Sowing the Wind and Reaping the Whirlwind? The Effect of Wind Turbines on Residential Well-Being

This paper investigates the effect of wind turbines on residential well-being in Germany, using panel data from the German Socio-Economic Panel (SOEP) and a unique, novel data set on wind turbines for the time period between 2000 and 2012. Using a Geographical Information System (GIS), it calculates the distance from households to the nearest wind turbines to determine whether an individual is affected by disamenities, e.g. through visual pollution. The depth of our unique, novel data set on wind turbines, which has been collected at the regional level and which includes, besides their exact geographical coordinates, their construction dates, allows estimating the causal effect of wind turbines on residential well-being, using difference-in-difference propensity-score and spatial matching techniques. We demonstrate that the construction of a new wind turbine in a treatment area of 4000 metres around households has a significantly negative impact on life satisfaction. Moreover, this effect is found to be of transitory nature. Contrasting the implicit monetary valuation with the damage through CO2 emissions avoided by wind turbines, wind power turns out to be a favorable technology despite robust evidence for negative externalities

Current methods and advances in forecasting of wind power generation

Wind power generation differs from conventional thermal generation due to the stochastic nature of wind. Thus wind power forecasting plays a key role in dealing with the challenges of balancing supply and demand in any electricity system, given the uncertainty associated with the wind farm power output. Accurate wind power forecasting reduces the need for additional balancing energy and reserve power to integrate wind power. Wind power forecasting tools enable better dispatch, scheduling and unit commitment of thermal generators, hydro plant and energy storage plant and more competitive market trading as wind power ramps up and down on the grid. This paper presents an in-depth review of the current methods and advances in wind power forecasting and prediction. Firstly, numerical wind prediction methods from global to local scales, ensemble forecasting, upscaling and downscaling processes are discussed. Next the statistical and machine learning approach methods are detailed. Then the techniques used for benchmarking and uncertainty analysis of forecasts are overviewed, and the performance of various approaches over different forecast time horizons is examined. Finally, current research activities, challenges and potential future developments are appraised

Local ownership, smart energy systems and better wind power economy

Increasing wind power shares enhances the need to integrate wind power into the energy system and to improve its economy. In this study we propose two ways of achieving this end. One is to increase the value of wind power by integrating the heat and power markets, and thus ensures that wind power is never sold at a lower price than the most expensive heat alternative.The other is to lower the average costs of wind power by building more onshore wind power capacity, and proportionally less offshore wind power. This is facilitated by local and regional majority ownership models that increase the acceptance rate of onshore wind.The economy of wind power is thus improved by both increasing its value and reducing its costs