CLNR Project Impact
The CLNR project produced research findings that will help electricity customers across the country, reducing both their energy costs and their carbon emissions in the years to come. These findings have ben implemented across the industry and have changed the way that energy providers view the flexibility and price sensitivity of consumers, contributing to a long term saving of around £8 billion in energy costs and 43 million tonnes of CO2 emissions.
This project allowed Northern Powergrid and its partners to trial smart grid solutions on the electricity distribution network, creating smart-enabled homes to give customers more flexibility over the way they use and generate electricity. The results have now been shared and embedded within industry working practices and guidance and will help the industry ensure electricity networks can cost effectively handle the mass introduction of low-carbon technologies like solar PV panels, electric cars and heat pumps.
Durham's Social Sciences research shaped the project in three ways:
(i) The energy/electricity systems are socio-technical systems involving partnerships among multiple actors beyond those directly engaged in electricity generation and transmission and where system performance depends on the interplay of technical, social and cultural parameters;
(ii) That rates and patterns (temporal, spatial) of electricity consumption are the result of social practices (norms and routines, shaped by experience and material environments) that differently enable and constrain the capacity of households to “flex” their demand;
(iii) And that electricity consumption is materially embedded – i.e. occurs in relation to material practices and infrastructures – so that, for example, significant geographical differences (urban vs. rural) influence norms and routines of consumption in non-trivial ways.
These three ideas/concepts informed the design of trials and the analysis of results, generating a new understanding of the scope of the electricity system, NPG’s position and roles within it and how, through organisational (structural and cultural) change, DNOs like NPG can find new market opportunities by repositioning themselves as providers of grid flexibility services.
CLNR was a large piece of collaborative research. The nature of the research process, the scale and scope of the project, and the evidence base it generated combined to create significant opportunities for learning by the project partner. CLNR generated the largest social science household data survey on consumer smart meter behaviour in Europe, linking customer-side innovation with network-side technology. The scale of the project created a robust data set for evaluating customer responses to smart-meter technology, both on their own and in combination with time-of-use tariffs, incentives to manage demand and a suite of low carbon technologies.
The Learning outcomes from the project are provided below, whilst the publications and resources produced by project can be found via this link
For further information on outcomes from the project visit http://www.networkrevolution.co.uk/resources/learning-outcomes/
Learning outcome 1 What are customer’s current, emerging and possible future load and generation characteristics? The data gathered for LO1 enabled us to update the industry’s current understanding of electricity consumption and generation profiles across a representative cross-section of customer and demographic groups.
View the Learning Outcome 1 results here
Learning outcome 2 To what extent are customers flexible in their load and generation, and what is the cost of this flexibility? LO2 set out to establish the extent to which customers were willing to be flexible in their energy usage and generation, and what the cost of this flexibility might be.
View the Learning Outcome 2 results here:
Learning outcome 3 To what extent is the network flexible and what is the cost of this flexibility? Traditionally, distribution network operators (DNOs) have met new demands placed on the powergrid by reinforcing the network. In this part of the project we exploried smarter alternatives which could allow conventional reinforcement and its costs to be avoided or deferred.
View the Learning Outcome 3 video here: Video
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 focused on expanding and refining the knowledge and outputs from Learning Outcomes 1, 2 and 3. We carried out detailed analysis of the trial results and (other research which became available during the course of the project) to identify the optimum solution for resolving network constraints and we assessed the interaction between customer flexibility and network flexibility to better understand the role which each plays in the overall smart grid solution.
View the Learning Outcome 4 results here:
View the High Level Summary Report on Electric vehicles here:
View the High Level Summary Report on Domestic Heat Pumps here:
Learning outcome 5 What are the most effective means to deliver optimal solutions between customer, supplier and distribution network operator? In LO5, we reviewed the outputs from all the other Learning Outcomes drew our conclusions. We produced the outputs and tools needed to transition CLNR learning into business as usual and identified the optimum solutions for a range of given circumstances, from the non-network and network options available.
View the Learning Outcome 5 results here:
For further information on the CLNR project visit the project website at http://www.networkrevolution.co.uk/