New Thames Valley Vision Project (NTVV)


The £500m Low Carbon Network Fund (LCNF) was set up by Ofgem in 2010 to enable network operators to understand what they need to do to provide security of supply at value for money as the UK moves towards a low carbon future. The New Thames Valley Vision (NTVV) is a LCNF project led by Scottish and Southern Energy Power Distribution (SSEPD). The aim of the £30m project is to investigate the most effective ways to manage, monitor and model the network. As part of the project the University of Reading is modelling household and low-voltage (LV) substation level data in order to better understand current and future network demand behaviour as well as look at how smart control can be combined with network storage to reduce peak demand and balance the networks. In particular this project focuses on the LV Network in Bracknell and the surrounding Thames Valley area. Bracknell and the Thames Valley is home to many large companies and the local network is typical of much of Britain's network, and therefore the lessons we learn can quickly be applied nationwide.









A number of posters detailing aspects of the project have recently been developed for the Low Carbon Network Fund conference in Liverpool in November 2015.

These are:

Forecasting for low voltage networks

Long term forecasts of take up of low carbon technologies

Low Voltage network modelling

Modelling the electricity profiles for SMEs

Key results to date

  •  Categorisation

Categorisation was one of the crucial tasks of the project which was instrumental with both reducing monitoring requirements and developing bespoke forecasting methodologies. We also need to know the different types of behaviour before we can really understand the main drivers and changes in future behaviour. To date a versatile segmentation method is developed which takes both real and categorical variables and enable us to classify different types of behaviour[1].

[1] Haben, S., Singleton, C. and Grindrod, P. (2016) Analysis and clustering of residential customers energy behavioral demand using smart meter data. IEEE Transactions on Smart Grid, 7 (1). pp. 136-144. ISSN 1949-3053 doi:10.1109/TSG.2015.2409786

  • Forecasts

Forecasting at the household level is extremely useful for control, demand side response, and energy storage. At individual level, creating reliable forecasts is much more challenging than the typical forecasting which is performed at the smoother, more regular high voltage (HV) level. Accurate forecasting at the LV level would help DNOs to manage and plan the network, including considering the risks of the higher uptake of low carbon technologies in the future. We developed a new technique to evaluate different forecasts at the individual level that can cope with volatility (or 'peaks') in the data. [4] We also created several new forecasts methods.[2,3,4,5] and developed agent-based models of LCT uptake[5].

[3] Singleton, C. and Charlton, N. (2014) A refined parametric model for short term load forecasting. International Journal of Forecasting, 30 (2). 364 - 368. ISSN 0169-2070 doi: 10.1016/j.ijforecast.2013.07.003

[4] Haben, S., Ward, J., Vukadinovic Greetham, D., Singleton, C. and Grindrod, P. (2014) A new error measure for forecasts of household-level, high resolution electrical energy consumption. International Journal of Forecasting, 30 (2). pp. 246-256. ISSN 0169-2070 doi: 10.1016/j.ijforecast.2013.08.002

[5] Charlton, N., Vukadinovic Greetham, D. and Singleton, C. (2013) Graph-based algorithms for comparison and prediction of household-level energy use profiles. In: IEEE International Workshop on Intelligent Energy Systems, 14 Nov 2013, Wien, pp. 119-124.

[6] Poghosyan, A., Vukadinovic Greetham, D., Haben, S. and Lee, T. (2015) Long term individual load forecast under different electrical vehicles uptake scenarios. Applied Energy, 157. pp. 699-709. ISSN 0306-2619 doi:10.1016/j.apenergy.2015.02.069

  • Smart Control

The short term forecasts have been used with smart control algorithms in order to plan the charging and discharging of a battery on the network[7]. Significant reductions in peak demand can be produced on the network when smart control is used with battery storage by planning ahead using the day-ahead forecasts [8].

[7] Haben, S., Rowe, M., Vukadinovic Greetham, D., Grindrod, P., Holderbaum, W., Potter, B. and Singleton, C. (2013)Mathematical solutions for electricity networks in a low carbon future. In: Electricity Distribution (CIRED 2013), 22nd International Conference and Exhibition on , 10-13 June 2013, Stockholm , 0857-0857. doi: 10.1049/cp.2013.0972

[8] Rowe, M., Yunusov, T., Haben, S., Singleton, C., Holderbaum, W. and Potter, B. (2014) A peak reduction scheduling algorithm for storage devices on the low voltage network. IEEE Transactions on Smart Grid, 5 (4). pp. 2115-2124. ISSN 1949-3053 doi: 10.1109/TSG.2014.2323115


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