Marco Rivera

With the rapid global expansion of wind energy and increasing deployment of large offshore turbines, achieving reliable sensorless control of wind energy conversion systems (WECSs) has become increasingly important. This paper presents a comprehensive review of modern sensorless control techniques for the doubly fed induction generator (DFIG), which is a dominant technology in variable-speed WECSs. This review focuses on control strategies and rotor speed and position estimation techniques, covering their theoretical foundations, operational characteristics, and emerging research trends. In addition, sensorless operations of the DFIG beyond power generation, including grid synchronisation and stand-alone operation, are presented, providing a foundation for future research in the field.

This paper presents a comprehensive review of control techniques, simulation mechanisms, and validation methods applied to DC-DC converters, with a focus on high-step-up topologies used in renewable energy systems such as photovoltaic and wind power applications. Control strategies including classical PID, fuzzy logic, sliding mode, and model predictive control (MPC) are analyzed in terms of performance, robustness, and implementation complexity. Simulation platforms and hardware-in-the-loop (HIL) validation frameworks are also discussed as key enablers for rapid prototyping. The findings reveal a clear trend toward intelligent and hybrid control schemes that combine nonlinear techniques with artificial intelligence to address the inherent nonlinearities and parametric uncertainties of DC-DC converters. However, challenges remain in real-time implementation due to computational demands, which drives the need for future developments focused on the (i) integration of AI-based controllers with low-cost embedded platforms, (ii) standardization of HIL-based validation workflows, and (iii) optimization of converter topologies for specific applications such as electric vehicle charging and photovoltaic grid integration. Looking forward, the convergence of advanced control algorithms, real-time validation platforms, and application-specific converter design is expected to define the next generation of power electronics systems, enabling more efficient, reliable, and scalable renewable energy integration.

Climate change compels the built environment to adopt adaptive Building-Integrated PhotoVoltaic (BIPV) designs balancing energy yields with indoor environmental quality. However, current dynamic facades suffer from algorithmic opacity and an operational bias prioritising generation over human comfort. This study systematically reviews the energy–comfort nexus within intelligent building skins. Following PRISMA 2020 guidelines, we searched nine databases (including Scopus and Web of Science) for empirical studies (2016–early 2026) evaluating dynamic BIPV control. Methodological quality and bias risk were assessed via a rigorous framework evaluating control transparency and simulation validity. From 2423 initial records, 92 high-quality studies were selected and structured into six adaptive BIPV design clusters (e.g., Double-Skin Facades, kinetic shading, semi-transparent glazing). The synthesis reveals a polarisation between thermo-mechanical integration and computational–geometric optimisation. An energy-centric bias persists. Power generation dominates over 92% of research, whereas glare evaluation remains below 20%. Additionally, nearly 60% of studies fail to explicitly define their control parameters. Current evidence limitations include a heavy reliance on idealised 1D simulations, a strong Northern Hemisphere geographical concentration, and a scarcity of robust data on dynamic bifacial systems. Bridging the gap between PV hardware and intelligent control, this study delivers a comprehensive theoretical framework and an actionable roadmap.

This paper presents the design, digital implementation, and experimental validation of a new 50 W scaled prototype of a quadratic buck converter (48 V to 5 V, 10 A) controlled by a finite control set model predictive control (FCS-MPC) strategy. The converter utilizes its quadratic step-down topology to achieve high voltage conversion gain without extreme duty cycles, making it suitable for low-power applications requiring precise voltage regulation. The proposed methodology encompasses the theoretical design of the power stage, the development of the experimental prototype based on a C2000 Digital Signal Processor DSP, and a comparative performance assessment between the proposed FCS-MPC and a conventionally tuned PD controller. An iterative tuning and real-time validation process is employed to optimize both the converter parameters and the control law, ensuring closed-loop stability and enhanced dynamic response under line and load disturbances. The experimental results demonstrate that the FCS-MPC strategy significantly outperforms the PD controller in terms of output voltage regulation, settling time (4.2 s vs. 5 ms), and disturbance rejection (<2 ms recovery). The main contribution of this work is the construction of a new scaled prototype and the experimental validation of a predictive control strategy for a high-gain DC–DC converter, positioning the FCS-MPC-controlled quadratic buck converter as a viable solution for modern applications demanding high energy efficiency and robustness.

Sustainable power systems increasingly require scheduling methods that can coordinate renewable generation, distributed flexibility, and conventional energy-conversion units under forecast uncertainty while directly supporting carbon-emission reduction. However, many data-driven scheduling models still enforce operational constraints through soft penalties or post-processing corrections, which may lead to infeasible schedules during deployment and weaken their reliability in digital-shadow-assisted operation. In addition, conventional cost-oriented scheduling objectives do not explicitly account for the carbon impact of real-time imbalances caused by forecast errors. To address these challenges, this paper proposes a digital-shadowing-enabled deep learning framework for carbon-aware day-ahead scheduling of integrated energy systems. The main methodological contribution is a feasibility-by-design neural decoder that embeds hard physical constraints directly into the network forward pass. By classifying devices into non-memory fast units, non-memory ramp-limited units, and memory-type storage devices, the decoder applies tailored transformations to enforce capacity limits, ramp-rate restrictions, state-of-charge dynamics, and terminal energy consistency by construction. Therefore, the generated schedules are physically feasible without relying on post-hoc repair. In parallel, a carbon-first objective is developed to minimize both scheduled emissions and imbalance-driven emissions, allowing the scheduler to reduce not only planned carbon output but also the carbon impact of real-time corrective actions. Forecast uncertainty is represented through a digital shadow that stores historical forecast-error patterns and generates augmented training scenarios. Case studies based on U.K. data show that the proposed framework produces fully feasible schedules and reduces annual CO₂ emissions by approximately 4.0% compared with a forecast-driven baseline, with larger benefits during high-demand periods. These results demonstrate that combining digital shadowing, constraint-embedded neural decoding, and carbon-aware optimization provides a practical and reliable pathway for low-carbon smart-grid scheduling under uncertainty.

The increasing penetration of distributed renewable energy sources and electric vehicles (EVs) introduces significant operational challenges for residential energy management systems (HEMS), including stochastic renewable generation, uncertain load demand, device coupling, and physical safety constraints. This paper proposes a digital-twin-aided safe multi-agent reinforcement learning framework for coordinated energy management in renewable-integrated residential systems. The proposed approach models the battery energy storage system and the EV as independent agents and employs a multi-agent soft actor–critic (MASAC) algorithm with a centralised critic to capture the interactions among distributed energy resources. To improve decision quality under uncertainty, a digital twin module is developed to maintain a virtual representation of the residential energy system, synchronise operational states, update degradation-sensitive parameters, and generate short-term predictive information on photovoltaic (PV) generation and household load. The updated digital twin states and forecasts are incorporated into the observations of the reinforcement learning agents. In addition, a safety projection layer is incorporated to improve operational feasibility during both training and deployment. The environment considers realistic residential characteristics, including time-of-use electricity prices, battery degradation, EV mobility patterns, and grid energy trading. Simulation results show that the proposed framework reduces daily energy costs compared with rule-based baselines while maintaining EV charging reliability and operational feasibility. These results highlight the potential of combining predictive information, safety-constrained action execution, and multi-agent reinforcement learning for intelligent residential energy management.

This paper presents a comparative analysis of three-level (3L-NPC) and five-level (5L-NPC) Neutral-Point-Clamped converters using Finite Control Set Model Predictive Control (FCS-MPC) for reactive power compensation. The research addresses a critical gap by providing a direct performance comparison under identical operating conditions, supported by simulation and experimental validation of a 3L-NPC prototype. The study evaluates harmonic performance, dynamic response, and DC-link balance. Results demonstrate that the 5L-NPC topology significantly outperforms the 3L-NPC, achieving a simulated grid current Total Harmonic Distortion (THD) of 3.36% compared to 7.84% for the 3L-NPC. This 57.1% reduction in THD allows the 5L-NPC to comply with the IEEE Std. 519-2022 limit (<5%), whereas the 3L-NPC experimental results (9.9% THD) highlight the impact of practical non-idealities such as dead time and sensor noise. While the 5L-NPC offers superior power quality, it entails higher hardware complexity, evaluating 125 switching states compared to 27 in the 3L-NPC. These findings provide quantitative guidelines for selecting NPC topologies in high-performance grid compensation systems.