Siemens-EPSRC: Cloud-based solar forecasting for improved grid management

Completed

Other Cross-Cutting Technologies or Research(Energy Models)
Other Power and Storage Technologies(Electricity transmission and distribution)
Renewable Energy Sources(Solar Energy)

Basic and strategic applied research

PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics)
ENVIRONMENTAL SCIENCES (Earth Systems and Environmental Sciences)

Systems Analysis related to energy R&D (Energy modelling)

Dr Y Wu
Faculty of Engineering
University of Nottingham

Standard

EPSRC

01 March 2022

28 February 2023

12 months

£50,372

Energy

East Midlands

Energy : Energy

Dr Y Wu, Faculty of Engineering, University of Nottingham

Dr M Sumner, Electrical and Electronic Engineering, University of Nottingham

Project Contact, Polysolar Ltd
Project Contact, Nottingham City Council

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.

20/04/22