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Interannual weather variability and the challenges for Great Britain’s electricity market design

Coker, P. J., Bloomfield, H. C., Drew, D. R. and Brayshaw, D. J. (2020) Interannual weather variability and the challenges for Great Britain’s electricity market design. Renewable Energy, 150. pp. 509-522. ISSN 0960-1481

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To link to this item DOI: 10.1016/j.renene.2019.12.082

Abstract/Summary

Global growth in variable renewable generation has brought significant attention to the challenge of balancing electricity supply and demand. However, inter-annual variability of energy resources has only recently begun to feature in energy system assessments and receives limited recognition in policy discussion, let alone policy design. Meteorological reanalysis datasets that blend modern modelling techniques with historic weather records are seeing increased application in energy system studies. This practice offers insights for market and policy design implications as governments seek to manage the changing energy landscape, as seen with the UK’s introduction of the Electricity Market Reform policy package. Here we apply a concise, Load Duration Curve based approach to consider the market and policy implications of increasing variability in the Great Britain (GB) energy system. Our findings emphasise the growing inter-annual variability in operating opportunity for residual mid-merit and even baseload generation, alongside implications for capacity assurance approaches. The growth in wind generation is seen to bring an accompanying opportunity for increased solar generation, with its lower inter-annual variability and largely uncorrelated annual characteristic. The results underscore the need for an increased recognition of inter-annual variability when addressing market design and incentive mechanisms.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of the Built Environment > Construction Management and Engineering > Transition Pathways to a Low-Carbon Economy
Faculty of Science > School of Mathematical, Physical and Computational Sciences > NCAS
Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:88163
Publisher:Elsevier

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