Learning Outcome 4
What is the optimum solution to resolve network constraints driven by the transition to a low carbon economy?
This phase of the project is focused on expanding and refining the knowledge and outputs from learning outcomes
1,
2 and
3. We will carry out detailed analysis of the trial results (and any other research which becomes available during the course of this project) in order to identify the optimum solution for resolving network constraints. ie we are seeking to assess the interaction between customer flexibility and network flexibility and to understand the role which each has to play in a smart grid.
This detailed analysis will form a baseline of what are the roles of customer, distributor and supplier in the transition to a low carbon economy.
This phase of the project will model, simulate and emulate technology combinations to gain a more rounded understanding of the issues. Where appropriate, the modelling, simulation and emulation work will reflect the outcomes of the trials, but will include those combinations potentially not piloted in the field. The modelling, simulation and emulation is designed to add value to the practical trials and to extend the learning outcomes.
The network combinations which will be considered will include the following technologies:-
Customer-side
Network-side
- Enhanced Automatic Voltage Control
- Real Time Thermal Rating
- Energy Storage System
This work will help us to understand how useful each of the various customer-side and network-side solutions will be, as some may not be viable or economic.
The value gained in learning outcome 4 is in being able to balance supply and demand while deferring investment in conventional reinforcement of the electricity network, and so facilitating the transition to a low-carbon economy while avoiding additional reinforcement costs.