Due to the aging of electric infrastructures, conventional power grid is being modernized towards smart grid that enables two-way communications between consumer and utility, and thus more vulnerable to cyber-attacks. However, due to the attacking cost, the attack strategy may vary a lot from one operation scenario to another from the perspective of adversary, which is not considered in previous studies. Therefore, in this paper, scenario based two-stage sparse cyber-attack models for smart grid with complete and incomplete network information are proposed. Then, in order to effectively detect the established cyber-attacks, an interval state estimation (ISE) based defense mechanism is developed innovatively. In this mechanism, the lower and upper bounds of each state variable are modeled as a dual optimization problem that aims to maximize the variation intervals of the system variable. At last, a typical deep learning, i.e., stacked auto-encoder (SAE), is designed to properly extract the nonlinear and non-stationary features in electric load data. These features are then applied to improve the accuracy for electric load fore-casting, resulting in a more narrow width of state variables. The uncertainty with respect to forecasting errors is modeled as a parametric Gaussian distribution. The validation of the proposed cyber-attack models and defense mechanism have been demonstrated via comprehensive tests on various IEEE benchmarks.

Renewable energy generations have been attracting sustained attentions in academic and industry. Current-fed isolated bidirectional DC/DC converters (CF-IBDCs) are widely adopted in low voltage high current applications such as solar photovoltaic, fuel cell and energy storage. This paper gives an overview and a comprehensive comparative evaluation of CF-IBDCs. The active clamped, dual half-bridge, L-L type dual active bridge, resonant type, naturally clamped and other type topologies of CF-IBDCs are investigated, analyzed and compared regarding their circuit topological structures, characteristics, modulation methods and soft-switching technologies. In addition, component cost models and loss models of converters are deduced and presented. On this basis, quantitative and comprehensive performance comparison of six typical CF-IBDC topologies selected from each type are conducted in terms of costs, losses, weight, volume and power density for the given system specifications and design constraints.

Black phosphorus quantum dots (BP QDs) are facilely loaded on MXene nanosheets through van der Waals self-assembly. On the one hand, the BP QDs possess large surface areas and abundant active sites, offering fairly high electrocatalytic activity. On the other hand, the MXene nanosheets serve as a 2D substrate to confine the BP QDs, preventing them from aggregation and, thus, electrolyte inaccessibility. They can further provide excellent electronic conductivity and superior structural robustness. The BP QDs, in turn, act as a spacer to isolate the nanosheets from restacking, thus preserving the active sites from being lost. The resulting BP QDs/MXene nanohybrids, as a bifunctional electrocatalyst, exhibit remarkable synergy in both hydrogen and oxygen evolution reactions (HER/OER) in alkaline media. Specifically, an overpotential of 360 mV (vs. RHE) and a Tafel slope of 64.3 mV dec⁻¹ are achieved for the OER, and an overpotential of 190 mV (vs. RHE) and a Tafel slope of 83.0 mV dec⁻¹ for the HER in 1.0 M KOH solution are achieved. These values are significantly lower than those of their components (BP QDs and MXene nanosheets) and even approach those of commercialized RuO₂ or Pt/C. When the BP QDs/MXene nanohybrids are employed as both the cathode and anode in a full cell for overall water splitting, a current density of 10 mA cm⁻² is quickly reached at a potential of only 1.78 V. Additionally, the underlying mechanism of the superior electrocatalytic performance of the BP QDs/MXene nanohybrids is fundamentally explored by density functional theory calculations.