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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 胡明哲(Ming-Che Hu) | |
dc.contributor.author | Zih-Hao Li | en |
dc.contributor.author | 李子豪 | zh_TW |
dc.date.accessioned | 2021-06-17T08:19:51Z | - |
dc.date.available | 2020-08-20 | |
dc.date.copyright | 2019-08-20 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-08-13 | |
dc.identifier.citation | Aalami, H., Moghaddam, M. P., & Yousefi, G. J. A. E. (2010). Demand response modeling considering interruptible/curtailable loads and capacity market programs. 87(1), 243-250.
Abrate, G., Bompard, E., Napoli, R., & Wan, B. (2006). Multi-agent models for consumer choice and retailer strategies in the competitive electricity market. Retrieved from Barbarosoǧlu, G., & Arda, Y. J. J. o. t. o. r. s. (2004). A two-stage stochastic programming framework for transportation planning in disaster response. 55(1), 43-53. Birge, J. R., & Louveaux, F. (2011). Introduction to stochastic programming: Springer Science & Business Media. Cappers, P., Goldman, C., & Kathan, D. J. E. (2010). Demand response in US electricity markets: Empirical evidence. 35(4), 1526-1535. Dept. of Household Registration, M. O. I. (2019). 中華民國 內政部戶政司 全球資訊網-人口統計資料. Retrieved from https://www.ris.gov.tw/app/portal/346 Green, R. J., & Newbery, D. M. J. J. o. p. e. (1992). Competition in the British electricity spot market. 100(5), 929-953. Hobbs, B. F., Metzler, C. B., & Pang, J.-S. J. I. t. o. p. s. (2000). Strategic gaming analysis for electric power systems: An MPEC approach. 15(2), 638-645. Houthakker, H. S. J. T. E. J. (1951). Electricity tariffs in theory and practice. 61(241), 1-25. Huang, C.-H. (2019). 用數據看台灣-台灣及時用電資訊. Retrieved from https://www.taiwanstat.com/realtime/power/ Huang, G., & Loucks, D. P. J. C. E. S. (2000). An inexact two-stage stochastic programming model for water resources management under uncertainty. 17(2), 95-118. Mahmoudi-Kohan, N., Moghaddam, M. P., & Sheikh-El-Eslami, M. J. E. P. S. R. (2010). An annual framework for clustering-based pricing for an electricity retailer. 80(9), 1042-1048. Marcotte, P. J. M. p. (1986). Network design problem with congestion effects: A case of bilevel programming. 34(2), 142-162. Mulvey, J. M., & Vladimirou, H. J. M. S. (1992). Stochastic network programming for financial planning problems. 38(11), 1642-1664. Pieper, H. (2001). Algorithms for mathematical programs with equilibrium constraints with applications to deregulated electricity markets: Stanford University Stanford, Calif. Raghunathan, A. U., Biegler, L. T. J. C., & engineering, c. (2003). Mathematical programs with equilibrium constraints (MPECs) in process engineering. 27(10), 1381-1392. Rudkevich, A., & Duckworth, M. J. T. E. J. (1998). Strategic bidding in a deregulated generation market: implications for electricity prices, asset valuation and regulatory response. 11(1), 73-83. Siddiqui, S., Gabriel, S. A. J. N., & Economics, S. (2013). An SOS1-based approach for solving MPECs with a natural gas market application. 13(2), 205-227. Torriti, J., Hassan, M. G., & Leach, M. J. E. (2010). Demand response experience in Europe: Policies, programmes and implementation. 35(4), 1575-1583. Xie, Y., Huang, G., Li, W., Li, J., & Li, Y. J. J. o. e. m. (2013). An inexact two-stage stochastic programming model for water resources management in Nansihu Lake Basin, China. 127, 188-205. Zhou, L., Liao, Z., Wang, J., Jiang, B., & Yang, Y. J. A. e. (2014). MPEC strategies for efficient and stable scheduling of hydrogen pipeline network operation. 119, 296-305. 政府網站資料開放宣告. (2018). 電價成本-經營資訊-資訊接露-台灣電力股份有限公司. 國家發展委員會. (2018). 台灣電力公司_各縣市住宅、服務業及機關用電統計資料. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74098 | - |
dc.description.abstract | 近年來,為了讓人民能自由選擇電力來源以及減輕台灣在用電高峰時發電機組的負載,政府開始推動電力市場自由化,希望更多公司提供新的電力來源。最近,需量反應也被認為是提供電力的新方式,因此需量反應將會是本研究主要的研究目標。然而,電力市場自由化後,成效還不顯著可歸咎於鮮少有對於未來台灣電力市場中競爭的分析及模擬,以至於潛在參與者對於市場的不確定性感到不安。因此,本研究提出將電力自由化後的台灣電力市場模擬成mathematical program with equilibrium constraints(MPEC) 問題。模式中包含利用stochastic programming以及Nash-Cournot equilibrium找出最佳的電力抑低量以及台電的需量反應獎勵金額。要解MPEC問題不是很容易,因此本研究使用GAMS中nlpec的solver,將MPEC reformulation,再找出最佳解。目前在台灣尚未有學者將台灣電力市場模擬成MPEC,而我們認為此模式可以精確的模擬台灣電力市場競爭狀況,並給政府或是想進入市場的玩家有擬訂政策或是策略的依據。 | zh_TW |
dc.description.abstract | In recent years, in order to allow people to freely choose power sources and reduce the load on generators during peak hours in Taiwan, the government has begun to promote the liberalization of the electricity market, hoping that more companies will provide new sources of electricity. Besides, the demand response has also been considered as a new way to provide electricity, so the demand response will be the main research target of this study. However, after the liberalization of the electricity market, the results are not significant, which can attribute to the lack of analysis and simulation of competition in the future Taiwan electricity market and the potential players are concerned about the uncertainty of the market. Therefore, this study proposes to simulate the Taiwanese electricity market after power liberalization as a mathematical program with equilibrium constraints (MPEC). The model includes the use of stochastic programming and Nash-Cournot equilibrium to find the optimal amount of power reduction and the amount of demand response rewards for Taipower. To solve the MPEC problem is not very easy, so this study uses nlpec solver in GAMS, reformulate this MPEC problem, and then find the best solution. At present, no scholars in Taiwan have simulated the Taiwan power market as MPEC, and we believe that this model can accurately simulate the competition in the Taiwan power market and provide a basis for policy or strategy for the government or players who want to enter the market. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T08:19:51Z (GMT). No. of bitstreams: 1 ntu-108-R06622023-1.pdf: 1169462 bytes, checksum: 2d5b25765f29055783cc4b8702a07130 (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 摘要 I
Abstract III List of Contents V List of Figures VII Chapter 1 Introduction 1 Chapter 2 Literature Review 5 2.1 Electricity Market 5 2.2 Mathematical Program with Equilibrium Constraints 6 2.3 Demand response 8 Chapter 3 Methodology 10 3.1 Mathematical Program with Equilibrium Constraints 10 3.2 Two Stage Stochastic Programming 11 3.3 Nash-Cournot Model 13 3.4 Karush–Kuhn–Tucker Conditions 17 Chapter 4 The Model and Case Study 20 4.1 Model Description 20 4.2 Model Assumption 21 4.3 The Model – formulation 23 4.4 The Model – 2(Reformulation) 28 4.5 Case study 33 Chapter 5 Result and Discussion 35 5.1 Cost of Buying Electricity and Electricity Demand Fixed 35 5.1.1 Electricity Generation Cost Adjustment 35 5.1.2 Generation Capacity Adjustment 36 5.1.3 Relation between demand response effect on environment and money paid by Taipower Company 37 5.2 Electricity Demand Varies with Time 38 5.3 Buying Electricity Cost Varies with Time 39 Chapter 6 Conclusion 41 Reference 42 | |
dc.language.iso | en | |
dc.title | MPEC模式於電力市場需量反應之分析 | zh_TW |
dc.title | MPEC Analysis of Demand Response in Electricity Markets | en |
dc.type | Thesis | |
dc.date.schoolyear | 107-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 余化龍(Hwa-Lung Yu),溫在弘(Tsai-Hung Wen),陳聿宏(Yu-Hong Chen),蔡孟伸(Meng-Shen Tsai) | |
dc.subject.keyword | mathematical program with equilibrium constraints,Nash-Cournot equilibrium,需量反應,電力自由化,最佳化策略, | zh_TW |
dc.subject.keyword | mathematical program with equilibrium constraints,Nash-Cournot equilibrium,demand response,liberalization of electricity market,strategy optimization, | en |
dc.relation.page | 43 | |
dc.identifier.doi | 10.6342/NTU201903289 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2019-08-14 | |
dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
dc.contributor.author-dept | 生物環境系統工程學研究所 | zh_TW |
顯示於系所單位: | 生物環境系統工程學系 |
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