利用廣域同步相角量測與分散式計算技術進行智慧電網之即時電壓穩定度評估

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研究生：	劉建宏 Liu, Jian-Hong
論文名稱：	利用廣域同步相角量測與分散式計算技術進行智慧電網之即時電壓穩定度評估 Applications of Wide-Area Synchrophasor Measurements and Distributed Computation Techniques for Real-Time Voltage Stability Assessment in Smart Power Grids
指導教授：	朱家齊 Chu, Chia-Chi
口試委員:	盧展南 Chan-Nan Lu 劉志文 Chih-Wen Liu 廖聰明 Chang-Ming Liaw 鄭博泰 Po-Tai Cheng 朱家齊 Chia-Chi Chu
學位類別：	博士 Doctor
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：	2014
畢業學年度：	102
語文別：	英文
論文頁數：	132
中文關鍵詞：	修正型直接法、C級級數、電壓穩定度、耦合單埠模型、虛功響應、感應發電機、短期電壓穩定度、修正型耦合單埠模型、可傳輸容量、分散式演算法
外文關鍵詞：	Modified Direct Methods, Gram-Charlier Expansions, Voltage Stability, Coupled Single-Port Circuit, Reactive Power Response, Induction Generators, Short-Term Voltage Stability, Modified Coupled Single-Port Models, Available Transfer Capability, Distributed Algorithms
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隨著電網供電日益趨緊與大量的再生能源併入電網，電壓穩定度評估問題已成為監測與控制目前電力系統安全性的關鍵議題。基於電網的安全考量，系統操作者需要仰賴可靠的電壓穩定度評估工具，以即時分析電網的電壓安全度。傳統上，電壓穩定度評估法通常採用潮流方程式為基礎的解析法，雖然大多數的解析法已能精確地評估電網的電壓穩定度，但是其計算複雜度卻成為應用於即時環境上的阻礙，近年來，隨著廣域同步相角量測技術與分散式計算技術的進步，這些新型技術開啟了發展即時電壓穩定度評估法的新方向。因此，本論文的目標將採用廣域同步相角量測技術與分散式計算技術，發展新型的即時電壓穩定度評估法。
首先，本論文將提出一修正型耦合單埠模型，用以改善現有耦合單埠模型在即時電壓穩定度評估上的低估情形。藉由負載的即時同步量測資訊，電網的擴增Ward-type等效電路中的虛功響應可定義出現有模型與實際電網的虛功不匹配程度，修正型耦合單埠模型再以此虛功響應定義一緩和因子，此緩和因子可做為修改現有耦合單埠模型電路參數之依據，以此修改完成修正模型之建構。此外，基於提出的修正模型，我們也發展具應用性的即時電壓穩定度指標。
之後，再提出一基於同步相角量測之短期電壓穩定度指標，用以即時偵測感應發電機之短期電壓不穩定。短期電壓不穩定現象很可能使得感應發電機過度加速，以致其轉速過高而無法回到原先的穩定平衡工作點。此短期電壓穩定度指標是藉由合併感應發電機之等效模型與修正型耦合單埠模型所建構而成。
此外本文將提出一可即時評估電網可傳輸容量的疊代型分散式演算法。三種特定的分散式演算法將應用於發展此疊代型分散式演算法，分別為(i)預測與修正型近似乘數法、(ii)輔助問題原理法與(iii)交替方向乘數法。疊代型分散式演算法可實現在一分散式系統中，電網可經由結構性的切割，分割為多個不重疊的子系統以及與其連結的邊界子系統之分散式系統，再以一平行運算架構計算電網的可傳輸容量。
最後，本論文將分析大型風能注入電網所造成的機率性負載裕度變化，因此擬提出一修正型直接法，同時結合C級級數機率分析法，用以分析負載裕度的機率分佈，此修正型直接法將分兩階段開發，首先研究一分散型雙向滑動法，採用分散式計算法計算連結多部風電系統的電網潮流解，第二階段再以一修正型直接法取代分散型雙向滑動法中的潮流計算步驟，以評估電網的負載裕度，因修正型直接法可以較少N + 1維度取代舊有2N + 1維度的負載裕度計算，所以具有應用於即時計算環境之優勢。本論文提出的即時電壓穩定度評估法將測試於多個典型的IEEE測試系統中，用以驗證提出之評估法的可行性與精確性。

As the power system becomes more stressed and the penetration of renewable energies increases, voltage stability assessment (VSA) becomes a key issue for monitoring and controlling the security of modern bulk power grids. For security considerations, system operators require powerful tools to analyze voltage security of the bulk power system in real-time environments. Traditionally, the VSA is accomplished based on model-based approaches. Although various analytical approaches have been proposed along this direction, their computational complexities may impede their real-time applications. In recent years, with advances of wide-area synchrophasor measurements and distributed computation techniques, these new technologies have opened new perspectives for developing real-time tools for VSA. To this end, this dissertation aims to develop new methodologies for enhancing the real-time voltage stability by using wide-area synchrophasor measurements and distributed computation techniques.
First, based on real-time PMU measurements of individual load bus, a modified coupled single-port model will be proposed for measurement-based VSA. This model will improve underestimations of existing coupled single-port models since the reactive power response extracted from the extended Ward-type equivalent is explored to compensate the reactive power mismatch in the existing coupled single-port model. Then, a mitigation factor based on this reactive power response will be defined to provide a direction for adjusting circuit parameters of the current model, and modified models can be constructed accordingly. In addition, based on this modified coupled single-port model, several voltage stability indicators are developed for real-time VSA.
Second, the phenomenon of the short-term voltage instability of induction generators will be investigated. This phenomenon will lead to over-accelerations of induction generators such that the induction generators with the high slip may not return to the pre-fault equilibrium point. In order to identify this short-term voltage instability of induction generators in real-time manners, a synchrophasor-based short-term voltage stability indicator will be developed by incorporating induction generator equivalent models into modified coupled single-port models.
Third, multi-area Available Transfer Capability (ATC) assessments will be investigated in the distributed computation environments. Three distributed schemes, including (i) Predictor-Corrector Proximal Multiplier Method, (ii) Auxiliary Problem Principle Method, and (iii) Alternative Direction Multiplier Method, will be applied to distributed ATC assessments. System partition with non-overlapping and boundary sub-systems will be employed to a distributed system for implementing proposed iterative distributed algorithms in a distributed manner.
Finally, probabilistic load margin predictions under large-scale penetration of wind generations will be studied. Under wind speed variations, a new computational framework will be proposed to conduct probabilistic load margin estimations. A distributed bi-directional sweep method will be employed in multiple wind generators connected to the main grid for power flow computations. A modified direct method will be presented such that conventional 2N + 1 non-linear equations can be replaced by N + 1 equations for load margin calculations. Accordingly, corresponding probabilistic estimations can be calculated by integrating both modified direct method and Gram-Charlier expansions. Simulations on several IEEE test systems have been used to validate the feasibility and the accuracy of our proposed techniques.

CHINESE ABSTRACT . . . . . . . . . . . . . . . . . . . .. .I
ENGLISH ABSTRACT . . . . . . . . . . . . . . . . . . . .  II
ACKNOWLEDGEMENTS . . . . . . . . . . . . . . . . . . . .  IV
Contents . . . . . . . . . . . . . . . . . . . . . . . . . V
List of Figures . . . . . . . . . . . . . . . . . . . . VIII
List of Tables . . . . . . . . . . . . . . . . . . . .  XIII
Introduction . . . . . . . . . . . . . . . . . . . . . . 1
1 Overview . . . . . . . . . . . . . . . . . . . . . . . 1
2 Contributions of The Dissertation . . . . . . . . . . .5
Wide-Area Measurement-Based Voltage Stability Indicators by Modified Coupled Single-Port Models . . . . . . . . . . 9
1 Background . . . . . . . . . . . . . . . . . . . . .  10
1.1 Coupled Single-Port Model . . . . . . . . . . . .   10
1.2 Voltage Stability Indicators . . . . . . . . . . .  12
1.3 Limitations of The Current Model . . . . . . . . .  13
2 Modified Coupled Single-Port Model by Extended Ward Equivalent . . . . . . . . . . . . . . . .. . . . . . . . 16
2.1 Extended Ward Equivalent and Reactive Power Response . . . . . . . . . . . . . . . . . .. . . . . . . .. . . . . 16
2.2 Applications to Coupled Single-Port Model . . . . . 17
2.3 Solution Algorithm . . . . . . . . . . . . . . . .  22
3 Limitations of The Proposed Model . . . . . . . . .   25
4 The Transition from PV Bus Mode to PQ Bus Mode . . .  26
5 Simulation Results . . . . . . . . . . . . . . . . .  28
5.1 IEEE 57-Bus System Without Considering Generator Reactive Power Limit . . . . . . . .. . . . . . . .. . . .28
5.2 IEEE 57-Bus System With Considering Generator Reactive Power Limit . . . . . . . . .. . . . . . . .. . . . . . . 30
5.3 IEEE 118-Bus System Without Considering Generator Reactive Power Limit . . . . . . . .. . . . . . . .. . . .32
5.4 IEEE 118-Bus System With Considering Generator Reactive Power Limit . . . . . . . . .. . . . . . . .. .  36
6 Summary . . . . . . . . . . . . . . . . . . . . . . . 36
Short-Term Voltage Instability Detections of Multiple Induction Generators in Distribution Networks Using Synchrophasors . . . . . . . . . . . . . . . . . . . . . .38
1 Measurement-Based IG Models . . . . . . . . . . . . . 39
2 Mechanisms for Short-Term Voltage Instability of IGs .41
2.1 Influences of Mechanical Torque Tm . . . . . . . . .41
2.2 Influences of Grid Voltage Levels . . . . . . . . . 43
3 Short-Term Voltage Stability Indicator of IGs . . . . 46
3.1 Short-Term Voltage Instability Indicator of A Single IG . . . . . . . . . . . . . . . . . . .. . . . . . . .. .46
3.2 Extensions to A Power Grid with Multiple IGs . . . .47
4 Simulation Results . . . . . . . . . . . . . . . . . .51
4.1 IEEE 57-Bus System . . . . . . . . . . . . . . . . .51
4.2 IEEE 118-Bus System . . . . . . . . . . . . . . . . 56
5 Summary . . . . . . . . . . . . . . . . . . . . . . . 62
Iterative Distributed Algorithms for Real-Time Available Transfer Capability Assessment of Multi-Area Power Systems . . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . 63
1 Problem Formulations . . . . . . . . . . . . . . . . .64
2 Distributed Algorithms for ATC Assessment . . . . . . 65
2.1 Overview . . . . . . . . . . . . . . . . . . . . . .65
2.2 Predictor-Corrector Proximal Multiplier Method (PCPM) . . . . . . . . . . . . . .. . . . .. . . . .. . . . .. . 67
2.3 Auxiliary Problem Principle Method (APPM) . . . . . 68
2.4 Alternative Direction Multiplier Method (ADMM) . . .70
3 Framework of Real-Time ATC Assessment . . . . . . . . 71
3.1 Framework of A Distributed System . . . . . . . . . 71
3.2 System Partition . . . . . . . . . . . . . . . . . .73
3.3 Computational Procedure . . . . . . . . . . . . . . 75
4 Simulation Results . . . . . . . . . . . . . . . . . .77
4.1 IEEE 14-Bus System . . . . . . . . . . . . . . . . .77
4.2 IEEE 57-Bus System . . . . . . . . . . . . . . . . .78
4.3 IEEE 118-Bus System . . . . . . . . . . . . . . . . 83
5 Summary . . . . . . . . . . . . . . . . . . . . . . . 88
Probabilistic Load Margin Estimations of Power System with Wind Farms . . . . .. . . . .. . . . .. . . . .. . . . .  94
1 Power Flow Studies of Power Systems with Wind Farms . 94
1.1 Power Flow Model of Wind Farms . . . . . . . . . . .95
1.2 Bi-Directional Sweep Method . . . . . . . . . . . . 97
2 Modified Direct Methods for Load Margin Computations .98
2.1 Proposed Theory . . . . . . . . . . . . . . . . . . 99
2.2 Efficient Newton Method . . . . . . . . . . . . . .102
2.3 Solution Algorithm . . . . . . . . . . . . . . . . 103
2.4 Load Margin Estimations of Line Outages . . . . . .104
3 Probabilistic Estimations of Load Margins . . . . . .106
3.1 Latin Hypercube Sampling Method . . . . . . . . . .106
3.2 The Gram-Charlier Expansion . . . . . . . . . . . .108
4 Simulation Results . . . . . . . . . . . . . . . . . 109
4.1 Probabilistic Characteristics of Load Margins . . .110
4.2 Load Margin Predictions for N-1 Contingencies . . .113
5 Summary . . . . . . . . . . . . . . . . . . . . . . .116
Conclusions and Future Works . . . . .. . . . .. . . . 117
1 Conclusions . . . . . . . . . . . . . . . . . . . . .117
2 Future Works . . . . . . . . . . . . . . . . . . . . 119
References . . . . .. . . . .. . . . .. . . . .. . . . . 121
Vita . . . . .. . . . .. . . . .. . . . .. . . . .. . . .130
                                

[1] IEEE/CIGRE Joint Task Force on Stability Terms and Definitions, “Definition and classification of power system stabilit,” IEEE Trans. on Power Systems, vol. 19, no. 2, pp. 1387–1401, May 2004.
[2] C. A. Canizares, F. L. Alvarado, C. L. DeMarco, I. Dobson, and W. F. Long, “Point of collapse methods applied to AC/DC power systems,” IEEE Trans. on Power Systems, vol.
7, no. 2, pp. 673–683, May 1992.
[3] V. Ajjarapu and C. Christy, “The continuation power flow: a tool for steady state voltage stability analysis,” IEEE Trans. on Power Systems, vol. 7, no. 1, pp. 416–422, February 1992.
[4] I. Dobson, “Computing a closest bifurcation instability in multidimensional parameter space,” Journal of Nonlinear Science, vol. 3, no. 1, pp. 307–327, 1993.
[5] C. A. Canizares, “Calculating optimal system parameters to maximize the distance to saddle node bifurcations,” IEEE Trans. on Circuit and Systems-I Fundamental Theory and Applications, vol. 45, no. 3, pp. 225–237, March 1998.
[6] G. D. Irisarri, X. Wang, J. Tong, and S. Mokhtari, “Maximum loadability of power systems
using interior point nonlinear optimization method,” IEEE Trans. on Power Systems, vol.12, no. 1, pp. 6162–172, February, 1997.
[7] G. Verbic and F. Gubina, “A new concept of voltage-collapse protection based on local
phasors,” IEEE Trans. on Power Delivery, vol. 19, no. 2, pp. 567–581, April 2004.
[8] G. Verbic and F. Gubina, “Fast voltage-collapse line-protection algorithm based on local phasors,” IEE Proceeding of Generation, Transmission and Distribution, vol. 150, no. 4, pp.482–486, July 2003.
[9] M. Glavic and T. V. Cutsem, “A short survey of methods for voltage instability detection,” Proceeding of the 2011 IEEE/PES Winter Meeting, pp. 1–8, July 2011.
[10] K. Vu, M. M. Bagovic, D. Novosel, and M. M. Saha, “Use of local measurements to estimate voltage stability margin,” IEEE Trans. on Power Systems, vol. 14, no. 3, pp. 1029–1035,
August 1999.
[11] M. Parniani and M. Vanouni, “A fast local index for online estimation of closeness to load limit,” IEEE Trans. on Power Systems, vol. 25, no. 1, pp. 584–585, February 2010.
[12] B. Venjatesh, A. Rost, and L. Chang, “Dynamic voltage collapse index-wind generation application,” IEEE Trans. on Power Delivery, vol. 22, no. 1, pp. 90–94, January 2007.
[13] A. Wiszniewski, “New criteria of voltage stability margin for the purposed of load shedding,” IEEE Trans. on Power Delivery, vol. 22, no. 3, pp. 1367–1371, July 2007.
[14] K. Seethalekshimi, S. N. Singh, and S. C. Srivastava, “A synchrophasor assisted frequency and voltage stability based load shedding scheme,” IEEE Trans. on Smart Grid, vol. 2, no. 2, pp. 221–230, June 2011.
[15] Y. V. Makarov, P. Du, S. Lu, T. B. Nguyen, X. Guo, J. W. Burns, J. F. Gronquist, and M.
A. Pai, “PMU-based wide-area security assessment: concept, method, and implementation,” IEEE Trans. on Smart Grid, vol. 3, no. 3, pp. 1325–1332, September, 2012.
[16] C. W. Taylor, “The future in on-line security assessment and wide-area stability control,” Proceeding of the 2000 IEEE/PES Winter Meeting, vol. 1, January 2000.
[17] W. Li, Y. Wang, and T. Chen, “Investigation on the Thevenin equivalent parameters for online estimation of maximum power tansfer limits,” IET Generation, Transmission, vol.18, no. 1, pp. 121–127, February 2010.
[18] Y. Wang, I. R. Pordanjani, W. Li, W. Xu, and E. Vaahedi, “Strategy to minimise the load shedding amount for voltage collapse prevention,” IET Generation, Transmission, vol. 5, no. 3, pp. 307–313, September 2010.
[19] Y. Wang, I. R. Pordanjani, W. Li, W. Xu, T. Chen, E. Vaahedi, and J. Gurney, “Voltage stability monitoring based on the concept of couple single-port circuit,” IEEE Trans. on Power Systems, vol. 26, no. 4, pp. 2154–2163, November 2011.
[20] W. Xu, I. Pordanjani, Y.Wang, and E. Vaahedi, “A network decoupling transform for phasor data based voltage stability analysis and monitoring,” IEEE Trans. on Smart Grid, vol. 3, no. 1, pp. 261–270, March 2012.
[21] F. F. Wu and A. Monticelli, “Critical review of external network modelling for online security analysis,” Int. J. Electrical and Energy Systems, vol. 5, no. 4, pp. 222–235, October 1983.
[22] P. M. De Oliveira-De Jesus, E. D. Castronuovo, and M. T. Ponce de Leao, “Reactive power response of wind generators under an incremental network-loss allocation approach,” IEEE Trans. on Energy Conversion, vol. 23, no. 2, pp. 612–621, June 2008.
[23] P. Kessel and H. Glavitsch, “Estimating the voltage stability of a power system,” IEEE Trans. on Power Delivery, vol. 1, no. 3, pp. 346–352, July 1986.
[24] H. Jia, X. Yu, and Y. Yu, “An improved voltage stability index and its application,” Int. J. Elec. Power and Energy Systems, vol. 27, no. 8, pp. 567–574, October 2005.
[25] T. L. Baldwin, L. Mili, M. B. Boisen, and R. Adapa, “Power system observability with
minimal phasor measurement placement,” IEEE Trans. on Power Systems, vol. 8, no. 2, pp. 707–715, May 1993.
[26] Federico Milano, “Power System Analysis Toolbox (PSAT),” version 2.0.0, February 2008.
[27] A. Monticelli, S. Deckmann, A. Garcia, and B. Sccot, “Real-time external equivalents for static security analysis,” IEEE Trans. on Power Apparatus and Systems, vol. PAS-98, no. 2, pp. 498–503, March 1979.
[28] P. Kansal and A. Bose, “Bandwidth and latency requirements for smart transmission grid applications,” IEEE Trans. on Smart Grid, vol. 3, no. 3, pp. 1344–1352, September 2012.
[29] B. Milosevic and M. Begovic, “Voltage-stability protection and control using a wide-area network of phasor measurements,” IEEE Trans. on Power Systems, vol. 18, no. 1, pp. 121–127, February 2003.
[30] D. J. Hill, “Nonlinear dynamic load models with recovery for voltage stability studies,” IEEE Trans. on Power Systems, vol. 8, no. 1, pp. 166–176, February 1993.
[31] Y. Sekine and H. Ohtsuki, “Cascaded voltage collapse,” IEEE Trans. on Power Systems, vol. 5, no. 1, pp. 250–256, February 1990.
[32] C. D. Vournas and C. A. Manos, “Modelling of stalling motors during voltage stability studies,” IEEE Trans. on Power Systems, vol. 13, no. 3, pp. 775–781, August 1998.
[33] H. Bai and V. Ajjarapu, “A novel online load shedding strategy for mitigating fault-induced delayed voltage recovery,” IEEE Trans. on Power Systems, vol. 26, no. 1, pp. 294–304, August 1998.
[34] D. H. Popovi´c, I. A. Hiskens, and D. J. Hill, “Stability analysis of induction motor networks,” Int. J. Elec. Power and Energy Sys., vol. 20, no. 3, pp. 475–487, 1998.
[35] M. Paramasivam, A. Salloum, V. Ajjarapu, V. Vittal, N. B. Bhatt, and S. Liu, “Dynamic Optimization based reactive power planning to mitigate slow voltage recovery and short term voltage instability,” IEEE Trans. on Power Systems, vol. 28, no. 4, pp. 3865–3873, November 2013.
[36] S. Dasgupta, M. Paramasivam, U. Vaidya, and V. Ajjarapu, “Real-time monitoring of shortterm
voltage stability using PMU data,” IEEE Trans. on Power Systems, vol. 28, no. 4, pp. 3702–3711, November 2013.
[37] H. Wu. and I. Dobson, “Cascading stall of many induction motors in a simple system,” IEEE Trans. on Power Systems, vol. 27, no. 4, pp. 2116–2126, November 2012.
[38] C. Duclut, S. Backhaus, and M. Chertkov, “Hysteresis, phase transitions, and dangerous transients in electrical power distribution systems,” Physical Review, E, vol. 87, no. 6, 062802, 2013.
[39] J. H. Liu and C. C. Chu, “Wide-area measurement-based voltage stability indicators by modified coupled single-port models,” IEEE Trans. on Power Systems, vol. 29, no. 2, pp.
756–764, March 2014.
[40] P. M. Anderson and A. Bose, “Stability simulation of wind turbine systems,” IEEE Trans. on Power Systems, vol. 102, no. 12, pp. 3791–3795, December 1983.
[41] A. R. Bergen and V. Vittal, Power System Analysis, 2nd ed., New Jersey, Prentice Hall, 2000.
[42] G. C. Ejebe, J. Tong, J. G. Waight, J. G. Frame, X. Wang, and W. F. Tinney, “Available transfer capability calculations,” IEEE Trans. on Power Systems, vol. 13, no. 4, pp. 1521–1527, November 1995.
[43] H. D. Chiang and H. Li, “On-Line ATC Evaluation for Large-Scale Power Systems: Framework and Tool,” Ch. 5 of Applied Mathematics for Restructured Electric Power Systems,
Spring-Verlag, 2005.
[44] A. G. Bakirzis and P. N. Biskas, “A decentralized solution to the DC-OPF of interconnected power systems,” IEEE Trans. on Power Systems, vol. 18, no. 3, pp. 1007–1013, August 2003.
[45] C. H. Lin and S. Y. Lin, “Distributed optimal power flow with discrete control variables of large distributed power systems,” IEEE Trans. on Power Systems, vol. 23, no. 3, pp.1383–1392, August 2008.
[46] B. H. Kim and R. Baldick, “Coarse-grained distributed optimal power flow,” IEEE Trans.on Power Systems, vol. 12, no. 2, pp. 932–939, May 1997.
[47] B. H. Kim and R. Baldick, “A comparison of distributed optimal power flow algorithms,” IEEE Trans. on Power Systems, vol. 15, no. 2, pp. 599–604, May 2000.
[48] S. Talukdar and V. C. Rameh, “A multi-agent technique for contingency constrained optimal power flows,” IEEE Trans. on Power Systems, vol. 9, no. 2, pp. 855–861, May 1994.
[49] R. Ebrahimian and R. Baldick, “State estimation distributed processing,” IEEE Trans. on Power Systems, vol. 15, no. 14, pp. 1240–1246, November 2000.
[50] D. M. Falcao, F. F. Wu, and L. Murphy, “Parallel and distributed state estimation,” IEEE Trans. on Power Systems, vol. 10, no. 2, pp. 724–730, May 1995.
[51] W. Yan, L.Wen, W. Li, C. Y. Chung, and K. P.Wong, “Decomposition–coordination interior point method and its application to multi-area optimal reactive power flow,” Int. J. Electric Power and Energy Systems, vol. 33, no. 1, pp. 55–60, January 2011.
[52] J. Contreras, A. Losi, M. Russo, and F. F. Wu, “Simulation and evaluation of optimization problem solutions in distributed energy management systems,” IEEE Trans. on Power Systems, vol. 17, no. 1, pp. 57–62, February 2002.

[53] P. Sulc, S. Backhaus, and M. Chertkov, “Optimal distributed control of reactive power via the alternating direction method of multipliers,” arXiv preprint arXiv:1310.5748, 2013.
[54] Q. Yang, J. A. Barria, and T. C. Green, “Communication infrastructures for distributed control of power distribution networks,” IEEE Trans. on Industrial Informatics, vol. 7, no. 2, pp. 316–327, May 2011.
[55] Q. Morante, N. Ranaldo, A. Vaccaro, and E. Zimeo, “Pervasive grid for large-scale power systems contingency analysis,” IEEE Trans. on Industrial Informatics, vol. 2, no. 3, pp. 165–175, August 2006.
[56] L. Min and A. Abur, “Total transfer capability computation for multi-area power systems,” IEEE Trans. on Power Systems, vol. 21, no. 3, pp. 1141–1147, August 2006.
[57] J. Zhao, P. Xu, and F. S. Chen, “A distributed computation method for voltage stability assessment of a sub-grid within large interconnected power system,” 17th power system computation conference, Stockholm, Sweden, August 22-26, 2011.
[58] L. Xie, Y. Chen, and H. Liao, “Distributed online monitoring of quasi-static voltage collapse in multi-area power systems,” IEEE Trans. on Power Systems, vol. 27, no. 4, pp. 2271–2279, November 2012.
[59] G. Cohen, “Auxiliary problem principle and decomposition of optimization problems,” Journal of Optimization Theory and Application, vol. 32, no. 3, pp. 277–305, November 1980.
[60] G. Cohen, “Optimization by decomposition and coordination: an unified approach,” IEEE Trans. on Automatic Control, vol. AC-32, no. 2, pp. 222–232, April 1978.
[61] G. Chen and M. Teboulle, “A proximal-based decomposition method for convex minimization problems,” Math. Programming, vol. 64, pp. 81–101, 1994.
[62] S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Stanford University, Stanford, CA, Technical Report, January 2011.
[63] M. Shahidehpour and Y. Wang, Communication and Control in Electric Power Systems, Application of Parallel and Distributed Processing. Piscataway, NJ: IEEE Press, pp. 47–192, 2003.

[64] C. H. Lin and S. Y. Lin, “A new dual-type method used in solving optimal power flow problems,” IEEE Trans. on Power Systems, vol. 12, no. 4, pp. 1667–1675, November 1997.
[65] P. Zhang and S. T. Lee, “Probabilistic load flow computation using the method of combined culmants and Gram-Charlier expansion,” IEEE Trans. on Power Systems, vol. 19, no. 1, pp. 676–682, February 2004.
[66] C. Hamon, M. Perninge, and L. Soder, “A stochastic optimal power flow problem with stability constraints, part I: approximating the stability,” will appear in IEEE Trans. on Power Systems, 2013.
[67] C. Hamon, M. Perninge, and L. Soder, “A stochastic optimal power flow problem with stability constraints, part II: the optimization problem,” will appear in IEEE Trans. on
Power Systems, 2013.
[68] J. F. Zhang, C. T. Tse, W. Wang, and C. Y. Chung, “Voltage stability analysis based on probabilistic power flow and maximum entropy,” IET Trans. Generation, Transmission, and Distribution, vol. 4, no. 4, pp. 530–537, April 2012.
[69] C. L. Su, “Probabilistic load-flow computation using point estimate method,” IEEE Trans. on Power Systems, vol. 20, no. 4, pp. 1843–1851, November 2005.
[70] E. Haesen, C. Bastiaensen, J. Driesen, and R. Belmans, “A probabilistic formulation of load margins in power systems with stochastic generation,” IEEE Trans. on Power Systems, vol. 24, no. 2, pp. 951–958, May 2009.

[71] M. Perninge, F. Lindskog, and L. Soder, “Importance sampling of injected powers for electric power system security analysis,” IEEE Trans. on Power Systems, vol. 27, no. 1, pp. 3–11, February 2012.
[72] J. Usaola, “Probabilistic load flow with correlated wind power injection,” Int. J. Electric and Energy Systems, vol. 80, no. 5, pp. 528–536, May 2010.
[73] S. Q. Bu, W. Du, H. F. Wang, Z. Chen, L. Y. Xiao, and H. F. Li, “Probabilistic analysis of small-signal stability of large-scale power system as affected by penetration of wind generation,” IEEE Trans. on Power Systems, vol. 27, no. 2, pp. 762–770, May 2012.
[74] G. Moore and A. Spence, “The calculation of turning points of nonlinear equations,” SIAM J. Numer. Anal., vol. 17, pp. 567–576, 1980.
[75] D. K. Molzahn and B. C. Lesieutre, “Initializing dynamic power system simulations using eigenvalue formulations of the induction machine and power flow models,” IEEE Trans. on Circuit and System-I: Regular Papers, vol. 60, no. 3, pp. 690–702, March 2013.
[76] D. R. Vega, T. I. A. Olivares, and D. O. Salinas, “An approach to the initialization of dynamic induction motor models,” IEEE Trans. on Power Systems, vol. 17, no. 3, pp. 747–751, August 2002.
[77] J. F. M. Padron and A. E. F. Lorenzo, “Calculating steady-state operating conditions for doubly-fed induction generator wind turbines,” IEEE Trans. on Power Systems, vol. 25, no. 2, pp. 922–928, May 2010.
[78] E. Janecek and D. Georgiev, “Probabilistic extension of the backwward/forward load flow analysis method,” IEEE Trans. on Power Systems, vol. 27, no. 2, pp. 695–704, May 2012.
[79] Y. Q. Shen and T. J. Ypma, “Newton’s method for singular nonlinear equations using approximate
left and right nullspaces of the Jacobian,” Applied Numerical Mathematics, vol. 54, pp. 256–265, 2005.
[80] X. Yang, X. Zhou, and Z. Du, “Efficient solution algorithms for computing fold points of power flow equations,” Int. J. Electric Power and Energy Systems, vol. 33, no. 2, pp. 229–235, 2010.
[81] A. Hussein, K. Chen, and H. Wan, “On adapting test function methods for fast detection of fold bifurcations in power system,” International Journal of Bifurcation and Chaos, vol. 12, no. 1, pp. 179–185, 2002.
[82] A. J. Flueck, R. Gonella, and J. R. Dondeti, “A new power sensitivity method of ranking branch outage contingencies for voltage collapse,” IEEE Trans. on Power Systems, vol. 17, no. 2, pp. 265–270, May 2002.
[83] R. L. Iman, “Latin Hypercube Sampling,” Encyclopedia of Statistical Sciences. Hoboken, NJ: Wiley, pp 408–411, 2006.
[84] H. Yu, C. Y. Chung, K. P. Wong, H. W. Lee, and J. H. Zhang, “Probabilistic load flow evaluation with hybrid latin hypercube sampling and cholesky decomposition,” IEEE Trans.
on Power Systems, vol. 24, no. 2, pp. 661–667, May 2009.
[85] G. Moore and A. Spence, “The calculation of turning points of nonlinear equations,” SIAM J. Numer. Anal., vol. 17, pp. 567–576, 1980.
[86] H. D. Chiang, A. J. Flueck, K. S. Shah, and N. Balu, “CPFLOW: a practical tool for tracing power system steady-state stationary behaviour due to load and generation variation,” IEEE Trans. on Power Systems, vol. 10, no. 2, pp. 623–633, May 1995.

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