As more electrical energy is generated by wind turbines, older generation technologies such as coal and gas are being displaced. This situation presents a challenge in the sense that the additional services once provided by fossil generators must now be sourced from elsewhere. Our work provides real-world data showing the capabilities of wind generators in providing the specific service of secondary frequency regulation (automatic generation control, AGC).

The growing worldwide level of renewable power generation requires innovative solutions to maintain grid reliability and stability. In this work, twelve sites in Switzerland are chosen for a 100 % renewable energy microgrid feasibility study. For all of these sites, a combination of wind and PV performs consistently better than wind only and PV only. Five of the sites are found to be potentially economically viable, if investors would be prepared to make extra investments of 0.05–0.2 $/kWh.

An important design criterion for the electric drive system of a wind turbine is the fulfilment of grid codes given by transmission system operators. The grid codes state how wind turbines/farms must behave when connected to the grid in normal and abnormal conditions. A type of testing equipment that comprises the use of fully-rated voltage source converter in back-to-back configuration for grid code testing is proposed. Test results of a 4 MW wind turbine and an 8 MW test equipment are shown.

Often in wind power forecasting the mean wind speed is forecasted at a plant, converted to power, and multiplied by the number of turbines to predict the plant's generating capacity. This methodology ignores the variability among turbines caused by localized weather, terrain, and array orientation. We show that the wind farm mean wind speed approach for power conversion is impacted by Jensen's inequality, quantify the differences, and show machine learning can overcome these differences.

This paper focuses on the use of scanning lidars for very short-term forecasting of wind speeds in a near-coastal area. An extensive data set of offshore lidar measurements up to 6 km has been used for this purpose. Using dual-doppler measurements, the topographic characteristics of the area have been modelled. Assuming Taylor's frozen turbulence and applying the topographic corrections, we demonstrate that we can forecast wind speeds with more accuracy than the benchmarks persistence or ARIMA.