Postgrad LIVE! Study Fairs

Birmingham | Edinburgh | Liverpool | Sheffield | Southampton | Bristol

Wellcome Trust Featured PhD Programmes
Coventry University Featured PhD Programmes
Engineering and Physical Sciences Research Council Featured PhD Programmes
Swansea University Featured PhD Programmes
University of Manchester Featured PhD Programmes

Collective Decision Making for Energy Efficiency and Intelligent Mobility

This project is no longer listed in the FindAPhD
database and may not be available.

Click here to search the FindAPhD database
for PhD studentship opportunities
  • Full or part time
    Dr D Bauso
  • Application Deadline
    Applications accepted all year round

Project Description

In "future cities" end use customers use electric plug-in vehicles to go from a source node to a destination node, and choose routing policies and charging policies (where and when to buy-sell energy to/from the power network).

You will deal with the analysis and design of market mechanisms, incentive schemes, business models to induce socially optimal behaviors of end-use customers. You will develop micro-macro models capturing the interactions between individuals, groups and the environment. You will use mean-field game theoretic models to describe how individuals respond to a population behavior and how the population behavior evolves if individuals are rational decision-makers. You will build coalitional game models to study incentives for people to join demand-side-management programs.

You will also have opportunities to engage with colleagues from the Behavioral and Evolutionary Theory Lab ( of the Computer Science Department, to gain insights on biologically-inspired collective decision making processes.

You will be interacting with scientists with complementary skills which will help you
1. enhance your capability to look at things from different perspectives in a holistic approach
2. learn about alternative paths to research or academia and how to exploit them to develop a unified modeling framework
3. build a platform of relationships with other sectors that will strengthen the impact of your research

Prospective Applicants should have a good first degree and/or Masters degree in mathematics or engineering or related subject. In addition, they should have a background in systems and control theory, optimization, operational research, or game theory as well as a familiarity with computing software (MATLAB or similar). An interest in interdisciplinary studies is also desirable.

Funding Notes

Applicants can apply for a Scholarship from the University of Sheffield but should note that competition for these Scholarships is highly competitive. it will be possible to make Scholarship applications from the Autumn with a strict deadline in late January 2018. Specific information is avaialable at:


T. D. Seeley, P. Kirk Visscher, T. Schlegel, P. M. Hogan, N. R. Franks, J. A. R. Marshall. Stop Signals Provide Cross Inhibition in Collective Decision-Making by Honeybee Swarms. Science, 335(6064) 108--111, 2012

Z Ma, DS Callaway, I Hiskens. Decentralized charging control of large populations of plug-in electric vehicles. IEEE Transactions on Control Systems Technology, 21(1), 67--78, 2013

E. Bayens, E. Y. Bitar, P.P. Khargonekar, K. Poolla. Coalitional Aggregation of Wind Power, IEEE Transactions on Power Systems, 28(4) 3774—3784, 2013

F. Bagagiolo and D. Bauso. Mean-field games and dynamic demand management in power grids. Dynamic Games and Applications, 4(2), 155--176, 2014.

D. Bauso, T. Mylvaganam, and A. Astolfi. Crowd-averse robust mean-field games: approximation via state space extension. IEEE Transactions on Automatic Control, in print.

D. Bauso, H. Tembine, and T. Basar. Robust mean-field games. Dynamic Games and Applications, online, 6 June 2015, 10.1007/s13235-015-0160-4.

D. Bauso, X. Zhang, and A. Papachristodoulou. “Density Flow over Networks: A Mean-Field Game Theoretic Approach”. Proceedings of the 53rd IEEE Conf. on Decision and Control, 2014.

How good is research at University of Sheffield in General Engineering?

FTE Category A staff submitted: 21.80

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

FindAPhD. Copyright 2005-2018
All rights reserved.