Abstract
With the latest smart technologies in the electricity sector, the consumers of electricity got the opportunity to reduce their electricity consumption cost by participating in the demand response programs offered by the utility companies. In this paper, a model of energy management system is introduced for the energy scheduling at home. Residential automatic smart appliances of general use are selected for energy scheduling. The energy controlling device in the EMS model receives the real time electricity price signals from the utility company and schedule the appliances according to the user requirements in such a way so that the electricity consumption cost could be minimized. The appliances are scheduled under real time pricing combined with inclined block rate pricing scheme so that the peak to average ratio of power could be maintained in the satisfactory range. This helps the utility companies in maintaining the system reliability. For the solution of the scheduling problem, particle swarm optimization algorithm is used due to its effectiveness and easy implementation. Finally, the results have been compared and verified against the results achieved by genetic algorithm.
References
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