2010 IREP Symposium – Bulk Power System Dynamics and Control – VIII (IREP), August 1-6, 2010, Buzios, RJ, Brazil

Probabil...
conventional capacity may increase in order to maintain
system reliability [5]. Note that the deterministic approach is
fa...
turbines. The method is as same as used for conventional
generator [10]. Then the probability of a given wind farm
output ...
The forced outage rate of a two-terminal transmission line can
be calculated by (6) [13]:

FORL = (λL × L × rL + 2λT × rT ...
to meet the 6% RPS target. The method presented in this subsection a simple and easy to use practical model that can be
ut...
300

10

EENS (GWh)

12

230

8

200

6

150

4

100

LEGEND
ADDITION OF CONVENTIONAL
UNITS
EENS

2

50

0

ADDITION OF CO...
TABLE II. TYPICAL INDUSTRY DATA FOR LINE OUTAGE

Parameter

Value

λL
λT

0.6488
0.1629

rL

20.8

rT

16.1

TABLE III. PR...
reliability cost of using de-rated transmission upgrade can be
calculated by the proposed method and framework presented
i...
Transmission at the California Independent System Operator.
His fields include power system stability and reliability,
eco...
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Probabilistic wind energy modeling for electric generation system

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Probabilistic wind energy modeling for electric generation system

  1. 1. 2010 IREP Symposium – Bulk Power System Dynamics and Control – VIII (IREP), August 1-6, 2010, Buzios, RJ, Brazil Probabilistic Wind Energy Modeling for Electric Generation System Reliability Assessment D. O. Koval, FIEEE Yi Zhang, SMIEEE A. A. Chowdhury, FIEEE Dept. of Electrical & Computer Operator University of Alberta Edmonton, Alberta Canada T6G 1G4 California Independent System Operator 151 Blue Ravine Road Folsom, CA 95630 USA California Independent System Operator 151 Blue Ravine Road Folsom, CA 95630 USA Abstract The power grid reliability impacts could be significant when a large amount of variable wind generation is integrated with the electric power system. The widely used deterministic reliability assessment method is invalid when modeling intermittency of wind energy sources. The energy based probabilistic reliability assessment models are required in system reliability impact assessment in order to consider the stochastic characteristic of wind resources. This paper investigates different stochastic characteristics in wind energy integration, including resource availability, generation facility outages and transmission availability. A probabilistic framework of reliability modeling for renewable resource integration such as wind energy conversion system is proposed in this paper. Using the proposed reliability models and framework, the cost of wind energy integration with the power gird for maintaining system adequacy and reliability can be evaluated realistically. The IEEE Reliability Test System (IEEE-RTS) system is utilized to demonstrate the developed models and methods. I. Introduction Renewable energy resources such as wind and solar energy conversion systems play an important role to eliminate reliance on fossil fuels as well as in reducing the greenhouse gas emissions. With the wind energy technology advancements in the past couple of decades, it is expected that a large amount of electric energy supply requirements will be met by non-conventional energy sources such as wind, solar and geothermal technologies. Many countries have adopted aggressive Renewable resource Portfolio Standard (RPS) targets in order to reduce their reliance on imported oil, and on environmentally harmful fossil fuels. For example, in the state of California, a 33% RPS target by 2020 is under consideration [1]. Wind resource integration with the power system has received increased attention by power system researchers and engineers in both planning and operation phases. Intermittency and variability of energy production 978-1-4244-7467-7/10/$26.00 © 2010 IEEE associated with any renewable technologies need to be reflected and accurately modeled in system reliability performance assessments. The inability of modeling the stochastic characteristics of power system is not a new problem for widely deterministic methods; however, it becomes a serious problem when considering the integration of wind resources with the power system. For example, in the current practice of system planning, the deterministic method is used in generation interconnection to identify and to resolve the stability issues [2]. The transmission upgrades identified based on the worst case scenarios may be underutilized if the capacity factor of wind resource is low. A solution that has been used in deterministic study is to adopt a de-rated capacity as the target capacity of transmission upgrade [3]. The selection of the derated target capacity is normally based on the average of historical data or field measurements. The deterministic reliability assessment is a snapshot study that is difficult to cover all possible scenarios. The system addition identified by deterministic methods cannot adequately model power system capability to accommodate different types of generation technologies including intermittent sources. The probability of loss of load may increase when wind resource penetration and system load increase [4 and 5]. Probabilistic reliability techniques are required to model the impacts of wind energy resources on system reliability and adequacy. The energy-limited and intermittent characteristics of wind generation, especially wind generation, have been studied using the probabilistic reliability assessment techniques. The capacity state probability model of energylimited generation in probabilistic reliability assessment has been developed in [6 and 7]. The capacity state probability model of wind generation output is dependent of wind speed and wind turbine outage [8 and 9]. The impacts on system reliability from wind generation have been investigated based on probabilistic reliability assessment from different view points [4, 5, 10 and 11]. It has been recognized that the system reliability may be degraded when wind generation penetration increases in power system, because of the intermittent characteristic of wind resource. The requirements of additional
  2. 2. conventional capacity may increase in order to maintain system reliability [5]. Note that the deterministic approach is favored in calculating the operating limit and transmission capability, which are needed as input to probabilistic reliability assessments. A new approach to reliability cost assessment is presented in this paper, which is an extension of the model developed in [5]. The impacts of wind resource on system reliability can be quantified by the cost of additional conventional capacity. Several reliability modeling issues, which may affect the accuracy of probabilistic reliability assessment for wind resource integration, are investigated in this. As an extension of [10], the impact of wind turbine outage on system reliability is analyzed first. Then the correlation between the wind capacity factor and the outage rate of wind turbine is discussed. Another modeling issue is related to the capacity of transmission system. It is assumed that the transmission capacity has been obtained in deterministic study. An equivalent probabilistic reliability model is developed to represent the composite system of transmission and wind generator. Using the developed model, the impact of transmission upgrade on system reliability can be assessed when different target capacity of transmission upgrade is adopted. The outages of transmission line and other facilities can also be incorporated into the developed model. Based on the proposed models and methods, a framework of identifying appropriate target capacity of transmission upgrades and additional generation capacities can be developed. The IEEERTS system is used to illustrate the developed probabilistic models. II. Modeling Wind Resources in Generation System Adequacy Evaluation Different reliability indices can be used to quantify the system reliability. LOLE (Loss of Load Expectation), LOLP (Loss of Load Probability) and EENS (Expected Energy Not Supplied) have been widely used in probabilistic reliability assessment in power systems reliability evaluations. Similar to [10], EENS is used in this paper as the reliability index. It has been verified that the reliability assessment results using EENS in this paper are consistent with the results using hourly LOLE. Only the results using EENS are presented. According to [6], EENS can be calculated as. n ∑E P k k k =1 Ek is the energy curtailed when the capacity is at state k. EENS has energy unit, such as MWh or GWh. B. Capacity states of a wind farm Assuming there is no energy storage facility associated with the wind energy conversion system, the wind generation can be modeled as a conventional generation with multiple capacity states with corresponding probability reflecting the energy availability at various levels. [7]. The capacity states of wind generation can be sampled from historical profiles of wind generation. The philosophy of using the historical profiles of wind generation is that both wind energy availability and wind turbine availability have been reflected in the historical profiles. In the absence of live data, the wind generation output is normally calculated based on the wind speed of the location where the wind turbine is installed and the wind turbine technology [8, 10 and 11]. In a wind farm that has many wind turbines, the availability of individual wind turbines is another factor that may affect the output of the wind farm. A joint capacity state method has been developed in [8 and 9] to calculate the capacity states of wind farm considering the availability of both wind energy and wind turbine. This model is further employed in generation system reliability assessment for wind generation integration in [10]. The same idea is extended in this paper to investigate the correlation of the wind turbine outage and the wind farm capacity factor. The capacity factor of a wind farm is the ratio between the average capacity and the maximum capacity in the study period, which normally is a calendar year. A large wind capacity factor implies that the probabilities of high wind conditions are large. Capacity factor can be calculated as A. Reliability index, expected energy not supplied (EENS) EENS = where n is the number of system capacity states; Pk is the probability of a capacity state; (1) Capacity factor = Average capacity of study period (2) Maximum capacity In order to calculate the joint states of a wind farm, two capacity state tables need to be created first. One is the state table of available wind. It can be represented as a pair of wnd , Pjwnd ), available wind capacity and its probability ( C j for j = 1 to s; s is the number of wind capacity states. The wind capacity availability and its probability can be obtained from the historical or field-measured wind profiles. The other state table is for the capacity of available wind turbines and its probability. The state table for the wind turbine capacity can be calculated using the forced outage rate (FOR) of wind
  3. 3. turbines. The method is as same as used for conventional generator [10]. Then the probability of a given wind farm output C k can be obtained by: n ⎛ s ⎛C ⎞⎞ P(C k ) = ∑ ⎜ Pi wtg × ∑ Pjwnd × U ⎜ k − C wnd ⎟ ⎟ j ⎜ ⎝ i ⎠⎟ i =1 ⎝ j =1 ⎠ farm is connected to the system through two transmission lines. The wind generation and its associated transmission upgrades can be modeled as a system in series connection from the reliability assessment standpoint. (3) where, n is the number of states of wind turbine availability; Piwtg is the probability of the wind turbine availability at state i; U(•) is a step function. Fig. 1. An example of wind farm interconnection From equation (3), it can be seen that the probability of wind capacity is the weight of the probability of wind turbine availability in calculating the probability of the joint capacity. Then a large wind capacity factor implies that the wind turbine availability has large impact on the probability of the joint capacity, especially, under high wind conditions. Therefore, it is necessary to evaluate and revise the wind profile by incorporating the wind turbine availability when wind capacity factor is large. C. Joint capacity of wind generation and transmission Interconnecting wind generation to the grid may need long distance lines since the wind resources are normally located at remote areas. The transmission path between the wind generation and the main grid may consist of one or multiple transmission lines and reactive power support devices. Normally, the capacity of the transmission path is a function of the availability of transmission lines and the status of the reactive power compensation devices. The deterministic reliability assessment normally identifies the transmission upgrade based on a de-rated capacity instead of the full capacity of wind farm. The target capacity of transmission upgrade that is often used in current practice is the average MW value on hourly wind profile during the peak-load or offpeak load hours, depending on which scenario is selected for the deterministic study. In some wind resource areas, the average wind capacity during summer peak-load hours may be 60% of the wind farm capacity and even lower; during the offpeak hours it can be as high as 80% [3]. It is important to select the target capacity appropriately so that both the system reliability and the utilization of green energy will be economically optimized. Probabilistic reliability models can permit the selection of the optimal target capacity of transmission upgrade. In this section, a probabilistic reliability model of the composite system of wind generation and transmission lines is developed based on reliability mathematics [12]. A typical wind generation interconnection is shown in Fig. 1, where a wind The series connection system is shown in Fig. 2. The input and output of the series system are wind capacity and the delivered power to the main grid, respectively. The capacity of the wind farm, transmission lines and the output of the composite system are denoted by x, y, and z, respectively. In this series connection system, the output z is the minimum of x and y, i.e. z = min( x, y ) (4) Probabilities of x and y are denoted by Px and Py , respectively; hence the probability of the composite system output Pz can be obtained by Pz ( z) = Px ( z) × ∑ Py ( y) + Py ( z) × ∑ Px ( x) (5) y≥ z x≥ z The capacity state pair (x, Px ) of the wind farm can be obtained by the method discussed in Sub-section II.B. The derivation of capacity states of transmission lines, denoted by y, is discussed in the following section. Fig. 2. A series connection system If the forced outages of transmission lines are ignored, the transmission path has two capacity states, path rating and 0. The probabilities of these two capacity states are 1 and 0, respectively. Considering line forced outages, each transmission line still has two capacity states, but the probability of capacity states need to be revised according to the outage rate of the line. For the transmission path with two parallel lines as shown in Fig. 1, its capacity states can be obtained by convoluting the capacity states of two lines. The algorithm is the same as for calculating the capacity states of generation system with multiple units, which is not repeated here instead the reader is referred to [6].
  4. 4. The forced outage rate of a two-terminal transmission line can be calculated by (6) [13]: FORL = (λL × L × rL + 2λT × rT ) / T (6) where, λL L rL λT rT T is the frequency of line related occurrence/year/mile, normally is given in miles; is the length of a line, mile; is the mean duration of line related hour/occurrence; is the frequency of terminal related occurrence/year; failure, per 100 failure, failure, is the mean duration of terminal related failure, hour/occurrence; is the hours of a cycle, e.g. 8760 hours of a year. demonstrate the reliability modeling of wind energy integration using the proposed models and methods. In this sub-section, a wind farm as given in [10] is added into the system. The wind capacity availability of this wind farm is shown in Table I. The wind capacity factor is 83%. The capacity of the wind farm is adjusted so that the consumed wind energy is 6% of the total consumed energy. This reflects the 6% RPS target. Assume that each wind turbine generator has 2 MW capacity and 10% FOR. The base case scenario has 2400 MW of peak load and 0 MW of wind generation. The EENS of this base case scenario is used as the reliability criterion of the system. For any other studied scenarios, if the EENS is less than the EENS of the base case scenario, then the studied scenario is deemed reliable; otherwise is not reliable. Fig. 3 shows that the EENS increases rapidly as the load increases. In order to maintain system reliability, conventional capacity is added into the system. In this example, assume the capacity of the additional units is 25 MW and the FOR is 6.3%. The addition of wind generation and conventional capacity that are needed to meet the 6% RPS target and to maintain the system reliability are shown in Fig. 4. It can be seen in this example that the addition of conventional capacity increases rapidly and will be more than the new wind generation when the peak load exceeds certain level. This implies that the wind penetration is limited for a particular system depending on the peak load, which is shown in Fig. 5. TABLE I. WIND ENERGY AVAILABILITY AND THE PROBABILITY % of Capacity 0 8 20 30 42 56 70 84 100 The benefit of using de-rated transmission mainly is the saving on transmission investment. From the standpoint of system reliability however, the de-rated transmission upgrade may reduce the contribution of wind generation to system reliability improvement. A benefit/cost assessment therefore is needed to determine the optimal capacity of the transmission upgrade for wind generation interconnection. The cost may include additional conventional capacity that is needed to maintain system reliability, and the additional wind capacity to meet the mandatory RPS target. Additional transmission upgrade for the higher capacity may also be needed. The reliability approach proposed in this paper can be used to identify the cost of de-rated transmission upgrade, which will be discussed in detail in the following section. EENS (GWh) D. Benefit/cost assessment of de-rated transmission upgrade Probability 0.0933332 0.0162963 0.0192593 0.0222222 0.0251852 0.0059259 0.0118519 0.0029630 0.8029630 BASE CASE PEAK LOAD The transmission capacity of the transmission path is also affected by the status of the reactive power devices, such as shunt capacitors or series compensators. If some reactive power devices are out of service, the transmission capacity may need to be de-rated. The capacity states considering the outages of reactive power devices can be calculated using the series connection system model as shown in Fig. 2. For simplicity, only the line outage is considered in this paper. Note that the available capacity of the transmission path between the wind generation and the main grid is determined separately, normally by deterministic reliability assessment method. III. Reliability Assessment of a Wind Resource E. Reliability cost of wind integration The IEEE-RTS [13] system is used in this paper to Fig. 3. EENS increases as wind penetration and peak load increase
  5. 5. to meet the 6% RPS target. The method presented in this subsection a simple and easy to use practical model that can be utilized in large system assessments. 700 650 600 550 500 LEGEND Addition of conventional capacity Addition of wind capacity 450 400 350 300 250 200 150 100 50 0 2300 2400 2500 2600 2700 2800 2900 3000 3100 3200 3300 Peak load (MW) Penetration Fig. 4. Addition of new conventional capacity to improve system reliability Fig. 5. Wind penetration change along with peak load increases Similar studies have been performed using different wind capacity factors. The same conclusions can be made, although the results are not shown here because of space limitation. Note that the size and FOR of the additional thermal units that are used in the earlier assessments will affect the total capacity need of additional thermal units, but the conclusion will still be valid. In order to meet the RPS goal and to maintain the system reliability, close coordination between the developments of wind resource and conventional capacity is always needed in wind resource integration. The additional conventional capacity causes reliability capacity cost of wind resource integration, which can only be adequately determined using the the probabilistic reliability models. The reliability capacity cost can be obtained given the cost of additional thermal units. This cost needs to be added with other cost in the economic assessment for wind integration. Assuming the cost of thermal unit in this example is $0.3 million/MW, the reliability capacity cost of wind integration is shown in Fig. 6. It can be seen that the reliability capacity cost increases quickly as peak load increases, in order Fig. 6. Cost of capacity addition of wind integration F. Effect of wind capacity factor In this sub-section, the test system is modified such that the peak load is 2850 MW. A 570 MW wind farm is added into the system to replace 570 MW of existing generation that has higher FOR. EENS for scenarios with different wind capacity factors are computed, as well as the need of additional conventional capacity. The results are shown in Fig. 7, where the EENS is illustrated by wide columns and the need for additional conventional capacity is illustrated by narrow columns. As expected, the wind farms with low capacity factor have less contribution to the system reliability than the wind farms with high capacity factor. This is reflected in Fig. 7 that the EENS decreases when wind capacity factor increases. It is also seen that more additional conventional capacity is needed for the wind farms with low capacity factor than the wind farms with high capacity factor. G. Impacts of wind turbine availability A large wind farm normally includes many energy collection facilities, e.g. hundreds of the wind turbines in a wind farm. This sub-section will discuss the impact of wind turbine availability on the system reliability. The correlation of the wind turbine availability and the wind capacity factor will be analyzed.
  6. 6. 300 10 EENS (GWh) 12 230 8 200 6 150 4 100 LEGEND ADDITION OF CONVENTIONAL UNITS EENS 2 50 0 ADDITION OF CONVENTIONAL CAPACITY (MW) The same modified test system as considered in sub-section III.B is used. Two wind turbine outage rates, 4% and 20%, are compared. The changes of EENS that are resulted from the increase of wind turbine outage rate are shown in Fig. 8. 0 0% 10% 20% 30% 40% 50% CAPACITY FACTOR RATIO OF EENS CHANGE Fig. 7. EENS for different wind farms with different capacity factors Fig. 8. Ratio of EENS change between 4% and 20% wind turbine FOR It can be seen that the increase of wind turbine outage rate has limited impact on system reliability if wind capacity factor is low. The impact of wind turbine outage may become significant in the cases of high wind capacity factor. The results of reliability assessment may be unrealistic if wind profile does not incorporate wind turbine availability. Note that the availability of wind turbines is affected not only by the wind turbine outage, but also by the outage of collector system and the auxiliary facilities in the wind farm. The outage rate of collector system in the wind farm is normally small since underground cable is often used. On the other hand, the impact of collector system outage may be not small since a collector feeder outage will result in all wind turbines on the same feeder unavailable. Test result of the impact of collector system outage is not presented in this paper because of lack of outage data of collector system. Still, the joint capacity state model of wind generator and transmission line, which has been presented in Section II.C, can be used to create the capacity state probability model of a wind farm considering collector system outage. H. Target capacity for transmission upgrade Assume a 570 MW wind farm is interconnected with the system via the plan of interconnection shown in Fig. 1. Three scenarios of transmission upgrades are compared with different transmission capacity. Normally the upgrade is decided by deterministic criteria widely used in transmission system planning. In the first scenario, the capacity of the transmission upgrade is based on the full capacity of the wind farm, which is assumed to be 570 MW as two lines in service and 285 MW when one line in service. A remedial action scheme (RAS) of tripping wind generation may be needed when only one line is in service. The second scenario is that the transmission upgrade can deliver 80% of the nameplate capacity of the wind farm, which is 456 MW as two lines in service and 228 MW as one line in service. In the third scenario, the transmission upgrade is based on 60% of wind capacity, which are 342 MW and 171 MW as two lines in service and one line in service, respectively. Probabilistic reliability models proposed in Section II.C are used in system reliability assessment. Line outage data are given in Table II, which is the typical outage data of 230 kV lines used in some utilities in North America. Further assume that the length of the transmission lines is 200 miles to reflect the long distance of the wind interconnection. The outage rate of each line can be obtained by (6) and the capacity states of the two-line transmission system can be calculated using the method discussed in Section II.C. The capacity states of the transmission system with two parallel transmission lines are shown in Table III. First assume a 570 MW wind farm with 40% wind capacity factor is interconnected. The EENS of three scenarios of transmission upgrades are illustrated in Table IV. In the first column of Table IV, “Full capacity” means the target capacity of transmission upgrade is based on the full capacity of the wind farm; “80% capacity” and “60% capacity” mean the target capacity is 80% of wind farm capacity and 60% of wind farm capacity, respectively; “plus outage” means the line outage is modeled. Also listed in Table IV is the ratio of the consumed wind energy and the total consumed energy. It can be seen from Table IV that the availability of transmission capacity does impact the system reliability and the consumed wind energy, but not significant. The outage of transmission lines between the wind farm and the main grid has slight impact on the system reliability and the utilization of wind energy.
  7. 7. TABLE II. TYPICAL INDUSTRY DATA FOR LINE OUTAGE Parameter Value λL λT 0.6488 0.1629 rL 20.8 rT 16.1 TABLE III. PROBABILITY OF LINE CAPACITY Capacity Probability 0 One line in service Two lines in service with 60% capacity factor is integrated with the power system, while all other assumptions are as same as in the previous example. Table V shows the reliability assessment results. It can be seen that when the transmission capacity is 60% of the wind capacity, the system reliability and the utilization of wind energy reduced significantly, comparing to the 80% capacity and full capacity scenarios. The reliability capacity cost can be evaluated based on the addition of thermal units. Assume that the FOR of the additional thermal units is 0.12. The 60% capacity scenario needs 30 MW of additional thermal capacity to achieve the same reliability level as in the full capacity scenario. It is also noticed that extra wind generation capacity may be needed for the de-rated transmission upgrade scenarios depending on the RPS target. 0.00001354 0.00734615 0.99265031 TABLE IV. EENS COMPARISON FOR 40% WIND CAPACITY FACTOR Target transmission capacity EENS (GWh) Consumed wind energy/Total consumed energy (%) Full capacity Full capacity plus outage 80% capacity 80% capacity plus outage 60% capacity 60% capacity plus outage 3.967901 3.969822 3.975363 3.979477 4.058380 4.066654 12.94 12.92 12.63 12.61 11.53 11.51 TABLE V. EENS COMPARISON FOR 60% WIND CAPACITY FACTOR Transmission Model EENS (GWh) Consumed wind energy/Total consumed energy (%) Full capacity Full capacity plus outage 80% capacity 80% capacity plus outage 60% capacity 60% capacity plus outage 2.175580 2.179758 2.204843 2.212937 2.415480 2.429566 19.50 19.45 18.21 18.16 15.35 15.30 For the 570 MW wind farm with 40% capacity factor in this example, it may be appropriate to build the transmission lines whose capacity is 60% of the wind farm capacity from the probabilistic reliability standpoint. The transmission upgrade based on the full capacity of the wind farm does not provide much more benefit to the system than the upgrade based on 60% capacity. The wind farm capacity factor may affect the selection of the target capacity of transmission upgrade. Assume a wind farm IV. Conclusions Wind resource integration has significant impacts on the system reliability. Although the deterministic reliability study is often used in generation interconnection studies, it lacks the capability of considering the stochastic characteristics of wind resources. It is necessary to complement the deterministic approach with probabilistic models in system reliability assessments to identify the system upgrade and the associated cost. Probabilistic reliability assessment has been applied to wind resources integration in this paper. It has been demonstrated that the deployment of wind generation needs to be coordinated closely with the deployment of conventional capacity. The probabilistic reliability assessment can identify the reliability capacity cost for wind resource integration. The reliability capacity cost can be estimated based on the addition of the conventional capacity that is needed to maintain system reliability. Several modeling issues of wind resources integration in probabilistic reliability assessment have been discussed using several wind generation scenarios. The availability of wind turbines in the wind farm has impact on the wind generation output and the system reliability. The joint capacity state model of wind farm that considers wind capacity availability and wind turbine availability has been investigated. It is recommended that incorporating wind turbine availability in wind integration reliability assessment, especially when wind capacity factor is high is absolutely necessary. Another important issue that has been discussed is the de-rated transmission upgrade in wind interconnection. System reliability will be degraded when de-rated transmission upgrade is adopted. A joint capacity model of wind generation and transmission is proposed in order to identify the appropriate target capacity of transmission upgrade. The
  8. 8. reliability cost of using de-rated transmission upgrade can be calculated by the proposed method and framework presented in this paper. In conclusion, the probabilistic reliability models and methods presented in this paper are simple and easy to use and can be applied to practical large power systems to investigate the system reliability impacts from large wind energy penetration. Including Wind Energy”, Microelectronics and Reliability, Vol. 36, No.9, 1996, pp.1253-1261. [12] Athanasios Papoulis, “Probability, Random Variables, and Stochastic Processes,” WCB McGraw-Hill, Third Edition, 1991. [13] Reliability Test System Task Force, “IEEE Reliability Test System,” IEEE Trans. On Power Apparatus and Systems, Vol. PAS-98, No. 6, Nov./Dec., 1979, pp. 2047~2054. V. Disclaimer This paper does not reflect in any form and manner the position of the California ISO. Any errors and omissions are the sole responsibilities of the authors. VI. References [1] California Energy Commission, “2007 Integrated Energy Policy Report, CEC-100-2007-008-CMF”, pp. 101. [2] A.A. Chowdhury, Y. Zhang and et. al, “Practice of Large Wind Generation Interconnection Study in CAISO,” IEEE PES 2009 General Meeting, July 26~30, 2009, Calgary, Canada. [3] CAISO, “RETI Phase II - Conceptual Transmission Planning”, 2009. [4] R. Karki, R. Billinton and G. Bai, “Risk Based Equivalent Wind Capacity in Power Generation Systems”, 8th International Conference on Probabilistic Methods to Power Systems, Iowa State University, Ames, Iowa, Sept. 12-16, 2004. [5] Y. Zhang and A. A. Chowdhury, “Reliability Assessment of Wind Generation in Planning and Operating Phases in Generation System,” IEEE PES 2009 General Meeting, July 26~30, 2009, Calgary, Canada. [6] R. Billinton, R. Allan, “Reliability Evaluation of Power Systems,” Plenum Press, 2nd edition, 1996. [7] R. Billionton and P. Harrington, “Reliability Evaluation in Energy Limited Generating Capacity Studies,” IEEE Trans. On Power Apparatus and Systems, Vol. PAS-97, No. 6, Nov/Dec, 1978, pp. 2076~2085. [8] P. Giorsetto and K. F. Utsurogi, “Development of a new procedure for reliability modeling of wind turbine generators”, IEEE Transactions, Vol. PAS-102, No. 1, pp. 134-143, January 1983. [9] X. Wang, H. Dai, and R. Thomas, “Reliability modeling of large wind farms and associated electric utility interface systems”, IEEE Transactions, Vol. PAS-103, No. 3, pp. 569-575, March 1984. [10] A. A. Chowdhury, “Reliability Models for Large Wind Farms in Generation System Planning”, in Proceedings of 2005 IEEE PES Annual General Meeting, San Francisco, CA, June 12-16, 2005. [11] R. Billinton, H. Chen and R. Ghajar, “Time-Series Models for Reliability Evaluation of Power Systems VII. Biographies D. O. Koval (S’64 - M’65 - SM’78 - F’90) is a professor in the Department of Electrical and Computer Engineering at the University of Alberta, Edmonton, Alberta, Canada. He teaches classes in Reliability Engineering, Power Quality, Power System Analysis and “IEEE Gold Book”. He worked 12 years as a distribution special studies engineer for B.C. Hydro and Power Authority in Vancouver, B.C. and for two years as a subtransmission design engineer for Saskatchewan Power in Regina, Saskatchewan. He is a Registered Professional Engineer in the Provinces of Alberta and British Columbia and a Fellow of the American Biographical Institute and a Life Fellow of the International Biographical Centre (Cambridge, England). He serves on the Board of Directors of several international societies (e.g., IASTED, ICSRIC) . He has authored or co-authored over 300 technical publications in the fields of emergency and standby power systems, power system reliability, human reliability, power system disturbances and outages, power quality and computer system performance. He is listed in Marquis’s “Whos’ Who in the West”, “Who’s Who in America”, “Who’s Who in World”, “Personalities of the Americans”, “Who’s Who in Science and Engineering”, “5000 Personalities of the World”, and in the International Biographical Centre’s : “International Leaders of Achievement”, “International Who’s Who of Intellectuals”, and “Men of Achievement”. He was the Editor of IASTED international proceedings on “High Technology in the Power Industry”, 1996. He was co-chairman of the 1998 and chairman of the 2007 I&CPS Conferences held in Edmonton, Canada. He is chairman of IEEE Std. 493-1997 and 2007 (IEEE Gold Book). He was elected as one of the six Distinguished Lecturers of the IEEE Industry Applications society (IAS) for the period 2000-2001. He was appointed to the rank of Distinguished Visiting Professor and elected Fellow by the International Institute for Advanced Studies in Systems Research and Cybernetics in Germany. He was recently elected a Fellow of the Engineering Institute of Canada and awarded the IEEE Standards Medallion Award in 2008. Yi Zhang received the B.S. and M.S degrees from Tianjin University, Tianjin in 1993 and 1996, respectively, and the Ph.D. degree from Washington State University, Pullman, in 2007. He is currently with the Department of Regional
  9. 9. Transmission at the California Independent System Operator. His fields include power system stability and reliability, economic assessments of transmission expansion, and renewable resource integration. He was with EPRI of China from 1996 to 2001 as an engineer and project lead of several DMS, SCADA/EMS and Power Market projects. A. A. Chowdhury (F ’05) received his MSc degree with honors in electrical engineering from the Belarus Polytechnic Institute in Minsk, Belarus. His MSc and PhD degrees in electrical engineering with specialization in power systems reliability and security were earned at the University of Saskatchewan in Canada, and his MBA degree from the St. Ambrose University in Davenport, USA. Dr. Chowdhury is currently the Director of Planning and Infrastructure Development at the California Independent System Operator, Folsom, California. He has approximately 30 years of electric utility industry, electric equipment manufacturing industry, consulting experience, and teaching, research and development in power system reliability and security assessments, planning, and analysis. He is actively involved in the development of probabilistic models, criteria and software for use in power system planning, operating and maintenance. He has given invited lectures on theory and applications of power system reliability and value-based planning nationally and internationally. Dr. Chowdhury has performed original works on power systems reliability and value-based assessments, designed and conducted customer interruption cost surveys, and developed practical system models for use in reliability cost/reliability worth assessments in the emerging competitive electricity market. He has co-authored a book with Dr. Don Koval, “Power Distribution System ReliabilityPractical Methods and Applications”, authored and/or coauthored approximately 150 technical papers on power system analysis; planning and reliability published in different peer reviewed engineering journals in Asia, Europe, Australia, Africa, and North and South Americas. He has received five best prize paper awards from the IEEE, the International Institute for Advanced Studies in Systems Research and Cybernetics and the 8th International Conference on Probabilistic Methods Applied to Power Systems. Dr. Chowdhury has received the IEEE Region 4 “2003 Outstanding Engineer of the Year Award” for his contributions to the science of power system reliability evaluation. Under his chairmanship and also received the 2005 IEEE Technical Working Group Recognition Award for contribution to the revision and expansion of the IEEE Standard 762-2005: Standard Definitions for Use in Reporting Electric Generating Unit Reliability, Availability, and Productivity. He has received the IEEE Regional Activities Board (RAB) 2005 Achievement Award for his outstanding leadership and contributions and to IEEE and Engineering profession. Dr. Chowdhury has been listed in International Biographical Center’s (Cambridge, UK) “2004 Living Legends”, 2008/2009 2000 Outstanding Scientists of the World, Marquis’ Who’s Who in America, Who’s Who in the World, Who’s Who in Finance and Business, and Who’s Who in Science and Engineering. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), a Fellow of the British Institution of Engineering and Technology (IET), a Chartered Engineer in the United Kingdom, a Registered Professional Engineer in the State of Texas, and in the Province of Alberta, Canada.

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