Optimisation of Smart Grid performance using centralised and distributed control techniques

A massive change is currently taking place in the manner in which power networks are operated. Traditionally, power networks consisted of large power stations which were controlled from centralised locations. The trend in modern power networks is for generated power to be produced by a diverse array of energy sources which are spread over a large geographical area. As a result, controlling these systems from a centralised controller is impractical. Thus, future power networks will be controlled by a large number of intelligent distributed controllers which must work together to coordinate their actions. The term Smart Grid is the umbrella term used to denote this combination of power systems, artificial intelligence, and communications engineering. This thesis focuses on the application of optimal control techniques to Smart Grids with a focus in particular on iterative distributed MPC. A novel convergence and stability proof for iterative distributed MPC based on the Alternating Direction Method of Multipliers is derived. Distributed and centralised MPC, and an optimised PID controllers' performance are then compared when applied to a highly interconnected, nonlinear, MIMO testbed based on a part of the Nordic power grid. Finally, a novel tuning algorithm is proposed for iterative distributed MPC which simultaneously optimises both the closed loop performance and the communication overhead associated with the desired control.

Integration of micro- and macroscopic models for pedestrian evacuation simulation

Simulation of pedestrian evacuations of smart buildings in emergency is a powerful tool for building analysis, dynamic evacuation planning and real-time response to the evolving state of evacuations. Macroscopic pedestrian models are low-complexity models that are and well suited to algorithmic analysis and planning, but are quite abstract. Microscopic simulation models allow for a high level of simulation detail but can be computationally intensive. By combining micro- and macro- models we can use each to overcome the shortcomings of the other and enable new capability and applications for pedestrian evacuation simulation that would not be possible with either alone. We develop the EvacSim multi-agent pedestrian simulator and procedurally generate macroscopic flow graph models of building space, integrating micro- and macroscopic approaches to simulation of the same emergency space. By “coupling” flow graph parameters to microscopic simulation results, the graph model captures some of the higher detail and fidelity of the complex microscopic simulation model. The coupled flow graph is used for analysis and prediction of the movement of pedestrians in the microscopic simulation, and investigate the performance of dynamic evacuation planning in simulated emergencies using a variety of strategies for allocation of macroscopic evacuation routes to microscopic pedestrian agents. The predictive capability of the coupled flow graph is exploited for the decomposition of microscopic simulation space into multiple future states in a scalable manner. By simulating multiple future states of the emergency in short time frames, this enables sensing strategy based on simulation scenario pattern matching which we show to achieve fast scenario matching, enabling rich, real-time feedback in emergencies in buildings with meagre sensing capabilities.

Pervasive handheld computing systems

The technological role of handheld devices is fundamentally changing. Portable computers were traditionally application specific. They were designed and optimised to deliver a specific task. However, it is now commonly acknowledged that future handheld devices need to be multi-functional and need to be capable of executing a range of high-performance applications. This thesis has coined the term pervasive handheld computing systems to refer to this type of mobile device. Portable computers are faced with a number of constraints in trying to meet these objectives. They are physically constrained by their size, their computational power, their memory resources, their power usage, and their networking ability. These constraints challenge pervasive handheld computing systems in achieving their multi-functional and high-performance requirements. This thesis proposes a two-pronged methodology to enable pervasive handheld computing systems meet their future objectives. The methodology is a fusion of two independent and yet complementary concepts. The first step utilises reconfigurable technology to enhance the physical hardware resources within the environment of a handheld device. This approach recognises that reconfigurable computing has the potential to dynamically increase the system functionality and versatility of a handheld device without major loss in performance. The second step of the methodology incorporates agent-based middleware protocols to support handheld devices to effectively manage and utilise these reconfigurable hardware resources within their environment. The thesis asserts the combined characteristics of reconfigurable computing and agent technology can meet the objectives of pervasive handheld computing systems.