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This paper presents a novel online technique, which simultaneously exploits the global search ability of differential evolution (DE) and rapid convergence of Newton Raphson (NR) methods (named as DE-NR) to solve intricate simultaneous transcendental trigonometric set of harmonic elimination pulse width modulation equations for modular multilevel cascade converters based power systems. Major contribution of this paper is a harmonically efficient online algorithm with rapid convergence. Switching angles for an extensive range of modulation index (M,0.86⩽M≤9.97) are computed online to demonstrate the harmonically efficient working of proposed method. Total harmonic distortion values of the filtered line-to-line output voltages during online operation are restricted to 0.35% of the fundamental component, which depict successful removal of unwanted harmonics and are significantly better than the values allowed by IEEE standard 519-2014. Comparison between DE-NR, differential evolution and Newton Raphson methods demonstrates the rapid convergence behavior of DE-NR as it requires only (on average) 4 iterations compared to 490 and 182 iterations respectively. It has also shown superior harmonic control than the recently devised online Middle-Level Selective Harmonic Elimination Pulse-Amplitude Modulation method. Successful simulation and experimental validations of DE-NR method have been done by developing three-phase modular multilevel cascade converter in MATLAB-Simulink and single-phase eleven-level modular multilevel cascade converter hardware prototype.

In life cycle assessments of wind turbines and, more generally, of Renewable Energy Systems (RES), environmental impacts are usually normalized by electricity production to express their performance per kilowatt-hour. For most RES, manufacture and installation dominate the impacts. Hence, results are sensitive to parameters governing both impacting phases and electricity production. Most available studies present the environmental performance of generic wind turbines with assumed fixed values for sensitive parameters (e.g. electricity production) that often vary between studies and fail to reflect specificities of wind farm projects. This study presents an approach to build a comprehensive parameterized model that generates unique wind turbine life cycle inventories conditioned by technologically, temporally and geographically-sensitive parameters. This approach allows for the characterization of the carbon footprint of five sets of turbines in Denmark, where wind power is highly developed. The analysis shows disparities even between turbines of similar power output, mostly explained by the service time, load factor and components weights but also by background processes (evolution of electricity mix and recycled steel content). Project-specific inventories with technologically, temporally and geographically-sensitive parameters are essential for supporting RES development projects. Such inventories are especially important to evaluate highly-renewable electricity mixes, such as that of Denmark.

This article presents an advanced control strategy for power quality enhancement in standalone power supply systems (PSSs) with grid forming four-leg voltage source inverters (FL-VSIs). Indeed, an online adaptive reference generator (ARG) with a gray wolf optimizer (GWO) is proposed to sustain the control performances of a feedback linearization control (FLC) strategy and improve its robustness against load-side disturbances and system parameters' uncertainties. The key purpose of the proposed GWO-based ARG is to compensate for load and phase disturbances through smooth reference adjustments, in order to improve the voltage waveforms' quality and symmetry and conform to the existing power quality standards and metrics. The design methodology of the proposed control approach is thoroughly detailed, and its effectiveness is asserted through simulation and experimental tests, demonstrating its superiority in maintaining the voltage waveforms within the required standard limitations even under unbalanced and nonlinear loading conditions.

In this work, we propose a framework for energy-efficient trajectory design of an unmanned aerial vehicle (UAV)-based portable access point (PAP) deployed to serve a set of ground nodes (GNs). In addition to the PAP and GNs, the system consists of a set of intelligent reflecting surfaces (IRSs) mounted on man-made structures to increase the number of bits transmitted per Joule of energy consumed measured as the global energy efficiency (GEE). The GEE trajectory for the PAP is designed by considering the UAV propulsion energy consumption and the Peukert effect of the PAP battery, which represents an accurate battery discharge profile as a non-linear function of the UAV power consumption profile. The GEE trajectory design problem is solved in two phases: in the first, a path for the PAP and feasible positions for the IRS modules are found using a multi-tier circle packing method, and the required IRS phase shift values are calculated using an alternate optimization method that considers the interdependence between the amplitude and phase responses of an IRS element; in the second phase, the PAP flying velocity and user scheduling are calculated using a novel multilap trajectory design algorithm. Numerical evaluations show that: neglecting the Peukert effect overestimates the available flight time of the PAP; after a certain threshold, increasing the battery size reduces the available flight time of the PAP; the presence of IRS modules improves the GEE of the system compared to other baseline scenarios; the multi-lap trajectory saves more energy compared to a single-lap trajectory developed using a combination of sequential convex programming and Dinkelbach algorithm. 12 pages, 11 Figures, accepted as a Paper in IEEE Transactions on Vehicular Technology

Urbanisation poses new and complex sustainability challenges. Socio-economic activities drive material and energy flows in cities that influence the health of ecosystems inside and outside the urban system. Recent studies suggest that these flows, under the urban metabolism (UM) metaphor, can be extended to encompass the assessment of urban ecosystem services (UES). Advancing UM approaches to assess UES may be a valuable solution to these arising sustainability challenges, which can support urban planning decisions. This paper critically reviews UM literature related to the UES concept and identifies approaches that may allow or improve the assessment of UES within UM frameworks. We selected from the UM literature 42 studies that encompass UES aspects, and analysed them on the following key investigation themes: temporal information, spatial information, system boundary aspects and cross-scale indicators. The analysis showed that UES are rarely acknowledged in UM literature, and that existing UM approaches have limited capacity to capture the complexity of spatio-temporal and multi-scale information underpinning UES, which has hampered the implementation of operational decision support systems so far. We use these results to identify and illustrate pathways towards a UM-UES modelling approach. Our review suggests that cause–effect dynamics should be integrated with the UM framework, based on spatially-specific social, economic and ecological data. System dynamics can inform on the causal relationships underpinning UES in cities and, therefore, can help moving towards a knowledge base tool to support urban planners in addressing urban challenges.

This contribution proposes an implementation for next generation smart homes, where heterogeneous data, coming from multiple sensors (medical, wellbeing, energy, contextual, etc.) and house equipment (smart fridge, smart TV, etc.), need to be managed, secured and visualized. As a first step, it focuses only on energy and health data. However, it aims to lay the foundations to manage any type of information towards the development of smart interactions with the house, which might include artificial intelligence and machine learning. These data are securely collected using a central Web of Things gateway, located inside the smart home. For the e-health part, a set of possible use-cases is provided, along with the current progress of the implantation. In this regard, the main idea is to link the next generation smart homes with external medical entities in order to provide, first, quick intervention in the event of an abnormality being detected, and to be able to provide basic medical services such as remote consultations with a doctor for a particular health issue. This vision can be very promising, particularly in rural areas, where access to medical services is difficult. As for the energy part, the aim is to collect users’ energy consumption inside the smart home, which can be supplied from different sources (heat, water, gas, or electricity), and to enable the use of advanced algorithms to predict and manage local energy consumption and production (if any). This approach combines data collected from smart meters, operational information of the smart energy devices (the status of smart plugs), user’s requests and external network signals such as energy prices. By using a home energy management system that accepts such input parameters, the operation of in-home devices and appliances can be optimally controlled according to different objectives (e.g., minimizing energy costs and maximizing user’s comfort level).