Rapid advances in the smart grid technology are making it possible to tackle a lot of problems in the aged power systems. High-speed data acquisition system, high-voltage power electronic equipment, advanced utility and customer interaction technologies, as well as distributed renewable generation are enabling the revolution in the electric power generation, delivery and distribution. Through the implementation of ubiquitous metering and communication networks, the customers would no longer be a passive receiver of the electrical energy, but instead, an active participant in the power system and electricity market. They can not only sell their own energy to the utility, but also take part in the emergency restoration in the power grid. Nonetheless, some technical barriers are encountered during this revolution, such as difficulties in integrating home automation, smart metering, customer interaction and power system operation into the whole system. This thesis proposes a customer involved load shedding algorithm for both the power system frequency control and the micro-grid islanding. This new algorithm possesses the features of centralized load control and distributed load control, which fully utilizes the advantages of hierarchical communication networks along with the home automation. The proposed algorithm considers the reliability of the power grid as well as the comfort of the electricity users. In the power distribution system, the high-level control centre is responsible for coordinating the local load controllers, whilst the local controller takes charge of frequency monitoring and decision making. In the micro-grid, a centralized control strategy is adopted to better serve the system with the wide set of information available at the micro-grid control centre. The simulation results have demonstrated the correctness and feasibility of the proposed algorithm. Finally, the hardware implementation further tests the validity of the wireless sensor networks serving as the system’s monitoring and communication technology. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2013-09-24 20:01:37.098