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.

Recent years have witnessed an exponential growth of model scale in deep learning-based recommender systems---from Google's 2016 model with 1 billion parameters to the latest Facebook's model with 12 trillion parameters. Significant quality boost has come with each jump of the model capacity, which makes us believe the era of 100 trillion parameters is around the corner. However, the training of such models is challenging even within industrial scale data centers. We resolve this challenge by careful co-design of both optimization algorithm and distributed system architecture. Specifically, to ensure both the training efficiency and the training accuracy, we design a novel hybrid training algorithm, where the embedding layer and the dense neural network are handled by different synchronization mechanisms; then we build a system called Persia (short for parallel recommendation training system with hybrid acceleration) to support this hybrid training algorithm. Both theoretical demonstrations and empirical studies with up to 100 trillion parameters have been conducted to justify the system design and implementation of Persia. We make Persia publicly available (at github.com/PersiaML/Persia) so that anyone can easily train a recommender model at the scale of 100 trillion parameters.