An adaptive decentralized regulation strategy for the cluster with massive inverter air conditionings

To deal with the power fluctuation caused by renewable energy integration and load growth, it has been a general recognition to provide regulation services for local power system or main grid by flexible loads. There is a certain range of adaption to temperature for users and thermal inertias for building components, making it possible for air conditioning (AC) loads to participate in demand response (DR). Large cluster of ACs offers a resource akin to that of a distributed energy storage system, which is also an important regulation module of virtual power plant (VPP). Inverter air conditioning (IAC) is gradually occupying the market proportion owing to comfort and power savings. It has become a key technology in the new power system on how to coordinate massive IAC resources with heterogeneous parameters and status to output reliable load regulation services. To this end, this paper proposes a consensus control strategy for the IACs cluster based on lightweight decentralized architecture. The task is distributed fairly while ensuring the occupants’ comfort. The target load curve can be accurately tracked with high adaptability and robustness. The interactive parameters are generated from the bottom up based on individuals’ response ability, which facilitates rapid convergence. Together with the intrinsic adaptability and robustness, the hosting mechanism for the coordinator is designed to guarantee high fault-tolerance, and therefore plug-and-play can be satisfied. Besides, dimensionality reduction and locality for the interaction data ensure a higher level of privacy protection. The advantages are illustrated in case studies and compared with other approaches. To the best of our knowledge, the proposed method has the overwhelming convergence performance over the present literatures. These characteristics indicate a sufficient basis for its application in the variable conditions with massive users and can be extended to more scenarios using consensus control.