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

A collaborative EPSRC research project between Leeds University and Nottingham University



Formalities

Funding body and details of grant



Researchers involved



Overview

This project 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 and industrial 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, but with the likelihood of national energy and climate targets being devolved down to a more local level, that is set to change. As well as an increasing pressure to meet targets, a more integrated approach to energy planning would help local authorities address the challenges of; reducing carbon emissions, ensuring access to energy services, and tackling fuel poverty in economically-deprived areas.

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. Why this vision? The exciting developments in complexity science have not thus far been applied to the problem of modelling city level energy futures, which crucially incorporate both technology and human/organisational aspects and especially their interactions. Currently models to aid policy-making exist which look at aggregate system properties such at the economics of various low-carbon technology options or the adoption rates of different technologies. Of course, in reality the behaviour of end-users of energy (domestic, industrial, commercial and public sector) within the city, and the decision-making processes of a multitude of other stakeholders, play a large role in the success or otherwise of energy-related policies and initiatives. We aim to develop a modelling tool kit using dynamical-network modelling methods as well as agent-based simulations which address limitations in current policy-modelling tools by incorporating both interactions between networks of actors and individual behaviours and decision-making.

Our approach to the project is as follows:

  1. A base case will be established for the current energy system within the City of Leeds and will be tested with historical data.
  2. Interventions that could be implemented by the local authority will be assessed using the developed models, including support for energy efficiency measures for homes and businesses and local generation of electricity and heat.
  3. The models will be used to look at different scenarios of internal and external interventions and unplanned events (such as fuel price fluctuations or changes in national policy).

A key aim of our models will be to assess the likely technical, economic, social and environmental outcomes of particular energy-related interventions or projects. Thus, we will be monitoring the system evolution in terms of:

  • Energy use (electric and heat)
  • CO2 emissions (and savings, direct and indirect)
  • Costs to users

The goal of the project is, through the application of complexity science, to provide decision makers at city level with the means for developing flexible and responsive energy policies. In this way the proposed research has the potential to enable cities across the UK to deliver their vital contribution to overall UK energy sustainability.



Outputs

Refereed Journal Publications

  1. Zhang T, Siebers PO and Aickelin U (2016) Simulating User Learning in Authoritative Technology Adoption: An Agent Based Model for Council-led Smart Meter Deployment Planning in the UK. Technological Forecasting and Social Change, 106, pp.74-84 [2014 IF 2.058] [link]

  2. McCullen NJ, Rucklidge AM, Bale CSE, Foxon TJ, and Gale WF (2013). Multi-parameter models of innovation diffusion on complex networks. SIAM Journal on Applied Dynamical Systems, 12(1), 515-532. [link]
    • News: How does innovation take hold in a community? Math modeling can provide clues. [link]
    • The model used in the paper [link]

  3. Bale CSE, Foxon TJ, Hannon MJ, and Gale WF (2012). Strategic energy planning within local authorities in the UK: A study of the city of Leeds. Energy Policy, 48, 242-251. [link]

  4. Zhang T, Siebers PO, and Aickelin U (2012). A three-dimensional model of residential energy consumer archetypes for local energy policy design in the UK. Energy Policy, 47, 102-110. [link]

  5. Zhang T, Siebers PO, and Aickelin U (2011). Modelling electricity consumption in office buildings: An agent based approach. Energy and Buildings, 43, 2882-2892. [link]

  6. McCullen NJ, Ivanchenko MV, Shalfeev VD, and Gale WF (2011). A dynamical model of decision-making behaviour in a network of consumers with application to energy choices. International Journal of Bifurcation and Chaos, 21, 2467-2480. [link]

Refereed Conference Publications

  1. Bale CSE, McCullen NJ, Foxon TJ, Rucklidge AM, and Gale WF (2012). Modelling diffusion of energy efficiency measures on a social network: Integration of real world data' (Poster). In: Proceedings of the Complexity Science and Social Science at the Interface to the Real World Conference 2012, 24-25 September, Newport Pagnell, UK. [link]

  2. Zhang T, Siebers PO and Aickelin U (2012). 'Modelling the effects of user learning on forced innovation diffusion'. In: Proceedings of the UK OR Society Simulation Workshop 2012 (SW12), 26-28 March, Worcestershire, UK. [link]

  3. Zhang T, Siebers PO and Aickelin U (2011). 'A three-dimensional model of residential energy consumer archetypes for local energy policy design in the UK'. In: Proceedings of Energy and People: Futures, Complexity and Challenges Conference, 20-21 September, Oxford, UK. [link]

  4. Bale CSE, McCullen NJ, Foxon TJ, Rucklidge AM, and Gale WF (2011). 'Local authority interventions in the domestic sector and the role of social networks: A case study from the city of Leeds'. In: Proceedings of Energy and People: Futures, Complexity and Challenges Conference, 20-21 September, Oxford, UK. [link]

  5. Zhang T, Siebers PO and Aickelin U (2010). 'Modelling Office Energy Consumption: An Agent Based Approach'. In: Proceedings of the 3rd World Congress on Social Simulation (WCSS2010), 5-9 Sep, Kassel, Germany. [link]

Workshop Organisation

  1. UKERC Energy & Complexity Workshop 2012, 5 July, Oxford, UK. [link]

Conference Presentations

  1. Bale CSE, Abuhussein AM, Foxon TJ, Hannon MJ, and Gale WF (2012). 'Delivering national policy at the local level: The role for local authorities in the implementation of the UK's flagship Green Deal policy'. 18th Annual International Sustainable Development Research Conference, 2426 June, Hull, UK. [link]

  2. Knowland T and Bale CSE (2011). 'Bringing together science, policy and design: Case study from the City of Leeds'. Ecobuild 2011, 13 March, London, UK. [link]

Other Presentations

  1. Foxon TJ (2011). 'Complex systems methods for informing energy decision-making in cities'. Presented at the PANDA Meeting, 6 April, Surrey, UK. [link]

  2. McCullen NJ (2011). 'Modelling energy technology diffusion on networks'. Presented at the IMA Seminar Series, 22 March, Nottingham, UK. [link]

  3. Zhang T (2011). 'Modelling office energy consumption: An agent based approach'. Presented at the IMA Seminar Series, 22 March, Nottingham, UK. [link]

Published Models

  1. McCullen (2013). Interface for Model of Diffusion of Innovation on Networks. [link]

  2. Zhang T (2013). Simulating User Learning in Authoritative Technology Adoption. [link]



This site uses cookies to anonymously measure how people use it!