【摘要】 海量需求侧数据的传输需求使得通信网络的压力成倍增加，容易造成通信延迟、拥塞、中断等现象，影响供需互动业务实时性，不利于负荷调控的进一步实施。针对上述问题，以实现通信受限情况下新能源发电的精准消纳为目标，提出一种计及通信资源优化的负荷精细化调控策略。首先基于信息物理融合技术，建立考虑通信网络影响的温控负荷调控机制。进而基于通信网络模型和计及通信时延的改进温控负荷模型，采用自适应权重与反向学习策略相结合的改进粒子群算法，实现考虑通信资源均衡的温控负荷精细化调控。最后通过算例仿真，验证了所提算法能够合理分配通信资源，使得温控负荷在通信链路时延较大的情况下仍具备良好的消纳能力。更多还原

【Abstract】 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 thermostaticallycontrolled 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.更多还原