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ERCOT Planning October 2010

Target Reserve Margin and Effective Load Carrying Capability of Installed Wind Capacity for the ERCOT System - Methodology. ERCOT Planning October 2010. Overview.

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ERCOT Planning October 2010

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  1. Target Reserve Margin and Effective Load Carrying Capability of Installed Wind Capacity for the ERCOT System - Methodology ERCOT Planning October 2010

  2. Overview • The scope of this study is to evaluate the impact of system volatility on the relationship between generation reserve levels and system reliability. • The following components of system volatility are being considered: • The forced outage and derating of generating facilities. • Load forecast uncertainty related to weather. • The intermittent nature of wind power.

  3. Reliability Indices • The following are the three main reliability indices used in this study: • Loss-of-load events (LOLEV): The number of times in a year that available generation was incapable of meeting demand. LOLEV provides information about the frequency of events. • Loss-of-load hours (LOLH): The number of hours in a year that available generation was incapable of meeting demand. LOLH provides information about the duration of events. • Expected unserved energy (EUE): The total amount of MWh in a year of demand that could not be met by available generation.

  4. Modeling assumptions • Transmission • The transmission network is not being modeled. The ERCOT system is being represented as a single node. • Generation other than wind • For each generator, for each iteration, a sequence of periods during which the unit is available and unavailable to provide energy is generated using a MATLAB model (sequential Monte Carlo approach). • Scheduled outages are programmed in UPLAN.

  5. Modeling assumptions • Wind generation • Since the transmission network is not being considered, the wind-farm-specific hourly profiles are aggregated. To capture the randomness of wind generation, daily wind profiles are generated by randomizing the available daily profiles using a MATLAB model. • Forced outages of individual wind turbines are not being modeled. • A family of hourly chronological load patterns have been developed in order to capture weather related uncertainty.

  6. Input Data – Generation • All existing, as well as future resources with a signed interconnection agreement, that are expected to be in service in year 2012 are being considered, including units under reliability must-run (RMR) review. • The import capacity of the DC ties is not taken into account. • Hydroelectric capacity is not being considered. • Generation and load from private use networks are included in this study. • Monthly capacity multipliers are applied in order to model the seasonal capacity ratings of thermal units. The seasonal values from the RARF are used for that purpose. • Forced and scheduled outages are being modeled in accordance with available NERC GADS data. Unit Specific data has been used whenever provided.

  7. Input Data – Load • Hourly chronological load profiles for year 2012 prepared by ERCOT. • Five load scenarios were developed in order to capture weather related uncertainty (extreme summer, warmer than average, average, cooler than average and much cooler than average weather year scenarios). • Each of them have an associated probability of occurrence which is later to be used in the calculation of reliability indices. • Economic growth assumption behind all scenarios is based on Moody’s base economic forecast.

  8. Input data - Wind • Representative hourly wind energy availability data for each wind plant provided by AWS Truepower. • Based on the wind generation assessment report prepared for ERCOT by AWS Truepower provided for the CREZ analysis. • The data is being aggregated for single node analysis.

  9. Generator forced outage modeling • Outage rates were evaluated based on the four-state model. NERC GADS was the source for all data needed for the EFORd calculation. (1-Ps) / T Reserve Shutdown In service EFORd = (FOHd + EFDHd) / (SH + FOHd) x 100%, where: FOHd = f x FOH f = (1/r + 1/T) / (1/r + 1/T + 1/D) r = Average Forced Outage Duration = FOH / # of FO occurrences D = Average Demand Time = SH / # of unit actual starts T = Average Reserve Shutdown Time = RSH / # of unit attempted starts EFDHd = (EFDH – EFDHRS) if reserve shutdown events reported, or EFDHd = (fp x EFDH) if no reserve shutdown events reported, fp = SH / (SH + RSH) 1/D 0 2 μ μ λ Ps/ T 1/T Forced out but not needed Forced out in period of need 1/D 1 3

  10. Why is a Four State Model needed? • In the case of generating equipment with relatively long operation cycles, the unavailability (FOR) is an adequate estimator of the probability that the unit under similar conditions will not be available for service in the future. • The two-state model based FOR formula does not, however, provide an adequate estimate when the demand cycle, as in the case of a peaking or cycling operating unit, is relatively short and therefore the unit is operating in more than two states (producing energy or on a forced outage). • The most critical period in the operation of a unit is the start-up period, and in comparison with a base load unit, a peaking unit will have fewer operating hours and many more start-ups and shut-downs. • These aspects must be included in arriving at an estimate of unit unavailability at some time in the future and are captured in the EFORd calculation.

  11. Generator outage modeling • Outages are modeled sequentially, using random draws from two exponential distributions. • The time on outage for each unit is randomly drawn from an exponential distribution with mean equal to the mean time to repair (MTTR): • MTTR = (FOH + EFDH) / # of FO occurrences • The time in service for each unit is randomly drawn from an exponential distribution with mean equal to the mean time to failure (MTTF): • MTTF = SOAF x MTTR x [(1/ EFORd) – 1 )] • SOAF is the scheduled outage adjustment factor equal to [(8784 – SOH) / 8784] which is applied while calculating MTTF to account for any loss of outage time due to overlap of forced outages with scheduled outages. • The outage modeling described above results in unit unavailability due to forced outage equal to EFORd.

  12. Flowchart Start Read generation, wind and load data from an EXCEL file For every load scenario Generate random outages and randomize available wind capacity for a pre-specified number of years Evaluate the hourly margin between resource and load (Margin = Resources – Demand) Update reliability metrics (LOLEV EUE, LOLH) Check whether the stopping criteria are met no yes Proceed with the next load scenario Calculate probability based reliability indices Print results Stop

  13. Flowchart Start • For every unit, build sequences of generator availability and unavailability periods using MTTF and MTTR respectively. • For every day, create random daily wind profiles. To randomly choose a day’s wind profile, a span of +/- 7 days is used. • Generate hourly resource profiles by summing up the hourly capacity available (wind and non-wind). Read generation, wind and load data from an EXCEL file For every load scenario Generate random outages and randomize available wind capacity for a pre-specified number of years • Once hourly resource profiles are available, the margin is calculated. • A negative margin indicates a loss of load hour and its value the amount of load that could not be served. A sequence of loss of load hours is treated as one loss of load event. Evaluate the hourly margin between resource and load (Margin = Resources – Demand) Update reliability metrics (LOLEV EUE, LOLH) • The reliability metrics (LOLEV, EUE and EUE) are updated based on the hourly margin obtained. Check whether the stopping criteria are met no • Once a (maximum) number of iterations has been run or the stopping criteria have been met, proceed to the next load scenario. See next slide for the stopping criteria. yes Proceed with the next load scenario • Based on the probability associated with each of the load scenarios, calculate the study-wide reliability indices. Calculate probability based reliability indices Print results Stop

  14. Stopping criteria • Each load scenario is simulated for a (maximum) number of iterations or until the stopping criteria are met. • The stopping criteria are: • A minimum number of iterations (set to 1,000). • The LOLEV halfwidth for a 95% confidence interval is less than a percentage (set to 5%) of the LOLEV average. • The total number of loss of load events is greater than a minimum value (set to 1).

  15. ELCC calculation • Methodology for the ELCC calculation: • ERCOT will be evaluating the ELCC of wind units by comparing them to the 2012 planned fleet. • First, the reliability metrics are evaluated for a base case scenario. Then after the wind units have been competed removed, a capacity multiplier is applied to the remaining fleet until the same level of reliability for each metric is achieved. • The ELCC value is equal to the ratio of the capacity of the remaining fleet that was added divided by the total installed capacity of wind, in per cent.

  16. Comparison to the 2007 study by Global Energy Decisions • The current study will differ from the 2007 Global Energy Decisions study in the following ways: • ERCOT will be using a family of load profiles representing different weather conditions. • All hours of the year are being modeled instead of a representative week from each month. • Generator outages are modeled sequentially. • Wind volatility is modeled in a more dynamic way. • The ELCC of existing wind will be compared to the reliability of the existing fleet rather than hypothetical new generation. • A much higher number of iterations will be performed in order to ensure the statistical significance of the results.

  17. Study Output • The following random variables will be estimated for year 2012 and for different reserve margin levels: • The annual loss-of-load events (LOLEV). • The annual loss-of-load hours (LOLH). • The annual expected unserved energy (EUE). • The calculated ELCC of wind generation will be reported based on LOLEV. • The calculated target reserve margin will be reported based on 0.1 LOLEV per year. • This is equivalent to 1 loss of load event every 10 years.

  18. Schedule for Results • The study results will be presented for consideration by WMS at the 10/20 meeting and at the November TAC and Board meetings • The official ELCC and target reserve margin may or may not change upon consideration of the study results by these groups • If any change is warranted, and is approved by the ERCOT Board, it would be used for the December CDR update

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