Recent Advances in Online Optimisation Theory and Application

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• Iman Shames, University of Melbourne
• Thursday 20 June 2019, 11:00-12:00
• 1109B (CAP seminar room) .

If you have a question about this talk, please contact Giordano Scarciotti.

Abstract: In this talk, recent results in the area of online convex optimisation are presented. First, the problem of incorporating future information in the form of the estimated value of future gradients in online convex optimisation is investigated. This is motivated by demand response in power systems, where forecasts about the current round, using historical data e.g., the weather or the loads’ behaviour, can be used to improve on predictions. Specifically, we introduce an additional predictive step that follows the standard online convex optimisation step when certain conditions on the estimated gradient and descent direction are met. We show that under these conditions and without any extra assumptions on the predictability of the environment, the predictive update strictly improves on the performance of the standard update. We present two types of predictive update for various family of loss functions. We provide a regret bound for each of our predictive online convex optimisation algorithms. We apply our framework to an example based on demand response which demonstrates its superior performance to a standard online convex optimisation algorithm. Second, a two level online optimisation algorithm for optimising the building’s energy management under uncertainty and limited information is introduced. A mixed-integer linear program scheduling level is first used to set an energy management objective for every hour using only averaged data. Then, an online convex optimisation algorithm is used to track in real-time the objective set by the scheduling level. For this purpose, a novel penalty-based online convex optimisation algorithm for time-varying constraints is developed. The regret of the algorithm is shown to be sublinearly bounded above. This ensures, at least on average, the feasibility of the decisions made by the algorithm. A case study in which the two-level approach is used on a building located in Melbourne, Australia is presented. The approach is shown to satisfy all constraints 97.32% of the time while attaining a positive net revenue at the end of the day by providing ancillary services to the power grid.

Biography: Iman Shames is currently a Senior Lecturer at the Department of Electrical and Electronic Engineering, the University of Melbourne. He had been a McKenzie fellow at the same department from 2012 to 2014. Previously, he was an ACCESS Postdoctoral Researcher at the ACCESS Linnaeus Centre, the KTH Royal Institute of Technology, Stockholm, Sweden. He received his Ph.D. degree in engineering from the Australian National University, Canberra, Australia in 2011. His current research interests include, but are not limited to, optimisation theory and numerical optimisation, mathematical systems theory, and security and privacy in cyber-physical systems.

This talk is part of the Control and Power Seminars series.

This talk is included in these lists:

• 1109B (CAP seminar room)
• Andrea Picciau's list
• Control and Power Seminars

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