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Since the early 1990s, deregulation of the power industry and the introduction of electricity market has unbundled the vertically integrated optimization of power system operation into distinct different optimization problems for generation and transmission operation separately. On top of that distribution network operation is also separately optimised. The increasing integration of renewable generation challenges the power system operation and the system operator plays an increasingly crucial role to organise the delivery of electricity through power trading associated with different forms of contracts. Among all the duties undertaken by the system operator, congestion management has become increasingly difficult but it is of absolute importance. However, most congestion management methods only concern with solutions from the supply side but assuming that the demand side remains unchanged. As the supply side is becoming less controllable and less predictable under the new generation environment, the quest for solution from the demand side arises. This thesis focuses on the investigation of network congestion arising in liberalised electricity markets and the management of congestion from the demand side with respect to identified existing and future challenges. To this purpose, new methodologies based on Demand Side Management are developed and modelled on Matlab platform. A simple but practical index, namely Economical Demand Management (EDM) index, is proposed for finding solution to manage the congestion from demand side based on Optimal Power Flow calculation and Locational Marginal Pricing. The efficacy of the proposed index has been validated on IEEE 14 and IEEE 30 systems. The values of the indices can be used to allocate the optimal load adjustment and determine the amount of demand side participation. Furthermore, a new load control methodology in the form of smart appliances that can be employed in the smart grid frame is also developed in this thesis. The proposed methodology is illustrated as a generalised technique based on load shifting, which has been mathematically formulated as a linear-constrained quadratic minimization problem. The proposed algorithm allows different control strategies to be applied according to the different attributes of the appliances, which is sufficiently general to be implemented in the real world. The implementation of the proposed load control methodology using case studies on a set of nationwide domestic smart appliance load shifting have been carried out with UK Power System data. Furthermore, combination of the proposed load control methodology and EDM index for congestion relief has been simulated on a network model. Also, their potential impacts from both economical and environmental aspects have been evaluated.