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Wind integration: experience, issues, and challenges

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Abstract The challenge of wind integration is to make best use of the variable and uncertain power source while maintaining the continuous balance between consumption and generation and high level of reliability in the power system. There is already experience of operating power systems with large amounts of wind power and integration studies give estimates on wind power impacts. Power systems are equipped to handle variability and uncertainty that comes from the electricity consumption, the load. Short‐term wind forecasting is required to manage large amounts of wind power. The main impacts of wind integration are investments in grid infrastructure and efficiency losses in power plants when following the increased variations and uncertainty in the power system. Wind power will lower emissions while replacing energy produced by fossil fuels and can also replace some power plant capacity. However, wind's lower capacity value compared to conventional power plants is one integration impact of wind power, meaning higher total installed capacity in power systems with high wind penetration. Managing options for wind integration impacts includes proper wind power plant grid‐connection rules, increasing transmission capacity and increasing flexibility that is available from generation plants and demand side. Further development of models and tools is required to study how entire power systems can be operated during the hours and days of very high penetration levels covering 60–80% of load. This article is categorized under: Wind Power > Systems and Infrastructure Wind Power > Climate and Environment Fossil Fuels > Systems and Infrastructure
Impacts of wind power on power systems, displayed by time and spatial scales relevant for the studies. Primary reserve is here denoted for reserves activated in seconds (system‐wide frequency activated reserve; regulation; automatically activated reserve of the balancing zones). Secondary reserve is here denoted for reserves activated in 10–15 min (minute reserve, load following reserve, manually activated).
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Capacity credit of wind power, results from eight studies. The Ireland estimates were made for two power system configurations, with 5 GW and 6.5 GW peak load.
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Results from estimates for the increase in balancing and operating costs due to wind power for different wind penetration levels. The currency conversion used here is 1 € = 0.7 £ and 1 € = 1.3 US$. For UK, according to a 2007 study, the average cost is presented here, the range in the last point for 20% penetration level is from 2.6 to 4.7 €/MWh.
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Results for the increase in reserve requirement due to wind power, presented as percent of installed wind capacity, for different wind penetration levels.
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Decrease of forecast error of prediction for aggregated wind power production due to spatial smoothing effects. Error reduction = ratio between root‐mean‐square error (rmse) of regional prediction and rmse of single site, based on results of measured power generation of 40 wind farms in Germany. (Reproduced by permission of Energy & Meteo Systems).
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Predictability of wind power is better for shorter time horizons and for larger areas/several sites. Example of average absolute prediction error for a single wind‐power plant and four distribute wind power plants when forecasting horizon is from one to 36 h ahead.
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Variability of wind power will smooth out with aggregation of wind power plants. Real data from Germany where the data are from the same time period and are normalized to the mean output of each group of wind turbines. (Reproduced by permission of ISET.)
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Wind power will add to the variability that power systems experience. One week of hourly data from West Denmark (January 10–16, 2005) that has 24% wind penetration level on yearly basis, showing the variability of load and wind (upper graph) and resulting net load: Net load = load–wind power (lower graph). Source of data: http://www.energinet.dk.
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Outline of possible active power control functions from wind power plants. The plots show the possible power and the actual achieved power when different control functions are activated (Reproduced by permission of Jesper Runge Kristoffersen.)
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