TVV has set out to achieve the following:

  • Understand consumption behaviour to determine potential network performance issues

This requires the installation of new monitoring equipment on our network to identify, in far greater detail than we currently have, what is happening where, when and why.

  • Anticipate future changes to identify new network management requirements

Advanced mathematical modelling techniques, developed by the University of Reading, will allow us to determine what solutions are needed to manage the network on a daily basis and how to build it in the future.

  • Support the necessary changes to network management through new technology and commercial solutions

New technology will allow greater control as to how and when electricity is used. This includes storing energy to optimise low carbon generation, so that it can be used later to overcome certain restrictions on the network. We can also use new techniques to control consumption within certain properties (demand response), and this requires the development of new commercial terms for the consumer who provide such services.

TVV will promote existing Government schemes to support energy efficiency and reduce the overall burden on the network; and promote the use of renewable generation to determine how much can be readily achieved, both technically and commercially.

New technologies are also being used in the deployment of monitoring equipment on the network to minimise disruption to consumers; and there will be increased use of communication technologies to bring this data back to SEPD that will require integration into the necessary network management systems and business processes.

In order to maximise the benefits to the Bracknell network, the Thames Valley and the UK electricity industry as a whole, TVV has targeted five high level learning outcomes:

  • What do we need to know about customer behaviour in order to optimise network investment?
  • How can improved modelling enhance network operational, planning and investment management systems?
  • To what extent can modelling reduce the need for monitoring and enhance the information provided by monitoring?
  • How might a DNO implement technologies to support the transition to a Low Carbon Economy?
  • Which commercial models attract which customers and how will they be delivered?