计及通信资源优化的温控负荷调控策略

A scheduling strategy for thermostatically-controlled loads considering communica⁃tion resource optimization

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。

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.