Estimation of variability and predictability of large‐scale wind energy in The Netherlands

Abstract

This paper presents a data-driven approach for estimating the degree of variability and predictability associated with large-scale wind energy production for a planned integration in a given geographical area, with an application to The Netherlands. A new method is presented for generating realistic time series of aggregated wind power realizations and forecasts. To this end, simultaneous wind speed time series-both actual and predictedat planned wind farm locations are needed, but not always available. A 1-year data set of 10-min averaged wind speeds measured at several weather stations is used. The measurements are first transformed from sensor height to hub height, then spatially interpolated using multivariate normal theory, and finally averaged over the market resolution time interval. Day-ahead wind speed forecast time series are created from the atmospheric model HiRLAM (High Resolution Limited Area Model). Actual and forecasted wind speeds are passed through multi-turbine power curves and summed up to create time series of actual and forecasted wind power. Two insights are derived from the developed data set: the degree of long-term variability and the degree of predictability when Dutch wind energy production is aggregated at the national or at the market participant level. For a 7.8 GW installed wind power scenario, at the system level, the imbalance energy requirements due to wind variations across 15-min intervals are ±14% of the total installed capacity, while the imbalance due to forecast errors vary between 53% for down-and 56% for up-regulation. When aggregating at the market participant level, the balancing energy requirements are 2-3% higher.

Kinetic energy from distributed wind farms: Technical potential and implications

“…These time series were generated based on data from weather stations, using appropriate techniques for extrapolation to hub height and spatial interpolation [3]. The locations are actual or proposed wind farm sites, and are shown in Fig.…”

A market-based investigation of large-scale Renewable Energy integration in Northwestern Europe

“…3. A multi-turbine approach [20] was used for each considered location with a wind speed correlation decay length of 723 km for onshore and of 500 km for offshore locations.…”

“…3. A multi-turbine approach [20] was used for each considered location with a wind speed correlation decay length of 723 km for onshore and of 500 km for offshore locations.The wind-speed time series at 90 m height above ground level for the year 2007 (a medium wind year) are based on a meso-scale regional re-analysis model with a grid of 9x9 km (see Acknowledgements). The locations of the onshore wind farms were considered as per 20 September 2011 and were taken for Germany from [18] and for the rest of the countries from [19], and the capacities were scaled up.…”

Impact of wind power on the unit commitment, operating reserves, and market design

“…The work in [34] and [35] investigated the impact of using cross border scheduling for integration of wind power in the Netherlands. The results indicate clear benefits for sharing of balancing reserves over system borders.…”