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Optimising regulation of aggregated thermostatically controlled loads based on multi-swarm PSO

Optimising regulation of aggregated thermostatically controlled loads based on multi-swarm PSO

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Thermostatically controlled loads (TCLs) have been shown to be able to provide regulation service in ancillary service markets. In this work, an online optimisation model of TCLs was established to track the automatic generation control signal. In the optimisation model, the TCLs are regulated with different control commands, and the cluster-based control structure of the TCLs is proposed for implementation. To solve the optimisation problem, a mapping between the temperature setpoint variation and the on/off state of TCLs was established. Hence, the problem was transformed into a 0–1 non-linear programming, which can be solved by the binary dynamic multi-swarm particle swarm optimisation with cooperative learning strategy (DMS-PSO-CLS). Simulation results demonstrate that the binary DMS-PSO-CLS algorithm is an efficient method to solve the optimisation problem. It is promising to control the TCLs individually to serve for the frequency regulation in power grid.

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