Abstract
This work focuses on designing nonlinear control algorithms for dual half-bridge converters (DHBs). We propose a two-layer controller to regulate the current and voltage of the DHB. The first layer utilizes a change in the control variable to obtain a quasi-linear representation of the DHB, allowing for the application of simple linear controllers to regulate current and power flow. The second layer employs a nonlinear control allocation algorithm to select control actions that fulfill (pseudo) power setpoints specified by the first control layer; it also minimizes peak-to-peak currents in the DHB and enforces voltage balance constraints. We apply the DHB and this new control strategy to manage power flow in a hybrid energy storage system comprising of a battery and supercapacitors. Numerical simulation results demonstrate that, in comparison with state-of-the-art approaches, our control algorithm is capable of maintaining good transient behavior over a wide operating range, while reducing peak-to-peak current by up to 80%.

Abstract
This paper proposes a multi-objective stochastic optimization framework that can be used by governments to run auctions and select the best community energy storage system (CESS) projects to support. The framework enables CESS providers and energy community members to equitably benefit from the economic value generated by CESSs. The auction accepts offers from competing CESS providers that constitute the data of the CESS location, size, install time, technology, provider, investment cost, and energy trading price. The auction is run by a government agency which selects CESS projects that maximize the economic benefits and distribute them equitably among CESS providers and community members. The multi-objective stochastic optimization accounts for the multi-year uncertainties of photovoltaic (PV) generation, real and reactive energy consumption, energy trading prices, and PV installations. We exploit the Monte Carlo simulation and scenario trees to model the aforementioned uncertainties. The K-Means clustering method is used to reduce the number of scenarios, and thereby, lessen the computational burden of the optimization problem. Our experiments on an Australian low-voltage network with a community of prosumers and consumers demonstrate that government financial support can accelerate the installation of CESSs and enhance their business viability. This can be achieved by boosting the economic benefits shared between CESS providers and communities and ensuring these benefits are distributed equitably. Also, our experiments show that the economic benefits of all stakeholders are further improved with a high growth of the number of PV installations, and a slight reduction of energy import and export prices over the planning period.

Abstract
To achieve lower switching losses and higher frequency capabilities in converter design, researchers worldwide have been investigating Silicon carbide (SiC) modules and MOSFETs. In power electronics, wide bandgap devices such as Silicon carbide are essential for creating more efficient, higher-density, and higher-power-rated converters. Devices like SiC and Gallium nitride (GaN) offer numerous advantages in power electronics, particularly by influencing parasitic capacitance and inductance in printed circuit boards (PCBs). A review paper on Silicon carbide converter designs using coupled inductors provides a comprehensive analysis of the advancements in SiC-based power converter technologies. Over the past decade, SiC converter designs have demonstrated both efficiency and reliability, underscoring significant improvements in performance and design methodologies over time. This review paper examines developments in Silicon carbide converter design from 2014 to 2024, with a focus on the research conducted in the past ten years. It highlights the advantages of SiC technology, techniques for constructing converters, and the impact on other components. Additionally, a bibliometric analysis of prior studies has been conducted, with a particular focus on strategies to minimize switching losses, as discussed in the reviewed articles. © 2025 by the authors.

Abstract
Water stress is a significant issue in many countries, including Portugal, which has seen a 20% reduction in water availability over the last 20 years, with a further 10-25% reduction expected by the end of the century. To address potable water consumption, this study aims to identify the optimal rainwater harvesting (RWH) system for a commercial building under various non-potable water use scenarios. This research involved qualitative and quantitative methods, utilizing the Rippl method for storage reservoir sizing and ETA 0701 version 11 guidelines. Various scenarios of non-potable water use were considered, including their budgets and economic feasibility. The best scenario was determined through cash flow analysis, considering the initial investment (RWH construction), income (water bill savings), and expenses (energy costs from hydraulic pumps), and evaluating the net present value (NPV), payback period (PB), and internal rate of return (IRR). The energy savings obtained were calculated by sizing a hybrid system with an RWH system and a photovoltaic (PV) system to supply the energy needs of each of the proposed scenarios and the water pump, making the system independent of the electricity grid. The results show that the best scenario resulted in energy savings of 92.11% for a 7-month period of regularization. These results also demonstrate the possibility for reducing potable water consumption in non-essential situations supported by renewable energy systems, thus helping to mitigate water stress while simultaneously reducing dependence on the grid.

Abstract
The increasing integration of distributed energy generation into the electrical grid has led to changes in the structure and organization of energy markets over the past years. Market trading has become increasingly demanding due to the different types of production profiles. A forecast of the total production of all assets is made to bid for energy. Whenever there are differences between the forecast and the actual produced energy, a deviation occurs, which is assigned to the agent responsible for its settlement. This article proposes the application of a linear regression algorithm supported by a clustering method to forecast energy production. Based on the historical production profile of the installations in each cluster, it is possible to predict the production pattern for a period with no available data, thus standardizing this data for other assets belonging to the same cluster. © 2025 IEEE.

Abstract
A notable shift from an internal combustion engine vehicles (ICEVs) fleet to an electric vehicles (EVs) fleet is expected in the medium term due to increasing environmental concerns and technological breakthroughs. In this context, this paper conducts a systematic literature review on life cycle assessment (LCA) research of EVs compared to ICEVs based on highly impactful articles. Several essential aspects and characteristics were identified and discussed, such as the assumed EV types, scales, models, storage technologies, boundaries, lifetime, electricity consumption, driving cycles, combustion fuels, locations, impact assessment methods, and functional units. Furthermore, LCA results in seven environmental impact categories were gathered and evaluated in detail. The research indicates that, on average, battery electric vehicles are superior to ICEVs in terms of greenhouse gas (GHG) emissions (182.9 g CO2-eq/km versus 258.5 g CO2-eq/km), cumulative energy demand (3.2 MJ/km versus 4.1 MJ/km), fossil depletion (49.7 g oil-eq/km versus 84.4 g oil-eq/km), and photochemical oxidant formation (0.47 g NMVOC-eq/km versus 0.61 g NMVOC-eq/km) but are worse than ICEVs in terms of human toxicity (198.1 g 1,4-DCB-eq/km versus 64.8 g 1,4-DCB-eq/km), particulate matter formation (0.32 g PM10-eq/km versus 0.26 g PM10-eq/km), and metal depletion (69.3 g Fe-eq/km versus 19.0 g Fe-eq/km). Emerging technological developments are expected to tip the balance in favor of EVs further. Based on the conducted research, we propose to organize the factors that influence the vehicle life cycle into four groups: user specifications, vehicle specifications, local specifications, and multigroup specifications. Then, a set of improvement opportunities is provided for each of these groups. Therefore, the present paper can contribute to future research and be valuable for decision-makers, such as policymakers.