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Details of Grant 

EPSRC Reference: EP/R023484/2
Title: System-wide Probabilistic Energy Forecasting
Principal Investigator: Browell, Dr J
Other Investigators:
Researcher Co-Investigators:
Project Partners:
National Grid Scottish and Southern Energy (SSE) Scottish Power
Department: School of Mathematics & Statistics
Organisation: University of Glasgow
Scheme: EPSRC Fellowship
Starts: 02 August 2021 Ends: 31 July 2022 Value (£): 5,040
EPSRC Research Topic Classifications:
Wind Power
EPSRC Industrial Sector Classifications:
Energy
Related Grants:
Panel History:  
Summary on Grant Application Form
The UK has binding targets to reduce carbon emission by 80% from 1990 levels by 2050. To achieve this, our energy systems are changing rapidly with a growing portion of electricity coming from renewable energy sources, and electrification of heating and transport. The result of this transition is an electricity system that is increasingly dependent on the weather: as well as having to manage variable amounts of power available from wind and solar resources, demand for electricity is becoming increasingly weather-dependent. Electricity network operators, generators and suppliers must rely on weather forecasts to plan their operations and ensure that supply meets demand, and they must do so in the knowledge that weather forecasts are imperfect, and therefore that future generation and demand uncertain.

This research will develop new energy forecasting methodologies to address the needs of the energy industry in this new paradigm. Energy forecasts are required for all weather-dependent elements of the electricity system, and their uncertainty must be quantified. Critically, there is a high degree of interdependence between uncertainty across the electricity system which must be captured to correctly characterise overall uncertainty. Furthermore, the precise nature of that interdependence will vary depending on specific weather conditions. The methodologies developed here will provide a framework for system-wide energy forecasting considering large-scale meteorological conditions, and provide decision-makers with valuable information about forecast uncertainty.

In addition, specific decision-support tools will be derived to condense voluminous and complex probabilistic forecast information into actionable analytical support. Tools to aid operational decision for power system operators, such as deciding how much back-up power to have available and how to manage constrains on the gird will be developed. Similarly, tools for generators and suppliers will be produced to enable more efficient participation in electricity markets. The overall objective of this work is to reduce the cost, and increase the reliability, of power systems with a high penetration of renewables.

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Organisation Website: http://www.gla.ac.uk