Distributed Cooperative Economic Optimization Strategy of a Regional Energy Network Based on Energy Cell–Tissue Architecture

摘要

The conventional centralized control method can effectively solve the influence of system power imbalance on the optimal dispatching of a power network and stabilize the power fluctuation of tie lines when the control precision is relatively high. However, when there are disturbances and uncertainties in the system, the robustness is insufficient. The distributed control method in economic optimization scheduling has the advantages of simple communication and high reliability and is well adapted to the energy dispersion characteristics of a distributed generator within the regional energy network. However, the control accuracy is slightly less than that of the centralized control method, and when the distributed power encounters a random input or exit, the corresponding sparse network matrix needs to be updated every time, complicating the use of this system for economic operation. In addition, when message loss or communication noise occurs in the communication path, the robustness of distributed control is greatly reduced. To address these issues, this paper proposes a novel power allocation framework to solve the economic optimization problem of a regional energy network that requires only local information exchange among neighboring energy cells and tissues. Correspondingly, to meet the supply-demand balance, a distributed cooperative approach based on the equal incremental principle is used to impart decentralized autonomy to the whole system. In addition, the concept of virtual consistency variables is introduced to manage topological change caused by power excursions in the energy cell and to implement plug-and-play capabilities. Relative to the conventional collaborative control method, the case studies demonstrate the scalability, plug-and-play capability, and robustness of decentralized autonomous control strategies in terms of communication delay, communication failure, and message loss, among other aspects.