The increasing integration of electric vehicles (EVs) and photovoltaic (PV) generation introduces significant uncertainty into modern distribution grids. This paper presents a dual-stage, AI-driven framework for resilient energy management that combines machine learning-based forecasting with generative modelling for scenario-based control. The first stage uses a hybrid forecasting architecture: long short-term memory (LSTM) and convolutional LSTM (ConvLSTM) models for EV demand prediction and eXtreme gradient boosting (XGBoost) for PV generation forecasting. The second stage employs a generative adversarial network (GAN) to produce realistic EV and PV scenarios, capturing both typical variability and a wide range of operating conditions. The framework is validated on a modified IEEE 33-bus distribution system with integrated EV charging and stationary storage. Results show that the dual-model forecasting approach achieves high accuracy across diverse temporal patterns, while GAN-based scenario generation improves the adaptability of control decisions. Scenario-based optimisation enhances performance under uncertainty, especially at high-variance nodes, and offers greater flexibility than deterministic control in balancing energy cost and demand satisfaction.

The four-level single flying capacitor converter (4L-SFC) is an efficient topology for medium-voltage applications, offering a reduced component count over traditional four-level flying capacitor converters. However, maintaining balanced capacitor voltages is a key challenge in this topology, which is critical for stable and reliable operation. In this paper, an active voltage balancing method for 4L-SFC converters is presented, using a straightforward algorithm to adjust voltage levels in response to real-time measurements of capacitor voltages and phase currents. As a result of the proposed method, capacitor voltages remain balanced across a wide range of modulation indexes and operating conditions without significantly increasing switching frequency. Simulation results have demonstrated the effectiveness of the proposed approach, demonstrating that it is capable of maintaining stable capacitor voltages.

Renewable energy sources (RES) such as solar photovoltaic (PV) are employed in electric vehicle (EV) charging stations to promote sustainable transportation and reduce the load on the AC grid. This paper introduces a modular power converter topology that interfaces with solar PV, EV batteries, and the AC grid. The proposed topology supports EV battery charging in two modes: DC/DC (from PV modules) and AC/DC (from the AC grid). Additionally, during peak hours, EV batteries can function as energy storage units (ESUs) in vehicle-to-grid (V2G) mode. A central element of the proposed topology is the submodule (SM), which is implemented as a single-stage isolated bidirectional Cuk-based converter. This converter is chosen for its exceptional features, including high efficiency and inherent power factor correction (PFC) due to continuous input and output currents. The paper first details the operating modes of the Cuk-based SM. To improve performance, an extra switching state is added in the AC/DC (rectifier) mode, enabling the second-order harmonic from the AC grid to be stored within the Cuk SM instead of being passed to the battery. Additionally, power losses at the SM level are examined, and the effectiveness of the proposed topology is demonstrated through experimental results.

Wireless power transfer (WPT) systems’ efficiency is significantly impacted by non-monotonic variations in the coupling coefficient. For very short distances or strong-coupling cases, the WPT efficiency is minimal at the natural resonant frequency, with two peaks around this frequency, known as the frequency splitting phenomenon. On the other hand, WPT capability decreases for long distances or weak coupling cases. Therefore, adaptive matching is required for WPT systems with varying distances, like wireless charging systems for electric vehicles (EVs). This paper first presents a detailed analysis of the frequency splitting phenomenon by studying the root locations of the WPT system’s transfer function. Then, a real-time fixed-frequency adaptive impedance matching (IM) method is proposed, in which the amplitude and phase of the input impedance is estimated using the average active power, the average reactive power, and the amplitude of input voltage. Unlike traditional search-and-find techniques, the proposed method calculates the optimal IM network parameters only in a single iteration, which improves the convergent speed. A scaled-down 20-Watt prototype controlled by the TMSF2812 is fabricated and used to validate the effectiveness of the proposed method over a wide range of coil-to-coil distances.