A framework for planning of offshore wind energy projects based on multi-objective optimisation and multi-criteria decision analysis. - Mytilinou, Varvara (Cranfield University, 2018)
The wind industry is determined to lower the costs of producing energy in all phases of the offshore wind project. During 2015–2016, projects achieved a levelized cost of energy (LCOE) of £97 and more recently it was announced that Ørsted guaranteed £57.5/MWh. Significant price increases on structural materials directly impact on larger scale wind projects, the overall cost of turbines, establishing effective supply chains, improving the consent procedures for new developments, governmental mechanisms and support, improving grid connections and finally reducing overall uncertainty and costs etc. The most important decisions at the planning stage of new investment are the selection of a profitable, cost-effective suitable offshore location and a support structure type, which greatly impact on the overall Life Cycle Costs (LCC). This research aims to introduce and apply a scalable framework to reveal and select the optimal offshore location deployment and support structure in Round 3 zones in the UK by considering the interplay of LCC aspects at the planning stage of development. This research produced a portfolio of five studies while developing the framework above. First, a comparative Political Economic Social Technological Legal Environmental (PESTLE) analysis on wind energy was performed. The analysis focused on Europe, Germany, the UK and Greece, where the UK was selected in this research as the world leader in offshore wind energy. Second, three state-of-the-art Multi-Objective Optimisation (MOO) algorithms were employed to discover optimum locations for an offshore wind farm. The 7-objective optimisation problem comprises of some of the most important techno-economic LCC factors that are directly linked to the physical aspects of each site. The results of Non-dominated Sorting Genetic Algorithm (NSGA II), NSGA III and SPEA 2 algorithms follow a similar trend, where NSGA III demonstrated its suitability by revealing more uniform and clear optimum non-dominated solutions, also known as Pareto Front (PF), because of its main design compared to the other optimisers. Based on their frequency of appearance in the PF solutions, Seagreen Alpha, Seagreen Bravo, Teesside C, Teesside D, and the Celtic Array South West Potential development Area were discovered as the most appropriate. Since PF includes solutions from all regions, this provides the developer with the flexibility to accordingly assign costs in different development phases, as required, and to choose whether to invest the available budget on the installation or the maintenance stage of the project. Third, in order to reveal optimum locations for UK Round 3 offshore zones and each zone individually, three different wind farm layouts and four types of turbines were considered in an 8-objective formulation, where five LCC factors are directly linked to the physical aspects and restrictions of each location. NSGA II discovered Moray Firth Eastern Development Area 1, Seagreen Alpha, Hornsea Project One, East Anglia One and Norfolk Boreas in the PF solutions. Although layouts 1 and 2 were mainly selected as optimum solutions, the extreme case (layout 3) also appeared in the PF a few times. All this demonstrates the scalability and effectiveness of the framework. Fourth, the effectiveness of coupling MOO and Multi-Criteria Decision Making (MCDM) methods is demonstrated, so as to select the optimum wind farm Round 3 location in order to help stakeholders with investment decisions. A process on the criteria selection is also introduced, and seven conflicting criteria are considered by using the two variations of Technique for the Order of Preference by Similarity to the Ideal Solution (TOPSIS) in order to rank the optimum locationsthat were discovered by NSGA II. From the prioritisation list, Seagreen Alpha was found as the best option, three times more preferable than Moray Firth Eastern Development Area 1. Fifth, experts‘ opinions were employed in an MCDM process to select the support structure type in an offshore wind farm. For comparison, six deterministic MCDM methods and their stochastic expansion were employed; WSM, WPM, TOPSIS, AHP, ELECTRE I and PROMETHEE I in order to account for uncertainties systematically. It was shown that the methods can relate to each other and can deliver similar results. The jacket and monopile support structures were ranked first in most deterministic and stochastic approaches. Overall, the effectiveness of the introduced research framework to meet the aim of the research is demonstrated. The framework combines a) a prototype techno-economic model for offshore wind farm deployment by using the LCC and geospatial analysis, b) MOO by using NSGA II and c) survey data from real-world experts within MCDM by using a deterministic and stochastic version of TOPSIS.
Keywords: Decision-making; LCC; PESTLE; round 3; support structure; stochastic TOPSIS