Presentation on theme: "Preliminary Impacts of Wind Power Integration in the Hydro-Qubec System."— Presentation transcript:
Preliminary Impacts of Wind Power Integration in the Hydro-Qubec System
Purpose of the Study Hydro-Québec’s installed hydro capacity stands at 97% of its total generation capacity of about 40 GW. It has been decided that the installed wind capacity will have to reach 10% of that of hydro by 2016. The impacts of this rapid integration of wind power on Power System’s reliability / security are being evaluated.
Wind Share being Studied The studies are based on the addition into the power system of 3000 MW of wind power capacity over 23 wind power plants.
Scope of Study The reliability/security-related aspects that are being studied include the impacts of wind power on Operating Reserves Balancing Reserves Wind Power Capacity Credit
Operating Reserves To mitigate the effects of sudden changes in the power system (contingencies) and of slower frequency deviations due to load and wind generation variability in the intra- hourly time frame. Operating Reserves have following types: Stability Reserve (10 Mins, 30 Mins) AGC Load Following Typical Value of operating Reserves in HQ : 1000 MW
Balancing Reserves To mitigate the consequences of inherent load and wind generation forecast errors over the time horizon of 1 to 48 hours. These reserves in HQ vary to: 1500/1200 MW (winter/summer) in the day-ahead time frame. 500 MW in real-time two-hours ahead.
Wind Power Capacity Credit To address reliability aspects of long term supply adequacy taking into account coincident load and wind power series with real weather conditions, and further the forced outages of the wind turbines induced by very cold temperatures (under −30°C). Today’s turbines are with a standard operational limit of −20°C, or with a “cold package” limit of −30°C. The peak load of Quebec usually around –30°C or lower during two or more consecutive days. With the Quebec climate, turbines face forced stoppages in periods of low temperature.
Modelling of System for Simulation HQ simulated following important variables at each wind power plant site under consideration Hourly time series of wind speed Air Temperature and Wind generation covering a period of 36 years(1971−2006) Stations and Meteorological Mats Wind Power Plant layouts Local Topography Information
Preparation of Simulation Data Hourly demand and wind generation forecasts were derived for the year 2016 as follows 2016 demand forecasts based on the actual 2006 demand profile and on realistic load growth assumptions. Hourly demand data from 1995 to 2006 were simulated observed from 1995 to 2006. The hourly wind generation data comes from 11-year historical reconstitutions of the anticipated 3000-MW wind plants generation capacity. The minute by minute demand and wind generation data were then interpolated according.
ORNL Method: The main idea is to use the increase in the standard deviation of the regulation signals as a measure of the impact of wind integration. N (AGC) = 4 N (LF) = 2
BPA Method: The principle is to establish the total reserve capacity requirement, and then to attribute to the proportions that the wind and load components each contributes to the total using the covariance allocation concept.
Methodology: It is based on the criterion of loss of load probability (LOLP).It is equivalent to the probability that the available generation, including reserves, is not sufficient to satisfy completely the demand.
Data Acquisition: The inputs to this method are the distributions of all the forecast errors over the lead times from 1 to 48 hours. These distributions were developed a posteriori from actual past forecasts and their corresponding measurements by subtracting one from the other. The anticipated risk was then computed at each forecast lead time. Value of a function of the net forecast error distribution corresponding to a predetermined level of balancing reserves. If given a target level of risk, the associated balancing reserve requirements can be quantified.
Methodology: Monte-Carlo simulation model was used. Wind and Load data series were matched on an hourly time-step, over a 36 year period using real weather data combined with seven different weekday load patterns. Forecasting errors and Conventional generation outages were also considered. Two Simulations were being carried out – A first simulation includes the 3000 MW wind power scenario. – In the second simulation, the wind power is replaced by conventional generation resources having a 0% outage rate.
Operating Reserves- Stability Reserve (10 Mins, 30 Mins) Preliminary analysis rapidly identified that the contingency-related reserve categories are not sensitive to wind energy integration because of following reasons: Wind plants are limited in size (less than 200 MW) Geographically dispersed over 1000 km.
After one full year of operations simulation with detailed transmission network and generation dispatch, the simulator calculated that 3000-MW of wind generation could increase the number of alternators start-ups and shut- downs by approximately 1340. With a maximum daily increase of 34 starts-stops and an average increase of 4 starts-stops per day.
Balancing Reserves: With current HQ balancing reserves being relatively high and risk levels relatively low. Little additional balancing reserves are required to integrate 3000 MW of wind power capacity most of the time. The 5% maximum risk level was revealed by the present study. It seems to be acceptable, since current practice in operations planning seems satisfactory.
WIND POWER CAPACITY CREDIT Capacity contribution of 3000 MW of wind power was equivalent to 900 MW of firm conventional generation. Results were very sensitive to wind data during a limited number of extreme cold events over the 36 years period. That finding suggests that such evaluations are improved by long time series and by better on site weather data covering critical historical events.
Basic Assumptions / Limitations: Simulation approach was being used for generation of regulation signal for ORNL\ BPA method as on frequency deviation was considered and tie-line power imbalances were ignored. Geographic and climate conditions will remain same till 2016. Meteorological conditions will remain similar till 2016. Load Profile of 2016 will be in accordance with present load profile (2006).
Conclusions: It is accepted that the evaluation of the impacts using a statistical model to generate system data is not as accurate as an approach based on simulation. Need of balancing reserves may reach as high as 13% of wind generation in some instances. The frequency of the occurrence of such an event depends on the meteorological data. The capacity credit was established at 30% (900 MW) of total wind capacity (3000 MW).
Next Phase: Priority had been given to evaluate the impact of a “3000 MW of wind in 2016”integration scenario on the reliability related aspects of the Hydro- Quebec system. Supplemental studies will now address the impacts of wind power that are specific to the water management and market related processes.