Uncertain Multi-Criteria analysis of offshore wind farms projects investments – Case study of the Polish Economic Zone of the Baltic Sea
Year: 2021
Type: article
Abstract: Investments in offshore wind farms can be an effective development stimulus for an economy recovering from the crisis caused by the COVID-19 pandemic. Therefore, an important issue is a multi-criteria analysis of planned investments in offshore wind farms. Such an analysis allows to identify the most important projects from the perspective of sustainable development. Taking into account the uncertainties and risks related to wind energy, and additionally taking into account the uncertainty of ex-ante evaluation, such studies need to analyse the uncertainty as widely as possible. The article presents a comprehensive methodological approach that allows for the maximum capture of uncertainties and risks related to energy investment projects in the area of wind farms. This approach involves using the fuzzy multi-criteria decision analysis (FMCDA) method, sensitivity analysis and robustness analysis based on the Monte-Carlo simulations. Additionally, the uncertainty related to the applied methodology was taken into account and the same problem was solved by using different FMCDA methods. Based on the applied approach, a case study was conducted and a ranking of offshore wind farms projects in Poland was created. It was found that the most promising project is Bałtyk II, followed by Bałtyk I, both projects being carried out by the Polenergia/Equinor consortium. However, if the preference model is not known, the Bałtyk I project has the greatest chance of taking the leasing place in the ranking (31.7%), ahead of the following projects: Baltic Power (20.4%) owned by PKN Orlen and Bałtyk III (18.6%) which is also owned by Polenergia/Equinor.
Source: Applied Energy
Author: Paweł Ziemba
Institution: University of Szczecin
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $4,020
Funder: Narodowe Centrum Nauki
Award: G6748497230
OpenAlex ID: W3216636910
