Jon Olauson

Net load variability in Nordic countries with a highly or fully renewable power system

Increasing the share of intermittent renewable energy (IRE) resources such as solar, wind, wave and tidal energy in a power system poses a challenge in terms of increased net load variability. Full ...

Wind Energy Converters and Photovoltaics for Generation of Electricity after Natural Disasters

Abstract During recovery and reconstruction after a natural disaster, an autonomous power supply might be needed for an extended period of time. In this work, the feasibility of using small‐scale wind power and battery storage for power supply is evaluated and compared with systems containing photovoltaics. The investment cost per yearly produced and for an optimized energy system supplying small loads (2 or 20 KW peak) has been calculated for 32 sites, predominantly in frica and the iddle ast. The sites represent foreign activities of the wedish ivil ontingencies gency at the end of 2012. Since wind speed measurement series often have a lot of missing data, autoregressive moving average models were trained and used to generate hourly time series of wind speed. This methodology proved robust, even when data availability was very low or when measurements were only taken every third hour. The results of the simulations show that photovoltaic/battery systems outperform wind/battery systems at all evaluated sites. This can be explained by lower investment cost per yearly produced and smoother daily/weekly power output over the year for the photovoltaic system. The proportion of wind power for optimized systems comprising wind, photovoltaics and battery bank is generally very low and the system cost is almost identical to the corresponding photovoltaic/battery systems. In conclusion, at lower latitudes and with little time for a proper wind measurement campaign, photovoltaics should be the primary candidate for replacing or complementing conventional diesel generators.

Using the MIUU Model for Prediction of Mean Wind Speed at Low Height

This paper presents an evaluation of using the MIUU mesoscale model to predict mean wind speed at 10 meters above ground. This was done by comparing the model output to 128 meteorological measurements in Sweden. Although some terrain-dependent bias was seen, the model worked surprisingly well. Mean absolute error (MAE) was 0.58 m/s or 0.52 m/s if a few measurements taken in highly complex terrain were excluded. By using a multiple regression model, taking into account the terrain at site, the latter value was lowered to 0.39 m/s. The model described in this paper can be useful for a first evaluation of the wind conditions for small-scale wind power or when measurements are not feasible.