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Development of a commercially viable carbon capture and sequestration (CCS) technology for fossil fuel power generation is vital if the anticipated effects of global warning are to be avoided. Chemical-looping combustion (CLC) is an indirect combustion process that utilises a regenerable solid oxygen sorbent (oxygen carrier, OC), typically a metal oxide, to transfer oxygen from the combustion air to the fuel such that direct contact between air and fuel is avoided. CLC is a variant on an oxy-fuel carbon capture system that offers the potential for a much lower energy penalty as CO2 separation is achieved intrinsically such that additional energy-intensive gas separation steps are avoided. Our research focuses on the development and optimisation of OCs for CLC systems using biomass and biomass derived fuels. The development of a CLC process utilising biomass is of particular interest as it has the potential to result in negative CO2 emissions i.e. a net removal of CO2 from the atmosphere. Thermochemical conversion of biomass typically results in the formation of significant quantities of refractory tar compounds which are difficult to combust and can lead to reduced fuel conversion efficiencies. Decomposition of the tars on the surface of the OC can result in severe coking and temporary deactivation. Coking of the OC also limits the overall CO2 capture efficiency of the process as regeneration of the OC in air produces CO2 which cannot be captured. This thesis documents the progress made towards the development of a robust laboratory based system for testing the effects of biomass tars on the long term performance of a chemical-looping combustion process. The work completed in this thesis can be divided into two main areas: the first involved developing optimised fabrication strategies for the production of inexpensive iron-based oxygen carrier particles of high reactivity and robust physical characteristics that could be used in CLC systems utilising biomass as the fuel. The second research focus involved the development of a reactor and analysis protocol for studying the interactions between biomass pyrolysis tars and the cheap, synthetic iron-based oxygen carrier materials. A range of pure iron oxide and iron oxide supported with 40 wt.% Al2O3 oxygen carrier materials were prepared via simple scalable fabrication techniques based on wet granulation for use in CLC systems utilising biomass or gasified biomass as a fuel. The oxygen carrier particles were subjected to rigorous testing using a range of analytical methods to assess their physical and chemical properties and suitability for use in large-scale systems. The effect of fabrication method and alumina precursor material used for producing the supported iron oxide materials were found to have a considerable effect on the physical characteristics and reactivity of the oxygen carrier material. The reduction kinetics (the rate limiting step in the CLC of gaseous fuels) of the different OC materials prepared in this work were assessed using a thermogravimetric analyser (TGA). A simple particle model based on the concept of effectiveness factor was applied to determine the intrinsic kinetic information. Preparation of the Al2O3 supported iron oxide oxygen carrier material using a Al(OH)3 alumina precursor gave the most porous oxygen carrier material with the highest surface area. This oxygen carrier was also the most reactive particularly at temperatures above 973 K and demonstrated very good thermal stability at temperatures up to 1173 K. The activation energy of the oxygen carrier was found to increase from 73 kJ mol-1 for the temperature range 823-1073 K to 123 kJ mol-1 at temperatures of 1073-1173 K. The increase in the activation energy was attributed to further conversion of Fe3O4 to FeAl2O4 which was more pronounced at the higher temperature range. Here we propose that the formation of FeAl2O4 was beneficial, acting to enhance the thermal stability, reactivity and oxygen transfer capacity of the iron oxide based oxygen carrier material. A new 500W laboratory-scale, two-stage fixed-bed reactor for simulating CLC with ex situ solid fuel gasification has been designed and constructed. Preliminary studies of the interactions between OC materials consisting of pure iron oxide and 60 wt.% Fe2O3 iron oxide supported on Al2O3 and a gas stream produced from the pyrolysis of biomass to emulate a fuel gas containing large quantities of tars were carried out. The presence of both OC materials at 973 K was found to significantly reduce the amount of biomass tars by up to 71 wt.% in the case of the 60 wt.% Fe2O3/40 wt.% Al2O3 OC material compared with analogous experiments in which the biomass tars were exposed to an inert bed of sand. Exposing the pyrolysis vapours to the oxygen carriers in their oxidised form favoured the production of CO2. The production of CO was favoured when the oxygen carriers were in their reduced forms. Both oxygen carrier materials were affected by carbon deposition. Carbon deposition was removed in the subsequent oxidation phase with no obvious deleterious effects on the reactivity of the oxygen carrier materials after exposure to the pyrolysis gases and vapours.

This thesis summarises a set of experiments towards the integration of concentrating optics into up-conversion photovoltaics. Up-conversion in rare earths has been investigated here. This optical process is non-linear therefore a high solar irradiance is required. High solar irradiance is achievable by solar concentration. Two concentrating approaches were investigated in this thesis: The first approach involved the concentration of the incident solar irradiance into optical fibres. An optical system with spherical lenses and dielectric tapers was designed accordingly. A solar concentration of 2000 suns was realised at the end of a single optical fibre. In addition to the total solar concentration, the spectral dependence was characterised to account for the effect of chromatic aberrations. Then, the solar concentration could be transferred into rare earth-doped fibres. For this reason, a series of experiments on double-clad erbium-doped silicate fibres was carried out. Although up-conversion in this type of fibre is minimised, the measured power dependence agrees with up-conversion via excited state absorption. In the second approach, concentrating optics were integrated in up-conversion solar cells. The role of the optics was to couple the photons transmitted by the solar cell to the rare earth up-converter. Therefore, imaging and non-imaging optics were investigated, with the latter exhibiting ideal coupling characteristics; concentration and high transmission of the incident irradiance, but also efficient collection of the up-converted emission. Out of the non-imaging optics, the dielectric compound parabolic concentrator fulfilled these characteristics, indicating its novel use in up-conversion solar cells. Two erbium-doped up-converters were utilised in this approach, beta-phase hexagonal sodium yttrium tetrafluoride (β-NaYF4:25%Er3+) and barium diyttrium octafluoride (BaY2F8:30%Er3+). The latter performed best, with an external quantum efficiency (EQE) of 2.07% under 1493 nm illumination, while the former exhibited an EQE of 1.80% under 1523 nm illumination both at an irradiance of 0.02 W/cm2. This corresponds to a relative conversion efficiency of 0.199% and 0.163% under sub-band-gap illumination, respectively, for a solar cell of 17.6% under standard AM1.5G conditions. These values are among the highest in literature for up-conversion solar cells and show the potential of the concentrating concept that can be important for future directions of photovoltaics.

The Economic Load Dispatch (ELD) problem is an optimisation task concerned with how electricity generating stations can meet their customers’ demands while minimising under/over-generation, and minimising the operational costs of running the generating units. In the conventional or Static Economic Load Dispatch (SELD), an optimal solution is sought in terms of how much power to produce from each of the individual generating units at the power station, while meeting (predicted) customers’ load demands. With the inclusion of a more realistic dynamic view of demand over time and associated constraints, the Dynamic Economic Load Dispatch (DELD) problem is an extension of the SELD, and aims at determining the optimal power generation schedule on a regular basis, revising the power system configuration (subject to constraints) at intervals during the day as demand patterns change. Both the SELD and DELD have been investigated in the recent literature with modern heuristic optimisation approaches providing excellent results in comparison with classical techniques. However, these problems are defined under the assumption of a regulated electricity market, where utilities tend to share their generating resources so as to minimise the total cost of supplying the demanded load. Currently, the electricity distribution scene is progressing towards a restructured, liberalised and competitive market. In this market the utility companies are privatised, and naturally compete with each other to increase their profits, while they also engage in bidding transactions with their customers. This formulation is referred to as: Bid-Based Dynamic Economic Load Dispatch (BBDELD). This thesis proposes a Smart Evolutionary Algorithm (SEA), which combines a standard evolutionary algorithm with a “smart mutation” approach. The so-called ‘smart’ mutation operator focuses mutation on genes contributing most to costs and penalty violations, while obeying operational constraints. We develop specialised versions of SEA for each of the SELD, DELD and BBDELD problems, and show that this approach is superior to previously published approaches in each case. The thesis also applies the approach to a new case study relevant to Nigerian electricity deregulation. Results on this case study indicate that our SEA is able to deal with larger scale energy optimisation tasks.

The statistical method of Principal Component Analysis (PCA) is developed here from a time-series analysis method used in nonlinear dynamical systems to a forecasting tool and a Measure-Correlate-Predict (MCP) and then applied to wind speed data from a set of Met. Office stations from Scotland. PCA for time-series analysis is a method to separate coherent information from noise of measurements arising from some underlying dynamics and can then be used to describe the underlying dynamics. In the first step, this thesis shows that wind speed measurements from one or more weather stations can be interpreted as measurements originating from some coherent underlying dynamics, amenable to PCA time series analysis. In a second step, the PCA method was used to capture the underlying time-invariant short-term dynamics from an anemometer. These were then used to predict or forecast the wind speeds from some hours ahead to a day ahead. Benchmarking the PCA prediction against persistence, it could be shown that PCA outperforms persistence consistently for forecasting horizons longer than around 8 hours ahead. In the third stage, the PCA method was extended to the MCP problem (PCA-MCP) by which a short set of concurrent data from two sites is used to build a transfer function for the wind speed and direction from one (reference) site to the other (target) site, and then apply that transfer function for a longer period of data from the reference site to predict the expected wind speed and direction at the target site. Different to currently used MCP methods which treat the target site wind speed as the independent variable and the reference site wind speed as the dependent variable, the PCA-MCP does not impose that link but treats the two sites as joint observables from the same underlying coherent dynamics plus some independent variability for each site. PCA then extracts the joint coherent dynamics. A key development step was then to extend the identification of the joint dynamics description into a transfer function in which the expected values at the target site could be inferred from the available measurements at the reference site using the joint dynamics. This extended PCA-MCP was applied to a set of Met. Office data from Scotland and benchmarked a standard linear regression MCP method. For the majority of cases, the error of the resource prediction in terms of wind speed and wind direction distributions at the target site was found to be between 10% and 50% of that made using the standard linear regression. The target mean absolute error was also found to be only the 29% of the linear regression one.