The growing interest in decarbonising electricity systems together with advances in communication and information technologies that may support the application of demand and generation solutions to solve network problems has initiated reviews of traditional operational practices and security grid standards in a number of jurisdictions. The key concern is that these historical practices and standards, mostly developed in the 1950‘s, might be inappropriate for the new emerging systems as they may pose entry barriers for both renewable generation and smart grid technologies. This thesis presents a probabilistic cost-benefit framework for the development of future efficient operating and design strategies and network security standards enabled by new technologies. By optimally balancing the costs of network constraints with various operational measures composed of preventive and corrective control actions, considering potential outages of network and generation facilities, optimal network capacity that could be released to network users in real time is determined along with its impacts on network design. This framework is compatible with smart grid concepts which integrate new generation, network, and demand technology. Together with the aforementioned framework, a full optimisation model that serves to scrutinise the characteristics of the proposed probabilistic standards in the presence of high penetration of wind is developed by means of a Benders algorithm. To reduce the computational times and memory usage, a novel technique that eliminates redundant scenarios (i.e. outages) that do not contribute towards the optimum solution and hence simplifies the optimisation procedure is presented and successfully tested. The studies demonstrate that various operational measures (such as generation and demand response) can be effectively used to release additional network capacity with small (or even nil) increases in risk. It is also demonstrated that the GB system would benefit in terms of network investment and congestion costs if the changes proposed were adopted.

The main theme of this thesis is adaptive echo cancellation. Two novel independent approaches are proposed for the design of long echo cancellers with improved performance. In the first approach, we present a novel structure for bulk delay estimation in long echo cancellers which considerably reduces the amount of excess error. The miscalculation of the delay between the near-end and the far-end sections is one of the main causes of this excess error. Two analyses, based on the Least Mean Squares (LMS) algorithm, are presented where certain shapes for the transitions between the end of the near-end section and the beginning of the far-end one are considered. Transient and steady-state behaviours and convergence conditions for the proposed algorithm are studied. Comparisons between the algorithms developed for each transition are presented, and the simulation results agree well with the theoretical derivations. In the second approach, a generalised performance index is proposed for the design of the echo canceller. The proposed algorithm consists of simultaneously applying the LMS algorithm to the near-end section and the Least Mean Fourth (LMF) algorithm to the far-end section of the echo canceller. This combination results in a substantial improvement of the performance of the proposed scheme over both the LMS and other algorithms proposed for comparison. In this approach, the proposed algorithm will be henceforth called the Least Mean Mixed-Norm (LMMN) algorithm. The advantages of the LMMN algorithm over previously reported ones are two folds: it leads to a faster convergence and results in a smaller misadjustment error. Finally, the convergence properties of the LMMN algorithm are derived and the simulation results confirm the superior performance of this proposed algorithm over other well known algorithms.

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