Integrated Energy Systems (IESs) are important vehicles for achieving energy conservation and emission reduction. However, operating an IES smoothly is difficult due to source–load fluctuations and the complexity of the multiple timescales of different energy flows. To tackle the challenges, this paper proposes a two-stage dual-loop optimization framework for IESs, where the two stages comprise the first stage: day-ahead cooperative optimization of source-storage-demand (DCOS), and the second stage: intraday dual-loop rolling optimization control (IDRO). In DCOS, energy storage, and integrated demand response models are established, and a carbon emission trading mechanism is introduced to achieve an economically low-carbon operating plan. In IDRO, an electric power rolling optimization model based on model predictive control is established in the inner loop, and a cooling and heating power output adjustment strategy based on user comfort event-trigger mechanism is developed in the outer loop. The proposed optimization strategy enables the coordinated operation of multiple energy flows across various time scales, effectively mitigating the imbalance between production and demand during intraday operations under source–load fluctuations scenario. In case studies, this strategy is applied to a typical IES, with simulations conducted to evaluate its performance during typical summer and winter seasons.

Power batteries and their management technology are crucial for the safe and efficient operation of electric vehicles (EVs). The life and safety issues of power batteries have always plagued the EV industry. To achieve an intelligent battery management system (BMS), it is crucial to accurately estimate the internal state of the power battery. The purpose of this review is to analyze the current status of research on multi-state estimation of power batteries, which mainly focuses on the estimation of state of charge (SOC), state of energy (SOE), state of health (SOH), state of power (SOP), state of temperature (SOT), and state of safety (SOS). Moreover, it also analyzes and prospects the research hotspots, development trends, and future challenges of battery state estimation. It is a significant guide for designing BMSs for EVs, as well as for achieving intelligent safety management and efficient power battery use.

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.