This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractDetailed residential energy consumption data can be used to offer advanced services and provide new business opportunities to all participants in the energy supply chain, including utilities, distributors and customers. The increasing interest in the residential consumption data is behind the roll-out of smart meters in large areas and led to intensified research efforts in new data acquisition technologies for the energy sector. This paper introduces a novel model for generation of residential energy consumption profiles based on the energy demand contribution of each household appliance and calculated by using a probabilistic approach. The model takes into consideration a wide range of household appliances and its modular structure provides a high degree of flexibility. Residential consumption data generated by the proposed model are suitable for development of new services and applications such as residential real-time pricing schemes or tools for energy demand prediction. To demonstrate the main features of the model, an individual household consumption was created and the effects of a possible change in the user behaviour and the appliance configuration presented. In order to show the flexibility offered in creation of the aggregated demand, the detailed simulation results of an energy demand management algorithm applied to an aggregated user group are used.

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Citation: Jahromizadeh, Soroush (2013). Joint rate control and scheduling for providing bounded delay with high efficiency in multihop wireless networks. (Unpublished Doctoral thesis, City University London) This is the unspecified version of the paper.This version of the publication may differ from the final published version. factor. The alternative optimisation problem is solved by a distributed scheduling algorithm incorporating a duality-based rate control algorithm at its inner layer, where optimal link prices correlate with their average queueing delays. The proposed approach is then realised by a scheduling algorithm that runs jointly with an integral controller whereby each source regulates the queueing delay on its paths at the desired level, using its utility weight coefficient as the control variable. Since the proposed algorithms are based on solving the alternative concave optimisation problem, they are simple, distributed and lead to maximal link utilisation. Hence, they avoid the limitations of the previous approaches. The proposed algorithms are shown, using both theoretical analysis and simulation, to achieve asymptotic regulation of end-to-end delay given the step size of the proposed integral controller is within a specified range. Permanent

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Abstract:This paper uses problem decomposition to show that optimal dynamic home energy prices can be used to reduce the cost of supplying energy, while at the same time reducing the cost of energy for the home users. The paper makes no specific recommendations on the nature of energy pricing, but shows that energy prices can normally be found that not only result in optimal energy consumption schedules for the energy provider's problem and are economically viable for the energy provider, but also reduce total users energy costs. Following this, the paper presents a heuristic real-time algorithm for demand management using home appliance scheduling. The presented algorithm ensures users' privacy by requiring users to only communicate their aggregate energy consumption schedules to the energy provider at each iteration of the algorithm. The performance of the algorithm is evaluated using a comprehensive probabilistic user demand model which is based on real user data from energy provider E.ON. The simulation results show potential reduction of up to 17% of the mean peak-to-average power estimate, reducing the user daily energy cost for up to 14%.