Battery swapping stations (BSSs) present an alternative way of charging electric vehicles (EVs) that can lead toward a sustainable EV ecosystem. Although research focusing on the BSS strategies has been ongoing, the results are fragmented. Currently, an integrated way of considering stochastic EV station visits through planning and operations has not been fully investigated. To create comprehensive and resilient battery swapping stations, a two-stage optimization with recourse is proposed. In the planning stage, the investment for battery purchases is recommended even before the EV station visit uncertainties are made known. In the operation stage, the battery allocation decisions, such as charging, discharging, and swapping are then coordinated. To apply the recourse strategy in creating representative scenarios, the EV station visit distribution techniques are also proposed using a modified K-means clustering method. Aside from the sensitivity analysis made with swapping prices and charging intervals, the strategy comparisons with conventional strategies have also demonstrated the practicality of the BSS coordination to future electricity and transportation networks.

The stability of dual active bridge converter (DAB) is threatened when feeding the constant power loads (CPLs). This article proposes a deep reinforcement learning-based backstepping control strategy to solve this problem. First, a nonlinear disturbance observer is adopted to estimate the large-signal nonlinear disturbance. Then, a backstepping controller is used to stabilize the voltage response of the DAB under the large-signal disturbance. Finally, a compensation method based on deep reinforcement learning is developed to intelligently minimize output voltage tracking error and improve the operating efficiency of the system. The proposed controller can guarantee system stability under the large-signal disturbance of the CPL and achieve a fast dynamic response with accurate voltage tracking; it is more adaptive by using the deep reinforcement learning technique through the learning of its neural networks. The effectiveness of the proposed controller is verified by experiments.

A high-performance three-phase pulse width modulation (PWM) converter with reduced dc-link capacitor under unbalanced ac voltage conditions is proposed in this paper. The unique feature of the converter lies in that sinusoidal symmetrical ac currents and ripple free dc voltage can be achieved simultaneously without using a bulky capacitor, which was thought to be a dilemma in the past. The control scheme is implemented in an α-β frame, so the complex rotate transformations are avoided, and it does not require the extraction of positive and negative sequence currents, which simplifies the control algorithm. A two-quadrant operation active power compensator together with its controller is developed to suppress the voltage ripple in the dc-link. Compared with the passive compensation method, the compensator is equipped with a smaller capacitor to reduce the system size, weight, and cost. Moreover, the compensator could be integrated with the conventional three-phase three-leg PWM converter seamlessly. The effectiveness of the proposed control schemes is verified by the experiment results.

This paper proposes a modified one cycle control (OCC) for active power filter (APF) to compensate only harmonic components of load currents. The conventional OCC-based APF cannot differentiate between the harmonic and reactive components of load currents, which leads to the increment in device current rating because of compensation of large reactive currents besides harmonics. The reference signals of the OCC control core have been reconstructed and the associated control equations have been developed, following the control philosophy of simplicity. The modified scheme is easy to implement and only three more sensors are required for measuring load currents compared with the conventional OCC. This modified OCC guarantees the excellent performance of harmonic compensation alone and retains the advantages of the conventional OCC, such as no phase locking loop and the constant switching frequency. The feasibility and performance of the proposed OCC has been validated by experimental studies on a 5 kVA APF hardware platform developed in laboratory.

Wide area control (WAC) is highly desirable for maintaining the stability of large-scale smart grid. It crucially requires the support of real-time data communications with low latency. This requirement leads to the consideration of the multicast technique for routing of data packets of various measurements, control signals and commands. While the feasibility of multicast in WAC communications has been investigated, there is a lack of a general framework to formulate and solve the multicast routing problem particularly for WAC. To tackle this problem, this paper presents a systematic approach to constrained optimization of multicast routing in WAC over wide area networks (WANs) with a publish-subscribe architecture. To fulfil the requirements of real-time WAC communications, the optimization is formulated to minimize the end-to-end delay over WANs subject to bandwidth constraints. Then, the constrained optimization is solved theoretically by using Lagrangian Relaxation. The theoretic results are implemented in an iterative bandwidth-constrained minimum Steiner tree (BCMST) algorithm. Simulations are conducted to demonstrate the approach presented in this paper.

The DC solid-state transformer (DCSST) is a key component of the DC microgrid, and stability issues due to operation with constant power loads (CPLs) have become increasingly prominent. To stabilize the system of DCSST with CPLs, in this paper, a novel intelligent backstepping controller based on a nonlinear disturbance observer (NDO) is proposed, and the control coefficients of the backstepping control are automatically adjusted by an auxiliary deep deterministic policy gradient (DDPG) controller. More specifically, the NDO estimates the large-signal nonlinear disturbance of the load power variation, and a backstepping controller is designed to improve the stability of the system under the estimated large-signal disturbance. Then, an input voltage equalizing compensator is added to ensure the identical input voltage to each sub-module of DCSST. Furthermore, a DDPG sub-controller is introduced into the backstepping control to intelligently adjust the control coefficients to improve the operating efficiency of the system. The results of the simulation and experiment show that the system is more robust due to the learning ability of the proposed controller, and the proposed controller can improve the stability of the system under the large-signal disturbance and intelligently ensure fast dynamic response with accurate tracking over different operating environments.