In real-word applications, distributed generators (DGs) cooperate to accomplish the objectives of frequency restoration and active power sharing with time restrictions plays a crucial role in improving power quality of AC islanded microgrids. To address this issue, two novel distributed robust secondary control protocols, namely finite-time and fixed-time consensus algorithms, which subject to unknown external bounded disturbances are put forward. The main features of the proposed algorithms are distributed implementation, discrete control, asynchronous communication, and local computation. Take the event-triggered strategy into consideration, the developed algorithms have faster convergence speeds as long as the event-triggered parameters are limited in a certain range. Through Lyapunov synthesis, the conditions of maximum/off-line settling time for finite/fixed-time frequency restoration and active power sharing algorithms are first established. In addition, the detailed implementation process of distributed robust event-triggered control approach is provided. At the end of this paper, considerable amount of various simulations under the unified wide-time-scale framework are conducted to illustrate the performance of the proposed algorithms.

The distributed energy management technology based on communication channels plays an important role in the economic operation of the modern smart grid. However, the information exchange between participating agents, such as multiple heterogeneous generation devices and consumption loads, increases the risk of privacy leakage, which will reduce the optimization performance and even result in the collapse of the whole system. To address this issue, a novel optimal energy management strategy is proposed in this article with distributed consensus-based alternating direction method of multipliers (ADMM) algorithm and differential privacy scheme. The superior characteristics of the arranged strategy are: 1) can handle the cooperative optimal energy management problem of microgrids with conventional generators (CGs), battery energy storage stations (BESSs), wind turbines (WTs), and transmission loss and 2) can completely protect the privacy information of each agent from being stolen by neighboring nodes in the process of information interaction. At the end of this article, simulations are carried out on the modified IEEE 30-bus and simplified IEEE 118-bus systems to validate the developed differential privacy energy management strategy with distributed ADMM.

The fractional-order modeling with structure identification, parameter estimation and the ability of revealing natures of battery are considered. The correlative information criterion is proposed based on the 1/f noise assisted I/O data, which is adept in evaluating the reliability of model structure and adaptiveness of model parameters. Experimental results validate the above conclusions.

State of charge (SOC) estimation is the core algorithm of the battery management system. However, the commonly used model-based, data-driven, or experiment-based methods struggle to independently achieve accurate SOC estimation under different working conditions and temperatures, which affects battery performance and safety. To this end, this paper proposes an online SOC estimation method that combines the model-driven and double-data-driven approaches. The unscented Kalman filter (UKF) based on the first-order RC model is used to achieve robust SOC estimation, while the data-driven long short-term memory (LSTM) neural network is used to achieve fast SOC estimation. The former model has an excellent dynamic performance and the latter has high steady-state accuracy. The SOC estimation results are input into the SOC estimation model of series LSTM so that the stable but inaccurate SOC values estimated by UKF in the first part and the accurate but fluctuating SOC values estimated by LSTM can be correlated and corrected, achieving a fast and accurate SOC estimation under various working conditions. The estimation results show that the above method has strong robustness and high accuracy, and effectively reduces model complexity and data redundancy. In addition, the root mean square error of SOC estimation under different working conditions is controlled within 1–2.3% at 0 °C, 25 °C, and 45 °C, which is better than the traditional single-SOC estimation method.