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Abstract Smart Transformer (ST)-based Meshed Hybrid AC/DC Microgrids (MHM) present advantages concerning the performance of conventional AC/DC microgrids (MG). Despite this, MHMs present challenges in managing and controlling electronic converters associated with the ST and Distributed Energy Resources (DER), a scenario that makes it complex to achieve optimal system performance. Additionally, the high penetration of DER would impact the voltage profile, reduce the reactive compensation, and increase the losses in the distribution lines at a high cost in operation. This work proposes an optimal power management computational model for the day-ahead optimal operation planning of ST-based MHM. The management algorithm delivers the optimal operating points of each of the converters of the MG so that it allows the control of active power on both the AC and DC sides; likewise, it will enable reactive power injection control according to the availability of the solar photovoltaic resource and Battery Energy Storage Systems (BESS). According to the tests performed under different operation scenarios with varying degrees of penetration of Photovoltaic Generator (PVG), the benefits of implementing the ST as an energy router according to the operating set points by the optimization algorithm are evidenced. Integrating the ST in MHM and optimization algorithms is a suitable alternative for managing microgrids with high penetration of distributed energy resources. This allows for improving the voltage profile in the MHM, reducing losses in the lines and energy exchange costs with the distribution network.

Abstract Variable renewable energies are leading the energy transition and power system flexibility has become a global priority. Incorporating short-term assessment into long-term planning is essential to capture the operational characteristics of generation and address flexibility issues. This paper presents a novel model for generation and transmission expansion planning with economic dispatch. In the generation expansion planning, the capacity expansion is optimized. In the economic dispatch problem, power plant operation, hydropower generation, and line transmission power flow are optimized. The model is a mixed-integer linear programming problem solved in MATLAB using Gurobi toolbox. A model is proposed to optimize the reservoir of hydropower systems and run-of-river. A case study for supplying Brazilian demand in 2050 is presented. A scenario with greenhouse gas emission costs and no deficit is proposed. This study has shown that the current penalty cost for loss of load is not sufficient to avoid a deficit. It is possible to supply 2050 demand, without large reservoirs or deficits, with a 5.5% curtailment, using 13% of total storage capacity, however; peaking power plants are required.

Abstract Power distribution utilities effort to ensure the quality of energy to their consumers and the reliability of their power distribution system. It is necessary that maintenance activities are planned with the aim of maintaining or improving reliability indicators in supply to consumers. In this paper, a computational model based on integer nonlinear multiobjective programming is presented to improve the maintenance of equipment in the power distribution system. Since it is a reliability-centered approach, a probabilistic failure model is first used to obtain equipment reliability values at each time point through fuzzy inference. Three objective functions are optimized: i) minimizing maintenance cost, ii) minimizing failure frequency, and iii) maximizing equipment reliability. The optimization problem is also formed by three sets of constraints: i) individual and collective continuity indicators; ii) task execution time; and iii) maintenance limit for each type of equipment. Lichtenberg's algorithm is used to solve the model. A case study is performed for a feeder section consisting of twenty-eight distribution equipment. The results obtained using the Pareto constraints show scenarios that can help maintenance teams to make decisions and develop the preventive maintenance planning. Adding constraints on the duration and frequency of collective interruptions indicators improves the power quality of the distribution system; however, it requires an increase in investment by 36%.

Abstract Energy sources, like hydro and wind power, present uncertain nature, characteristic that affects the electric power systems operation planning. Such factors require using methodologies capable to handle these uncertainties in interconnected hydro-thermal-wind power systems operation. This work proposes a computational model for the day-ahead unit commitment problem for interconnected hydro-thermal-wind power systems, considering the uncertainties related to wind power, water inflow, and energy demand, employing the Robust Optimization (RO). The optimization model consists in a three-level mixed integer linear programming problem, which is resolved using a two-stage decomposition approach, solved using the column-and-constraint generation algorithm. The model is validated using the 30 nodes IEEE and the 33 nodes Brazilian test system. The model proposed proved to have an operating cost proportional to the level of uncertainty and the methodology employed provides a preventive view of what may occur during the hydro-thermal-wind systems operation, allowing the central system operator to take certain actions in order to ensure reliable operating conditions. In addition, the model allowed the identification of important aspects related to computational costs, such as the verification that constraints that couple the problem in time and space significantly impact the simulation times of the problem.

Electric mobility is a sustainable alternative for mitigating carbon emissions by replacing the conventional fleet. However, the low availability of data from charging stations makes planning energy systems for the integration of electric vehicles (EVs) difficult. Given this, this work focuses on developing an adaptive computational tool for charging simulation, considering many EVs and mobility patterns. Technical specifications data from many EVs are considered for charging simulation, such as battery capacity, driving range, charging time, charging standard for each EV, and mobility patterns. Different simulations of charging many EVs and analyses of weekly charging load profiles are carried out, portraying the characteristics of the different load profiles and the challenges that system planners expect. The research results denote the importance of considering different manufacturers and models of EVs in the composition of the aggregate charging load profile and mobility patterns of the region. The developed model can be adapted to any system, expanded with new EVs, and scaled to many EVs, supporting different research areas.

Smart Transformer (ST)-based Meshed Hybrid AC/DC Microgrids (MHMs) present a promising solution to enhance the efficiency of conventional microgrids (MGs) and facilitate higher integration of Distributed Energy Resources (DERs), simultaneously managing active and reactive power dispatch. However, MHMs face challenges in resource management under uncertainty and control of electronic converters linked to the ST and DERs, complicating the pursuit of optimal system performance. This paper introduces a Data-Driven Distributionally Robust Optimization (DDDRO) approach for day-ahead operation planning in ST-based MHMs, focusing on minimizing network losses, voltage deviations, and operational costs by optimizing the reactive power dispatch of DERs. The approach accounts for uncertainties in photovoltaic generator (PVG) output and demand. The Column-and-Constraint Generation (C&CG) algorithm and the Duality-Free Decomposition (DFD) method are employed. The initial mixed-integer non-linear planning problem is also reformulated into a mixed-integer (MI) Second-Order Cone Programming (SOCP) problem using second-order cone relaxation and a positive octagonal constraint method. Simulation results on a connected MHM system validate the model’s efficacy and performance. The study also highlights the advantages of the meshed MG structure and the positive impact of integrating the ST into MHMs, leveraging the multi-stage converter’s flexibility for optimal energy management under uncertain conditions.

The smart transformer (ST) is a multiport and multi-stage converter that allows for the formation of meshed hybrid microgrids (MHMs) by enabling AC-DC ports in medium and low voltage. This type of microgrid has advantages over the performance of conventional hybrid AC-DC microgrids (HMGs); however, the number of degrees of freedom of the ST increases the complexity of the energy management systems (EMSs), which require adequate and accurate modeling of the power flow of the converters and the MG to find the feasible solution of optimal power flow (OPF) problems in the MHM. An ST’s equivalent power flow model is proposed for formulating the MHM OPF problem and developing low-frequency equivalent models integrated with a decoupled hierarchical control architecture under a real-time simulation approach to the ST-based MHM. A simulation model of the MHM in the Simulink® environment of Matlab® 9.12 is developed and implemented under a digital real-time simulation (DRTS) approach on the OPAL-RT® platform. This model allows for determining the accuracy of the developed equivalent models, both low-frequency and power flow, and determining the MHM performance based on optimal day-ahead scheduling. Simulation test results demonstrated the ST equivalent model’s accuracy and the MHM’s accuracy for OPF problems with an optimal day-ahead scheduling horizon based on the model-in-the-loop (MIL) and DRTS approach.

Abstract In recent years, cold atmospheric plasma (CAP) is used for surface disinfection. However, little is known about its ability to improve biocompatibility of metallic surfaces when compared to thermal plasma methods. In this context, the study aimed to evaluate the response of human endothelial cells (Ea.hy926) on titanium surfaces treated by non-thermal plasma method and thermal plasma method under nitriding atmosphere. The wettability was characterized by the sessile drop method, the topography and roughness were evaluated by atomic force microscopy (AFM), and the microstructure by grazing angle X-ray diffraction (GIXRD). Endothelial cells were cultured and evaluated for morphology by scanning electron microscopy and viability by an MTT (3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide) assay. CAP treatment reduced the contact angle of the Ti surface (13.43° ± 1.48; p

Local drug delivery systems used in dental implants need to have uniform pore size distribution, adequate wettability, chemical composition, and biocompatibility. In the present work, titanium was treated by pulsed plasma electrolytic oxidation (PPEO), using 0.025 M sodium dihydrogen phosphate and 0.25 M calcium acetate as electrolytes, aiming at use in drug delivery systems. Pulse with Ton/Toff (width pulse/repetition time) of 50 μs/100 μs or 100 μs/50 μs, duty cycle 0,33, and 0.67, respectively, were used. After treatment, Ca/P ratio, wettability, crystalline phase, pore size and distribution were determined. The average pore size ranged from 1.5 μm to 2.3 μm according to the increase of energy supplied to the system. Pore distributions with lower dispersion were verified for the Ton/Toff condition of 50 μs /100 μs, using a current density of 30 mA/dm2. On the other hand, the 100 μs/50 μs conditions produce larger pores, but with greater dispersion. In general, conditions with lower currents (30 mA/dm2 and 38 mA/dm2) and Ton/Toff ratio = 50/100 were the most appropriate for use in drug delivery systems, due to their size and distribution of uniform pores, greater hydrophilicity, and Ca/P ratio close to desirable (1.67) was obtained.

Valendo-se de referenciais provenientes da pragmática linguística e dos princípios da plenitude da defesa e da soberania dos veredictos, os quais permeiam o caráter democrático do tribunal do júri, defende-se a importância do discurso racional, em procedimento aberto, público e transparente, como forma de produzir decisões fundamentadas argumentativamente, que dissipem o arbítrio e o isolacionismo subjetivista. Defende-se, para isso, a comunicabilidade dos jurados, seja a partir de projeto de lei, seja, sobretudo, com os novos recursos trazidos pela filosofia da linguagem. Intenta-se, nesse sentido, tematizar uma incursão jusfilosófica que garanta o aprofundamento da tese ora defendida, interpelando a possibilidade de um processo participativo capaz de alterar o paradigma do livre convencimento subjetivo por um procedimento linguístico que abone decisões públicas justificadas discursivamente.

Abstract The insertion of new distributed energy resources, such as distributed generation (DG), energy storage systems (ESS), demand response (DR), and microgrids (MG), is emerging, bringing new challenges to the current distribution network. In this regard, the active distribution networks (I) with multi-microgrids concept appears. The present paper proposes a hierarchical (master-slave problem) computational model to achieve optimal coordinated operation of multi-microgrids connected to an I. Day-ahead operation planning of an I was formulated as a multiperiod non-linear optimal power flow model, resulting in a non-linear optimization problem, additionally, the day-ahead operation planning of MGs was formulated as a multiperiod linearized optimal power flow resulting in a mixed-integer linear optimization problem. Numerical results on four different test-system microgrids connected to a 359-nodes ADNs test-system belonging to a Brazilian distribution company show the effectiveness of the proposed model and solution strategy. Three cases have been tested: with a maximum load-shedding restriction, without this restriction, and considering insertion of DG. Besides, the hierarchical model can evaluate how much losses and load shedding take effect without integrated operation and expansion planning of emerging distributed networks. This study showed the importance of analyzing the systemic impact of integrating multi-MGs and I synergistic operation interactions, resulting in improvements in the voltage quality levels, operation costs, and power losses. The results showed that, including DG in the system, the costs were reduced by 13,48% compared to the case base.

Abstract The benefits of complementarity between renewable energy sources have been presented as having a positive impact, but it has been neglected in generation expansion planning studies. In this paper the complementarity between renewable energy sources and regions of Brazil is characterized and the correlation coefficients are analyzed. A methodology is also proposed to optimize the complementarity between the sources, reducing the energy storage requirements. The methodology is applied to the Brazilian case to meet the projected demand for 2050 with a 100% renewable system and the complementarity is analyzed. The characterization of the data allowed us to propose five solar regions, four wind regions, three hydro regions and a region to represent the biomass. It was possible to find complementarity (negative correlation) of: 0.97 between hydro regions; 0.86 between wind and hydro regions and; 0.96 between biomass and hydro. The proposed methodology allowed us to know which regions and their respective technologies best benefit the electrical system, being the North for solar and the Northeast for wind. Finally, it was possible to reduce the demand for water storage with sources and regions that complement the seasonal hydro drought period.

Smart Transformer (ST)-based Meshed Hybrid AC/DC Microgrids (MHMs) present a promising solution to enhance the efficiency of conventional microgrids (MGs) and facilitate higher integration of Distributed Energy Resources (DERs), simultaneously managing active and reactive power dispatch. However, MHMs face challenges in resource management under uncertainty and control of electronic converters linked to the ST and DERs, complicating the pursuit of optimal system performance. This paper introduces a Data-Driven Distributionally Robust Optimization (DDDRO) approach for day-ahead operation planning in ST-based MHMs, focusing on minimizing network losses, voltage deviations, and operational costs by optimizing the reactive power dispatch of DERs. The approach accounts for uncertainties in photovoltaic generator (PVG) output and demand. The Column-and-Constraint Generation (C&CG) algorithm and the Duality-Free Decomposition (DFD) method are employed. The initial mixed-integer non-linear planning problem is also reformulated into a mixed-integer (MI) Second-Order Cone Programming (SOCP) problem using second-order cone relaxation and a positive octagonal constraint method. Simulation results on a connected MHM system validate the model's efficacy and performance. The study also highlights the advantages of the meshed MG structure and the positive impact of integrating the ST into MHMs, leveraging the multi-stage converter's flexibility for optimal energy management under uncertain conditions. [ABSTRACT FROM AUTHOR]

Electric mobility is a sustainable alternative for mitigating carbon emissions by replacing the conventional fleet. However, the low availability of data from charging stations makes planning energy systems for the integration of electric vehicles (EVs) difficult. Given this, this work focuses on developing an adaptive computational tool for charging simulation, considering many EVs and mobility patterns. Technical specifications data from many EVs are considered for charging simulation, such as battery capacity, driving range, charging time, charging standard for each EV, and mobility patterns. Different simulations of charging many EVs and analyses of weekly charging load profiles are carried out, portraying the characteristics of the different load profiles and the challenges that system planners expect. The research results denote the importance of considering different manufacturers and models of EVs in the composition of the aggregate charging load profile and mobility patterns of the region. The developed model can be adapted to any system, expanded with new EVs, and scaled to many EVs, supporting different research areas. [ABSTRACT FROM AUTHOR]

Abstract Power distribution systems have become more complex in recent years due to the integration of new technologies. Motivated by these challenges, this paper describes a model to solve the multi‐period expansion planning problem of active distribution systems, considering uncertainty, reliability, distributed generation, self‐healing, reactive power support, switches, and energy storage devices. The objective is to determine the best installation times and locations of new components, minimizing the total cost, and ensuring desired reliability levels. The approach consists of a three‐level decomposition. The first level identifies expansion proposals, the second level finds the worst‐case scenario using adaptive robust optimization, and the third level performs a Monte Carlo Simulation to compute reliability indexes. The main contributions are the introduction of a novel reliability sensitivity matrix to improve computational performance and the representation of the hours of the day inside the expansion planning formulation. The proposed method is illustrated using the IEEE 123‐bus test system. The analyses show high computational efficiency as compared with similar works. The impacts on the number of expansion components placed in the system and on the total cost are presented and discussed using cases varying the uncertainty budget and not considering some of these components.

Abstract The optimization of microgrids present challenges such as managing distributed energy resources (DERs) and the high reliance on intermittent generation such as PV and wind turbines, which present an aleatory behavior. The most popular techniques to deal with the uncertainties are stochastic optimization, which comes with a high computational burden, and adaptive robust optimization (ARO), which is often criticized for the conservativeness of its solutions. In response to these drawbacks, this work proposes a mixed-integer linear programming (MILP) model using a data-driven robust optimization approach (DDRO) solved by a two-stage decomposition using the column-and-constraint generation algorithm (C&CG). The DDRO model uses historic data to create the bounds of its uncertainty set, eliminating the conservativeness created by the arbitrary definition of the uncertainty set that is seen in ARO while maintaining a low computational burden. The DDRO model applied was not previously utilized in MGs, only in bulk power systems. A benchmark MG system was simulated for a 24-hour period without uncertainties, with uncertainties using ARO (15% uncertainty budget) and with uncertainties and DDRO. The operational costs without uncertainty were $124,600,60, while the ARO approach rose those costs by 32.5% ($ 165,137.18). Finally, the DDRO approach managed to keep the costs in $ 126,934.54, a mere 1.8% increase from the base case without uncertainty. All simulations were performed in less than 1 minute. The results confirm a) the advantages of bounding the uncertainty set with historical data instead of an arbitrary definition of bounds and b) the fast-converging times of DDRO.

Abstract The behaviour of ions at solid–liquid interfaces underpins countless phenomena, from the conduction of nervous impulses to charge transfer in solar cells. In most cases, ions do not operate as isolated entities, but in conjunction with neighbouring ions and the surrounding solution. In aqueous solutions, recent studies suggest the existence of group dynamics through water-mediated clusters but results allowing direct tracking of ionic domains with atomic precision are scarce. Here, we use high-speed atomic force microscopy to track the evolution of Rb+, K+, Na+ and Ca2+ nano-domains containing 20 to 120 ions adsorbed at the surface of mica in aqueous solution. The interface is exposed to a shear flow able to influence the lateral motion of single ions and clusters. The results show that, when in groups, metal ions tend to move with a relatively slow dynamics, as can be expected from a correlated group motion, with an average residence timescale of ~ 1–2 s for individual ions at a given atomic site. The average group velocity of the clusters depends on the ions’ charge density and can be explained by the ion’s hydration state. The lateral shear flow of the fluid is insufficient to desorb ions, but indirectly influences the diffusion dynamics by acting on ions in close vicinity to the surface. The results provide insights into the dynamics of ion clusters when adsorbed onto an immersed solid under shear flow.

Abstract: Although nonlinear Alternating Current (AC) Optimal Power Flow (OPF) may provide more accurate results compared to linearized OPF models, many ACOPF problems are complex and may require high, sometimes infeasible, computational time. The linear Direct Current OPF (DCOPF) has been widely used in power system analysis; however, it has limitations regarding reactive power analysis, and it may cause significant error in distribution networks with a high R/X ratio of feeders. Moreover, most of the existing formulations for the linear ACOPF do not consider mutual impedance for the lines and simplify the network as a single-phase system, which does not significantly represent unbalanced loads. This paper proposes a novel linear AC three-phase OPF formulation for unbalanced ZIP loads using current injection and considering delta and wye loads. The formulation is extended to include shunt capacitors, voltage regulators, and mutual impedance. The proposed methodology is illustrated using the IEEE 123-bus test system in case studies including/not-including voltage regulators, distributed generation, high R/X ratio, and mutual impedance. The results are very accurate compared with a nonlinear three-phase ACOPF model. The high computational performance and the accuracy of the proposed model considering distributed generation and high R/X ratio show its effectiveness in active distribution networks.

In the plasma electrolytic oxidation (PEO) process, microdischarges are produced at the titanium surface immersed in an appropriate electrolyte by applying a voltage higher than dielectric breakdown of the oxide film. During this discharging stage, lasting between 30 and 300 μs, many species are transferred from electrolytic to surface producing the coatings. In the present work, pulses of electric current with duration of 50 μs and 100 μs were used in order to interrupt the microdischarges and, thus, control the PEO process. The effect of pulse duration, using different current densities and duty cycles, was investigated by the changes in energy consumption, in addition to the topographical, microstructural and chemical properties of the surface, in order to meet the requirements of a hypothetical surface for drug delivery from titanium implants. It was shown that surfaces treated with pulse duration of 50 μs had uniform pores and smaller mean diameters, better anatase/rutile and calcium/phosphorus ratios than those treated with pulses of 100 μs. Our results reveal that using a 50 μs current pulse, the process has lower energy consumption and more suitable properties for use in titanium implants with controlled drug delivery.

Two surface modification techniques, the glow discharge plasma nitriding (GDPN) and the cathodic cage plasma nitriding (CCPN), were compared regarding the mechanical and tribological behavior of layers produced on AISI 316 stainless-steel surfaces. The analyses were carried out at the micro/nanoscale using nanoindentation and nanoscratch tests. The nitriding temperature (°C) and time (h) parameters were 350/6, 400/6, and 450/6. Morphology, structure, and microstructure were evaluated by X-ray diffraction, scanning electron and optical interferometry microscopies, and energy-dispersive X-ray spectroscopy. GDPN results in stratified modified surfaces, solidly integrated with the substrate, with a temperature-dependent composition comprising nitrides (γ’-Fe4N, ε-Fe2+xN, CrN) and N-solid solution (γN phase). The latter prevails for the low treatment temperatures. Hardness increases from ~2.5 GPa (bare surface) to ~15.5 GPa (450 °C). The scratch resistance of the GDPN-modified surfaces presents a strong correlation with the layer composition and thickness, with the result that the 400 °C condition exhibits the highest standards against microwear. In contrast, CCPN results in well-defined dual-layers for any of the temperatures. A top 0.3–0.8 µm-thick nitride film (most ε-phase), brittle and easily removable under scratch with loads as low as 63 mN, covers a γN-rich case with hardness of 10 GPa. The thickness of the underneath CCPN layer produced at 450 °C is similar to that from GDPN at 400 °C (3 µm); on the other hand, the average roughness is much lower, comparable to the reference surface (Ra ~10 nm), while the layer formation involves no chromium depletion. Moreover, edge effects are absent across the entire sample´s surface. In conclusion, among the studied conditions, the GDPN 400 °C disclosed the best tribo-mechanical performance, whereas CCPN resulted in superior surface finishing for application purposes.

Cutting tools, such as machining inserts, which in addition to having a complex geometry need to handle different demands on more than one face during their application, stimulate investigation for new coating techniques. Even though many hard film deposition techniques are well accepted or even established among researchers and industries, these processes still need to meet a limitation that requires the repositioning of the sample for new treatments in order to cover all of its surfaces. This paper proposes a new methodology for coating deposition on complex geometry pieces, through a structure for three-dimensional plasma treatment, using a Rotary Cathodic Cage Plasma Nitriding (RCCPN). TiN films were deposited on cemented carbide inserts applied in machining processes using the RCCPN technique. The plasma parameters were kept constant and deposition time was the only variable input. The deposited films were characterized by SEM, EDS, XRD, and Vickers microhardness. It was observed that the RCCPN technique was useful in producing a uniform TiN layer in all insert surfaces, with a thickness proportional to deposition time.

Abstract The electrical system is becoming more robust with the insertion of distributed energy resources (DERs) and the need for energy autonomy by consumers, given that the current scenario is a growth in demand for electric energy. This paper aims to apply a computational model capable of determining the optimal hourly allocation of controllable loads in residence, as well as studying the optimal dispatch of residential microgrids considering management on the demand side. In addition, this paper presents an economic feasibility analysis of residential microgrids considering distributed generation from wind and solar sources, distributed storage, electric vehicles, and residential controllable loads. Thus, it was possible to conclude that in residence, the insertion of distributed energy generation and storage elements can present a significant reduction in electric energy costs, which can be even greater if these elements are associated with optimized controllable load management.

The smart transformer (ST) is a multiport and multi-stage converter that allows for the formation of meshed hybrid microgrids (MHMs) by enabling AC-DC ports in medium and low voltage. This type of microgrid has advantages over the performance of conventional hybrid AC-DC microgrids (HMGs); however, the number of degrees of freedom of the ST increases the complexity of the energy management systems (EMSs), which require adequate and accurate modeling of the power flow of the converters and the MG to find the feasible solution of optimal power flow (OPF) problems in the MHM. An ST's equivalent power flow model is proposed for formulating the MHM OPF problem and developing low-frequency equivalent models integrated with a decoupled hierarchical control architecture under a real-time simulation approach to the ST-based MHM. A simulation model of the MHM in the Simulink® environment of Matlab® 9.12 is developed and implemented under a digital real-time simulation (DRTS) approach on the OPAL-RT® platform. This model allows for determining the accuracy of the developed equivalent models, both low-frequency and power flow, and determining the MHM performance based on optimal day-ahead scheduling. Simulation test results demonstrated the ST equivalent model's accuracy and the MHM's accuracy for OPF problems with an optimal day-ahead scheduling horizon based on the model-in-the-loop (MIL) and DRTS approach. [ABSTRACT FROM AUTHOR]