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Details of Grant 

EPSRC Reference: EP/R023484/1
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: Electronic and Electrical Engineering
Organisation: University of Strathclyde
Scheme: EPSRC Fellowship
Starts: 29 June 2018 Ends: 01 August 2021 Value (£): 312,934
EPSRC Research Topic Classifications:
Wind Power
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
07 May 2018 Energy Fellowship Interviews May 2018 Announced
06 Dec 2017 Engineering Prioritisation Panel Meeting 6 December 2017 Announced
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.

Key Findings
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Potential use in non-academic contexts
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Date Materialised
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Organisation Website: http://www.strath.ac.uk