Offshore Wind Power Data: Deliverable no: 16.1

Wind power development scenarios are critical when trying to assess the impact of the demonstration at national and European level. The work described in this report had several objectives. The main objective was to prepare and deliver the proper input necessary for assessing the impact of Demo 4 – Storm management at national and European level. For that, detailed scenarios for offshore wind power development by 2020 and 2030 were required.
The aggregation level that is suitable for the analysis to be done is at wind farm level. Therefore, the scenarios for offshore wind power development offer details about the wind farms such as: capacity and coordinates. Since the focus is on the impact of storm fronts passage in Northen Europe, the offshore wind power scenarios were estimated only for the countries at North and Baltic Sea. The sources used are public sources, mentioned in the reference list. The scenarios are split in baseline – the conservative one, most likely to happen, and high – the optimistic scenario. During the time of the work, EWEA has published their estimation for 2020 and 2030. The scenarios estimated in this work are in good accordance with EWEA’s.
A second task described in this work was to create a dataset containing forecast and realised wind power time series with hourly resolution. The database should cover all Europe, i.e. onshore and offshore and it will be further used in the project for the economic assessment impact, Tasks 16.2.2 and 16.2.3. For the onshore wind power development, the approach used in the TradeWind project has been used. This approach considered a first aggregation level for wind power at a grid node, and then a second aggregation at wind power regions. With this approach, wind power for a country can be expressed in one or several wind power nodes and one or several wind power regions. For onshore wind power, the estimated installed capacity was upscaled to meet the number published by EWEA in the Pure Power report.
Wind speed time series were extracted from the WRF dataset available at DTU Wind Energy and interpolated to the exact location of the wind power points with CorWind. Wind speed forecast errors were calculated using the Scenario Tree Tool developed in the WILMAR project.
Finally, wind power time series were simulated using the wind speed time series and adequate power curves. The resulted wind power time series were briefly analysed with respect to the distribution of wind power forecast errors and the results show that the wind power forecast error distribution manages to capture the area smoothening effect.