Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

A modelling framework for characterising the impacts of uncertainty on energy systems

Zhao, Yongning, Qadrdan, Meysam and Jenkins, Nick 2020. A modelling framework for characterising the impacts of uncertainty on energy systems. Presented at: The 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, The Netherlands, 25-28 October 2020.

[img]
Preview
PDF - Accepted Post-Print Version
Download (668kB) | Preview

Abstract

A modelling framework was developed for characterising the impacts of variability and uncertainty of renewable generation and load on a multi-vector and multi-scale energy system. A time series synthesis algorithm was proposed to produce a large number of daily profiles for wind, PV and load, representing possible variation features. Based on the synthetic time series, four models with different uncertainty characteristics were built and applied to generate forecast scenarios for wind and PV. Using the generated scenarios, the operation of a combined gas and electricity system was formulated as a two-stage stochastic mixed-integer linear optimisation problem. A simplified Great Britain's energy system was investigated under different flexibility options and uncertainty characterisations. Results indicate that electricity storage was the most effective measure to reduce operation cost. The impacts of uncertainty characterisation methods were significant only when system flexibility was insufficient.

Item Type: Conference or Workshop Item (Paper)
Status: In Press
Schools: Engineering
Energy Systems Research Institute (ESURI)
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Date of First Compliant Deposit: 29 June 2020
Date of Acceptance: 20 April 2020
Last Modified: 29 Jun 2020 12:15
URI: http://orca.cf.ac.uk/id/eprint/132806

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics