A scheduling strategy for thermostatically-controlled loads considering communication resource optimization

Due to the transmission demand for massive demand-side data， the pressure on communication networks has doubled and redoubled， leading to communication delays， congestion， interruptions， and other problems， which impact the real-time interaction of supply and demand services and hinder further implementation of load scheduling. To address the aforementioned issues and achieve precise consumption of new energy generation under communication constraints， a refined load scheduling strategy considering communication resource optimization is proposed. Firstly， based on information physical fusion technology， a scheduling mechanism for thermostatically-controlled loads considering the influence of communication networks is established. Subsequently， by use of a communication network model and an improved thermostatically-controlled load model that takes account of communication delay， an improved particle swarm optimization （PSO） that combines adaptive weighting and reverse learning is utilized， a refined thermostatically -controlled load scheduling considering communication resource balance is achieved. Finally， numerical simulation demonstrates that the proposed method can reasonably allocate communication resources and thermostatically-controlled loads maintain good consumption capability even under significant communication link delays.