The non-resonant single-phase dual-active-bridge (NSDAB) dc-dc converter has been increasingly adopted for isolated dc-dc power conversion systems. Over the past few years, significant research has been carried out to address the technical challenges associated with modulations and controls of NSDAB dc-dc converter. The aim of this paper is to review and compare these recent state-of-the-art modulation and control strategies. Firstly, the modulation strategies for NSDAB dc-dc converter are analyzed. All possible phase-shift patterns are demonstrated, and the correlation analysis of the typical phases-shift modulation methods for NSDAB dc-dc converter is presented. Then, an overview of steady-state efficiency optimization strategies is discussed for NSDAB dc-dc converter. Moreover, a review of optimized techniques for dynamic responses is also provided. For both the efficient and dynamic optimizations, thorough comparisons and recommendations are provided in this paper. Finally, to improve both steady state and transient performances, a combination approach to optimize both efficiency and dynamics for NSDAB dc-dc converter based on the reviewed methods is presented in this paper.

Energy management is critical to reduce the size and operating cost of hybrid energy systems, so as to expedite on-the-move electric energy technologies. This paper proposes a novel knowledge-based, multi-physics-constrained energy management strategy for hybrid electric bus (HEB), with an emphasized consciousness of both thermal safety and degradation of onboard lithium-ion battery (LIB) system. Particularly, a multi-constrained least costly formulation is proposed by augmenting the over-temperature penalty and multi-stress-driven degradation cost of LIB into the existing indicators. Further, a soft actor-critic deep reinforcement learning strategy is innovatively exploited to make an intelligent balance over conflicting objectives and virtually optimize the power allocation with accelerated iterative convergence. The proposed strategy is tested under different road missions to validate its superiority over existing methods in terms of the converging effort, as well as the enforcement of LIB thermal safety and the reduction of overall driving cost.

Energy management is an enabling technique to guarantee the reliability and economy of hybrid electric systems. This paper proposes a novel machine learning-based energy management strategy for a hybrid electric bus (HEB), with an emphasized consciousness of both thermal safety and degradation of the onboard lithium-ion battery (LIB) system. Firstly, the deep deterministic policy gradient (DDPG) algorithm is combined with an expert-assistance system, for the first time, to enhance the “cold start” performance and optimize the power allocation of HEB. Secondly, in the framework of the proposed algorithm, the penalties to over-temperature and LIB degradation are embedded to improve the management quality in terms of the thermal safety enforcement and overall driving cost reduction. The proposed strategy is tested under different road missions to validate its superiority over state-of-the-art techniques in terms of training efficiency and optimization performance.

In this paper, the cyber-security of smart microgrids is thoroughly discussed. In smart grids, the cyber system and physical process are tightly coupled. Due to the cyber system’s vulnerabilities, any cyber incidents can have economic and physical impacts on their operations. In power electronics-intensive smart microgrids, cyber-attacks can have much more harmful and devastating effects on their operation and stability due to low inertia, especially in islanded operation. In this paper, the cyber–physical systems in smart microgrids are briefly studied. Then, the cyber-attacks on data availability, integrity, and confidentiality are discussed. Since a false data injection (FDI) attack that compromises the data integrity in the cyber/communication network is one of the most challenging threats for smart microgrids, it is investigated in detail in this paper. Such FDI attacks can target state estimation, voltage and frequency control, and smart microgrids’ protection systems. The economic and physical/technical impacts of the FDI attacks on smart microgrids are also reviewed in this paper. The defensive strategies against FDI attacks are classified into protection strategies, in which selected meter measurements are protected, and detection/mitigation strategies, based on either static or dynamic detection. In this paper, implementation examples of FDI attacks’ construction and detection/mitigation in smart microgrids are provided. Samples of recent cyber-security projects in the world, and critical cyber-security standards of smart grids, are presented. Finally, future trends of cyber-security in smart microgrids are discussed.

An observer-based dc voltage droop and current feed-forward control for a dc microgrid has been proposed in this paper. With the proposed control scheme, dynamic response of dc voltage control can be improved effectively through using an observer. Moreover, the feedback current for dc voltage droop and the feed-forward current are both obtained from the observer without additional current measurement. Furthermore, system stability analysis shows that, the propose method has enhanced robustness and stability when compared to the traditional droop method under variations of system parameters, such as loads, cable impedances, and droop control gains. In a droop controlled dc microgrid, there exists a trade-off between the load demand sharing accuracy (which needs a high droop gain) and stability (which limits the droop gain). With the above mentioned advantages, the proposed method can effectively address this trade-off in such a system. The effectiveness of the proposed method is verified by experiments in a laboratory dc microgrid with two boost dc-dc converters based distributed generations and a buck type constant power dc load.

A novel unified control of the dc-ac interlinking converters (ICs) for autonomous operation of hybrid ac/dc microgrids (MGs) has been proposed in this paper. When the slack terminals in the ac and dc MGs are available, the ICs will operate in autonomous control of interlinking power between the ac and dc subgrids, with the total load demand proportionally shared among the existing ac and dc slack terminals. With a flexible control variable added in power control loop, design of the interlinking power control, and droop features of ac and dc MGs can be decoupled. Moreover, this control variable can be tuned flexibly according to different power control objectives, such as proportional power sharing in terms of capacity (which is considered in this paper), interlinking power dispatch, and other optimal power dispatch algorithms, ensuring a well-designed flexibility and compatibility. Furthermore, if the dc MG or the ac MG loses dc voltage control or ac voltage and frequency control capability due to failures of operation of its slack terminals, the ICs can automatically and seamlessly transfer to dc MG support or ac MG support control modes without operation mode detection, communication, control scheme switching, and control saturation. In order to enhance the stability of the proposed unified control in different modes with different control plants, a phase compensation transfer function has been added in the power control loop. After thorough theoretical analysis and discussions, detailed simulation verifications based on PSCAD/EMTDC and experimental results based on a hardware experimental MG platform have been presented.