| Citation |
Frost, C., Findlay, D., Macpherson, E., Sayer, P. and Johanning, L. A model to map levelised cost of energy for wave energy projects, Ocean Engineering,149: 438-451, 2017. https://doi.org/10.1016/j.oceaneng.2017.09.063. Cite this using DataCite |
| Author(s) |
Frost, C., Findlay, D., Macpherson, E., Sayer, P. and Johanning, L. |
| Project partner(s) |
University of Edinburgh, Albatern Ltd, University of Strathclyde, University of Exeter |
| Publisher |
Ocean Engineering,149: 438-451 |
| DOI |
https://doi.org/10.1016/j.oceaneng.2017.09.063 |
| Abstract |
An economic model has been developed which allows the spatial dependence of wave energy levelised cost of energy (LCOE) to be calculated and mapped in graphical information system (GIS) software. Calculation is performed across a domain of points which define hindcast wave data; these data are obtained from wave propagation models like Simulating WAves Nearshore (SWAN). Time series of metocean data are interpolated across a device power matrix, obtaining energy production at every location. Spatial costs are calculated using Dijkstra’s algorithm, to find distances between points from which costs are inferred. These include the export cable and operations, the latter also calculated by statistically estimating weather window waiting time. A case study is presented, considering the Scottish Western Isles and using real data from a device developer. Results indicate that, for the small scale device examined, the lowest LCOE hotspots occur in the Minches. This area is relatively sheltered, showing that performance is device specific and does not always correspond to the areas of highest energy resource. Sensitivity studies are performed, examining the effects of cut-in and cut-out significant wave height on LCOE, and month on installation cost. The results show that the impact of these parameters is highly location-specific.
Highlights- A model to map Levelised Cost Of Energy (LCOE) for wave energy is presented.
- Spatial methodology allows identification of device specific LCOE “hotspots”.
- Dijkstra&rquo;s algorithm is used for spatial cost estimation.
- Installation weather windows are estimated using the NMI statistical method.
- LCOE sensitivity to cut-in and cut-out sea state and installation season presented.
This work was partly funded via IDCORE, the Industrial Doctorate Centre for Offshore Renewable Energy, which trains research engineers whose work in conjunction with sponsoring companies aims to accelerate the deployment of offshore wind, wave and tidal-current technologies |
| Associated Project(s) |
ETI-MA2003: Industrial Doctorate Centre for Offshore Renewable Energy (IDCORE) |
| Associated Dataset(s) |
No associated datasets |
| Associated Publication(s) |
An Integrated Data Management Approach for Offshore Wind Turbine Failure Root Cause Analysis An investigation of the effects of wind-induced inclination on floating wind turbine dynamics: heave plate excursion Application of an offshore wind farm layout optimization methodology at Middelgrunden wind farm Characterisation of current and turbulence in the FloWave Ocean Energy Research Facility Characterization of the tidal resource in Rathlin Sound Comparison of Offshore Wind Farm Layout Optimization Using a Genetic Algorithm and a Particle Swarm Optimizer Component reliability test approaches for marine renewable energy Constraints Implementation in the Application of Reinforcement Learning to the Reactive Control of a Point Absorber Control of a Realistic Wave Energy Converter Model Using Least-Squares Policy Iteration Cost Reduction to Encourage Commercialisation of Marine in the UK Cumulative impact assessment of tidal stream energy extraction in the Irish Sea Design diagrams for wavelength discrepancy in tank testing with inconsistently scaled intermediate water depth Development of a Condition Monitoring System for an Articulated Wave Energy Converter Dynamic mooring simulation with Code(-)Aster with application to a floating wind turbine ETI Insights Report - Wave Energy Environmental interactions of tidal lagoons: A comparison of industry perspectives Exploring Marine Energy Potential in the UK Using a Whole Systems Modelling Approach Hybrid, Multi-Megawatt HVDC Transformer Topology Comparison for Future Offshore Wind Farms Hydrodynamic analysis of a ducted, open centre tidal stream turbine using blade element momentum theory Offshore wind farm electrical cable layout optimization Offshore wind installation vessels - A comparative assessment for UK offshore rounds 1 and 2 Optimisation of Offshore Wind Farms Using a Genetic Algorithm Quantifying uncertainty in acoustic measurements of tidal flows using a “Virtual” Doppler Current Profiler Re-creation of site-specific multi-directional waves with non-collinear current Reactive control of a two-body point absorber using reinforcement learning Reactive control of a wave energy converter using artificial neural networks Reliability and O & M sensitivity analysis as a consequence of site specific characteristics for wave energy converters Reliability prediction for offshore renewable energy: Data driven insights Resource characterization of sites in the vicinity of an island near a landmass Review and application of Rainflow residue processing techniques for accurate fatigue damage estimation Sensitivity analysis of offshore wind farm operation and maintenance cost and availability Simulating Extreme Directional Wave Conditions Testing Marine Renewable Energy Devices in an Advanced Multi-Directional Combined Wave-Current Environment Testing the robustness of optimal access vessel fleet selection for operation and maintenance of offshore wind farms The Industrial Doctorate Centre for Offshore Renewable Energy(IDCORE) - Case Studies The SPAIR method: Isolating incident and reflected directional wave spectra in multidirectional wave basins The effects of wind-induced inclination on the dynamics ofsemi-submersible floating wind turbines in the time domain The power-capture of a nearshore, modular, flap-type wave energy converter in regular waves UK offshore wind cost optimisation: top head mass (Presentation to All Energy, 10th May 2017) |
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