Soft computing methods for the implementation of aggregated load control of domestic electric water heaters

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University of New Brunswick


Power system operators have the task of maintaining the balance between the demand and generation of electric power. Currently, much research and attention is directed at finding more environmentally friendly sources of power generation. Naturally, more power is required when the load is at its peak value, and this tends to be when the most non-environmentally friendly sources of power generation are used. This thesis proposes a control strategy for peak load shaving by intelligently scheduling power consumption of domestic electric water heaters using three methods; fuzzy logic controller, a fuzzy neural network, and particle swarm optimization. Simulation studies evaluate the performance of the load control strategy. In doing so, the control strategy is shown to be an effective tool for reducing the aggregated peak load of electricity without compromising customer satisfaction.