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Collective Decision Making for Energy Efficiency and Intelligent Mobility

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

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 (http://staffwww.dcs.shef.ac.uk/people/J.Marshall/lab/About_Us.html) 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:
View Website


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

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