Natural wind variability triggered drop in German redispatch volume and costs from 2015 to 2016

Backup Capacity Planning Considering Short-Term Variability of Renewable Energy Resources in a Power System

Increasing renewable energy penetration rate in a power grid leads to an increase in the variability of the generated energy, which increases the system integration cost. To handle the output variations in the generation, it is necessary to secure sufficient flexible resources, such as energy storage units. Flexible resources can adjust the output quickly, which helps to increase the system flexibility. However, the electricity generation cost of the flexible resources is usually high. Because the renewable energy expansion policy is being implemented worldwide, it is necessary to evaluate the ability to manage the short-term variations of the renewable energy outputs to obtain a cost-effective long-term plan. In this study, the variability of renewable energy in Korea over the past five years was analyzed. Additionally, the backup capacity is determined to manage the variability of renewable energy output. The backup capacity is affected by system flexibility. In general, increasing system flexibility decreases the backup capacity and increases the total electricity production cost. In this study, a backup capacity planning method is proposed considering the short-term variability of renewable energy output and flexibility deficit in a power system. The numerical results illustrated the effectiveness of the proposed backup capacity planning method.

Wind Power Persistence Characterized by Superstatistics

Mitigating climate change demands a transition towards renewable electricity generation, with wind power being a particularly promising technology. Long periods either of high or of low wind therefore essentially define the necessary amount of storage to balance the power system. While the general statistics of wind velocities have been studied extensively, persistence (waiting) time statistics of wind is far from well understood. Here, we investigate the statistics of both high- and low-wind persistence. We find heavy tails and explain them as a superposition of different wind conditions, requiring q-exponential distributions instead of exponential distributions. Persistent wind conditions are not necessarily caused by stationary atmospheric circulation patterns nor by recurring individual weather types but may emerge as a combination of multiple weather types and circulation patterns. This also leads to Fréchet instead of Gumbel extreme value statistics. Understanding wind persistence statistically and synoptically may help to ensure a reliable and economically feasible future energy system, which uses a high share of wind generation.