Quantifying the System Benefits of Ocean Energy in the Context of Variability: A UK Example

Donald R. Noble (), Shona Pennock, Daniel Coles, Timur Delahaye and Henry Jeffrey
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Donald R. Noble: School of Engineering, Institute for Energy Systems, The University of Edinburgh, Edinburgh EH9 3DW, UK
Shona Pennock: School of Engineering, Institute for Energy Systems, The University of Edinburgh, Edinburgh EH9 3DW, UK
Daniel Coles: Department of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, UK
Timur Delahaye: CorPower Ocean, Västberga Allé 60, Hägersten, 126 30 Stockholm, Sweden
Henry Jeffrey: School of Engineering, Institute for Energy Systems, The University of Edinburgh, Edinburgh EH9 3DW, UK

Energies, 2025, vol. 18, issue 14, 1-24

Abstract: Recent studies have shown benefits of using tidal stream and wave energy in the electricity generation mix to improve supply–demand balancing on annual/subannual timeframes. This paper investigates this further by considering the variability of solar photovoltaic, onshore and offshore wind, wave, and tidal stream over multiple years. It also considers their ability to match with electricity demand when combined. Variability of demand and generation can have a significant impact on results. Over the sample of five years considered (2015–2019), demand varied by around 3%, and the availability of each renewable technology differed by up to 9%. This highlights the importance of considering multiple years of input data when assessing power system impacts, instead of relying on an ‘average’ year. It is also key that weather related correlations between renewable resources and with demand can be maintained in the data. Results from an economic dispatch model of Great Britain’s power system in 2030 are even more sensitive to the input data year, with costs and carbon emissions varying by up to 21% and 45%, respectively. Using wave or tidal stream as part of the future energy mix was seen to have a positive impact in all cases considered; 1 GW of wave and tidal (0.57% of total capacity) reduces annual dispatch cost by 0.2–1.3% and annual carbon emissions by 2.3–3.5%. These results lead to recommended best practises for modelling high renewable power systems, and will be of interest to modellers and policy makers.

Keywords: power system modelling; annual variability; input data sensitivity; renewable energy; wave energy; tidal stream energy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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