Original article
Development and application of a multiple-attribute decision-analysis methodology for site selection of floating offshore wind farms on the UK Continental Shelf

https://doi.org/10.1016/j.seta.2021.101440Get rights and content

Highlights

  • Qualitative and quantitative methodology outlined for floating wind site selection.

  • Case study applied to Northern coast of Scotland; 45/450 sites identified.

  • Sites are analysed and ranked against 3 main criteria and 16 basic criteria.

  • Top 4 sites found to be adjacent, hence large site identified for floating wind.

  • Evidential Reasoning is a viable tool in offshore wind energy site selection.

Abstract

This research presents the development of a methodology for determining the most suitable floating offshore wind farm locations for the northern coast of Scotland, through the application of multi-attribute decision-analysis. A large area off the northern coast of Scotland is defined and separated into coordinate grids. The environmental, logistical and facilities factors are first analysed in order to remove sites that fall within restricted areas. Following this, data is gathered for the remaining sites in terms of a set of Logistics, Facilities & Environmental, and Met-Ocean criteria. The logistical criterion consists of such factors as, depth, distance to ports and distance to substations. The Met-ocean criterion provides a data analysis of the wind, wave, tidal and current conditions of each site between 2011 and 2016, and the Facilities & Environmental criterion analyses the proximity of the sites to such criteria as Marine Protection Areas, Special Areas of Conservation, military training areas and subsea facilities. The compiled data is then applied to a Multiple Attribute Decision Analysis (MADA) algorithm which aggregates the data for each site and produces a utility ranking in order to determine the most suitable site for floating offshore wind. Validation is conducted through benchmark testing and correlation with government survey sites.

Introduction

Driven by issues of climate change, security of energy supply and economic development potential, the UK Government set ambitious plans for the growth of offshore wind by 2020. Overall, the UK had nominal targets for around 20 GW of installed offshore wind by 2020, however, the offshore wind capacity, as of December 2020 was approximately 10.5GW, which is 50% of what was projected for 2020. The UK’s current goal is to achieve an offshore capacity of 27 GW by 2026, and 40 GW by 2030 [19], [55]. The projected level of capacity is required to help deliver the UK’s carbon reduction targets through the de-carbonization of electricity production – a means of achieving an overall 15% reduction in carbon-based energy use. This represents a ten-fold increase in 2006 renewable energy consumption [30], [48]. Furthermore, offshore wind deployment is expected to reach 20–55 GW by 2050, depending on the UK’s broader energy mix and carbon reduction strategy [13].

Offshore wind in the UK is a world-leading industry in terms of installed capacity, which is approaching 7.5GW as of September 2018. One UK offshore site has recently begun supplying power while another is in the construction phase, both off the coast of Lincolnshire (See Fig. 1), the Hornsea 1 and 2 projects, respectively. Hornsea 1 was completed and installed in early 2020 and has an approximate capacity of 1.2GW (approximately 171 × 7 MW turbines). It is the world’s first offshore wind farm to produce over a Gigawatt of power, as well as being the largest wind farm in the world. Hornsea 2 was given consent to be constructed in August 2016, and is expected to be operational by 2022, with an approximate capacity of 1.4GW [49], [50], [51], [52].

All the offshore wind farms around the UK consist of conventional fixed-bottom foundation technology located in relatively shallow water depths (<60 m) and near to shore (<30 km), except for the Hywind Scotland farm which is floating (see Section 1.2), and the Hornsea Project which is more than 30 km from the shore [31], [50]. As installed capacity increases and the availability of near-shore sites is exhausted, it is inevitable that wind farms will need to be developed further from shore in deeper water. This poses great technical challenges and efforts to reduce costs. Hence, the application of Floating Offshore Wind (FOW) is gaining momentum along with unlocking the potential in near-shore deep water sites at a lower cost of energy than far-shore fixed- bottom locations. Therefore, it can be said that FOW is well suited to some areas of the UK, in particular the northern coast of Scotland. A combination of high wind speeds, abundant near-shore deep water sites, and the ability to leverage existing infrastructure and supply chain capabilities from the offshore oil and gas industry create the requisite conditions to position the UK, particularly Scotland, as a world leader in floating wind technology [11], [12], [71].

The Hywind Scotland wind farm, operated by Statoil in partnership with Masdar, consists of 5 × 6 MW turbines, with a total farm capacity of 30 MW, and has the potential to power approximately 20,000 households. Hywind is located 25 km offshore from Peterhead in Aberdeenshire, Scotland (See Fig. 1). Hywind is a floating wind turbine design based on a single floating cylindrical spar buoy moored by cables or chains to the seabed. Its substructure is ballasted so that the entire construction floats upright. Hywind combines familiar technologies from the offshore and wind power industries into a new design [65], [27].

The floating design allows Hywind wind turbines to be placed in waters too deep for conventional bottom-fixed turbines. Where a fixed wind turbine can operate in a maximum water depth of 60 m, the Hywind design can operate in waters up to 800 m deep. The wind farm is currently operating in a water depth of approximately 105 m. Hywind uses a ballasted catenary layout with three mooring cables with 60 tonne weights hanging from the midpoint of each anchor cable to provide additional tension. Control software on board constantly monitors the operation of the wind turbine and alters the pitch of the blades to effectively dampen the motion of the tower and maximise production. Electricity produced is taken to shore through subsea cables. Similarly, several logistical challenges were faced regarding the construction and installation of Hywind as it the structures were built in the Navantia Fene shipyard, which is in Ferrol, A Coruña, Spain. Thus, there was a significant distance between the construction site and the final installation site and further adds to the rationale of including the logistics criterion in this research [65], [20].

The Hywind Scotland array is a massive step towards implementing FOW farms in much deeper waters and further out to sea. Offshore winds are typically more consistent and stronger over the sea, due to the absence of topographic features that disrupt wind flow. Hence 80% of the wind resources available are located over the open ocean [65], [71].

The aim of this research is to develop a Multiple Attribute Decision-Analysis (MADA) methodology for application the selection of a suitable site for Floating Offshore Wind (FOW) farms. This will be conducted through several objectives which are reflected in the steps of the methodology outlined in Section 3. These objectives focus on outlining: 1) a specific area of the United Kingdom Continental Shelf (UKCS) to apply the methodology; 2) a suitable set of qualitative criteria to identify restricted zones, i.e. areas that cannot be utilised for FOW development due to environmental or legislative restrictions; 3) a further set of criteria and gather data relative to these criteria in order to apply a MADA algorithm and 4) conduct the quantitative analysis. Subsequently, ideal sites for FOW implementation are produced and partial validation of the model can also be conducted.

This paper is divided into several sections; Section 1 outlines a brief introduction into the current state of offshore wind on the UKCS. Section 2 presents the background into floating offshore wind and State-of-the-Art site selection methodologies. Section 3 outlines the MADA methodology, while Section 4 presents a case study focusing on a specific area of the UKCS. Section 5 provides the data aggregation and utility ranking, along with the validation of the MADA algorithm. Finally, Section 6 provides the conclusions and further work.

Section snippets

Offshore site selection methods and state-of-the-art

During the planning and development of offshore wind farms, the technical aspects and the design of the wind turbine structures tends to be at the forefront. However, the identification of areas where the energy resources are sufficient, and the environment is ideal for offshore wind development can be somewhat overlooked when considering floating devices. This can result in poor site selection which can be damaging not only in terms of underestimated economic performance and subsequent

Methodology

When developing a decision-making methodology, it is important to clearly define the domain that it is to represent. The criteria must be appropriately allocated, which careful attention being paid to what each attribute shall represent and where they shall rank in the evaluation hierarchy. The fundamental part of developing a coherent decision-making method, with the ability to deliver coherent results, lies in its evaluation hierarchy and the allocation the belief degrees and weights. To

Establish the domain and objective

To determine the size and location of the larger area for analysis, conversations and meetings were held with experts in the area of renewable energy and the legislation that surrounds implementing an offshore wind farm. These meetings (which formed part of the ARCWIND project) were held with experts from industry and academia who are heavily involved in the development and implementation of offshore wind farms. These experts consisted of members from the following renewable energy companies:

Aggregation assessment through evidential reasoning algorithm

The problem now is how the belief degrees can be aggregated to arrive at an assessment as to the most suitable site for FOW implementation. To demonstrate the procedure of the ER algorithm the detailed steps of the calculation shall be shown for generating the assessment for the criterion Logistics (X), by aggregating two basic criteria, Depth (e3) and Vicinity to Ports for Installation (e4), for site A15. The evaluation grades have been defined in Equation (1), and from Table 4 the following

Conclusion

This research set out to develop a MADA methodology for suitable site selection for floating offshore wind farms on the UK continental shelf. A large site was selected off the north coast of Scotland and was subsequently divided into a grid system with 450 individual sites. Initially, 11 evaluation criteria were determined in order to remove sites that fell into areas that are restricted for offshore development. The evaluation criteria included marine protected, military areas, landmass and

Funding

Thanks are given to the EU for its financial support under a European Commission funded project ARCWIND 2017 – 2021 (EAPA_344/2016), as well as to the European Marine Energy Centre (EMEC), the National Oceanography Centre (NOC) Liverpool and Ifremer (French Research Institute for the Exploitation of the Sea) for their data contributions. This research is also partially supported by EU H2020 RISE 2016 RESET – 730888.

CRediT authorship contribution statement

Sean Loughney: Conceptualization, Methodology, Investigation, Data curation, Software, Writing - original draft. Jin Wang: Conceptualization, Data curation, Supervision, Funding acquisition, Writing - original draft. Musa Bashir: Funding acquisition, Supervision, Writing - review & editing. Milad Armin: Validation, Software, Data curation. Yang Yang: Validation, Writing - review & editing.

Declaration of Competing Interest

The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: This paper is the opinion of the authors and does not represent the belief and policy of their employers.

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