A multi-objective optimisation approach applied to offshore wind farm location selection

This paper compares the three state-of-the-art algorithms when applied to a real-world case of the wind energy sector. Optimum locations are suggested for a wind farm by considering only Round 3 zones around the UK. The problem comprises of some of the most important techno-economic life cycle cost-related factors, which are modelled using the physical aspects of each wind farm location (i.e., the wind speed, distance from the ports, and water depth), the wind turbine size, and the number of turbines. The model is linked to NSGA II, NSGA III, and SPEA 2 algorithms, to conduct an optimisation search. The performance of these three algorithms is demonstrated and analysed, so as to assess their effectiveness in the investment decision-making process in the wind sector, more importantly, for Round 3 zones. The results are subject to the specifics of the underlying life cycle cost model

A framework for planning of offshore wind energy projects based on multi-objective optimisation and multi-criteria decision analysis.

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.EngD in Renewable Energy Marine Structures (REMS

A comparative study of multiple-criteria decision-making methods under stochastic inputs

This paper presents an application and extension of multiple-criteria decision-making (MCDM) methods to account for stochastic input variables. More in particular, a comparative study is carried out among well-known and widely-applied methods in MCDM, when applied to the reference problem of the selection of wind turbine support structures for a given deployment location. Along with data from industrial experts, six deterministic MCDM methods are studied, so as to determine the best alternative among the available options, assessed against selected criteria with a view toward assigning confidence levels to each option. Following an overview of the literature around MCDM problems, the best practice implementation of each method is presented aiming to assist stakeholders and decision-makers to support decisions in real-world applications, where many and often conflicting criteria are present within uncertain environments. The outcomes of this research highlight that more sophisticated methods, such as technique for the order of preference by similarity to the ideal solution (TOPSIS) and Preference Ranking Organization method for enrichment evaluation (PROMETHEE), better predict the optimum design alternative

Techno-economic optimisation of offshore wind farms based on life cycle cost analysis on the UK

In order to reduce the cost of energy per MWh in wind energy sector and support investment decisions, an optimisation methodology is developed and applied on Round 3 offshore zones, which are specific sites released by the Crown Estate for offshore wind farm deployments, and for each zone individually in the UK. The 8-objective optimisation problem includes five techno-economic Life Cycle Cost factors that are directly linked to the physical aspects of each location, where three different wind farm layouts and four types of turbines are considered. Optimal trade-offs are revealed by using NSGA II and sensitivity analysis is conducted for deeper insight for both industrial and policy-making purposes. Four optimum solutions were discovered in the range between £1.6 and £1.8 billion; the areas of Seagreen Alpha, East Anglia One and Hornsea Project One. The highly complex nature of the decision variables and their interdependencies were revealed, where the combinations of site-layout and site-turbine size captured above 20% of total Sobol indices in total cost. The proposed framework could also be applied to other sectors in order to increase investment confidence

A framework for the selection of optimum offshore wind farm locations for deployment

This research develops a framework to assist wind energy developers to select the optimum deployment site of a wind farm by considering the Round 3 available zones in the UK. The framework includes optimization techniques, decision-making methods and experts’ input in order to support investment decisions. Further, techno-economic evaluation, life cycle costing (LCC) and physical aspects for each location are considered along with experts’ opinions to provide deeper insight into the decision-making process. A process on the criteria selection is also presented and seven conflicting criteria are being considered for implementation in the technique for the order of preference by similarity to the ideal solution (TOPSIS) method in order to suggest the optimum location that was produced by the nondominated sorting genetic algorithm (NSGAII). For the given inputs, Seagreen Alpha, near the Isle of May, was found to be the most probable solution, followed by Moray Firth Eastern Development Area 1, near Wick, which demonstrates by example the effectiveness of the newly introduced framework that is also transferable and generic. The outcomes are expected to help stakeholders and decision makers to make better informed and cost-effective decisions under uncertainty when investing in offshore wind energy in the UK