Research areas

Introduction

Developing dynamic digital twins of interconnected energy and transport networks, which integrate accurate digital representations of existing and planned transport and energy infrastructure.

Explaining the science

The proposed electrification of the UK’s light-duty vehicle fleet will result in a far greater degree of interaction between energy and transportation networks. In particular, the charging of electric vehicles represents a direct link between private transportation and energy demand. However, much of our analysis of critical infrastructure remains siloed, relying on separate transport and energy models with limited interactions between them. Furthermore, long-term lifestyle changes arising from the COVID-19 pandemic (including substantial increases in homeworking) have fundamentally shifted the existing patterns of transport and domestic energy demand, which are the cornerstone of our simulations for operational management and long-term forecasting.  

To address these challenges, we intend to develop dynamic digital twins of interconnected energy and transport networks, which integrate accurate digital representations of existing and planned transport and energy infrastructure with (i) real-time monitoring of transport and energy network conditions and demand and (ii) agent-based simulations of joint transportation and domestic energy based on behavioural first principles. Using London as a case-study, this project will focus on the second of these thrusts, building on existing work to develop optimisation-based household scheduling models. 

By modelling activity participation from first principles, the dynamic behavioural dimension will allow the digital twin to provide user-centric predictions about how interconnected transport and energy networks will respond to both planned and unplanned changes. It is therefore anticipated that dynamic digital twins will support long-term strategic planning, network management, and system-wide sustainability and resilience analysis.  
 

Project aims

The expected outcomes from this research project are:  

  • Modelling the scheduling of key activities completed at home, understanding the trade-offs between activities and accounting for household interactions in the individual scheduling problem through the utility of social interactions and constraints on shared spaces 
  • Develop an agent-based simulation framework which captures both disaggregate travel and household energy demand 
  • Develop detailed synthetic populations for different Local Authority Districts (LADs) in London 
  • Creation of a working prototype model through which different scenarios will be investigated 
     

Applications

This project addresses these key concerns:  

  • Creation of a model to aid with strategic planning for diverse industry and governmental stakeholders on a number of crucial challenges. 
  • Supports the UK’s ambitious vehicle fleet electrification goals. 
  • Establish an open framework for human-centric demand modelling of interconnected infrastructure systems, which could support wider applications in industry and academia 
  • Support further research to establish common standards for digital twins 

Contact info

Tony Zemaitis
[email protected]

Funders