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Probabilistic dynamic stability analysis of microgrids considering wind turbines

Number of pages: 135 File Format: word File Code: 32154
Year: 2013 University Degree: Master's degree Category: Biology - Environment
Tags/Keywords: micro network - Power systems - Probabilistic analysis - Production certainty - Small signal stability - wind energy - Wind turbines
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  • Summary of Probabilistic dynamic stability analysis of microgrids considering wind turbines

    Master thesis in the field of electrical-power engineering

    Abstract

    Possible dynamic stability analysis of microgrids considering wind turbines

    In recent years, the high penetration of renewable energy sources, especially wind energy It has created new issues in power networks. One of the most important issues is the uncertainty in the power produced by wind turbines. The uncertainty created by wind energy in microgrids that have lower power and voltage levels can be much more effective. This issue specifies the need to perform a possible analysis in microgrids that use wind energy to generate power. In this thesis, small signal stability of microgrids will be studied under the influence of production uncertainty by wind energy. For this purpose, Monte-Carlo and quantize methods are used as numerical methods and two-point estimation method and the method based on Gram-Charlier expansion are used as possible numerical analysis methods. The advantages and disadvantages of these methods will be studied. In order to complete the studies in this field, the dynamics of wind turbines will also be examined in this thesis. To achieve this goal, three types of conventional wind turbines in power systems are fully modeled and their dynamic impact on the possibility of system instability is evaluated. Also, to obtain the equations of state of the system, a method specific to microgrids will be used, which provides great flexibility for modeling new components.

    Key words:

    Small signal stability, probabilistic analysis, microgrid, uncertainty, wind energy

    1-1. Wind energy

    Overview of wind energy

    The negative and non-negligible effect of burning fossil fuels[1] on the world's climate has been strongly noticed in recent years. Reducing the negative effects of these climate changes requires a huge reduction in the production of greenhouse gases [2], which can be achieved by reducing the burning of fossil fuels. According to estimates, it is necessary to reduce these gases by 60-80% by 2050 [1]. For this reason, in many countries, the use of energy production sources that, despite having a high reliability factor, produce little carbon monoxide and are economical in terms of energy has become one of the most important goals of policymakers in the field of energy.

    For this purpose, the use of renewable energy sources[3] has been put on the agenda of governments, so that in 2012, the amount of power generation capacity from all renewable sources exceeded 1,470 gigawatts. This amount of production capacity [4] is equivalent to 26% of the global production capacity and 21.7% of the power produced this year [2]. Meanwhile, wind energy[5] has had one of the fastest growth rates compared to other renewable energy sources. In 2012, the amount of power generation capacity from wind energy has reached 282 GW [3].

    Figure 1-1- Cumulative capacity of world wind energy

    In Figure 1-1, the "Reference" chart is based on the World Energy Outlook report in 2004 from the International Energy Agency[6], the "Moderate" scenario represents conditions that all necessary political measures to support renewable energy (under construction or planning) are taken, and in the "Advanced" scenario, it is assumed that all political solutions are in favor of the production and expansion of the use of wind energy. By examining Figure 1-1, which shows the forecast of the amount of wind power capacity produced in 2004 and comparing it with the actual values ??of wind power capacity in 2012, it can be clearly seen that the best and most optimistic predictions about the future of wind energy are far from reality [4]. Therefore, it can be concluded that in the coming years, wind energy will become one of the most effective and widely used energy sources in the world.

    Figure 1-2- Atlas of the world wind speed at a height of 80 meters for 2005

    Since the amount of power produced by wind turbines is very dependent on the wind speed, it is tried to choose the location of wind power plants in areas with relatively high wind speed. Figure 2-1 shows an example of a wind atlas that can be used for this purpose. In this figure, the wind speed in different regions of the world at a height of 80 meters from the ground is shown. In addition, Figure 1-3 shows Iran's wind atlas at a height of 80 meters above the ground. According to this figure, Iran has a high potential and ability to exploit wind energy [5].

    Figure 1-3- Iran wind speed atlas at 80 meters altitude

    Almost the working process of all wind turbines is the same, so that wind energy creates a rotational movement [7] in the turbine blades and this rotation of these blades causes the generator axis to move. electric [8] which is located inside the nozzle [9]. Then the rotational speed of the axis is increased by a gearbox [10] so that it is suitable for use by the electric generator. The generator converts rotational kinetic energy into electrical energy with the help of a magnetic field[11]. Finally, the voltage level is converted from about 700V to a suitable voltage for connecting to the network, for example 20KV, by a transformer. style="direction: rtl;"> 

    By

    Aslan Mojallal

     

    In the recent years high penetration of renewable energy sources and in particular wind energy in power systems has posed unforeseen problems. One of the most significant problems is the uncertainty in power which is generated by wind turbines. Uncertainty introduced by wind power has more influence on Microgrids where power and voltage levels are relatively low. This illustrates the necessity of conducting probabilistic analysis on Microgrids that employ wind energy to generate power. In this dissertation, small-signal stability of microgrids under the influence of uncertainty caused by wind energy will be investigated. To this end, Monte-Carlo and Quantize methods as examples of numerical methods and two point estimation approach and Gram-Charlier based method as analytical methods will be employed. Advantages and disadvantages of these approaches will be investigated. In order to conduct a comprehensive study on this field, the dynamics of wind turbines will be considered in this thesis. For this purpose, three types of conventional wind turbines are fully modeled and the influence of their dynamics on the probability of system instability is assessed. Furthermore, in order to obtain state equations of the system, a specific method for Microgrids is employed which introduces a large amount of flexibility.

  • Contents & References of Probabilistic dynamic stability analysis of microgrids considering wind turbines

    List:

    First Chapter 1

    1-1. wind energy 2

    1-1-1. An overview of wind energy. 2

    1-1-2. Different wind turbine technologies. 6

    1-1-2-1. Wind turbine with squirrel cage induction generator. 7

    1-1-2-2. Wind turbine with two-way induction generator. 8

    1-1-2-3. Wind turbine with full power converter. 9

    1-2. An introduction to microgrids 10

    1-2-1. Distributed production 10

    1-2-2     Microgrids 12

    1-3. Problem design and an overview of the conducted research 14

    1-3-1. An overview of the conducted research 14

    1-3-2. Definition of the problem. 16

    1-4. Head of chapters 17

    1-4-1. Second chapter: Modeling and definition of wind turbine equations. 17

    1-4-2. The third chapter: introduction and modeling of microgrid. 17

    1-4-3.     Chapter 4: Introduction of possible analysis methods. 18

    1-4-4. The fifth chapter: simulation and comparison. 18

    The second chapter 19

    2-1. Constant speed wind turbines [33] 20

    2-2. Variable speed wind turbines. 25

    2-2-1. Wind turbine with full power converter [35] 25

    2-2-1-1. Power system modeling. 27

    2-2-1-2. Control system modeling. 30

    2-2-2. Wind turbine with two-way induction generator. 38

    2-2-2-1. Modeling of the induction machine used in two-way feeding wind turbine. 39

    2-2-2-2. Modeling of converter control system used in two-way wind turbine. 41

    The third chapter 44

    3-1. Introduction of microgrid system. 45

    3-2. Microgrid modeling. 47

    3-2-1. Synchronous machine model. 47

    3-2-2. Microgrid model. 52

    3-2-3. General model of the system. 54

    Chapter Four. 56

    4-1. Numerical probabilistic investigation methods. 57

    4-1-1. Monte-Carlo method[25,41] 57

    4-1-2. Quantize method [43] 62

    4-2. Analytical probabilistic investigation methods. 63

    4-2-1.        Two-point estimation method [27-28, 43-44] 64

    4-2-2.       The method based on Gram-Charlier expansion [29-30, 45-47] 67

    Chapter 5 74

    1-5. Check the stability of the system without considering the uncertainty. 75

    5-2. Investigating the sensitivity of microgrid eigenvalues ??to system states. 85

    5-3. A probabilistic investigation of small signal stability considering a probabilistic variable. 92

    5-4. A probabilistic investigation of small signal stability considering several possible input variables. 104

    Sixth Chapter 114

    6-1. conclusion 115

    6-1-1. Results for wind turbines. 115

    6-1-2. Results related to possible methods used 115

    6-1. Suggestions. 116

    References... 118

     

    Source:

     

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    The World Wind Energy Association (WWEA), world Wind Energy Report 2012.

    Anaya-Lara, Olimpo, et al. Wind energy generation: modeling and control. John Wiley & Sons, 2011.

    Renewable Energy Organization of Iran (SUNA), Available online on: www.suna.org.ir

    Machowski, Jan, Janusz Bialek, and Jim Bumby. Power system dynamics: stability and control. John Wiley & Sons, 2011.

    Davis, Murray W. "Distributed resource electric power systems offer significant advantages over central station generation and T & D power systems. II." Power Engineering Society Summer Meeting, 2002 IEEE. Vol. 1. IEEE, 2002.

    Ackermann, Thomas, G?ran Andersson, and Lennart S?der. "Distributed generation: a definition." Electric power systems research 57.3 (2001): 195-204.

    Meliopoulos, AP Sakis. "Challenges in simulation and design of ?grids." Power Engineering Society Winter Meeting, 2002. IEEE. Vol. 1. IEEE, 2002.

    Dugan, Roger C. "Distributed resources and reliability of distribution systems." Power Engineering Society Summer Meeting, 2002 IEEE. Vol. 1. IEEE, 2002.

    Katiraei, F., and M. R. Iravani. "Power management strategies for a microgrid with multiple distributed generation units." Power Systems, IEEE Transactions on 21.4 (2006):

    El-Fouly, T. H. M., E. F. El-Saadany, and M. M. A. Salama. "Grey predictor for wind energy conversion systems output power prediction." Power Systems, IEEE Transactions on 21.3 (2006): 1450-1452. Louka, Petroula, et al. "Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering." Journal of Wind Engineering and Industrial Aerodynamics 96.12 (2008): 2348-2362.

    Kariniotakis, G. N., G. S. Stavrakakis, and E. F. Nogaret. "Wind power forecasting using advanced neural networks models." Energy conversion, IEEE transactions on 11.4 (1996): 762-767.

    Pelacchi, Paolo, and Davide Poli. "The influence of wind generation on power system reliability and the possible use of hydrogen storages." Electric Power Systems Research 80.3 (2010): 249-255.

    Black, Mary, and Goran Strbac. "Value of bulk energy storage for managing wind power fluctuations." Energy conversion, IEEE transactions on 22.1 (2007): 197-205. ?uri?, Milenko B., Zoran M. Radojevi?, and Emilija D. Turkovi?. "A reduced order multimachine power system model suitable for small signal stability analysis." International Journal of Electrical Power & Energy Systems 20.5 (1998): 369-374.

    Coelho, Ernane Antonio Alves, Porfirio Cabaleiro Cortizo, and Pedro Francisco Donoso Garcia. "Small-signal stability for parallel-connected inverters in stand-alone AC supply systems." Industry Applications, IEEE Transactions on 38.2 (2002): 533-542.

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