Predictive and optimization-based control of smart grids: theory and algorithms

Model predictive control (MPC) has long been identified as a leading candidate technique for control in future power networks and smart grids, because of its ability to handle constraints and optimize the performance or economy of the system. One of the main barriers to adoption of MPC for power system control (and, indeed, large-scale systems in general) is its inherently centralized nature, which is at odds with the structure of modern power systems as networks of decentralized, interconnected and interacting systems. A logical solution to this is the use of distributed or decentralized forms of MPC (in which several MPC controllers are spread throughout the network and make control decisions independently) but this raises its own questions and challenges: where should the MPC controllers be deployed and what plants, subsystems or devices should they control? What models and information should they employ to make decisions? How can system-wide stability be guaranteed?

The project will focus on developing theory and algorithms for MPC applied to the smart grid. The emphasis is on developing implementable (low-complexity) controllers with strong theoretical properties (guarantees of stability, constraint satisfaction).

Prospective candidates should have an excellent first degree (I or II.i) and/or Masters degree in a mathematical or engineering-related subject. A background in control/systems theory and convex optimization is desirable. Please note that the project will involve model predictive control or (exact/classical) optimization techniques applied to smart grids, and not metaheuristic, rule-based or fuzzy methods.