What is delaying the expansion of renewable energies?

Researchers and experts identify favourable and unfavourable features of potential wind energy areas

Survey results flow into AI-supported geoinformation system

Wind energy is an important building block for achieving climate neutrality in Germany. However, expansion is stalling. In order to find out which characteristics have a positive or negative influence on the realisation of wind farms, the research team at Bremerhaven University of Applied Sciences conducted a survey with experts as part of the WindGISKI joint project. This should help artificial intelligence (AI) to evaluate areas and thus predict the prospects of success of a wind energy project. The initial results of the survey have now been published.

It is well known that certain features, such as close development, airports or nature reserves, have an influence on the usability of areas for wind energy. But how big is the impact on the approval of new wind farms really? This was the question that the researchers at Bremerhaven University of Applied Sciences set out to answer. "We first used literature research to identify relevant, influential features in the designation of areas for wind energy. We then had these weighted by experts and evaluated in terms of their relevance. Our central question: What influence does feature XY have on the suitability of an area for wind energy on a scale of one to five from positive to negative influence? The weighting was based on the assumption that all legal requirements, such as distance regulations, are met and that sufficient wind potential is available," says Prof Dr Carsten Fichter, project manager at Bremerhaven University of Applied Sciences. 

Settlement structures, species protection and nature and landscape conservation most relevant

In order to incorporate different perspectives, the researchers interviewed more than sixty stakeholders from various areas of the wind energy industry. These included project planners, planning municipalities, legal firms, insurance companies, experts from research and development, nature conservation organisations, companies involved in the wind industry, approval authorities, participants in a community wind farm, experts and wind energy agencies and associations. The results of the non-representative survey show that they also consider local settlement structures, species protection and nature and landscape conservation to be particularly relevant criteria for the success of approval procedures. "The high importance of these feature categories can be explained by the relatively high number of complaints from the population.  Even if complaints are unjustified, they can delay the approval process because court reviews and, if necessary, the inclusion of expert opinions and expert reports as well as appeal options can develop into a time-consuming procedure," explains Dr Sandra Peters-Erjawetz, a member of the project team.

The assessment of the impact of aviation is not as clear-cut. According to the experts, it depends on the individual case. In principle, there are clear planning regulations and distance rules that ensure that wind turbines do not have a negative impact on air traffic. There is also good cooperation between the operators of wind turbines and airports and aviation authorities. As a result, conflicts are recognised at an early stage and resolved jointly. The situation is different with regard to air traffic in the military sector. "A high level of relevance is justified by the fact that air traffic matters, especially in the military sector, are often associated with secrecy, which can lead to unpleasant surprises for wind turbine operators and can even lead to unforeseen licence suspensions," says Prof. Fichter.

Economic efficiency as the most important overarching goal

By far the most frequently cited overarching objective is economic viability - across the entire consideration path from site designation and turbine planning through to implementation and operation. "From the planners' and project developers' point of view, wind farm projects should be as large as possible to ensure an ongoing cash flow and the realisation of high returns. Parameters such as the avoidance of financial risks, the stability of economic conditions when acquiring new sites and, in particular, the avoidance of plant downtimes are considered to be particularly important. As a precautionary measure, however, many companies are also focussing on the long-term, sustainable possibility of ensuring the operational reliability of wind turbines on the potential sites in question. Not only the current legal situation, but also possible risk potential with regard to a tightening of legislation, particularly in the area of nature and species protection, is already being assessed for the future," says Dr Sandra Peters-Erjawetz, summarising the results.

Results feed artificial intelligence

The experts' assessments serve as a data basis so that artificial intelligence can learn over the course of the project to evaluate areas across Germany with regard to their potential for the construction of wind turbines and thus predict the prospects of success of new wind energy projects. However, this is not equally feasible for all characteristics. "Features for which a data basis is available in the form of figures or geodata can, in principle, be suitable for an AI application. These can be categorised as 'Yes, applies' or 'No, does not apply', for example. This applies to nature conservation areas, for example. The AI can recognise whether a nature reserve exists or not," says Prof. Fichter. In the case of "qualitative" data, inclusion in the AI is most likely not possible. However, these could be incorporated into the project as recommendations for action. "Characteristics from the categories of settlement structure, air traffic, infrastructure, nature and landscape conservation, species conservation, forest, water protection, immission control and technical aspects can be taken into account in the AI model, as these mainly belong to the quantitative and categorical data types. However, many features were also identified, especially those assigned to the feature category 'sociological factors', which could not be categorised as quantitative data types," adds Dr Sandra Peters-Erjawetz. Further expert interviews are planned.

WindGISKI is a joint project of fk-wind: (Institute for Wind Energy at Bremerhaven University of Applied Sciences), the Institute for Statics and Dynamics at Leibniz Universität Hannover, the Institute for Integrated Production Hannover (IPH) gGmbH, the Institute for Social Sciences (UOL) at Carl von Ossietzky Universität Oldenburg, Nefino GmbH, LEE Landesverband Erneuerbare Energien Niedersachsen | Bremen e.V., ARSU-Arbeitsgruppe für regionale Struktur-und Umweltforschung GmbH and the Institute for Information Processing at Leibniz Universität Hannover. The project is organised by Zukunft - Umwelt - Gesellschaft (ZUG) gGmbH. The project is funded by the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU). 

Scientific contact person

Prof. Dr - Ing. Carsten Fichter
Chair of Wind Energy Technology, Energy Economics & Energy Storage 
Institute for Wind Energy fk-Wind:
Mail: carsten.fichter@hs-bremerhaven.de
Phone: +49 471 4823 546