PO192

A Path To Low Risk £75/MWh UK Offshore Wind

Ines Tunga 1 ,2, Stuart Bradley1, Lars Johanning4, Iraklis Lazakis3, Harry Van Der Weijde2
1Energy Technologies Institute, Loughborough, UK, 2The University of Edinburgh, Edinburgh, UK, 3Strathclyde University, Glasgow, UK, 4University of Exeter, Cornwall, UK

Abstract

 

Offshore Wind is an essential part of the UK's energy mix for future perspectives of low-carbon electricity production. With over 5GW installed in UK waters and an expected capacity in the order of 60GW by 2050 (ESME, 2016), the sector is on track to be one of the largest contributors to the UK electricity generation mix. Significant cost reduction is enabling this growth through technology innovation, risk-sharing within supply chain and transmission, improved operation and maintenance strategies and the positive impact of buying power. However long-term deployment is only possible if it is sustainable and competitive. Hence this project looks to determine where in the UK waters current turbine/foundation designs and operational practices could meet the £75/MWh cost target. The project will also research how and where alternative designs, practices or more advanced designs would be required.

 

Whist Previous studies by The crown Estate,  BVG Associates, S. Cavazzi (2015), the recent CRMF report (2015-16), Clean Pipeline surveys and ETI internal projects have focussed on specific areas of innovation to improve cost of energy (CoE), this study will complement the findings with an holistic approach used integrating the impact of the overall system by identifying  potential areas of cost reduction and determine the impact on the whole energy system environment using ESME model[1] and the impact on the geographical distribution in UK waters.

 

 

[1] ESME- ETI Energy System Modelling environment.

Method

Current trends and patterns of turbine top-head components, balance-of-plants, substructures, operation, practices and cost are mapped and analysed; potential areas of cost reduction are identified using Quality Function Deployment analysis (QFD).

Alongside the QFD analysis, cost optimisation is assessed using RiskOptimizer, a tool that takes an optimisation problem and replaces uncertain values with defined probability distribution functions. Within each defined trial solution, several scenarios are run with Monte Carlo simulation to find the combination of variables that provides the best simulation results. In this case, with the known investment, financial and operation and maintenance costs, the tool runs simulations to obtain lowest possible cost-of-energy (≤ £75/MWh) with highest return of investment. Confidence levels of constraints and correlations of "most influential" parameters are evaluated. 

Results

The determination of a possible innovation path against the impact on the whole system cost is presented alongside specific parameters to an optimised levelised cost of energy and net present value for Offshore Wind.

Details of the outcome is discussed and validated with ETI internal projects data.

 ( results and discussion to be obtained and discussed in paper/presentation or poster)

Conclusions

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Objectives

 Findings from this paper/poster provide a preliminary informed cost and reliability model of the current sites in the UK waters and evidence based potential areas for cost reduction. These results will then be used to model a spatial techno-economic model using a geographic information system (GIS) , identifying potential technologies and least cost combinations of offshore wind site.