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 com-munication network model and an improved thermostatically-controlled load model that takes account of communica-tion 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 communica-tion resources and thermostatically-controlled loads maintain good consumption capability even under significant communication link delays.
thermostatically-controlled loadload schedulingnew energy consumptioncommunication delayAO-MO particle swarm optimization
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