Nowadays the small-signal angular stability problem caused by insufficient damping of low- frequency oscillations threatens the security and integrity of power grids when the power system is developing towards large or even super large interconnected grids. The low-frequency oscillation often persists for a long period of time, and in some cases it may interrupt stable operation of the power system. One effective way of suppressing the oscillations is that the auxiliary controllers called power system stabilisers (PSS) are installed in the excitation system of generators, to provide additional damping to the low-frequency power oscillations. The work presented in this thesis focuses on applying the damping torque analysis to power systems. The main contributions of the work are the following two aspects. Firstly, by using the damping torque analysis, an in-depth study on the damping contribution and distribution in modern power system is carried out. This study provides valuable detailed information about how and why the damping is distributed in the system as well as identifying the most responsible components to the damping. The results of extended analysis can be used to guide planning, operation and control of power systems, and have great value in practice. Secondly, the work has demonstrated that damping torque analysis is not only useful in studying global model of power systems as it was before, but can also be applied in local model of power systems and the new energy power generation systems (such as the wind power generation system). Hence the work significantly enhances the potential of the damping torque analysis technique in practical applications thereby making it comparable to the conventional method of modal analysis.

The work presented in this manuscript concentrates on investigating the use of organosulfur compounds as potential electrolyte components for electrochemical energy storage systems, in particular in lithium-sulfur batteries. Novel glyme-functionalised sulfonium-based ionic liquids were synthesized and characterised before being tested as pure electrolytes for symmetrical supercapacitors based on activated carbon electrodes. The adaptation of the structure of the ionic liquids to the porosity of activated carbon was found to be of fundamental importance for the design of more efficient systems. For lithium-sulfur batteries, the study has enabled a better understanding of the mechanisms involved during the operation of the sulfur/diphenyl disulfide redox couple in a range of glyme-based solvents. Similarly, the influence of the glyme-based solvents on the chemical equilibria between organic and mineral polysulfides and on the system operation has been investigated. The initial results demonstrated that this is a particularly promising strategy in order to significantly improve the performances of lithium-sulfur batteries.

The demand for more sustainable social and economic development has resulted in a rapid growth in wind power generation largely due to the highly available of wind resource worldwide. Despite that various approaches have been proposed to improve the accuracy and to overcome the uncertainties associated with traditional wind power methods, the stochastic nature of wind still remains the most challenging issue. A temporally local Gaussian process (TLGP) for time series forecasting is proposed to enhance the time varying adaptation and overcome the computation complexity problem. The iterative techniques are employed to achieve multi-step forecasting. Statistical analysis of the application results in real wind farms shows that the method generates smaller residuals which better follows Gaussian distribution. Such a method overcomes the strict assumption made in standard Gaussian process and reduces the computation complexity involved. Further, the uncertainty propagation for iterative multi-step forecasting is analysed based on Taylor expansion. The key finding is that TLGP not only gives better accuracy but also is less sensitive to the multi-step uncertainty propagation. The probability of successful predictions in each confidence region of multi-step TLGP better fits a Gaussian distribution. Under some unique circumstances such as ramping events, the nearby local data will contribute little to predict new generations. A hybrid Gaussian process (HGP) is proposed, combining both recently collected data and historical similar patterns. The application of HGP to real wind farm shows improved prediction accuracy and response swiftness. In addition to accurate forecasting, efficient modelling and calculation techniques are investigated. Meta heuristic methods for nonlinear optimization have been studied and TLBO is deployed. Furthermore, efficient matrix operation is achieved by combining proper matrix decomposition and least square solution methods. Therefore, advanced Gaussian process modelling for probabilistic wind power forecasting under different circumstances is accomplished.

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