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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 劉志文(Liu, Chih-Wen) | |
| dc.contributor.author | Israjuddin | en |
| dc.contributor.author | 尹斯拉 | zh_TW |
| dc.date.accessioned | 2021-06-17T09:08:17Z | - |
| dc.date.available | 2019-12-02 | |
| dc.date.copyright | 2019-12-02 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-11-18 | |
| dc.identifier.citation | [1] 'Open Smart Grid,' Trilliant Holdings Inc., [Online]. Available: https://trilliant.com/home/smart-grid/. [Accessed 30 October 2019].
[2] International Energy Agency, World Energy Outlook 2018, Paris: IEA Publications, 2018. [3] REN21, Renewables 2018 Global Status Report, Paris: REN21 Secretariat, 2018. [4] P. Tielens and D. Van Hertem, 'The relevance of inertia in power systems,' Renewable and Sustainable Energy Reviews, vol. 55, pp. 999-1009, March 2016. [5] U. Tamrakar, D. Shrestha, M. Maharjan, B. Bhattarai, T. Hansen and R. Tonkoski, 'Virtual Inertia: Current Trends and Future Directions,' Applied Sciences, vol. 7, no. 7, p. 654, June 2017. [6] R. Ierna, J. Zhu, H. Urdal, A. J. Roscoe, M. Yu, A. Dysko and C. D. Booth, 'Effects of swing equation-based inertial response (SEBIR) control on penetration limits of non-synchronous generation in the GB power system,' in International Conference on Renewable Power Generation (RPG 2015), Beijing, China, 2015. [7] G. Delille, B. François and G. Malarange, 'Dynamic frequency control support by energy storage to reduce the impact of wind and solar generation on isolated power system's inertia,' IEEE Transactions on Sustainable Energy, vol. 3, no. 4, pp. 931-939, October 2012. [8] B. K. Poolla, D. Groß, T. Borsche, S. Bolognani and F. Dörfler, 'Virtual Inertia Placement in Electric Power Grids,' in Energy Markets and Responsive Grids, S. Meyn, Ed., New York, NY, Springer, 2018, pp. 281-305. [9] H.-P. Beck and R. Hesse, 'Virtual synchronous machine,' in 2007 9th International Conference on Electrical Power Quality and Utilisation, Barcelona, Spain, 2007. [10] Y. Chen, R. Hesse, D. Turschner and H.-P. Beck, 'Improving the grid power quality using virtual synchronous machines,' in 2011 International Conference on Power Engineering, Energy and Electrical Drives, Malaga, Spain, 2011. [11] J. Driesen and K. Visscher, 'Virtual synchronous generators,' in 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century, Pittsburgh, PA, 2008. [12] K. Sakimoto, Y. Miura and T. Ise, 'Stabilization of a power system with a distributed generator by a Virtual Synchronous Generator function,' in 8th International Conference on Power Electronics - ECCE Asia, Jeju, South Korea, 2011. [13] Y. Hirase, K. Abe, K. Sugimoto and Y. Shindo, 'A grid-connected inverter with virtual synchronous generator model of algebraic type,' Electrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi), vol. 184, no. 4, pp. 10-21, 2013. [14] M. P. Van Wesenbeeck, S. W. De Haan, P. Varela and K. Visscher, 'Grid tied converter with virtual kinetic storage,' in 2009 IEEE Bucharest PowerTech, Bucharest, Romania, 2009. [15] J. Alipoor, 'Studies on Stabilization of Power System With Distributed Generations Using Virtual Synchronous Generator' Doctoral Dissertation, Osaka University, Osaka, Japan, 2015. [16] M. Rezkalla, M. Pertl and M. Marinelli, 'Electric power system inertia: requirements, challenges and solutions,' Electrical Engineering, vol. 100, no. 4, p. 2677–2693, December 2018. [17] Q.-C. Zhong and G. Weiss, 'Synchronverters: Inverters That Mimic Synchronous Generators,' IEEE Transactions on Industrial Electronics, vol. 58, no. 4, pp. 1259-1267, April 2011. [18] A. Ulbig, T. S. Borsche and G. Andersson, 'Impact of low rotational inertia on power system stability and operation,' IFAC Proceedings Volumes, vol. 47, no. 3, pp. 7290-7297, August 2014. [19] P. S. Kundur et al., 'Definition and classification of power system stability IEEE/CIGRE joint task force on stability terms and definitions,' IEEE Transactions on Power Systems, vol. 19, no. 3, pp. 1387-1401, August 2004. [20] P. S. Kundur, N. J. Balu and M. G. Lauby, Power System Stability and Control, New York: McGraw-Hill, Inc., 1994. [21] R. A. Serway and J. . W. Jewett, Jr., Physics for Scientists and Engineers with Modern Physics, Ninth Edition, Boston: Brooks/Cole Publishing Co., 2014. [22] M. M. N. Rezkalla, 'Emulated Inertia and Frequency Support from Fast Acting Reserves' Ph.D. dissertation, Technical University of Denmark, Kongens Lyngby, Denmark, 2018. [23] H. Bevrani, T. Ise and Y. Miura, 'Virtual synchronous generators: A survey and new perspectives,' International Journal of Electrical Power & Energy Systems, vol. 54, pp. 244-254, 2014. [24] F. S. Rahman, T. Kerdphol, M. Watanabe and Y. Mitani, 'A study on the placement of virtual synchronous generator in a two-area system,' in 2018 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia), Singapore, Singapore, 2018. [25] B. K. Poolla, S. Bolognani and F. Dörfler, 'Placing rotational inertia in power grids,' in 2016 American Control Conference (ACC), Boston, MA, 2016. [26] 'Evolutionary Computation,' Wikimedia Foundation, [Online]. Available: http://en.wikipedia.org/wiki/Evolutionary_computation. [Accessed 31 October 2019]. [27] I. Erlich, G. K. Venayagamoorthy and N. Worawat, 'A mean-variance optimization algorithm,' in IEEE Congress on Evolutionary Computation, Barcelona, Spain, 2010. [28] J. C. Cepeda, J. L. Rueda, I. Erlich, A. W. Korai and F. M. Gonzalez-Longatt, 'Mean–Variance Mapping Optimization Algorithm for Power System Applications in DIgSILENT PowerFactory,' in PowerFactory Applications for Power System Analysis, Cham, Springer International Publishing, 2014, pp. 267-295. [29] S. B. Karanki, D. Xu, B. Venkatesh and B. N. Singh, 'Optimal location of battery energy storage systems in power distribution network for integrating renewable energy sources,' in 2013 IEEE Energy Conversion Congress and Exposition, Denver, CO, 2013. [30] M. Yang, C. Chen, B. Que, Z. Zhou and Q. Yang, 'Optimal placement and configuration of hybrid energy storage system in power distribution networks with distributed photovoltaic sources,' in 2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2), Beijing, China, 2018. [31] X. Huang, G. Zhang and L. Xiao, 'Optimal location of SMES for improving power system voltage stability,' IEEE Transactions on Applied Superconductivity, vol. 20, no. 3, pp. 1316-1319, June 2010. [32] W. Nakawiro, I. Erlich and J. L. Rueda, 'A novel optimization algorithm for optimal reactive power dispatch: A comparative study,' in 2011 4th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), Weihai, Shandong, China, 2011. [33] J. L. Rueda, J. C. Cepeda and I. Erlich, 'Estimation of location and coordinated tuning of PSS based on mean-variance mapping optimization,' in 2012 IEEE Power and Energy Society General Meeting, San Diego, CA, 2012. [34] J. L. Rueda Torres, A. M. Theologi, M. Ndreko, I. Erlich and P. Palensky, 'Metaheuristic approach for online optimal reactive power management in near-shore wind power plants,' in 2017 IEEE PES Innovative Smart Grid Technologies Conference - Latin America (ISGT Latin America), Quito, Ecuador, 2017. [35] R. M. Pringles and J. L. Rueda, 'Optimal transmission expansion planning using mean-variance mapping optimization,' in 2012 Sixth IEEE/PES Transmission and Distribution: Latin America Conference and Exposition (T&D-LA), Montevideo, Uruguay, 2012. [36] A. Gbadamosi, J. L. Rueda, D. Wang and P. Palensky, 'Application of mean-variance mapping optimization for parameter identification in real-time digital simulation,' in 017 Federated Conference on Computer Science and Information Systems (FedCSIS), Prague, Czech Republic, 2017. [37] J. L. Rueda and I. Erlich, 'Evaluation of the mean-variance mapping optimization for solving multimodal problems,' in 2013 IEEE Symposium on Swarm Intelligence (SIS), Singapore, Singapore, 2013. [38] J. L. Rueda and I. Erlich, 'Optimal dispatch of reactive power sources by using MVMOs optimization,' in 2013 IEEE Computational Intelligence Applications in Smart Grid (CIASG), Singapore, Singapore, 2013. [39] S. Wildenhues, 'Optimal Allocation and Sizing of STATCOM for Power System Dynamic Performance Enhancement' M.S. Thesis, University of Duisburg, Essen, Germany, 2013. [40] J. L. Rueda and F. Gonzalez-Longatt, 'Application of swarm mean-variance mapping optimization on location and tuning damping controllers,' in 2015 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA), Bangkok, Thailand, 2015. [41] DIgSILENT GmbH, 'PowerFactory 2019 - User manual,' DIgSILENT GmbH, Gomaringen, Germany, 2019. [42] DIgSILENT GmbH, 'PowerFactory 2019 - Technical Reference Documentation Synchronous Machine,' DIgSILENT GmbH, Gomaringen, Germany, 2019. [43] DIgSILENT GmbH, 'PowerFactory 2018 - Template documentation battery energy storing system template,' DIgSILENT GmbH, Gomaringen, Germany, 2018. [44] Y. Zhu, C. Liu, B. Wang and K. Sun, 'Damping control for a target oscillation mode using battery energy storage,' Journal of Modern Power Systems and Clean Energy, vol. 6, no. 4, p. 833–845, July 2018. [45] T. Kerdphol, F. S. Rahman, M. Watanabe, Y. Mitani, D. Turschner and H.-P. Beck, 'Enhanced virtual inertia control based on derivative technique to emulate simultaneous inertia and damping properties for microgrid frequency regulation,' IEEE Access, vol. 7, pp. 14422-14433, 2019. [46] S. P. Teeuwsen, I. Erlich and M. A. El-Sharkawi, 'Neural network based classification method for small-signal stability assessment,' in 2003 IEEE Bologna Power Tech Conference Proceedings, Bologna, Italy, 2003. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74824 | - |
| dc.description.abstract | 在集中式發電中，電力主要由同步發電機產生。在具有巨大同步發電機的電力系統中，頻率由轉子頻率決定，轉子頻率本身取決於原動機功率。在發生故障或干擾的情況下，保留在大型轉子和相關設備中的動能注入電力系統以維持能量平衡。而且，轉子質量的慣性可以防止頻率的突然變化並提高電力系統的穩定性。
現代電力系統從經典的同步發電演變為更加分佈式電力電子非同步發電，大規模滲透可再生能源，如風能和光伏發電。然而，這種新一代模式沒有自然慣性和阻尼特性，這是同步電機的經典特徵。在這樣的電力系統中，系統慣性的缺乏導致對頻率穩定性的負面的影響，導致電網的弱化。 許多研究人員展示如何將儲能器和逆變器與虛擬慣性控制演算法一起使用，從而使它們被視為電網的等效同步發電機，維護並提高了系統穩定性。 本文强调使用虚擬化的惯性控制的能量存储的最优位置，並研究使用基于群的均值方差映射优化算法於低惯性电网中的频率稳定性的重要性。 | zh_TW |
| dc.description.abstract | In centralized electric power generation, electric power mainly generated by Synchronous Generators (SGs). In a power system dominated by SGs, the frequency is determined by the rotation frequency, which depends on the prime mover power. In the event of a fault or disturbance, kinetic energy is stored in a massive rotor, and related equipment is injected into the power system to maintain energy balance. Besides, the inertia of the rotating mass prevents sudden changes in frequency and increases the stability of the power system.
Modern power systems evolved from classical synchronous generation to more distributed power electronic-based non-synchronous generation, with large-scale penetration of renewable energy sources (RESs) such as photovoltaic and wind turbines. However, this new generation mode does not have natural inertia and damping properties, which is a classic feature of synchronous machines. In such a power system, the lack of system inertia causes undesirable influence to frequency stability, leading to weakening of the grid. Many researchers have shown how to use energy storages and inverters with virtual inertia control algorithms so that they are recognized as synchronous generators to power grids, maintain and improve system stability. This thesis emphasizes the importance of the optimal placement of energy storage with virtual inertia control for frequency stability in a low inertia power grid using Swarm-Based Mean-Variance Mapping Optimization (MVMOS). | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T09:08:17Z (GMT). No. of bitstreams: 1 ntu-108-R07921097-1.pdf: 6627233 bytes, checksum: c47358a7cc2a489e2dd3d2db20c38fa8 (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
Acknowledgment ii 中文摘要 iii Abstract iv Contents v List of Figures viii List of Tables xi Chapter 1 Introduction 1 1.1 Background 1 1.2 Related Work 5 1.3 Research Questions and Objectives 6 1.4 Methodology 6 1.5 Thesis Organization and Outline 7 Chapter 2 Literature Review 8 2.1 Power System Stability in Regards to Inertia 8 2.2 Synchronous Inertia 11 2.3 System inertia in future power system 13 2.4 Methods to Emulate Virtual Inertia and Damping 13 2.4.1 VSYNC’s VSG topology 16 2.4.2 The IEPE’s topology 17 2.4.3 ISE Lab’s VSG topology 19 2.4.4 Kawasaki Heavy Industries’s VSG topology 20 2.4.5 Summary of VSG Topologies 21 2.5 Where to Place Virtual Inertia and Damping 22 Chapter 3 Optimization Algorithm 23 3.1 Problem Formulation 23 3.2 Energy Storage Placement Problem 25 3.3 Mean-Variance Mapping Optimization 26 3.3.1 Initialization of MVMO Algorithm and Normalization of Variables 27 3.3.2 De-normalize Each Variable and Evaluate its Fitness 27 3.3.3 Solution Archive 28 3.3.4 Offspring Generation 29 3.4 Swarm-Based Mean-Variance Mapping Optimization 30 3.5 Implementation 34 Chapter 4 Modeling 36 4.1 Power System Model in PowerFactory 36 4.2 Virtual Synchronous Generator Model 38 4.2.1 Battery Model 38 4.2.2 Power Conversion Systems 39 4.2.3 Controller Models 40 4.2.4 VSG model in PowerFactory 41 4.3 Test Case 42 Chapter 5 Simulation Results 44 5.1 MVMOS Parameter Tuning 44 5.1.1 Initial Swarm Size (Np) 45 5.1.2 Number of Variables to be Selected for Mutation (m) 47 5.1.3 Number of Independent Evaluation (M) 48 5.1.4 Variable Selection Strategy 49 5.2 Single Scenario Optimization 51 5.2.1 Placement Results for Generator Outage Case (Case-1) 52 5.2.2 Comparison with Particle Swarm Optimization Method 54 5.2.3 Placement Results for Load Step Case (Case-2) 56 5.2.4 Placement Results for Different Number of VSG Location 60 5.2.5 Placement Results with Different Weighting Factor 63 5.3 Multi-Scenario Optimization 66 Chapter 6 Conclusions and Recommendations 68 6.1 Conclusions 68 6.2 Recommendations for further work 69 REFERENCE 70 | |
| dc.language.iso | en | |
| dc.title | 應用群體均值方差映射優化於低慣性電網具虛擬慣性儲能設備之最佳配置 | zh_TW |
| dc.title | Optimal Placement of Energy Storage with Virtual Inertia Control on a Low Inertia Power Grid using Swarm-Based Mean-Variance Mapping Optimization | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳耀銘(Chen, Yaow-Ming),黃 世杰(Huang, Shyh-Jier) | |
| dc.subject.keyword | 儲能,頻率穩定性,可再生能源,虛擬慣性控制,群均方差映射优化, | zh_TW |
| dc.subject.keyword | energy storage,frequency stability,renewable energy sources,virtual inertia control,MVMOS, | en |
| dc.relation.page | 75 | |
| dc.identifier.doi | 10.6342/NTU201904296 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2019-11-19 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| 顯示於系所單位： | 電機工程學系 | |
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