Wind Power Impact on Power System Dynamic Part 11 potx

Impact of Real Case Transmission Systems Constraints on Wind Power Operation 335 distributed between nodes 2 and 4 (see table 5), the limited transmission capacity of L1 does no more impact wind power and this last one can be entirely transferred in the network (see table 6). This complete use of wind production was not feasible when some of the defined wind parks (24MW) were directly connected at L1 (via node 1; see Table 2 and Fig. 9). Installed capacity (MW) Connection node Wind park 1 8 Node 4 Wind park 2 6 Node 4 Wind park 3 12 Node 2 Wind park 4 1 Node 4 Wind park 5 3 Node 2 Wind park 6 4 Node 4 Wind park 7 5 Node 2 Wind park 8 4 Node 2 Wind park 9 5 Node 4 Table 5. Wind generation considered for the modified RBTS test system Annual energy wind park 1 (GWh/y) 7.5 Annual energy wind park 2 (GWh/y) 5.5 Annual energy wind park 3 (GWh/y) 25.0 Annual energy wind park 4 (GWh/y) 0.9 Annual energy wind park 5 (GWh/y) 6.3 Annual energy wind park 6 (GWh/y) 3.7 Annual energy wind park 7 (GWh/y) 10.4 Annual energy wind park 8 (GWh/y) 8.2 Annual energy wind park 9 (GWh/y) 4.8 Table 6. Annual wind energy for wind parks located in nodes 2 and 4 with limited transmission capacity of L1 (40MW) This result points out the utility of the developed tool in order to improve the management of wind generation. Indeed, thanks to the proposed software, the transmission system operator will now be able, not only, to quantify the maximal wind penetration in a given network, but also, to propose an adequate distribution of wind parks connection nodes. However, for this last point, note that environmental concerns for the establishment of wind parks must still be taken into account. 5. Wind generation management in a real case transmission system In order to point the utility of the developed tool for investments studies in modern networks, we have applied the proposed program to the real case Belgian transmission system. The major issue for this network concerns the large scale integration of offshore wind power. In that way, two projects (for an installed capacity of 630 MW) are actually built in the North Sea and are going to lead to the connection of respectively 300 MW at the 150 kV Slijkens connection node and of 330 MW at the 150 kV Zeebrugge node. Initially, the transmission capacity from Slijkens and Zeebrugge towards Brugge is highly sufficient as it reaches 800 MW. However, as illustrated in Fig. 10 (Van Roy et al., 2003), the integration of Wind Power 336 offshore wind power associated with the importation of electricity from France towards the Netherlands can lead to the apparition of congestions between Rodenhuize (Gent) and Heimolen (Antwerpen). Such a result is confirmed with our developed simulation tool as an increase of congestion hours over the line between Rodenhuize and Heimolen can be observed in Fig. 11 when 200 MW of wind power are installed in the North Sea and that 1 GW is imported form France towards the Netherlands. Simultaneously, the increase of installed offshore wind power does not change the amount of critical hours over the Slijkens – Brugge and Zeebrugge – Brugge lines. This last result confirms thus that the major issue of Belgian wind integration is mainly related to possible congestion hours inside the country (between Gent and Antwerpen). Fig. 10. Major active power flows over the Belgian transmission system after the large scale integration of offshore wind power Fig. 11. Evolution of congestion hours over major transmission lines in the Belgian high voltage system. Impact of the installed offshore capacity Impact of Real Case Transmission Systems Constraints on Wind Power Operation 337 In order to improve the offshore wind power integration and to consequently reduce the number of congestion hours over the Rodenhuize-Heimolen line, a grid extension of 150 MW between Koksijde and Slijkens was proposed (dashed curve in Fig. 10). With this new 150 kV line, simulation results (Fig. 12) clearly confirm a reduction of congestion hours between Gent and Antwerpen when the importation level is limited (and that the installed offshore wind power reaches 630 MW). However, after an increase to 2 GW of the electricity Fig. 12. Evolution of congestion hours between Rodenhuize and Heimolen with and without the added connection Koksijde-Slijkens (importation level of 1 GW and 630 MW installed offshore wind power) Fig. 13. Impact of the importation level on the offshore lost of energy (installed capacity set to 630 MW) Wind Power 338 exchange between France and the Netherlands, not only a reduction of the transmitted wind power can be computed (Fig. 13) but it can also be observed that the number of congestion hours dramatically increases over the Rodenhuize-Heimolen line (Fig. 14). Therefore, in the context of large scale interconnected European networks, it will obviously be necessary to imagine new reinforcements over the Belgian transmission system (connection of Zeebrugge node to the 380 kV network or reinforcement of the Heimolen-Rodenhuize line). Fig. 14. Impact of the importation level and of the offshore wind power (installed capacity set to 630 MW) over the Heimolen-Rodenhuize line Finally, it can thus be concluded that the proposed simulation tool permits to study reinforcement scenarii taking into account large scale integration of wind power. In that way, the developed program is thus perfectly suitable for the recent and future developments to be made over modern transmission systems. 6. Conclusion In this chapter, wind generation has been introduced into a transmission system analysis tool. This last one was composed of two parts: system states generation (non sequential Monte Carlo simulation) and analysis (economic dispatch, DC load flow and eventual load shedding). In order to take into account wind generation in this simulation tool, each part had thus to be modified. Finally, a useful bulk power system analysis software taking into account wind generation has been developed and has permitted to study the impact of wind generation not only on reliability indices but also on the management of the classical production park. In that way, situations of forced wind stopping were pointed out due to increased wind penetration and transmission system operation constraints. Moreover, the interest of the proposed software was demonstrated by adequately determining reinforcements to be made in order to optimize large scale wind penetration in modern real case electrical systems. Impact of Real Case Transmission Systems Constraints on Wind Power Operation 339 7. References Al Aimani S. (2004). Modélisation de différentes technologies d’éoliennes intégrées à un réseau de distribution moyenne tension, Ph.D. Thesis, Ecole Centrale de Lille, chap.2, pp.24-25, Dec. 2004. Allan R.N., Billinton R. (2000). Probabilistic assessment of power systems, Proceedings of the IEEE, Vol. 22, No.1, Feb. 2000. Billinton R., Kumar S., Chowdbury N., Chu K., Debnath K., Goel L., Kahn E., Kos P., Nourbakhsh, Oteng-Adjei J. (1989). A reliability test system for educational purposes – Basic data. IEEE Trans. On Power Systems, Vol. 4, No. 3, Aug. 1989, pp. 1238-1244. Billinton R., Chen H., Ghajar R. (1996). A sequential simulation technique for adequacy evaluation of generating systems including wind energy. IEEE Trans. On Energy Conversion, Vol. 11, No. 4, Dec. 1996, pp.728-734. Billinton R., Bai G. (2004). Generating capacity adequacy associated with wind energy. IEEE Trans. On Energy Conversion, Vol. 19, No. 3,Sept. 2004, pp. 641-646. Billinton R., Wangdee W. (2007). Reliability-based transmission reinforcement planning associated with large-scale wind farms. IEEE Trans. On Power Systems, Vol. 22, No. 1, Feb. 2007, pp. 34-41. Buyse H. (2004). Electrical energy production. Electrabel documentaion, available web site: www.lei.ucl.ac.be/~matagne/ELEC2753/SEM12/S12TRAN.PPT, 2004. Ernst B. (2005). Wind power forecast for the German and Danish networks. Wind Power in Power Systems, edited by Thomas Ackerman, John Wiley & Sons, chap.17, pp.365- 381, 2005. Mackensen R., Lange B., Schlögl F. (2006). Integrating wind energy into public power supply systems – German state of the art. International Journal of Distributed Energy Sources, Vol. 3, No.4, Dec. 2007. Maupas F. (2006). Analyse des règles de gestion de la production éolienne : inter- comparaison de trois cas d’étude au Danemark, en Espagne et en Allemagne. Working paper, GRJM Conference, Feb. 2006. Papaefthymiou G. (2006). Integration of stochastic generation in power systems. PhD. Thesis, Delft University, chap. 5 & 6, June 2006. Papaefthymiou G., Schavemaker P.H., Van der Sluis L., Kling W.L., Kurowicka D., Cooke R.M. (2006). Integration of stochastic generation in power systems. International Journal of Electrical Power & Energy Systems, Vol. 18, N°9, Nov. 2006, pp. 655-667. Sacharowitz S. (2004). Managing large amounts of wind generated power feed in – Every day challenges for a German TSO and approaches for improvements. International Association for Energy Economics (IAEE), 2004 North American Conference, Washington DC, USA, 2004. Vallee F., Lobry J., Deblecker O., (2008). System reliability assessment method for wind power integration. IEEE Trans. On Power Systems, Vol. 23, No. 3, Aug. 2008, pp. 1288-1297. Van Roy P., Soens J., Driesen Y., Belmans R. (2003), Impact of offshore wind generation on the Belgian high voltage grid, European Wind Energy Conference (EWEC), Madrid, Spain, June 2003. Wind Power 340 Wangdee W., Billinton R. (2006). Considering load-carrying capability and wind speed correlation of WECS in generation adequacy assessment. IEEE Trans. On Energy Conversion, Vol. 21, No. 3, Sept. 2006, pp. 734-741. 14 Wind Power at Sea as Observed from Space W. Timothy Liu, Wenqing Tang, and Xiaosu Xie Jet Propulsion Laboratory, California Institute of Technology, USA 1. Introduction With the increasing demand of electric power and the need of reducing greenhouse gas emission, the importance of turning wind energy at sea into electric power has never been more evident. For example, China is vigorously studying and pursuing the potential of wind energy to lessen dependence of coal consumption (McElroy et al., 2009). The White Paper on Energy (DTI, 2007) lays out an ambitious plan to the British Parliament in meeting the Renewables Obligation with offshore wind energy. The paper posted a challenge not only to Denmark, the leader of European offshore wind energy, but also to the world. New technology has also enabled floating wind-farms in the open seas to capture the higher wind energy and reduce the environmental impact on the coastal regions. Detailed distribution of wind power density (E), as defined in Section 4, at sea is needed to optimize the deployment of such wind farms. The distribution is discussed in Section 5. Just a few decades ago, almost all ocean wind measurements came from merchant ships. However, the quality and geographical distribution of these wind reports were uneven. Today, operational numerical weather prediction (NWP) also gives us wind information (Capps & Zender, 2008), but NWP depends on numerical models, which are limited by our knowledge of the physical processes and the availability of data. Recently, spacebased microwave sensors are giving us wind information with sufficient temporal and spatial sampling, night and day, under clear and cloudy conditions. Results from the most advanced passive sensor, which measures only wind speed, and active sensor, which measures both speed and direction, will be discussed. The principles of wind retrievals by active and passive microwave sensors are described in Section 2 and 3 respectively. The dependence of wind speed on height above sea level and on atmospheric stability is discussed in Section 6 and 7. 2. Scatterometer The capability of the spacebased scatterometer in measuring wind vector at high spatial resolution is discussed by Liu (2002) and Liu and Xie (2006). The scatterometer sends microwave pulses to the Earth’s surface and measures the backscatter power. Over the ocean, the backscatter power is largely caused by small centimeter-scale waves on the surface, which are believed to be in equilibrium with stress (τ). Stress is the turbulent momentum transfer generated by vertical wind shear and buoyancy. Liu and Large (1981) demonstrated, for the first time, the relation between measurements by a spacebased scatterometer and surface stress measured on research ships. Although the scatterometer Wind Power 342 has been known to measure τ, it has also been promoted as a wind-measuring instrument. The geophysical data product of the scatterometer is the equivalent neutral wind, U N , at 10 m height (Liu and Tang 1996), which, by definition, is uniquely related to τ, while the relation between τ and the actual winds at the reference level depends on atmosphere stability and ocean’s surface current. U N has been used as the actual wind, particularly in operational weather applications. The difference between the variability of stress and wind is assumed to be negligible because the marine atmosphere has near neutral stratification, and that the magnitude of ocean current is small relative to wind speed over most ocean areas. Because stress is small-scale turbulence generated by buoyancy and wind shear, its magnitude should have strong spatial coherence with sea surface temperature and its direction should show influence by current. These features that are driven by ocean processes may not be fully represented in winds that are subjected to larger-scale atmospheric factors, as discussed by Liu and Xie (2008) and Liu et al. (2010). NASA launched a Ku-band scatterometer, QuikSCAT, in June 1999. Level-2 data at 12.5 km resolution are obtained from the Physical Oceanography Distributed Active Archive Center. Seven years of the data, from June 2002 to May 2009 (coincide with radiometer data as discussed in Section 3), organized in wind vector cells along satellite swath, are binned into uniform 1/8 degree grids over global oceans and fitted to the Weibull distribution for the 7 year periods. There is hardly any in situ stress measurement. Even for winds, there is no in situ measurement that could represent the range of scatterometer data, particularly at the high and low ends, to evaluate the probability density function (PDF) from which E is derived. 3. Microwave radiometer Ocean surface wind speed can also be derived from the radiance observed by a microwave radiometer. It is generally believed that wind speed affects the surface emissivity indirectly through the generation of ocean waves and foam (Hollinger, 1971; Wilheit, 1979). Radiometers designed to observe the ocean surface operate primarily at window frequencies, where atmospheric absorption is low. To correct for the slight interference by tropospheric water vapor, clouds, and rainfall and, to some extent, the effect of sea surface temperature, radiances at frequencies sensitive to sea surface temperature, atmospheric water vapor, and liquid water are also measured (Wentz, 1983). The Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), on board of NASA’s Aqua satellite, was launched in May 2002 and has been measuring ocean parameters including wind speed and sea surface temperature. These parameters averaged to 0.25° grids for ascending and descending paths were obtained from Remote Sensing System. 4. Power density The Weibull distribution (Gaussian and Rayleigh distributions are special cases of it) has been often used to characterize the PDF of wind power (e.g., Pavia & O’Brien 1986). A two parameters Weibull distribution has the PDF (p) as a function of wind speed U, (1) where k is the dimensionless shape parameter, and c is the scale parameter. A number of methods to estimate Weibull parameters exist, with negligible difference in the results (Monahan, 2006). We used the simplest formula: Wind Power at Sea as Observed from Space 343 (2a) (2b) where U is the mean, σ is the standard deviation of wind speed, and Γ is the gamma function. The available wind power density E (which is proportional to U 3 ) may be calculated from the Weibull distribution parameters as (3) where ρ is the air density. E is essentially the kinetic energy of the wind. We will analyze PDF and E, which will provide the characteristics of not only the means and the frequencies of strong wind, but also the variation and higher moments critical in relating the non-linear effects of wind on electric power generation capability. 5. Geographic distribution Scatterometer climatology in forms of mean wind (e.g., Risien & Chelton, 2006), frequency of strong wind (Sampe & Xie, 2007), and power density (Liu et al., 2008a) have been produced before. The PDF of 7 year of wind speed at 10 m height above oceans between 75° latitudes (Fig. 1) shows the slight difference between QuikSCAT and AMSR-E. AMSR-E, which peaks at 7.5 m/s, has more high wind than QuikSCAT, which peaks at 7 m/s. The global distributions of E (Fig. 2 and 3) are very similar, with AMSR-E data giving a slightly larger dynamic range. The distributions of E, as shown in Fig. 2 and 3, confirm the conventional knowledge: strongest E is found over the mid-latitude storm tracks of the winter hemisphere, the relatively steady trade winds over the tropical oceans, and the seasonal monsoons. At mid- Fig. 1. Comparision of the probability density function of ocean surface wind speed from 7 years of QuikSCAT and AMSR-E measurements. Wind Power 344 Fig. 2. Distribution of power density of ocean surface wind (10 m) from QuikSCAT for (a) boreal winter (December, January, and February) and (b) boreal summer (June, July, and August). Fig. 3 Same as Fig. 2, but from AMSR-E. [...]... a WPS with specified requirements to EMC and electric power quality The present chapter is focused on a simulation-based spectral technique for power quality and EMC design of wind power systems including a power source or synchronous generator (G), an AC/DC/AC converter and electronic equipment with power supplies connected to a power distribution network A block diagram of a typical WPS is shown in... 551 11, Jönköping, 1Russia 2,3Sweden 2SP 1Mordovian 1 Introduction In off-grid wind power systems (WPS) a power source generates the power which is comparable to the consumed power Solving electromagnetic compatibility (EMC) problems in such a WPS is directly related to power quality issues High levels of low- and high-frequency conducted emissions in a WPS worsen the quality of consumed electric power, ... K.Xu, 2008b: Power density of ocean surface wind- stress from international scatterometer tandem missions Int J Remote Sens., 29(21), 6109- 6116 McElroy, M.B., X Lu, C.P Nielsen, and Y Wang, 2009: Potential for wind- generated electricity in China Science, 325, 1378-1380 352 Wind Power Monahan, 2006: The probability distribution of sea surface wind speeds Part I: theory and SeaWinds observations J Clim.,... Calculation of the filter includes an optimization procedure Objective function and constraints are defined based on application reasons For example, the total reactive power Q of the filter capacitors defines the volumetric dimensions of the filter, which in some applications is an important design criterion Minimization of the total reactive power of filter capacitors can be performed for a passive harmonic... 2000) However, they are still not well 358 Wind Power developed for independent power supply systems, where generated power and the consumed power are comparable The tendency to consider multi-phase systems in the variant design (Brown et al., 1989) should be addressed in the modelling software enabling a common CAD environment for electric power supply systems, consisting of elements with different number... merged product may need international cooperation in calibration, and maintaining them over time may require political will and international support It remains a technical challenge to generate electricity by wind off shore and transmit the power back for consumption efficiently, but satellite observations could contribute to realize the potential 350 Wind Power Fig 9 Fractional coverage, between 70°N... minimization of the total reactive power of filter capacitors can be performed along with solving the optimal control problem The filter optimization problem includes constraints regarding EMC and power quality in WPS nodes Power quality in WPS is presented by electric power quality indices, THD and DPF The constraints relate the filter component values to the electric power quality indices Constraints... calculated according to the following well-known equations in the node of WPS where power quality is monitored: N 2 THD = ( ∑ U n )1/2 / U 1 , n=2 (1) 356 Wind Power 1 Specification of WPS structure and parameters Specifying desired power quality EPQdesired Power quality regualtions 2 3 Regualtions for conducted emissions Specifying desired EMC Calculation of voltage and current spectra Forming updated... for Computer Aided Design of Wind Power Systems for EMC and Power Quality 357 specified for each power quality index, then the power quality problem has been solved Additionally, voltage and/or current spectra at the power supplies’ output have to be compared with EMC regulations for conducted emissions If a power quality and/or an EMC problem are identified, an expert decision has to be taken Otherwise,... by Kondo (1975) Wind Power at Sea as Observed from Space 347 Fig 5 Wind speed at 80 m height as a function of wind speed at 10 m under neutral stability for three formulations of drag coefficient Fig 6 Comparison of wind profiles under various stability conditions 7 Stability dependence Typical wind profiles at various stabilities are shown in Fig 6 At a given level, UN is larger than the actual wind . Case Transmission Systems Constraints on Wind Power Operation 337 In order to improve the offshore wind power integration and to consequently reduce the number of congestion hours over the. transmission systems. 6. Conclusion In this chapter, wind generation has been introduced into a transmission system analysis tool. This last one was composed of two parts: system states generation. wind penetration in modern real case electrical systems. Impact of Real Case Transmission Systems Constraints on Wind Power Operation 339 7. References Al Aimani S. (2004). Modélisation