Title: Model Predictive Control of Sea Wave Energy Converters
Abstract: Ocean waves provide a highly concentrated renewable energy resource. However wave energy technology has been much less developed compared with other renewable energies such as solar energy and wind energy. This talk will address how to use control to improve energy conversion efficiency and guarantee safe operation of wave energy converters. We will especially focus on model predictive control for a single device and further extend it to distributed model predictive control for an array of devices.
Dr Guang Li is a lecturer in Queen Mary University of London. He received his Bachelor degree and Master degree from Automation Department, University of Science and Technology Beijing in 2000 and 2003 respectively. He obtained his PhD degree in University of Manchester (UK) in 2007. Before joining Queen Mary, Dr Li conducted research works as a postdoc researcher in University of Bristol (UK), University of Exeter (UK) and Pennsylvania State University (USA). His current research interests include control theories and various control applications, such as marine energy, energy storage management and hybrid dynamics testing method, etc.
Title: Automated Energy Management Using a Situational Awareness-Centric Network in a Home Environment
Abstract: Energy management theory and techniques for home environments are facing several technical challenges in areas including real-time scheduling, power distribution, and automation of network of home appliances/renewables/storages for achieving maximum energy efficiency. In a smart home, apart from the electrical appliances, intelligent sensors are also consuming energy while transmitting data or when they are in idle mode. Situational awareness (SA) is an intelligent decision-making algorithm which substantially reduces the energy consumption by providing the valuable surrounding data in a home environment. SA-based ad-hoc network can further increase the lifetime of the sensors (doze mode vs idle mode), and make the energy monitoring/efficiency fully-automated without human intervention.
Dr Kamyar Mehran is a Lecturer in Power Engineering in Queen Mary University of London, UK. Prior to his current position, he worked in University of Warwick as a research fellow (2013-2015), Newcastle University and Imperial College London (2010 to 2013) as a research associate and Commercialization Manager for a spin-off company, OptoNeuro Ltd. He received his PhD degree in Electrical Engineering in Newcastle University, UK in 2009. Prior to his academic career, he collected over 8 years of industrial experience in companies like Sun Microsystems (Oracle), and National Iranian Oil Company. His current research interests include nonlinear dynamics, intelligent control and optimization and their applications in energy storage systems, high-switching power electronic converters, and home energy management systems.
