Residential demand response (RDR) is one of the demand side management (DSM) programs for smart grid applications that are designed to enable utility companies to manage the userside electrical loads and also for consumers to voluntarily lower their demand. Instead of adding more generators to the electrical power system, RDR programs pay residential energy users to reduce consumption. Due to the complex interactions between residential customers and the power utility companies; in this thesis, RDR is studied using an optimization approach for the reason that optimization of energy consumption, with consequent cost reduction, is among the primary problems of the present and future smart grid. In this thesis optimal control models are formulated to study household energy management under timeof-use (TOU) electricity pricing strategy. The initial optimal control mathematical model is developed where consumers attempt to find the best way to schedule their household electrical resources depending on the tariff provided by the utility and the incentive offered during peak times. Under such a setting, whenever customers have enough transferable appliances, significant energy cost savings can be achieved with proper modelling of appliance usage in a household. Consumer behaviour © University of Pretoria plays a crucial role in ensuring that RDR is achieved. It has been discovered in this thesis that; inconvenience, incentive, budget and coordination of appliances affect consumer’s energy consumption behaviour. Other areas that need attention in order to further enhance the solutions of the research question are investigated. It has been shown that by incorporating the storage and photovoltaic (PV) generator the consumer can increase cost savings and reduce their electricity peak consumption further as well as the total energy drawn from the grid. Insights on the complexity of the optimization problem are provided, to allow customers to better determine the trade-off between complexity, cost, and the need to schedule their energy resources. The derived models provide a blueprint for integrating demand-side management and scheduling of resources. The other part of the study proposes an optimal energy management system that combines DSM strategies for aggregated households; DR with a dedicated PV and battery which shows that the aggregated consumption can reduce the power demanded from a distribution system by a significant amount and thus relieve the power system network and afford some residential members significant collective savings. Further more, it is shown in this thesis that knowledge on carbon emissions can incentivize investment in renewable energy at household level. It is also demonstrated that the consumer’s preferences on the cost sub-functions of energy, inconvenience and carbon emissions affect the consumption pattern. These results are important for both the consumer and the electricity suppliers, as they illustrate the optimal decisions considered in the presence of multiple sub-objectives. In this work, field measurements are carried out to obtain the baseline appliance commitment and these are compared with the optimal solutions obtained through the inconvenience model.

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