Student: Oskar Sundström Program: Samhällsbyggnad år 4, TTGTM
Kontakt: firstname.lastname@example.org | +46706105876
1. Background and rationale
Previous research has shown that it is possible to translate Swedish and international standards and regulations concerning offshore wind farm siting into GIS data (Sundström, 2021). In this research a GIS model consisting of a multi-criteria decision making model (MCDM) as well as analytical hierarchy process (AHP) methods is presented. The results of this model can be used in order to evaluate the suitability of offshore areas in the context of offshore wind farm siting within the exclusive economic zone of Sweden. This model provides a good framework to expand on in further studies of the subject. Several similar studies have been performed that presents similar framework have also been performed in other parts of the world, notably a study in the Aegean Sea (Tercan et al., 2020) that determined the weights for the fuzzy AHP MCDM model by surveying both greek and turkish experts. Additionally, another study limited to greek territorial waters (Vasileiou et al., 2017) and a study considering offshore wind farms in the Emirate of Abu Dhabi (Saleous et al., 2016) provide similar frameworks as well as both evaluation and exclusion criterias to consider when evaluating potential sites for offshore wind farms.
In June 2020 the Danish legislature decided to initiate the construction of two energy islands. One in the Baltic Sea and one in the North Sea (Danish Ministry of Climate, Energy and Utilities, 2021). The idea with energy islands is to facilitate the constructions of offshore wind farms further offshore, to act as an offshore connection to regional and international power grids as well as to facilitate power-to-X technology.
Power-to-X (P2X) is a term for converting electrical power into something else. Within the context of offshore wind farms P2X most commonly refers to the process of producing hydrogen using water electrolysis when excess energy is being produced (Afry, 2021). According to the 2021 edition of the Offshore Wind Market Report, governments, energy companies and end users are increasingly looking att offshore wind power to produce green hydrogen (Musial et al., 2021).
2. Research objectives
Therefore the objective of this research is to answer the following questions:
What areas in the Baltic Sea region are suitable for the construction of energy islands?
What areas in the Baltic Sea region are suitable for the construction of offshore wind farms, if the potential of energy islands are considered?
What areas in the Baltic Sea region are suitable for the construction of offshore wind farms, if the potential of energy islands are not considered?
This will be done by applying a multi-criteria decision making (MCDM) and analytical hierarchy process (AHP) methods in a GIS. The GIS will then be evaluated in order to identify the most suitable areas. In order to increase the objectivity if the proposed method, authorities and experts will be surveyed to determine relevant evaluation- and exclusion-criterias as well as starting weights for the AHP evaluation.
3. Research methods and materials & planned activities
The approach to this study will be sequential with 5 well defined steps. These steps will be described in the following subheadings.
3.1. Literature study
The very first step of this study will be to do an extensive literature study in order to gather relevant materials on the subject. The literature search will aim to gather materials and provide insight in the following questions.
What parameters are important to consider when macro siting offshore wind farms?
What parameters are important to consider when siting energy islands:
If they are constructed artificial islands?
If the facilities are located on natural islands?
Does the introduction of power-to-Hydrogen technology impact the above considerations?
Once these questions have been answered it will be possible to identify relevant exclusion- and evaluation-criteria for the MCDM model and begin the process of data gathering and surveying experts.
3.2. Data gathering
Once the data needs have been identified the process of data gathering can begin. The main thing to consider here is that there needs to be a uniform data coverage for the entire study area. The preliminary identified data sources are:
Open source data
3.3. Survey authorities and experts
When the preliminary data sources have been gathered and potential exclusion and evaluation criteria have been identified the surveying of experts and authorities can begin. This will be done in two steps, the first survey round will aim to define relevant exclusion- and evaluation-criteria. The second round of surveying will aim to create initial weights for the AHP survey.
3.4. Perform AHP weighting
The AHP is a method of decision making for pairwise comparisons of different evaluation criteria (Saaty, 2008). The aim of implementing this method is to decrease inconsistencies in decision making and within the scope of a MCDM analysis AHP can be deployed in order to determine weights.
3.5. Perform MCDM analysis
The first step of the MCDM analysis will be to create a layer containing all the exclusion criterias, i.e. areas of military interest and protected nature, this layer will then be used to reduce the study area. The next step will be to create one layer for each evaluation criteria and rescale all values to a standardized scale. The final step will be to combine these layers using the results from the AHP weighting.
4. Expected results
The expected results are to identify areas that are suitable for the construction of offshore wind farms as well as energy islands. These results could provide valuable for decision- and policy makers.
6. List of main references
Afry, 2021. Power-to-X [WWW Document]. Power--X. URL https://afry.com/en/area/power-x (accessed 12.15.21).
Danish Ministry of Climate, Energy and Utilities, 2021. Denmark’s Energy Islands [WWW Document]. Dan. Energy Agency. URL https://ens.dk/en/our-responsibilities/wind-power/energy-islands/denmarks-energy-islands (accessed 12.15.21).
Musial, W., Spitsen, P., Beiter, P., Duffy, P., Marquis, M., Cooperman, A., Hammond, R., Shields, M., 2021. Offshore Wind Market Report: 2021 Edition. U.S. Department of Energy.
Saaty, T.L., 2008. Decision making with the analytic hierarchy process. Int. J. Serv. Sci. 1, 83. https://doi.org/10.1504/IJSSCI.2008.017590
Saleous, N., Issa, S., Al Mazrouei, J., 2016. GIS-BASED WIND FARM SITE SELECTION MODEL OFFSHORE ABU DHABI EMIRATE, UAE. ISPRS - Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. XLI-B8, 437–441. https://doi.org/10.5194/isprsarchives-XLI-B8-437-2016
Sundström, O., 2021. Multi-Criterion Macro-Siting Analysis of Offshore Wind Farm Potential in Sweden (Degree project in technology). Stockholm.
Tercan, E., Tapkin, S., Latinopoulos, D., Dereli, M.A., Tsiropoulos, A., Ak, M.F., 2020. A GIS-based multi-criteria model for offshore wind energy power plants site selection in both sides of the Aegean Sea. Environ. Monit. Assess. 192, 652. https://doi.org/10.1007/s10661-020-08603-9
Vasileiou, M., Loukogeorgaki, E., Vagiona, D.G., 2017. GIS-based multi-criteria decision analysis for site selection of hybrid offshore wind and wave energy systems in Greece. Renew. Sustain. Energy Rev. 73, 745–757. https://doi.org/10.1016/j.rser.2017.01.161