Energy 35 (2010) 5217e5222 Contents lists available at ScienceDirect Energy journal homepage: www.elsevier.com/locate/energy Adequacy evaluation of wind power generation systems M. Carolin Mabel a, *, R. Edwin Raj b, E. Fernandez c a Department of Electrical and Electronics Engineering, St. Xavier’s Catholic College of Engineering, Chunkankadai, Tamilnadu 629003, India Department of Mechanical Engineering, St. Xavier’s Catholic College of Engineering, Chunkankadai, Tamilnadu 629003, India c Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttranchal 247667, India b a r t i c l e i n f o a b s t r a c t Article history: Received 11 December 2009 Received in revised form 8 July 2010 Accepted 27 July 2010 Available online 15 September 2010 This paper attempts to assess the adequacy of wind power generation systems using the data collected from seven wind farms in Muppandal, Tamilnadu (India) with a total capacity of 37 MW. A Monte Carlo model simulation is incorporated in the algorithm to obtain the hourly power output of wind farms, which also takes into account the unavailability of wind turbines. A typical load demand profile is used to examine the chronological hourly wind power generation for each month. The reliability index of LOLE (loss of load expectation) is used to estimate the reliable contribution of wind farm power generation. Ó 2010 Elsevier Ltd. All rights reserved. Keywords: Adequacy evaluation Monte Carlo technique Reliability Wind power 1. Introduction In the recent years, renewable energy applications are rapidly increasing in power generation systems. Wind is one of the fastest growing energy resource and their penetration levels in power system are increasing worldwide. The benefit of wind power generation is in providing clean energy and saving fossil fuels thereby reducing emissions. Energy generation by wind power plants is without fuel cost but nature dependent. This demands the energy generated by wind is to be consumed fully in the grid to reduce the overall generation cost. A wind turbine installed at a potential site will generate electric power 70e85% of the time, but not always at rated power output. The power output characteristics of wind energy conversion system are different from that of any conventional generation systems. A conventional power plant can be considered as binary units, either fully available or not at all but the wind power plant output vary between zero and its rated capacity. When the power generation through wind is inadequate or not available, the conventional sources must meet the load demand during those quiescent periods. In spite of the intermittent and variable nature of wind, the wind power generation system contributes to a certain extent in meeting certain percentage of the base loads of power system. The analysis on reliability aspects of wind power finds more significance compared to the conventional power generation * Corresponding author. Tel.: þ91 9488073174; fax: þ91 4652259664. E-mail address: carolin_mabel@yahoo.co.in (M.C. Mabel). 0360-5442/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.energy.2010.07.044 systems. Several studies have been reported on the modeling of wind power generation and reliability of the power system incorporating wind energy [1e3]. Different reliability evaluation methods such as probabilistic methods, chronological simulations etc. have been applied [4e6]. The contribution of a wind plant to the reliability performance of a generating system mainly depends on wind energy penetration level and wind conditions [7,8]. An adequacy evaluation of wind farms is crucial in system planning for certain base loads and to determine appropriate generating resources to meet the expected total demand. For this study, the real data are collected from seven wind farms in Muppandal, Tamilnadu (India) for a period of 3 years. The total capacity of wind farms taken for study is 37 MW contributed by 137 wind turbines. Tamilnadu is one of the Indian states having large wind energy penetration with most of the wind farms located in Muppandal area. This paper presents a method to assess the reliability of wind power generation systems. The Monte Carlo simulation technique is incorporated in the method to obtain the hourly wind power generation. The reliability index, LOLE (loss of load expectation) is used to evaluate the adequacy of wind energy. 2. Wind energy production and evaluation 2.1. Power output of a wind turbine The power output of a wind energy conversion system depends upon the stochastic nature and chronological variability of the wind 5218 M.C. Mabel et al. / Energy 35 (2010) 5217e5222 speed. Fig. 1 shows the variation in power output of a wind turbine for different wind speeds [9]. The performance regions 1 and 2, shows the amount of power that can be produced from a wind power generation system corresponding to wind speeds VIeVR and VReVO. VI is the cut-in speed, VR is the rated speed and VO is the cut-out speed. The WECS power output PT can be calculated as [10]: Table 1 Wind potential of Muppandal station. Station Muppandal 8 0 >   > < A þ BV þ CV 2 PR PT ¼ > PR > : 0 0  V  VI VI  V  VR VR  V  VO V  VO At 25m height Annual average wind speed (m/s) Wind power density (W/m2) 7.00 406 B ¼ C ¼ 1 VR Þ2 ðVI 1 ðVI VR Þ2 1 ðVI VR Þ2 " " VI ðVI þ VR Þ 4ðVI VR Þ  VI þ VR 2VR 3 # #   VI þ VR 3 4ðVI þ VR Þ ð3VI þ VR Þ 2VR 2 Technical potential (MW) 712 2100 1600 demand in time period i, Pi(Ci < Li) is the probability that the load demand exceeds the available power generation in time period i. The percentage reliability (R) is calculated as given below: ReliabilityðRÞ ¼ A ¼ Gross potential (MW) (1) The constants A, B and C are found out using the following equations: " Wind density extrapolated to 50m height (W/m2)  #  VI þ VR 3 4 2VR (2) (3) (4) Generally, the cut-in speed of a wind turbine is in the range of 2.5e3.5 m/s and cut-out speed is in the range of 20e25 m/s.  1  LOLE no: of hours The reliability R value of 1 indicates that the wind generation system is able to meet all of the load demand. On the other hand, a lesser value of 1, for e.g., 0.4 represents that the wind energy system supplies only forty percentage of the load demand. The intermittent nature of wind and the constraints in the energy available in wind can degrade the system reliability. In a generation system study, the total system generation is examined to determine its adequacy to meet the system load requirement. Renewable energy systems like, wind power generation systems does contribute to the system reliability and reduces the capacity requirement of utility system. 2.2. Evaluation of wind power using reliability index 3. Description of wind farms taken for study The reliability index indicates the ability of wind generation capacity to meet the system demand and it is used in the utility industry in generation planning. The reliability index is defined by the term, loss of load expectation. LOLE is the expected period during which the load demand exceeds the available generation capacity [11]. The LOLE is defined as: The three-year data are collected from seven wind farms located in Muppandal, Tamilnadu (India). These wind farms cover 137 wind turbines with a total capacity of 37 MW. Tamilnadu is in the southern region of India at north latitude between 8 50 and 13 350 and east longitude between 76 150 and 80 20’. This state is ahead of other Indian states with more than 50% of the total wind energy installed capacity of India. The Muppandal region in Tamilnadu has the distinction of having one of the largest concentrations of wind turbines at a single location with installed capacity of more than 1000 MW. Table 1 gives the potential of Muppandal station [12,13]. Significant variations in seasonal or monthly average wind speed are common over most parts of the world. Fig. 2 shows the pattern of monthly average wind speed variation for three consecutive years and the average over the three years. In Muppandal, the wind speed is high during May to August and reaches a maximum of 30 m/s because of South-West winds. During December and January, the wind speed again increases because of North-East winds. The variation in energy output of wind farms depends upon the variation in wind speed. Fig. 3 shows the variation of energy LOLE ¼ n X Pi ðCi < Li Þ (5) i¼1 where i is the time step, an hour or a day, n is the no of hours, Ci is the power generation available in time period i, Li is the load Fig. 1. Variation in power output of a wind turbine for different wind speeds. Fig. 2. Pattern of monthly average wind speed variation. M.C. Mabel et al. / Energy 35 (2010) 5217e5222 5219 Fig. 3. Variation of energy generation per kW capacity with monthly average wind speed. generation per kW capacity with monthly average wind speed taken over a period of three years. For reliable energy generation, it is necessary to take into account the availability factor of the wind turbine. The availability factor of a wind turbine is the percentage of time, it is ready to generate power. Servicing, inspection, component failures and accidents, such as lightning strokes reduce the availability of wind turbine. The availability of each wind turbine in a wind farm may differ depending on the faults which occur. In this study, grid breakdown and maintenance period are also included in the calculation of availability factor since grid failure results in stalling of wind turbine. Fig. 4 shows the availability of wind farms for the three-year period. The availability is calculated using the expression: Availabilityð%Þ ¼   Uptime  100 Uptime þ Downtime (6) 4. Adequacy evaluation of wind farms Fig. 5. Algorithm for the evaluation of reliability index of wind farms. 4.1. Methodology The methodology to evaluate the adequacy of wind power generation systems uses Monte Carlo technique. Monte Carlo algorithm is one of the realistic engineering tools that facilitate to perform statistical analysis of the uncertainties involved in engineering problems [14,15]. The Monte Carlo simulation produces a series of random numbers and these numbers will have the same characteristics of the probability of their occurrences in the selected domain between 0 and 1. Sufficient number of iterations is required to arrive at statistically viable result and to approach the real value of a parameter. Fig. 5 gives the algorithm for the evaluation of reliability index, LOLE of wind farms, where, NWG is the number of wind turbine generators, WPGj is the power generation of jth wind turbine generator in MW, PD is the power demand in MW, AVj is the availability of the jth wind turbine generator, NWF is the number of wind farms and PGT is the total wind power generation in MW. Fig. 4. Availability of wind farms for the three-year period. 5220 M.C. Mabel et al. / Energy 35 (2010) 5217e5222 4.2. Analysis Wind farm evaluation consists of two steps: (1) Availability evaluation of each wind turbine and (2) the adequacy evaluation of wind farms. The hourly power output of the wind farms are obtained by the model simulation. With the available data of hourly mean wind speed and its standard deviation, normally distributed random wind speeds for every second are generated using Monte Carlo simulation. The generated wind speeds are used to calculate the wind turbine generator power output of each wind turbine as given by the Eq. (1), which gives the power output at any instant. At any instant, the total power output of the wind farm is the sum of the power output of each wind turbine. From the instant power output, the hourly wind farm power output is evaluated. In addition, the Monte Carlo technique generates random values and compares with the availability of each wind turbine of the wind farms to determine the inclusion of each wind turbine for determining power output of wind farms. The methodology is repeated for number of iterations to obtain the LOLE. The assessment of the reliability of wind farms in generation systems is essential from the planning point of view. The chronological Fig. 6. Load curve used for adequacy evaluation. hourly generation data for each month of the seven wind farms are used to match with a chronological hourly load demand model. The total capacity of the seven wind farms from which the data are collected is 37 MW. The maximum average hourly power generation is Fig. 7. Profiles of the wind farm energy production and the load demand. M.C. Mabel et al. / Energy 35 (2010) 5217e5222 5221 Fig. 7. (continued). 15 MW during the period taken for study. This power generation is rather low in comparison with the rated capacity due to the nature of wind, availability of wind turbines and grid etc. Therefore, the rated capacity of a wind turbine or a farm cannot be interpreted in the same way as that of a conventional generation system. The capacity value of any wind farm can be considered as the capacity factor multiplied by its rated capacity [8]. The wind farms taken for study are connected to the southern grid of India. The following assumptions are made to evaluate the adequacy of wind power generation systems: 1) It is considered that wind power generation systems alone meet the load demand. 2) The load demand curve pattern is the same throughout the year since the seasonal demand variation is negligible owing to the weather condition of the site. 3) In the scaled load curve, the peak load demand is taken as 90% of the maximum hourly average power generation in a year. For the adequacy evaluation, a suitable load model is essential. This load curve is derived from a typical load curve of southern region grid of India by scaling down the actual load demand curve pattern. The peak load demand is taken as 13.5 MW which is 90% of the maximum hourly average power generation. The obtained load curve for the study is of the similar pattern of typical load curve with difference in magnitude. Fig. 6 shows the derived load curve for the adequacy evaluation. The LOLE index is obtained by comparing hourly average generation of each month with the corresponding hourly load demand. The LOLE is obtained as 164.87 h per year. This means the wind farm reliability contribution is 42.75% in meeting the load demand. Fig. 7(a)e(l) shows the comparison of monthly hourly average daily generation curve with the load demand profile. It can be seen from Fig. 7(b)e(e), during the months of May to August the power generated from wind farms can meet the load demand for maximum number of hours. In Fig. 7(l) for the month of March, the power generation is too low and the load demand is not even met once. This shows how the seasonal variation of wind speed affects the wind power generation and thereby its reliability. From the Fig. 7(a)e(l) it can be noted that throughout the year, a base power generation of 2 MW is available from the wind farms taken for study. 5222 M.C. Mabel et al. / Energy 35 (2010) 5217e5222 Another feature of wind energy to be noted from this study is that during off peak hours, the wind power generation is high and during peak hours the power generation is low. This signifies that the wind energy must be considered only for the base load while integrating with the power systems. During the months of June, July and August in Fig. 7(c)e(e), the maximum power generation from the wind farms is higher than the peak load demand, but the time of the day does not match with peak demand. The adequacy analysis shows the hours during which the power generation is high and low, and accordingly the energy planners must handle the reserves in power system. 5. Conclusions A case study with respect to the reliability assessment of wind farms is carried out using the real data collected from Muppandal, Tamilnadu (India). It is seen that the reliability contribution of wind farms is 42.75% to meet the load demand pattern assumed for the evaluation. In addition, the study depicts that the maximum wind power generation period does not match with the peak load period. This signifies that in a power system, wind generated power must be utilized to the maximum to meet the base load demands to conserve conventional fuels and to reduce emissions. However, the total energy contribution by the wind farms to the power system is significant. While operating wind energy conversion systems with the other sources, it is necessary to know when and how much power will be available from wind in order to satisfy the load demand. This adequacy evaluation study is helpful to energy planners and operators in planning and decision-making. 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