程學研究所 | zh_TW |
| 顯示於系所單位： | 工業工程學研究所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| U0001-2806202201342200.pdf 未授權公開取用 | 2.74 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。