Criterion:
Prepare final reports on the trials carried out on the subjects listed in “Evidence 9.8” as well as an end of project report

Evidence:
• Use of modelling to support DNO investment decisions
• Use of modelling to support others (customers, industry, local government)

This document provides details of the Network Modelling Environment and the way in which it can be used to model the network using customer energy profile data, and assess the impact of low carbon technologies when they are connected to the network by customers. Be assessing the demands on the network in a more detailed and accurate manner than was possible with traditional methods, in particular the use of After Diversity Maximum Demand (ADMD) methodology, it is expected that greater confidence can be gained in the modelling outputs, and this will allow a more detailed assessment of the network locations where headroom is identified to be limited (or exceeded). This will support investment decisions that can be closely targeted. As confidence in forecasts grows it would be expected that the timing of the required reinforcement works can also be matched more closely to the real need. This report presents the findings identified in line with the evidence criteria specified for the Successful Delivery Reward Criteria (SDRC).

It is confirmed that:
· The Network Modelling Environment is described in terms of what it can do and the data requirements to facilitate its operation.
· The modelling techniques are applied and verified on real networks.
· Network modelling is improved with the use of energy profiles.
· The impact of disruptive technology penetration (eg electric vehicle charging) is modelled and assessed.
· Virtual monitoring (buddying) can be used effectively in the modelling environment.
· Forecasting methodologies can be used in conjunction with the modelling environment.
· The Network Modelling Environment provides both direct and indirect benefits to customers and industry.
· The relationship between the DNO and local authorities can be effectively supported using the Network Modelling Environment.