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.

This study utilises the optimization method to ascertain the levelized cost of hydrogen and life cycle carbon emissions of four water electrolysis hydrogen production systems across 31 provinces and regions of China, and compares these with hydrogen production from coal, natural gas and industrial by-products. The findings indicate that the grid-connected water electrolysis hydrogen production system has low-carbon advantages only in certain provinces, and time-of-use electricity prices can improve its economic competitiveness. The off-grid water electrolysis hydrogen production system can achieve near-zero carbon emissions, although additional investment is required to configure larger capacities for electricity energy storage and hydrogen storage. Projections indicate that by 2045–2050, this system could emerge as the most cost-effective to hydrogen production, a milestone that could be advanced by 5–15 years through the implementation of specific carbon reduction incentives or production subsidies. Prior to this timeframe, hydrogen production through industry by-products emerges as a viable alternative for the development of hydrogen energy.

Deep learning (DL) has been widely used in photovoltaic (PV) power forecasting due to its advantages in nonlinear processing and feature extraction. However, it faces an overfitting challenge when daily weather change, resulting in poor adaptability for practical applications. Conversely, the mapping strategy primarily applied in plant sacrifices the PV power fluctuation details for forecasting under complex weather conditions to improve adaptation. To keep long-term adaptations and accurately forecast fluctuation detail, this study proposes a novel two-stage hybrid DL (HDL) framework for day-ahead PV power forecasting with 15-min intervals. In the first numerical weather prediction (NWP) information mapping stage, motivated by forecasting strategies in plants, a mapping model is developed based on long short-term memory (LSTM) networks to forecast the general power trends with strong adaptation. Then, in the second stage of historical features (adjacency and similarity features) extraction, wavelet decomposition, LSTM, and a convolutional neural network are used to forecast more fluctuation details accurately. Moreover, a hyperparameter optimization method based on grid search and Bayesian optimization is proposed, which facilitates an unbiased development of DL. The developed framework is implemented on 10 MW and 100 kW plants and the results show that the proposed method can achieve correctness and adaptation among the rivals.

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.