Grid integration of wave energy & generic modelling of ocean devices for power system studies

The work presented in this thesis covers four major topics of research related to the grid integration of wave energy. More specifically, the grid impact of a wave farm on the power quality of its local network is investigated. Two estimation methods were developed regarding the flicker level Pst generated by a wave farm in relation to its rated power as well as in relation to the impedance angle ψk of the node in the grid to which it is connected. The electrical design of a typical wave farm design is also studied in terms of minimum rating for three types of costly pieces of equipment, namely the VAr compensator, the submarine cables and the overhead line. The power losses dissipated within the farm's electrical network are also evaluated. The feasibility of transforming a test site into a commercial site of greater rated power is investigated from the perspective of power quality and of cables and overhead line thermal loading. Finally, the generic modelling of ocean devices, referring here to both wave and tidal current devices, is investigated.

Dual-layer frequency-selective grid polarizers on thin-film substrates for THz applications

Dual-layer frequency-selective subwavelength grid polarizers on thin-film dielectric substrates are proposed for THz and sub-THz applications. The dual-layer grids possess enhanced (squared) polarizing efficiency at a sequence of discrete frequencies in reflection and within extended frequency bands in transmission as compared to conventional single grids.

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.

Hybrid integration and packaging of grating-coupled silicon photonics

This thesis covers both the packaging of silicon photonic devices with fiber inputs and outputs as well as the integration of laser light sources with these same devices. The principal challenge in both of these pursuits is coupling light into the submicrometer waveguides that are the hallmark of silicon-on-insulator (SOI) systems. Previous work on grating couplers is leveraged to design new approaches to bridge the gap between the highly-integrated domain of silicon, the Interconnected world of fiber and the active region of III-V materials. First, a novel process for the planar packaging of grating couplers with fibers is explored in detail. This technology allows the creation of easy-to-use test platforms for laser integration and also stands on its own merits as an enabling technology for next-generation silicon photonics systems. The alignment tolerances of this process are shown to be well-suited to a passive alignment process and for wafer-scale assembly. Furthermore, this technology has already been used to package demonstrators for research partners and is included in the offerings of the ePIXfab silicon photonics foundry and as a design kit for PhoeniX Software’s MaskEngineer product. After this, a process for hybridly integrating a discrete edge-emitting laser with a silicon photonic circuit using near-vertical coupling is developed and characterized. The details of the various steps of the design process are given, including mechanical, thermal, optical and electrical steps. The interrelation of these design domains is also discussed. The construction process for a demonstrator is outlined, and measurements are presented of a series of single-wavelength Fabry-Pérot lasers along with a two-section laser tunable in the telecommunications C-band. The suitability and potential of this technology for mass manufacture is demonstrated, with further opportunities for improvement detailed and discussed in the conclusion.

Development of germanium/silicon integration for near infrared detection

Silicon (Si) is the base material for electronic technologies and is emerging as a very attractive platform for photonic integrated circuits (PICs). PICs allow optical systems to be made more compact with higher performance than discrete optical components. Applications for PICs are in the area of fibre-optic communication, biomedical devices, photovoltaics and imaging. Germanium (Ge), due to its suitable bandgap for telecommunications and its compatibility with Si technology is preferred over III-V compounds as an integrated on-chip detector at near infrared wavelengths. There are two main approaches for Ge/Si integration: through epitaxial growth and through direct wafer bonding. The lattice mismatch of ~4.2% between Ge and Si is the main problem of the former technique which leads to a high density of dislocations while the bond strength and conductivity of the interface are the main challenges of the latter. Both result in trap states which are expected to play a critical role. Understanding the physics of the interface is a key contribution of this thesis. This thesis investigates Ge/Si diodes using these two methods. The effects of interface traps on the static and dynamic performance of Ge/Si avalanche photodetectors have been modelled for the first time. The thesis outlines the original process development and characterization of mesa diodes which were fabricated by transferring a ~700 nm thick layer of p-type Ge onto n-type Si using direct wafer bonding and layer exfoliation. The effects of low temperature annealing on the device performance and on the conductivity of the interface have been investigated. It is shown that the diode ideality factor and the series resistance of the device are reduced after annealing. The carrier transport mechanism is shown to be dominated by generation–recombination before annealing and by direct tunnelling in forward bias and band-to-band tunnelling in reverse bias after annealing. The thesis presents a novel technique to realise photodetectors where one of the substrates is thinned by chemical mechanical polishing (CMP) after bonding the Si-Ge wafers. Based on this technique, Ge/Si detectors with remarkably high responsivities, in excess of 3.5 A/W at 1.55 μm at −2 V, under surface normal illumination have been measured. By performing electrical and optical measurements at various temperatures, the carrier transport through the hetero-interface is analysed by monitoring the Ge band bending from which a detailed band structure of the Ge/Si interface is proposed for the first time. The above unity responsivity of the detectors was explained by light induced potential barrier lowering at the interface. To our knowledge this is the first report of light-gated responsivity for vertically illuminated Ge/Si photodiodes. The wafer bonding approach followed by layer exfoliation or by CMP is a low temperature wafer scale process. In principle, the technique could be extended to other materials such as Ge on GaAs, or Ge on SOI. The unique results reported here are compatible with surface normal illumination and are capable of being integrated with CMOS electronics and readout units in the form of 2D arrays of detectors. One potential future application is a low-cost Si process-compatible near infrared camera.

High-efficiency photovoltaics through mechanically stacked integration of solar cells based on the InP lattice constant

Solar Energy is a clean and abundant energy source that can help reduce reliance on fossil fuels around which questions still persist about their contribution to climate and long-term availability. Monolithic triple-junction solar cells are currently the state of the art photovoltaic devices with champion cell efficiencies exceeding 40%, but their ultimate efficiency is restricted by the current-matching constraint of series-connected cells. The objective of this thesis was to investigate the use of solar cells with lattice constants equal to InP in order to reduce the constraint of current matching in multi-junction solar cells. This was addressed by two approaches: Firstly, the formation of mechanically stacked solar cells (MSSC) was investigated through the addition of separate connections to individual cells that make up a multi-junction device. An electrical and optical modelling approach identified separately connected InGaAs bottom cells stacked under dual-junction GaAs based top cells as a route to high efficiency. An InGaAs solar cell was fabricated on an InP substrate with a measured 1-Sun conversion efficiency of 9.3%. A comparative study of adhesives found benzocyclobutene to be the most suitable for bonding component cells in a mechanically stacked configuration owing to its higher thermal conductivity and refractive index when compared to other candidate adhesives. A flip-chip process was developed to bond single-junction GaAs and InGaAs cells with a measured 4-terminal MSSC efficiency of 25.2% under 1-Sun conditions. Additionally, a novel InAlAs solar cell was identified, which can be used to provide an alternative to the well established GaAs solar cell. As wide bandgap InAlAs solar cells have not been extensively investigated for use in photovoltaics, single-junction cells were fabricated and their properties relevant to PV operation analysed. Minority carrier diffusion lengths in the micrometre range were extracted, confirming InAlAs as a suitable material for use in III-V solar cells, and a 1-Sun conversion efficiency of 6.6% measured for cells with 800 nm thick absorber layers. Given the cost and small diameter of commercially available InP wafers, InGaAs and InAlAs solar cells were fabricated on alternative substrates, namely GaAs. As a first demonstration the lattice constant of a GaAs substrate was graded to InP using an InxGa1-xAs metamorphic buffer layer onto which cells were grown. This was the first demonstration of an InAlAs solar cell on an alternative substrate and an initial step towards fabricating these cells on Si. The results presented offer a route to developing multi-junction solar cell devices based on the InP lattice parameter, thus extending the range of available bandgaps for high efficiency cells.

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.

Current methods and advances in forecasting of wind power generation

Wind power generation differs from conventional thermal generation due to the stochastic nature of wind. Thus wind power forecasting plays a key role in dealing with the challenges of balancing supply and demand in any electricity system, given the uncertainty associated with the wind farm power output. Accurate wind power forecasting reduces the need for additional balancing energy and reserve power to integrate wind power. Wind power forecasting tools enable better dispatch, scheduling and unit commitment of thermal generators, hydro plant and energy storage plant and more competitive market trading as wind power ramps up and down on the grid. This paper presents an in-depth review of the current methods and advances in wind power forecasting and prediction. Firstly, numerical wind prediction methods from global to local scales, ensemble forecasting, upscaling and downscaling processes are discussed. Next the statistical and machine learning approach methods are detailed. Then the techniques used for benchmarking and uncertainty analysis of forecasts are overviewed, and the performance of various approaches over different forecast time horizons is examined. Finally, current research activities, challenges and potential future developments are appraised.