Reducing energy demand from existing dwellings through occupant behaviour change is crucial for meeting UK carbon emission reduction targets. Dwellings account directly for 32% of UK energy consumption, and corresponding carbon emissions. While there are many reduction efforts aimed at new-build, a focus on existing dwellings is essential: 80% of the dwellings that will be in place in the UK in 2050 are already built. Attention to behaviour change is important - behavioural differences are estimated by DECC to account for 60% of the variance in demand. Demand related to heat is key - 80% of domestic energy demand is for heating. Using an interdisciplinary conceptual framework, our team of computer scientists, building engineers and sociologists will work together to explore the interaction of energy technologies and householder energy behaviours. For the first time household energy demand will be able to be analysed in great detail across a large number of homes and the effect of behavioural feedback evaluated over a multi-year period. The Smart Meter rollout planned to be complete by 2020 is intended to encourage householders to reduce their energy demand. These meters and the associated monitors create a feedback loop to householders in which energy-consumption information from the meters is provided to the householder on the monitor in the hope that this will cause him or her to change behaviours to reduce the amount of energy used, or the amount of money spent on energy, or the associated carbon emissions. This project's main goal is to construct an enhanced feedback loop which provides information to householders not just on their energy consumption, but also on what activities they are using energy, how much for each one, together with suggestions for what they might do to reduce their energy expenditure and use. We would hope to be able to tell the householder things like: "Last week you spent £10 on hot water for showers", or "Yesterday you spent £4 on heating your flat, if you turned off the heating at night you would probably have only spent £3 - you could save around £250 a year by doing this". We will construct this feedback loop and evaluate its effectiveness compared to standard Smart Meter type feedback by involving hundreds of households in a study over a three year period. We will involve a variety of types of households including single people, multi-adult dwellings, and families, and expect to have participants across income brackets. The feedback loop will use small unobtrusive wireless sensors in the dwellings to record data and transmit it over the internet to a large secure database; and a tablet PC to provide information back to householders. The data will be processed by software to tell the occupants how much energy, carbon and money they are spending on which energy-related activities - for example over the last day, week, month, and year. This feedback loop will run for several years (up to 3) and will provide the participants with a wealth of information that they can use to reduce their energy expenditure. We will compare how effective this feedback is with that provided by Smart Meters, that does not break down energy use into the important energy-using behaviours (particularly for gas use). At the end of the study we will ask participants if we can use the data we have gathered, with all personal information removed, in future studies. Those that agree will be contributing to a database that will be invaluable for future research efforts by us and others. If we can show that this loop is effective in helping people to reduce their energy demand, then we expect that energy suppliers and other companies will start to offer it as a service to households to help them keep their energy costs down. This will contribute to reducing energy poverty as well as the challenge of meeting UK 2050 carbon emission targets.

TRANSITION ZERO will make Net Zero Energy (E=0) refurbishments a market reality in the UK, France and The Netherlands. Energiesprong brokered a deal between housing associations and builders to refurbish 111,000 houses to E=0 levels in the Netherlands of which the roll-out will be further supported. Building on the same methodology and the inspiring example, a similar innovation trajectory will be facilitated in the UK and France through two deals of 5,000 houses per market and building a pipeline of more demand. TRANSITION ZERO will organize massive demand for a E=0 refurbishment proposition from social housing organizations, facilitates financers and governments to tune their financing products and regulations towards this product and challenges the construction sector to start an ambitious innovation process to deliver the proposition. The massive demand, the security that there will be finance available and an enabling regulatory environment will de-risk the innovation investment for the builders. The problem to solve to get these propositions to the market is not around technical challenges requiring breakthroughs. The problem is a set of market conditions that are not set right for the innovation process in the construction sector to take off. The consortium is therefore convinced that the market needs a new and independent actor to drive and coordinate actors to jointly develop all parts of the market solution in parallel. This independent actor is the TRANSITION ZERO market development team. The consortium has the partners and supporters to realize the objectives set out: National governments and specialized agencies; the three key financiers of social housing in the three countries; the European en all national umbrella organizations for social housing and 19 individual social housing organizations managing over half a million houses.

The project seeks to make a significant and original contribution to efforts being marshalled by the UK Research Councils - following government level advice - to improve the international profile and national strategic impact of energy modelling in the UK. It will provide valuable insights - both quantitative and qualitative - into questions of key importance fpr policymakers and the power sector as they seek to square the circle of emissions reduction and a viable, secure energy supply by 2050. It will address in the context of the electricity network, perceived general weaknesses in whole energy systems modelling - from the closely related standpoint of complex systems research - in the areas of end-use behaviour, technology dynamics, and energy in industry. According to the Committee on Climate Change, to meet the Government's challenging emissions reduction goals would require almost complete decarbonisation of electricity generation by around 2030. It is highly likely therefore that electricity will become even more significant than its current 37% share of emissions implies, as moves towards the electrification of heat and transport accelerate. This has huge implications for the electricity infrastructure. Another fundamental assumption is that there will be a restructuring of the electricity market as signalled in the Government's recent Draft Energy Bill. These potentially game-changing developments will shape the project. The approach used will be based on De Montfort University's innovative agent-based electricity market modeling Framework known as CASCADE, which so far has been used mainly to explore the relationship between end-users and smart technology; the expected rapid infiltration of distributed generators at low and medium voltage levels; more active participation by demand entities; new communication protocols; and smart energy controllers. The Framework will be developed to improve and expand its models of large-scale generation, the transmission network, the wholesale electricity market and the ability to model technology adoption and diffusion on long time-scales, in order to address the interrelationships and complex effects of such possible developments as Locational Pricing; richer agency models for Distribution Network Operators; congestion management based on economic signals; a restructured Balancing Mechanism. A key principle will be that the modelling methods, assumptions and and limitations will be made clear to stakeholders through accessible data description and highly focussed and structured dissemination activities.

It is widely acknowledged that the power industry faces a number of serious challenges including infrastructure, capacity constraints and the need to reduce greenhouse gas and other, but more complex issues have arisen from deregulation in many countries. This has resulted in a form of balkanisation that tends to cause additional stress to the legacy electricity grid, which has a structure based on centralised command and management of large scale generating plant, long-range high voltage transmission and local low voltage distribution networks. A number of interrelated problems on varying scales and at different levels need to be addressed, including the need for expensive standby capacity to meet peak loads, high capital cost and long lead-times for new plant, vulnerability to energy security threats of various kinds, and non-technical barriers to distributed energy resources (DERs) and more flexible and sophisticated energy services that might lead to greater energy efficiency.There are signs that a new paradigm for the modern electricity industry is being defined with a decentralised model based on recent and expected advances in DERs and electricity storage technology and, in particular, rapid developments in information and communication technology that will enable the wide scale deployment of smart devices. Particularly in the USA, this new concept - known as the smart grid - is attracting large scale investment and policy recognition, with some commentators comparing its development to that of the Internet and predicting change on a scale that could represent a paradigm shift of a similar kind for the electricity industry and its end-users. If this indeed occurs, then centralist theories, laws and techniques will at some point cease to be valid as the means of control.As well as being a new paradigm for business, the Internet has been considered to be a paradigm case for complexity theory and the parallel with the smart grid concept indicates the appropriateness of this new science as the means of articulating and answering the challenges it sets. The existing structure and organisation of the power industry provides the essential starting point and context for meaningful research into the mechanisms underlying the envisioned evolution, which may represent an example of a punctuated equilibrium. Complex systems thinking and modelling is all about the occurrence of such major, structural changes and the possible ways that the system may evolve under different policies and interventions. These factors combine to offer a unique opportunity to gain important insights into the emergence of self organisation and the evolution of complex adaptive systems in scenarios with extremely high relevance for a range of vital policy issues affecting energy security, carbon reduction and fuel poverty. Complexity science offers both a synergistic conceptual framework for the research questions raised and provides a set of tools and approaches particularly suited to their solution. This research will be based primarily on agent-based modelling, which enables simulation of the complexity arising from many non-linear, dynamic, history-dependent, multi-scale interactions with feedback effects that would defeat traditional equation-based and statistical modelling. Techniques not typical of previous modelling and simulation of this kind will be developed to reflect the special features of the problem domain, in particular the close coupling of socio-economic and technical systems, in which human and artificial intelligent agents are modelled and simulated together, and the need to find appropriate levels and forms of cognitive representation. The models will be based on evidence from the wealth of previous research into energy usage and supply issues and in particular from recent examples of small scale deployment of the technologies and mechanisms identified as key to the evolution of the smart grid as a complex adaptive system.