A modern DC microgrid often comprises renewable energy sources (RESs) such as Photovoltaic (PV) generation units, battery energy storage systems (BESSs), and local load, and it is also connected to the utility grid through a point of common coupling (PCC). While most existing approaches have to rely on communication links to achieve desirable control performance, this paper proposes a novel control strategy without resorting to the communication links. This is achieved by assigning BESSs as master units and regulating the DC bus voltage with a novel state-of-charge (SoC)-based droop control, where the BESSs coordinate the slave units (e.g. RES, utility grid) with the aid of the DC bus signaling (DBS) technique to avoid overcharging and over-discharging of these BESSs. In the proposed droop control, the reference voltage for these BESSs is designed for coordinated operation between BESSs and utility grid, it is maintained constant in normal SoC range, which can reduce DC voltage variation. Droop coefficients designed for SoC balance of BESS are dynamically adjusted based on their own SoC values. Furthermore, the preset maximum deviation between the reference voltage and DC bus voltage ensures reliable coordinated operation. Real-time hardware-in-loop (HIL) experiments considering three different scenarios are conducted to validate the effectiveness of the propose method.

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In railway traction power supply, co‐phase system with hybrid power quality conditioner (HPQC) is capable of tackling the power quality issues caused by single‐phase traction loads. To further reduce the overall carbon emissions in railway systems, this paper considers to integrate the renewable energy with railway power supply, which however leads to a more complicated system to model, design and control. This paper first investigates its modelling aspect. To reduce the operating capacity of HPQC while addressing the power unbalance, optimal design of the compensation scheme for co‐phase system is formulated as a multi‐objective optimization problem which is then solved by the nondominated sorting genetic algorithm‐II (NSGA‐II). To eliminate the impact of errors arising from imperfect predictions of the loads and renewable power, a hybrid optimal compensation control is proposed, yielding full and optimal compensations. Comprehensive simulation studies, considering three operation modes covering variable traction loads, renewable and regenerative braking power, are conducted. The simulation results confirm the validity of the proposed optimal compensation scheme, achieving an average of more than 20% reduction in HPQC capacity compared to the full compensation scheme. Meanwhile, the power quality requirement is satisfied, even in the presence of real‐time prediction errors.

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A novel spatial power limiter based on nonlinear frequency selective surface (FSS) is presented for high power electromagnetic (HPEM) wave protection. Embedded with Schottky diodes, the nonlinear FSS not only reflects out‐of‐band electromagnetic incidence like a filter, but also exhibits a power‐limiting characteristic, allowing low‐loss transmission for an in‐band low‐power incidence while rejecting a high‐power one. Such a FSS with 4 × 4 unit cells is designed, fabricated and measured. Results demonstrate its pass‐band centering at 2.5 GHz, power density threshold of about 0.27 W/m2 and shielding effectiveness (SE) up to 20 dB at 2.5 GHz.

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For investigating driver characteristic as well as control authority allocation during the process of human–vehicle shared control (HVSC) for an autonomous vehicle (AV), a HVSC dynamic mode with a driver’s neuromuscular (NMS) state parameters was proposed in this paper. It takes into account the driver’s NMS characteristics such as stretch reflection and reflex stiffness. By designing a model predictive control (MPC) controller, the vehicle’s state feedback and driver’s state are incorporated to construct the HVSC dynamic model. For the validation of the model, a field experiment was conducted. The vehicle state signals are collected by V-BOX, and the driver’s state signals are obtained with the electromyography instrument. Subsequently, the hierarchical least square (HLS) parameter identification algorithm was implemented to identify the parameters of the model based on the experimental results. Moreover, the Unscented Kalman Filter (UKF) was utilized to estimate the important NMS parameters which cannot be measured directly. The experimental results showed that the model we proposed has excellent accuracy in characterizing the vehicle’s dynamic state and estimating the driver’s NMS parameter. This paper will serve as a theoretical basis for the new control strategy allocation between human and vehicle for L3 class AVs.

The combined operation of hybrid wind power and a battery energy storage system can be used to convert cheap valley energy to expensive peak energy, thus improving the economic benefits of wind farms. Considering the peak–valley electricity price, an optimization model of the economic benefits of a combined wind–storage system was developed. A charging/discharging strategy of the battery storage system was proposed to maximize the economic benefits of the combined wind–storage system based on the forecast wind power. The maximal economic benefits were obtained based on scenario analysis, taking into account the wind-power forecast error, and costs associated with the loss of battery life, battery operation, and maintenance. Case simulation results highlight the effectiveness of the proposed model. The results show that the hybrid wind–storage system is not only able to convert cheap electricity in the valley period into expensive electricity in the peak period, thus resulting in higher economic benefits, but can also balance the deviation between actual output and plans for the wind power generator to decrease the loss penalty. The analyzed examples show that, following an increase in the deviation of the forecast wind power, the profit of the combined wind–storage system can increase by up to 45% using the charging/discharging strategy, compared with a wind farm that does not utilize energy storage. In addition, the profit of the combined wind–storage system can increase by up to 16% compared with separate systems, following an increase in the deviation penalty deviation coefficient.