This paper deals with power fluctuations from wind farms. The time range in focus is between one minute and up to a couple of hours. In this time range, substantial power fluctuations have been observed during unstable weather conditions. A wind power fluctuation model is described, and measured time series from the first large offshore wind farm, Horns Rev in Denmark, are compared to simulated time series. The comparison between measured and simulated time series focuses on the ramping characteristics of the wind farm at different power levels and on the need for system generation reserves due to the fluctuations. The comparison shows a reasonable agreement between simulations and measurements, although there is still room for improvement of the simulation model.

This paper deals with modelling of power fluctuations from large wind farms. The modelling is supported and validated using wind speed and power measurements from the two large offshore wind farms in Denmark. The time scale in focus is from 1min to a couple of hours, where significant power fluctuations have been observed from these wind farms. Power and wind speed are measured with 1s sampling time in all individual wind turbines in almost 1 year, which provides a substantial database for the analyses. The paper deals with diversified models representing each wind turbine individually and with aggregation of a wind farm to be represented by a single large wind turbine model. Copyright © 2007 John Wiley & Sons, Ltd.

Nowadays, power system inertia is changing as a consequence of replacing conventional units by renewable energy sources, mainly wind and photovoltaic power plants. This fact affects significantly the grid frequency response under power imbalances. As a result, new frequency control strategies for renewable plants are being developed to emulate the behaviour of conventional power plants under such contingencies. These approaches are usually called ‘virtual inertia emulation techniques’. In this study, an analysis of power system inertia estimation from frequency excursions is carried out by considering different inertia estimation methodologies, discussing the applicability and coherence of these methodologies under the new supply‐side circumstances. The modelled power system involves conventional units and wind power plants including wind frequency control strategies in line with current mix generation scenarios. Results show that all methodologies considered provide an accurate result to estimate the equivalent inertia based on rotational generation units directly connected to the grid. However, significant discrepancies are found when frequency control strategies are included in wind power plants decoupled from the grid. In this way, authors consider that it is necessary to define alternative inertia estimation methodologies by including virtual inertia emulation. Extensive discussion and results are also provided in this study.

This paper describes a comprehensive and unique open-access data set for research within hydrological and hydraulic modelling of urban drainage systems. The data come from a mainly combined urban drainage system covering a 1.7 km² area in the town of Bellinge, a suburb of the city of Odense, Denmark. The data set consists of up to 10 years of observations (2010–2020) from 13 level meters, 1 flow meter, 1 position sensor and 4 power sensors in the system, along with rainfall data from three rain gauges and two weather radars (X- and C-band), and meteorological data from a nearby weather station. The system characteristics of the urban drainage system (information about manholes, pipes, etc.) can be found in the data set along with characteristics of the surface area (contour lines, surface description, etc.). Two detailed hydrodynamic, distributed urban drainage models of the system are provided in the software systems MIKE URBAN and EPA Storm Water Management Model (SWMM). The two simulation models generally show similar responses, but systematic differences are present since the models have not been calibrated. With this data set we provide a useful case that will enable independent testing and replication of results from future scientific developments and innovation within urban hydrology and urban drainage systems research. The data set can be downloaded from https://doi.org/10.11583/DTU.c.5029124 (Pedersen et al., 2021a).