Future Energy Decision Making for Cities - Can Complexity Science Rise to the Challenge?

Lead Research Organisation: University of Nottingham
Department Name: School of Computer Science


This proposal addresses a key challenge for energy sustainability - how can individual cities play their vital role in the implementation of ambitious future UK energy sustainability policies between now and 2020, whilst mitigating conflicts with the local imperatives that until now have dominated local government decision making? With the UK's heavily urban population and commercial/industrial/institutional base, cities have a huge impact, for good or ill, on UK energy sustainability. The vast majority of UK cities have traditionally regarded energy as somebody else's problem. Hence cities not only have a lot of catching up to do but are also lacking in the knowledge, experience and tools needed to integrate energy sustainability into the core of their planning processes. Cities now find themselves in a position of having to make major future energy decisions which they are not equipped for and which, in aggregate, will have a huge impact on the future of the UK's energy sustainability and economic competitiveness. Developing energy planning tools on a city by city basis would not be cost-effective. Equally, the use of city specific toolkits would be a major barrier to the early and widespread adoption of evolving best practices for meeting national energy sustainability targets. However, there are nearly 70 cities in the UK and these differ markedly from each other and are internally inhomogeneous. Thus, modelling and analysing this problem at the city level is both essential and must be performed in a fashion that is adaptable enough to be truly applicable to any UK city, if we are to enable future energy sustainability at this level. We have assembled a highly skilled and interdisciplinary project team that is ready and able to tackle this challenge in close collaboration with our partners the City of Leeds, Arup and White, Young, Green.Our vision in response to the challenge is to deploy the tools of complexity science to deliver models that enable cities to define their current energy situation and then reach balanced decisions in their future energy planning, implementing UK sustainability targets. Why this vision? The exciting developments in complexity science have not thus far been applied to the problem of modelling city level energy futures, incorporating both technology and human/organisational aspects and especially their interactions. This proposal investigates the feasibility of a novel, complexity science based approach to filling the void between the traditional functions of city planning, for which energy has not been the focus and the need for cities to implement the ambitious UK carbon reduction targets through their future energy planning. The proposed research has the potential to enable cities across the UK to deliver their vital contribution to overall UK energy sustainability.The goals of the project are, through the application of complexity science, to provide cities with the means for developing flexible and responsive energy policies that incorporate the following: 1) the overall scope and targets set by evolving national energy policies; 2) the supply of multiple energy options, such as reliance on the national grid versus distributed power generation; 3) the challenge of the last few km of upgraded power distribution; 4) psychological and organisational factors influencing energy demand, generation and distribution at the local level; 5) lock in of existing energy systems in the built infrastructure; 6) highly distributed decision making by multiple stakeholders; 7) provision for unplanned, unpredictable external perturbations, in some cases of large magnitude; 8) potential for future expansion to integrate energy with other planning priorities with conflicting objectives.
Description The project established a base case for the current energy system within the City of Leeds and tested it with historical data. We then assessed interventions that could be implemented by the local authority including support for energy efficiency measures for homes and businesses and local generation of electricity and heat.The models used different scenarios of internal and external interventions and unplanned events (such as fuel price fluctuations or changes in national policy).

We found that complexity science in the shape of agent base dimulation wsa a very helpful tool tto understand these scenarios and generate what-if type question sand answers. The work culminated in the formlisation of diffeernt energy user archetypes and how policy makers should respond to them.
Exploitation Route feed into datamining approaches used by metering compnanies to profile customer populations
Sectors Energy,Environment

Description we are in discussions with energy companies how to take the work further . the future emphasis will probably be more on a data mining rather than simulation approach.
Sector Digital/Communication/Information Technologies (including Software),Energy