National Transmission System (NTS) Constraint Modelling

Completed

Fossil Fuels: Oil Gas and Coal(Oil and Gas, Refining, transport and storage of oil and gas)

Applied Research and Development

ENGINEERING AND TECHNOLOGY (Mechanical, Aeronautical and Manufacturing Engineering)

Not Cross-cutting

Project Contact
National Grid Gas Transmission

Network Innovation Allowance

Ofgem

01 May 2014

01 May 2017

36 months

£330,000

Technical Consultancy

London

Network Innovation Allowance

Project Contact, National Grid Gas Transmission

This programme will provide a unified set of automated NTS capacity forecasting tools, utilising the latest statistical methods and algorithms enhancing National Grid’s capacity management of the NTS with attendant benefits for all users. The programme will enhance the future capacity management of the NTS, providing a suite of flexible forecast tools that dovetail with National Grid’s existing network modelling software.

One aspect of the safe and efficient operation of the gas transmission system (NTS) is the ability to accurately forecast the network capability to maximise its operational effectiveness. This is particularly important in the area of network capacity as it is imperative that all users have unimpeded access to the NTS. System capacity constraints have to be imposed when the amount of gas users wish to flow onto or off the NTS would result in operation outside its allowed safe design limits. Improved capacity forecasting tools would give National Grid greater capacity management flexibility of the NTS. The programme will have two inter-related deliverables as follows:1. National Grid’s current procedure will be compared with the latest theoretical and practical techniques employing statistical emulators and algorithms to improve the current procedure. This will include analysis of the following: Refine end-of-day analysis to within-day transient; create a dashboard with potential risks (such as supply losses or compressor trips) that might cause constraints identified for chosen timescale e. g. day-ahead.; Include linepack changes within the analysis; Optimise compressor fuel usage. 2. The automation potential of some of the solution strategies used by the analysts in seeking to achieve feasible flows for a particular supply-demand scenario will be reviewed. Methods such as knowledge-based or rule-based expert systems or neural nets will be considered for this purpose. Change requirement to extend the project completion date is due to a delay in appointing the postdoctoral research fellow at Warwick University to undertake the above deliverables.Note : Project Documents may be available via the ENA Smarter Networks Portal using the Website link above

14/09/18