Multiple Time Scale Cooperation Optimization of Regional Multienergy Systems Based on Distributed Model Predictive Control

The multienergy systems (MES) can improve energy utilization efficiency through cascading energy utilization and have broad applications. However, the variation of different energy flows in the MES has multiple time scale characteristics, which affect the stable operation. Moreover, centralized optimization in large-scale energy systems is limited by communication pressure and computational burden, hindering rapid response to system. To overcome these challenges, a multiple time scale distributed model predictive control optimization strategy is proposed in this article. First, an energy imbalance model considering different time scales is proposed. Then, a communication network topology between intelligent agents is established. Furthermore, a supply–demand balance optimization model based on model predictive control is developed, while the dual decomposition and gradient method are adopted to achieve the distributed solution. Finally, a multiple time scale scheduling mechanism is designed to enable distributed cooperation optimization of different time scales. The effectiveness of the proposed strategy is demonstrated through various simulation scenarios, such as load changes, communication interruptions, plug-and-play, and scalability testing.