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Details of Grant 

EPSRC Reference: EP/W028581/1
Title: Siemens-EPSRC: Cloud-based solar forecasting for improved grid management
Principal Investigator: Wu, Professor Y
Other Investigators:
Sumner, Professor M
Researcher Co-Investigators:
Project Partners:
Nottingham City Council Polysolar Ltd
Department: Faculty of Engineering
Organisation: University of Nottingham
Scheme: Standard Research - NR1
Starts: 01 March 2022 Ends: 28 February 2023 Value (£): 50,372
EPSRC Research Topic Classifications:
Artificial Intelligence Energy Efficiency
Solar Technology
EPSRC Industrial Sector Classifications:
Energy Information Technologies
Related Grants:
Panel History:
Panel DatePanel NameOutcome
28 Oct 2021 Siemens EPSRC Digital Solutions for Energy Demand Reduction Announced
Summary on Grant Application Form
The contribution of PV energy to the electric grid continues to grow. Installed capacity in the UK in 2020 was 13.4 GW, (4.1% of total electricity generation compared with only 0.01% in 2010) and is expected to increase to 40 GW by 2030. Accelerating adoption of solar energy will present significant challenges to the electricity transmission and distribution system, as solar power is not dispatchable and therefore its incorporation as a major element of the generation mix requires the accurate estimation of solar energy production. The accurate estimation/prediction of solar energy generation is a significant challenge, especially in countries with widely varying weather patterns such as the UK, due to a poor understanding of the complex distribution of solar energy in the sky. Solar radiation is intermittent and the solar source at any given position on the plane of a PV array is highly dependent on the position of the sun, atmospheric aerosol levels, cloud cover and motion, etc. This inherent variability in the solar source directly affects solar-derived energy fed into power grids and can create severe imbalances between demand and the capacity/transport/distribution/storage of the grid, which can significantly impair grid reliability.

To counter these issues, the long-term aim is to develop a comprehensive digital platform for forecasting solar production (from very short to long term solar radiation forecasting) to significantly improve the prediction accuracy of meteorological parameters, reducing the power mismatch caused by solar forecast errors, and also reducing the continuing requirement for fossil fuel-based generation. To achieve this, the aim for this project is to build on our existing outdoor solar testing facility to significantly improve the prediction accuracy for intra-hour solar forecasting by developing and demonstrating a 'cloud'-based solar measurement and modelling platform to support multiple data sources and intensive prediction algorithms. The target is to achieve a prediction horizon of 20s to 1 hour with temporal resolution of 10s.

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Further Information:  
Organisation Website: http://www.nottingham.ac.uk