The adoption of the virtual synchronous generator (VSG) has attracted wide attention because of the equivalent operating mechanism as a synchronous generator, providing a feasible scheme for the distributed generation connected to utility grid. However, the delivered power and frequency of the VSG may easily oscillate when the dramatic power fluctuation occurred in the distribution generation system. Fortunately, the oscillation can be damped though adjusting the rotating inertia and damping coefficient. Thus, to investigate the influences of parameters perturbation on the active power and frequency for a VSG, the small-signal model is derived and the dynamic performances are analyzed in detail. Then, based on the results of parameters analysis, an adaptive control strategy is proposed in this paper. And an optimal damping ratio can be maintained throughout the whole process of operation to suppress the oscillation of power and frequency. Therefore, the dynamic performances of the VSG are enhanced since the dynamic indexes such as the response time and overshoots are optimized. In the end, a prototype of single-phase VSG has been built and the proposed adaptive control strategy has been verified through experimental results.

Due to the capacity limitation and the renewable energy's uncontrollability, multiple energy hubs (EHs) are connected by energy routers (ERs) to form a network structure and further build the future energy internet. With concave objective functions and/or nonlinear operation constraints, the centralized optimal dispatch of interconnected EHs is difficult to solve. In order to reduce the complexity of centralized optimal dispatch on large-scale systems and protect the information privacy of different EHs, a hierarchical optimization strategy is put forward for the ER-based energy internet. Specifically, the lower level is the optimal dispatch within EHs, where the dispatch model of EH is further optimized by considering variable dispatch factors, and the information privacy of different EHs is preserved. The upper level is the energy routing control between EHs, where the end-to-end power transmission according to contract path is realized through the controllability of ERs and energy routing protocols. Finally, the effectiveness of the hierarchical optimization strategy and model are verified by simulations.

Due to capacity constraints and the uneven distribution of resources, it becomes a trend that multiple microgrids are interconnected as a net structure through energy routers. With the large-scale penetration of distributed energy, the supply mode of energy networks has been gradually transformed into multi-source, multi-path and networked supply. In order to adapt to the energy supply mode and the real-time power transaction, a minimum loss routing (MLR) algorithm based on real-time transaction is proposed to realize the end-to-end energy transmission. On the basis of bidding information, the real-time transaction is introduced to determine the source and destination address of power transmission, the amount and transmission time of power flows. Then, an MLR is selected for transaction power, which minimizes the power loss caused by conversion and transmission. As the key of power real-time transaction, the solutions to transmission time difference, single loop or double loop power supply modes and congestion managements are incorporated with the Dijkstra algorithm to find a no-congestion MLR in this paper. Finally, the effectiveness of the proposed optimization algorithm in the selection of MLR and congestion managements are verified by simulations.

Confronted with the increasing penetration of renewable energy and hydrogen-powered vehicles (HVs), energy and transportation systems need to be optimized coordinately to improve the economy and reduce carbon emissions. However, the effect of dynamic hydrogen pricing on the optimization performance of energy and transportation systems is scarcely investigated. To realize the low-carbon and economic operation of energy and transportation systems, a hierarchical game between the distributed energy station (DES) and HVs under dynamic hydrogen pricing is put forward. First, a novel dynamic hydrogen pricing mechanism related to the proportion of renewable energy in the energy supply is presented and incorporated into the game optimization, which will promote hydrogen production using renewable energy and minimize the DES operation cost. At the game equilibrium, the HVs completing equivalent aggregation realize balanced hydrogen refueling at the guidance of dynamic hydrogen pricing. Meanwhile, the raised routing optimization method can select a minimum cost route for HVs to refuel based on traffic information. Finally, the effectiveness of the proposed hierarchical game optimization strategy and model is verified by simulations.

Addressing the issue of power quality degradation caused by parameter uncertainty and the problem of voltage and frequency deviation due to disturbance in islanded AC microgrids (MGs) in harsh environments, this article proposes a hierarchical robustness strategy combining model-free prediction and fixed-time control (CMFP-FTC). Firstly, an ultra-local model (ULM) of an LC-filter is established, and the unknown disturbances in the ULM are observed using the designed fixed-time sliding model observer to enhance the robustness and dynamic performance of the system. Additionally, the Artstein transform theory is employed to convert the model with communication delay into a delay-free model. Furthermore, a distributed fixed time secondary controller (DFTSC) based on saturation function is designed to avoid the control signal from exceeding the physical limitations of the actuator, and the deviations in frequency and voltage are corrected by DFTSC for accurate power sharing. Finally, the simulation and experimental results demonstrate that the proposed CMFP-FTC strategy exhibits strong robustness and has the ability to rapidly and accurately restore frequency and average voltage to rated values, as well as achieve active and reactive power sharing.

Accurate photovoltaic (PV) power prediction technology plays a crucial role in effectively addressing the challenges posed by the integration of large-scale PV systems into the grid. In this article, we propose a novel PV power combination prediction model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) in conjunction with a hybrid neural network. To mitigate the influence of strong fluctuations in PV power on prediction outcomes, we employ CEEMDAN to decompose the PV data into several subsequences. Subsequently, sample entropy (SE) is used to quantify the complexity of each subsequence. Subsequences with similar SE values are then restructured to reduce computational load. Moreover, to overcome the limitations of a single neural network in capturing historical data features of PV power, a hybrid sequential convolutional neural network-gate recurrent unit (CNN-GRU) neural network is proposed. The effectiveness of our proposed model is validated through case studies involving PV power stations in two regions. To provide a comprehensive assessment, we conduct comparative validation by building and evaluating alternative models, including long-short term memory (LSTM), GRU, CEEMDAN-LSTM, CEEMDAN-GRU, and CNN-GRU. The results unequivocally demonstrate that the model presented in this article exhibits exceptional prediction performance, characterized by high accuracy and robust generalization.