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Photovoltaic (PV) solar generation is gaining an increasing attention due to technological advances such as higher efficiency and life of PV cells and cost reduction. Due to its vast territory, Brazil is composed of regions that can explore renewable energy sources for electricity generation, and the solar resource is found satisfactorily in several areas of the country. This article presents a solar irradiance prediction mechanism developed using data collected in Fortaleza-CE, Brazil. Due to the fact of its characteristic of unpredictability for this resource, many researchers look for several methods to take the generation of this type of energy. The predictions were performed using a Radial Basis Function (RBF) a computational model based on the human nervous system, it is a technical and effective for time series forecasting, which is a relatively complex problem, Artificial Neural Network (ANN) with the advancement of 1 hour. In the ANN performance, a total of 34.4% forecasts underestimated solar energy availability, 7% of the forecasts obtained error 0 and 58.6% of forecasts overestimated the solar resource. A total of 62.33% of forecasts was between -10% and 10% of forecast error. The prediction mean error was 5.93% and the Mean Absolute Percentage Error (MAPE) was 11.43%.

The increasing of photovoltaic plant installations at different scales promotes the development of monitoring systems that facilitate the communication, control and automation of the generating units, allowing to guarantee the predicted energy generation performance. Monitoring systems are composed of different interfaces that involve sensing and capturing data; conversion, treatment, pre-storage and transmission of data; and publishing and final storage through graphic interface. This article focuses on describing the growth of decentralized plants, as well as the increasing demand for monitoring and data acquisition system, commenting the limitations of current commercial models and presenting alternative developed monitoring systems with different platforms.

This work contributes to the research on three-phase photovoltaic systems via a new more robust online adaptive neuro-fuzzy maximum power point tracking (MPPT) control technique considering real partial shading and load conditions. The trapping in local minima and high computational cost in the existing neuro-fuzzy structure are addressed by incorporating B-spline function in the proposed control method. The system parameters are adjusted online via an adaptive neuro-fuzzy inference system rules acquired from the MPPT error. The optimization part of the proposed control law is performed through an online learning gradient-descent back-propagation algorithm. The superiority of the proposed control in terms of energy conversion efficiency, MPPT error, and output power is checked under the same operating conditions with well-known used traditional and intelligent MPPT control algorithms. Finally, the robustness of the proposed control is confirmed through a complete day simulation and comparison indexes.

This paper describes and evaluates a hybrid propulsion system based on diesel generator and supercapacitors (SCs) as energy storage system (ESS) for a rubber tyre gantry (RTG) container crane, which currently operates within the yard of the Algeciras port terminal (Spain) powered by diesel electric generator for supplying the electric drives and motors (hoist and trolley). The SCs, which are connected to the DC bus through a bidirectional DC/DC converter, are controlled by a control strategy based on the DC-bus voltage. The SCs reference current is limited depending on their state-of-charge (SOC). All main components and control strategy of the RTG crane are modelled and simulated in SimPowerSystems. The current and hybrid configuration are simulated and compared under the real working cycle of the RTG crane. The results show the technical viability, the validity of the proposed control strategy, the improvements in the energy efficiency and diesel fuel consumption, and the economic viability of the hybrid propulsion system for the RTG crane.

Introduction: The Sydney system proposal for the study and reporting of lymphadenopathy by fine-needle aspiration (FNA) constitutes one of the first attempts to standardize this procedure. Here, we review its applicability. Materials and Methods: A retrospective study in which all ultrasound-guided FNAs (USFNAs) of superficial lymphadenopathy (palpable or not) performed by interventional pathologists in 2 specialized hospital centers were quantified over 2 years. The procedure was systematized, and the diagnoses were reclassified according to the Sydney system categories. Results: We analyzed 363 USFNAs of lymphadenopathies. The distribution of cases by categories was as follows: insufficient (n = 13; 3.58%), benign (n = 208; 57.30%), atypia of uncertain significance (n = 7; 1.93%), suspicious (n = 21; 5.79), and malignant (n = 114; 31.40%). The risks of malignancy calculated for categories I, II, III, IV, and V were 27%, 3%, 50%, 100%, and 100%, respectively. Conclusion: The implementation of the Sydney system allows the systematization and standardization of the lymph node FNA methodology, with increased efficacy and efficiency. Assimilating the recommendations enables the qualification of the diagnostic procedure.

This paper presents an energy management system (EMS) based on a novel approach using model predictive control (MPC) for the optimized operation of power sources in a hybrid charging station for electric vehicles (EVs). The hybrid charging station is composed of a photovoltaic (PV) system, a battery, a complete hydrogen system based on a fuel cell (FC), electrolyzer (EZ), and tank as an energy storage system (ESS), grid connection, and six fast charging units, all of which are connected to a common MVDC bus through Z-source converters (ZSC). The MPC-based EMS is designed to control the power flow among the energy sources of the hybrid charging station and reduce the utilization costs of the ESS and the dependency on the grid. The viability of the EMS was proved under a long-term simulation of 25 years in Simulink, using real data for the sun irradiance and a European load profile for EVs. Furthermore, this EMS is compared with a simpler alternative that is used as a benchmark, which pursues the same objectives, although using a states-based strategy. The results prove the suitability of the EMS, achieving a lower utilization cost (−25.3%), a notable reduction in grid use (−60% approximately) and an improvement in efficiency.

Maximum Constant Boost Control (MCBC) is one of the most suitable modulation techniques for Z-Source Inverters (ZSI) and quasi-ZSI (qZSI). Nevertheless, it cannot decouple the output voltage control and the fluctuations of the voltage source at the input of the inverter. This work proposes a new method based on this modulation technique that allows decoupling the control of these two variables, input and output voltages of a qZSI. The simulation results show that the Decoupled Maximum Constant Boost Control (DMCBC) has the same features and advantages than the one without decoupling. Voltage gain, stress and harmonic distortion are studied and the results show that the behavior is not modified, except for the reduction of the boost factor.

Mycobacterium kansasii extrapulmonary infections are infrequent in immunocompetent adults. Rifampin (RIF), clarithromycin (CLR), isoniazid (INH) and ethambutol (EMB) are included in all the standard regimens against M.kansasii. We report a case of a healthy 65-year-old male farmer who presented with isolated right supraclavicular lymphadenopathy. The lymph node FNA showed acid-fast-bacilli and granulomatous inflammation. Quantiferon TB Gold test, HIV serology, and functional immunological studies were all negative or normal. He was put on a standard 4 drugs anti-tuberculous regimen that was switched to RIF + CLR+ INH after the Microbiology lab demonstrated an EMB–resistant Mycobacterium kansasii isotype I strain. The patient was cured after 12 months of therapy. This is the 6th reported case of M. kansasii extrapulmonary lymphadenitis in an immunocompetent adult and the 2nd showing EMB resistance in the world literature. Antimycobacterial regimens against M. kansasii, classically resistant to pyrazinamide (PZA) might also exclude EMB due to its increasing resistance in Europe. A 612 months therapy with at least 2 effective antimycobacterial drugs including RIF + CLR might be enough to treat extrapulmonary M. kansasii infections in immunocompetents. Keywords: Mycobacterium kansasii, Cervical lymphadenitis, Immunocompetent, Resistance, Ethambutol, Quantiferon

The use of a battery energy-stored quasi-Z-source inverter (BES-qZSI) for large-scale PV power plants exhibits promising features due to the combination of qZSI and battery as energy storage system, such as single-stage power conversion (without additional DC/DC boost converter), improvements in the output waveform quality (due to the elimination of switching dead time), and continuous and smooth delivery of energy to the grid (through the battery energy storage system). This paper presents a new simplified model of a BES-qZSI to represent the converter dynamics with sufficient accuracy while using a less complex model than the detailed model (including the modelling of all switches and switching pulses). It is based on averaged values of the variables, voltage/current sources, and the same control circuit than the detailed model, except for the switching pulses generation. The simplified model enables faster time-domain simulation and is useful for control design and dynamic analysis purposes. Additionally, an energy management system has been developed to govern the power supply to grid under two possible scenarios: 1) System operator command following; or 2) economic dispatch of the stored energy. The results obtained from simulations and experimental hardware-in-the-loop (HIL) setup for different operating conditions of the grid-connected large-scale PV power plant with battery energy storage under study demonstrate the validity of the proposed simplified model to represent the dynamics of the converter and PV power plant for steady-state stability studies, long-term simulations, or large electric power systems.

In a developing world where the demand for energy increases every day, the use of hybrid systems is a viable and important solution. The rivers present a significant potential for the electrical generation from the hydrokinetic use of the river currents, as well as the use of the solar radiation by means of the generation of photovoltaic solar energy for the electrical supply in rural areas. This paper presents a combination of both forms of electricity generation, photovoltaic generators and river turbine, to configure a hybrid system together with a subsystem of support based on hydrogen and batteries to guarantee the electrical supply of a set of isolated loads. The complete design of a hybrid system satisfies the energy requirements of a load. The methodology includes the optimal location of the turbine in a cross section of the river to maximize the performance of the hydroelectric generator, and the techno-economic study of all components of the hybrid system for an efficient use of renewable energy resources. The results show that the hybrid system provides the power demanded by the loads, hydroelectric and solar photovoltaic generation are optimized, and the battery storage subsystem loading status is kept avoiding deep discharges.

As already happens with the electric vehicles (EVs) expansion, technology associated with their charge also must be improved. This paper presents a novel decentralized control method (DCM) for charging stations (CSs) based on a medium-voltage direct current (MVDC) bus. This kind of CSs is integrated in a microgrid with a photovoltaic system, a battery energy storage system (ESS), a local grid connection, and two units of fast charge. The main contribution of this paper resides in the cited DCM based on fuzzy logic that includes the state of charge (SOC) of the battery ESS as a control variable. This control contains two independent fuzzy logic systems (one for the battery ESS and other for the grid), whose function is to maintain the MVDC voltage and the battery ESS SOC within proper thresholds and to keep the power balance stable among the units of fast charge and the rest of the CS components. The new control method was tested in a considerable number of operating situations (two hundred cases studied) under different conditions of sun irradiance, initial SOC of battery ESS, and number of EVs connected to the CS with the objective of showing its correct performance in all the considered scenarios.

Rural electrification is one of the most significant issues faced by electricity companies. For this reason, these companies are choosing alternative sources to generate energy in isolated regions. Furthermore, hybrid generation systems are an effective option for supplying protected areas. In this context, this research aims at designing an autonomous hybrid system to meet the annual electricity demand of the inhabitants of a national park. Fluvial and solar energies are the best options to reduce environmental impact and to ensure the conservation of the endemic fauna and flora of the island at a low carbon footprint. The system comprises a series of subsystems modeled using commercial software for sizing and optimization. The main generation subsystem contains a hydrokinetic turbine and photovoltaic panels, the storage subsystem contains a battery bank, and the backup subsystem consists of a diesel generator used in case of lack of energy from the rest of suppliers of the hybrid system. The main results of the simulation show an optimized system that fulfills the energy demand while minimizing the use of the diesel generator to 5668 kWh/year (14.3%) of thorough generation. The hydrokinetic generator supplies 20,330 kWh/year (51.4% of the total generation) and the solar generator supplies 13,580 kWh/year (34.3%).

An ever-increasing penetration of electric vehicles (EVs) on the roads inevitably leads to an ever-stringent need for an adequate charging infrastructure. The emerging ultra-fast charging (UFC) technology has the potential to provide a refueling experience similar to that of gasoline vehicles; hence, it has a key role in enabling the adoption of EVs for medium-long distance travels. From the perspective of the UFC station, the differences existing in the EVs currently on the market make the sizing problem more challenging. A suitably conceived charging strategy can help to address these concerns. In this paper, we present a smart charging station concept that, through a modular DC/DC stage design, allows the split of the output power among the different charging ports. We model the issue of finding the optimal charging station as a single-objective optimization problem, where the goal is to find the number of modular shared DC/DC converters, and where the power rate of each module ensures the minimum charging time and charging cost. Simulation results show that the proposed solution could significantly reduce the required installed power. In particular, they prove that with an installed power of 800 kW it is possible to satisfy the needs of a UFC station composed of 10 charging spots.

Currently, the technology associated with charging stations for electric vehicles (EV) needs to be studied and improved to further encourage its implementation. This paper presents a new energy management system (EMS) based on a Biogeography-Based Optimization (BBO) algorithm for a hybrid EV charging station with a configuration that integrates Z-source converters (ZSC) into medium voltage direct current (MVDC) grids. The EMS uses the evolutionary BBO algorithm to optimize a fitness function defining the equivalent hydrogen consumption/generation. The charging station consists of a photovoltaic (PV) system, a local grid connection, two fast charging units and two energy storage systems (ESS), a battery energy storage (BES) and a complete hydrogen system with fuel cell (FC), electrolyzer (LZ) and hydrogen tank. Through the use of the BBO algorithm, the EMS manages the energy flow among the components to keep the power balance in the system, reducing the equivalent hydrogen consumption and optimizing the equivalent hydrogen generation. The EMS and the configuration of the charging station based on ZSCs are the main contributions of the paper. The behaviour of the EMS is demonstrated with three EV connected to the charging station under different conditions of sun irradiance. In addition, the proposed EMS is compared with a simpler EMS for the optimal management of ESS in hybrid configurations. The simulation results show that the proposed EMS achieves a notable improvement in the equivalent hydrogen consumption/generation with respect to the simpler EMS. Thanks to the proposed configuration, the output voltage of the components can be upgraded to MVDC, while reducing the number of power converters compared with other configurations without ZSC., This work was partially supported by Spain's Ministerio de Ciencia, Innovaci ' on y Universidades (MCIU), Agencia Estatal de Investigaci ' on (AEI) and Fondo Europeo de Desarrollo Regional (FEDER) Uni ' on Europea (UE) (grant number RTI2018-095720-B-C32), by the Federal Center for Technological Education of Minas Gerais, Brazil (process number 23062-010087/2017-51) and by the National Council of Technological and Scientific Development (CNPq-Brazil).

We introduce a new general class of metric f-manifolds which we call (nearly) trans-S-manifolds and includes S- manifolds, C-manifolds, s-th Sasakian manifolds and generalized Kenmotsu manifold studied previously. We prove their main properties and we present many examples which justify their study., Comment: 15 pages

We consider generalized ( κ , μ ) {(\kappa,\mu)} -paracontact metric manifolds satisfying certain flatness conditions on the ℳ {\mathcal{M}} -projective curvature tensor. Specifically, we study ξ- ℳ {\mathcal{M}} -projectively flat and ℳ {\mathcal{M}} -projectively flat generalized ( κ , μ ) {(\kappa,\mu)} -paracontact metric manifolds and, further, ϕ- ℳ {\mathcal{M}} -projectively symmetric generalized ( κ ≠ - 1 , μ ) {(\kappa\neq-1,\mu)} -paracontact metric manifolds. We prove that they are characterized by certain structures whose properties are discussed in some detail.

Electric vehicles are the most promising sustainable transport technology for solving problems linked to the internal combustion engine vehicles. Wireless charging reduce the main problems associated with electric vehicles, driving range, charging time and size and cost of the battery. Inductive power transfer is the most promising technology for dynamic wireless charging of electric vehicles, which can be used to supply the motors and charge the battery while moving. This paper presents a comparative study of a dynamic wireless power transfer system for charging electric vehicles driving on three stretches of traffic road (motorway, highway and urban stretch) in Cadiz (Spain). The study analyses the electricity consumption demanded by the dynamic wireless power transfer system and the battery state-of-charge of the electric vehicles that travel the stretch to evaluate the increase of autonomy, the length of the stretch or speed of the vehicle for achieving a specific increase of state-of-charge. The results show a great dependence on stretch of road, with large fluctuations in the urban stretch and more stability in the highway and motorway. This study could help to design stretches of roads with dynamic wireless power transfer and to quantify the power and energy demanded by the system.

Mycobacterium kansasii extrapulmonary infections are infrequent in immunocompetent adults. Rifampin (RIF), clarithromycin (CLR), isoniazid (INH) and ethambutol (EMB) are included in all the standard regimens against M.kansasii. We report a case of a healthy 65-year-old male farmer who presented with isolated right supraclavicular lymphadenopathy. The lymph node FNA showed acid-fast-bacilli and granulomatous inflammation. Quantiferon TB Gold test, HIV serology, and functional immunological studies were all negative or normal. He was put on a standard 4 drugs anti-tuberculous regimen that was switched to RIF + CLR+ INH after the Microbiology lab demonstrated an EMB–resistant Mycobacterium kansasii isotype I strain. The patient was cured after 12 months of therapy. This is the 6th reported case of M. kansasii extrapulmonary lymphadenitis in an immunocompetent adult and the 2nd showing EMB resistance in the world literature. Antimycobacterial regimens against M. kansasii, classically resistant to pyrazinamide (PZA) might also exclude EMB due to its increasing resistance in Europe. A 612 months therapy with at least 2 effective antimycobacterial drugs including RIF + CLR might be enough to treat extrapulmonary M. kansasii infections in immunocompetents.

In this paper, energy management and control of a microgrid is developed through supervisor and adaptive neuro?fuzzy wavelet?based control controllers considering real weather patterns and load variations. The supervisory control is applied to the entire microgrid using lower–top level arrangements. The top?level generates the control signals considering the weather data patterns and load conditions, while the lower level controls the energy sources and power converters. The adaptive neuro?fuzzy wavelet?based controller is applied to the inverter. The new proposed wavelet?based controller improves the operation of the proposed microgrid as a result of the excellent localized characteristics of the wavelets. Simulations and comparison with other existing intelligent controllers, such as neuro?fuzzy controllers and fuzzy logic controllers, and classical PID controllers are used to present the improvements of the microgrid in terms of the power transfer, inverter output efficiency, load voltage frequency, and dynamic response. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. Ministerio de Ciencia, Innovación y Universidades, MCIU: RTI2018?095720?B?C32; Deutsche Forschungsgemeinschaft, DFG: 350746631; Türkiye Bilimsel ve Teknolojik Araştirma Kurumu, TÜBITAK: 5190011 Funding: The work of Murat Karabacak and Tariq Kamal is sponsored by the Scientific and Technological Research Council of Turkey under project number 5190011. The work of Vedran S. Peri? is supported by the Bavarian Government and Deutsche Forschungsgemeinschaft (DFG) under project number 350746631. The work of Luis M. Fernández?Ramírez and Tariq Kamal is sponsored by the Spanish Ministry of Science, Innovation, and Universities under project number RTI2018?095720?B?C32.

This paper proposes an efficient bridgeless power factor corrected (PFC) modified single ended primary inductor converter (SEPIC) for arc welding power supplies (AWPS). The overall configuration is composed of two converters: (1) a modified bridgeless SEPIC PFC converter, which is controlled by a PI controller to achieve a high power factor and fast response; and (2) a full bridge buck converter with high-frequency transformer for high-frequency isolation to ensure arc welding stability. The proposed system is simulated under different operating conditions of an AWPS. It is also tested in real time by a hardware-in-the-loop system based on a dSPACE DS1103 control board. The system performances are evaluated based on power quality indices such as power factor, total harmonic distortions of the AC grid current, and voltage regulation. The obtained results show that the proposed controller enhances the weld bead quality by keeping a constant current at the output and a stable arc, meet the international power quality standards and robustness for voltage regulation.

Kılıç, Fuat (Balikesir Author), In this research, a new nonlinear and adaptive state feedback controller with a fast-adaptive robust differentiator is presented for grid-tied inverters. All parameters and external disturbances are taken as uncertain in the design of the proposed controller without the disadvantages of singularity and over-parameterization. A robust differentiator based on the second order sliding mode is also developed with a fast-adaptive structure to be able to consider the time derivative of the virtual control input. Unlike the conventional backstepping, the proposed differentiator overcomes the problem of explosion of complexity. In the closed-loop control system, the three phase source currents and direct current (DC) bus voltage are assumed to be available for feedback. Using the Lyapunov stability theory, it is proven that the overall control system has the global asymptotic stability. In addition, a new simple L filter design method based on the total harmonic distortion approach is also proposed. Simulations and experimental results show that the proposed controller assurances drive the tracking errors to zero with better performance, and it is robust against all uncertainties. Moreover, the proposed L filter design method matches the total harmonic distortion (THD) aim in the design with the experimental result.

This paper presents an application of the multi-port bidirectional three-phase ac-dc converter as interface between a microgrid composed by several power sources and an electric vehicle charging station (EVCS). The main advantage of using this converter is that it can integrate multiple power sources and loads into a single power conversion stage and thus control the power flow between them reducing the number of power conversion stages and / or devices as well as weight and volume of the entire system and the control architecture does not require communication strucure as main current solutions in this field present. The microgrid of this study was composed of a photovoltaic system, a battery energy storage system, two 48 kW fast charging units for electricle vehicles, a connection to the local grid and the multi-port bidirectional converter with minor changes to be able in this type of application with a rated power at 100 kW. Simulation results obtained from a system model are presented and discussed in order to validate that under different power sources conditions the converter operates effectively confirming the feasibility of using this type of application in EVCS microgrid technology.

In this paper, research on the control of photovoltaic (PV) using a novel higher order B-spline online adaptive neuro-fuzzy paradigm considering high external uncertainties in weather and load demand is presented. We optimize the existing neuro-fuzzy technique by incorporating third order B-spline membership functions in its antecedent part, which we solve using an on-line learning gradient-decent back propagation method. We fit the system parameters online through adaptive fuzzy rules extracted from the maximum power point tracking (MPPT) error and its derivative. Unlike many existing neuro-fuzzy techniques, our method addresses the trapping in local minima. The proposed controller is demonstrated to be stable using Lyapunov stability analysis. The performance of our control philosophy is checked in terms of output power tracking, efficiency, and MPPT error. Finally, we validate via simulation the high robustness and the self-adaptation ability of the proposed method over other existing traditional and intelligent techniques.

The global energy system is changing, mainly to achieve sustainable transport technologies and clean electrical generation based on renewable sources. Thus, as fuels, electricity and hydrogen are the most promising transport technologies in order to reduce greenhouse emissions. On the other hand, photovoltaic and wind energies, including energy storage, have become the main sources of distributed generation. This study proposes a new optimal-technical sizing method based on the Simulink Design Optimization of a stand-alone microgrid with renewable energy sources and energy storage to provide energy to a wireless power transfer system to charge electric vehicles along a motorway and to a hydrogen charging station for fuel cell-powered buses. The results show that the design system can provide energy for the charging of electric vehicles along the motorway and produce the hydrogen consumed by the fuel cell-buses plus a certain tank reserve. The flexibility of the study allows the analysis of other scenarios, design requirements, configurations or types of microgrids.

This work provides the dynamic operation and supervisory control of a hybrid renewable energy system which supplies power in stand-alone as well as in grid-connected mode. It contains a photovoltaic as a primary source controlled via fuzzy and a Proton Exchange Membrane Fuel Cell (PEMFC) as a secondary source controlled via Proportional Integral Differential (PID) controller. The high intermittency nature of photovoltaic is addressed through the integration of super-capacitor and battery bank in the proposed architecture. The overall strategy of the proposed system is achieved using dynamic power switches of the power converters. The strategy maximizes the use of available photovoltaic /fuel cell/ super-capacitor/battery power and reduces stress on the public grid under all conditions for 24 hours. The proposed test-bed is simulated under real weather pattern and load variations. Simulations conclude the effectiveness of the proposed architecture.

This paper describes a novel energy management system for the optimized operation of the energy sources of a grid-connected hybrid renewable energy system (wind turbine and photovoltaic) with battery and hydrogen system (fuel cell and electrolyzer). A multi-objective optimization problem based on the weight aggregation approach is formulated by combining three objective functions (operating costs, efficiency and lifetime of the devices) that can conflict with each. The multi-objective function to be optimized by the energy management system is obtained by solving the problem for all the possible cases. Then, the weights that provide the minimum value of the multi-objective function are selected. As the results demonstrate, the multi-objective function becomes a single-objective function that differs according to the net power (power to be generated by/stored in the energy storage devices) and has to be solved in the energy management system of the hybrid system. It simplifies considerably the multi-objective problem implemented in the energy management system, while taking into account the three control objectives that can conflict with each other, which is the main contribution of this paper. This optimal energy management system is solved using the Particle Swarm Optimization (PSO) method, tested by simulations of the hybrid power generation system throughout 25 years (the expected lifetime of the system), and compared with the results obtained by the energy management systems based on optimizing each single-objective function separately, and by that based on optimizing the multi-objective function combining the three single-objective functions equally weighted. The results demonstrate that this energy management system achieves reasonable operating costs, efficiency and degradation of the devices.

Although electric vehicles (EVs) are experiencing a considerable upsurge, the technology associated with them is still under development. This study focused on the control and operation of a medium-voltage direct-current (MVDC) microgrid with an innovative decentralized control system, which was used as a fast charging station (FCS) for EVs. The FCS was composed of a photovoltaic (PV) system, a Li-ion battery energy storage system (BESS), two 48 kW fast charging units for EVs, and a connection to the local grid. With this configuration and thanks to its decentralized control, the FCS was able to work as a stand-alone system most of the time though with occasional grid support. This paper presents a new decentralized energy management system (EMS) with two options to control the power sources of the FCS. The choice of the power source depends on the MVDC bus voltage, the state-of-charge (SOC) of the BESS, and the control option of the EMS. This control was tested by simulating the FCS, when connected to several EVs and under different sun irradiance conditions. Simulation results showed that the FCS operated smoothly and effectively, which confirms the feasibility of using this technology in EVs.

Control focused on optimizing the lifecycle costs of a stand-alone hybrid system.Combination of renewable sources, battery and hydrogen systems.The modeling includes the electric models of the components.The system assures reliable electricity support for stand-alone applications. This paper presents an Energy Management System (EMS) for hybrid systems (HS) composed by a combination of renewable sources with the support of different storage devices (battery and hydrogen system) that allow its operation without the necessity of grid connection (i.e. a stand-alone system).The importance of the proposed EMS lies in taking into account economic issues that affect to the decision of which device of the HS must operate in each moment. Linear programming was used to meet the objective of minimizing the net present value of the operation cost of the HS for its whole lifespan. The total operation costs depend largely on the reposition costs of its components. Instead of considering predefined reposition years for each component and calculate their net present cost from them (as is commonly considered in other works), in this work it was proposed to use lifetime degradation models - based on the well-known statement that the lifetime depends on the hours of operation and the power profiles that the components are subjected to- from which the repositions are made to check how they affect to the cost calculation and, consequently, to the EMS performance.The behavior of the proposed control is checked under a long term simulation, in MATLAB-Simulink environment, whose duration is the expected lifespan of the HS (25 years). A conventional state-machine EMS is used as a case study to validate and compare the results obtained. The results demonstrate that the proposed HS and EMS combination assures reliable electricity support for stand-alone applications subject to different techno-economic criteria (generation cost and sustenance of battery SOC and hydrogen levels), achieving to minimize the operation cost of the system and extend their lifespan.

This paper presents an overview of the state-of-the-art of research on control strategies or energy management systems for DC networks and microgrids. For this purpose, a systematic literature review was performed, which classified the research on the topic and identified the interconnections between the different approaches described in the publications selected. The work reviewed was initially classified into two main categories, based on a set of distinguishing characteristics identified in the control strategies. These categories were the following: (1) active load sharing (which includes the subcategories of centralized control and of master–slave control; (2) droop control methods. A third ad hoc category was also established for the approaches that did not fit in the other two categories. Additionally, secondary topics affecting the control strategies were also analyzed, such as the configuration of DC networks, commonly used control methods and factors, communication system relevance, and finally, the simulation and experimental techniques used for validation. Moreover, this systematic review permitted the identification of current research gaps as well as the proposal of new research lines. The results obtained not only are useful as a guide for researchers, who are starting out in the field, but they also highlight interesting research questions that more experienced researchers can actively pursue.

In this paper, the idea of solving algebraic equations (AEs) through the fixed-point iterative method (FPIM) is generalized for the differential algebraic equations (DAEs) of power systems in order to analyze transient stability problems, which are particularly relevant when the number of DAEs in high-order models increase. In the loop form, reducing the number of variables by explicitly solving AEs is no longer required. It also allows for adding or removing the equations in order to easily analyze the effect of equations in the system's response. Furthermore, through the loop solving (LS) mechanism, the simplification assumption about power consumption in the PQ buses to fixed impedances is not necessary and the loads can be assumed with each arbitrary model. The LS mechanism is the first innovation provided hereby which facilitates the programming of high-order models and increases the accuracy of the system's response. On the other hand, because consistent and redundant variables are in place, solving DAEs in the loop form requires an iterative method with strong a convergence property that provides a convergent solution to the load flow (LF) AEs, and then a convergence solution for the DEs of the machines. This can be developed by extending the FPIM to the traditional Gauss-Siedel (GS) method, called modified GS (MGS), which is the second innovation herein. It can converge the solution of LF equations to the equilibrium point despite the numerical anomalies. Moreover, in order for the same performance as that of the MGS to be achieved, the Newton-Raphson (NR) method is first developed by a new formulation to full complex form, called complex based NR (CNR), which is the third innovation addressed hereby, and then applied with the same technique as that of the MGS to modified NR (MNR). The CNR increases the speed and simplicity of the LF computations and does not require decomposition of AE for both real and imaginary components; therefore, it simplifies the simulation training problems and reduces the computational time for large system dimensions. The proposed method is implemented in Simulink/MATLAB, tested and validated for the Western System Coordinated Council (WSCC) IEEE 9-bus system and compared with the results obtained by power system simulators, such as PowerWorld (PW) and SymPowerSystems (SPSs), and previous works published in the literature. Then, the experience gained from the first test is also applied to the IEEE 57-bus test system as a large scale system. The simulation results show the ability of the proposed method to represent the system's response for severe transient conditions, with better results than those achieved by previous methods. The new results are obtained from the effect of the network's transient on mechanical response of some synchronous machines. Also, the importance of removing damping coils in the system's transient response and the transition of response divergence during the severe fault with the method proposed hereby can be observed.

Wireless power transfer technology is being widely used in electric vehicle wireless-charging applications, and foreign object detection (FOD) is an important module that is needed to satisfy the transmission and safety requirements. FOD mostly includes two key parts: metal object detection (MOD) and living object detection (LOD), which should be implemented during the charging process. In this paper, equivalent circuit models of a metal object and a living object are proposed, and the FOD methods are reviewed and analyzed within a unified framework based on the proposed FOD models. A comparison of these detection methods and future challenges is also discussed. Based on these analyses, detection methods that employ an additional circuit for detection are recommended for FOD in electric vehicle wireless-charging applications.

Inverters are critical for injecting power from renewable energy sources into the grid or grid-integrated load. This paper provides a novel improved adaptive neuro-fuzzy control of inverter for grid-coupled photovoltaic systems. The improvement is performed by the integration of Jacobi wavelet in the existing neuro-fuzzy controller. Unlike existing neuro-fuzzy controller, the new proposed controller is capable to locate the exact local minima, which sequentially improves the performance of the proposed controller. Simulations are performed to show that the proposed controller is better than the existing state-of-the- art in terms of power transfer, inverter output efficiency, and load voltage frequency.

Globally, there has been a significant evolution in the development of wind energy. Nevertheless, the major difference between the highly stochastic nature of wind speed and the desired demands regarding the electrical energy quality and system stability is the main concern in wind energy system. Hence, wind energy generation according to the standard parameters imposed by the power industry is unachievable without the essential involvement of advanced control technique. In this book chapter, a novel indirect adaptive control for wind energy systems is proposed considering real load demand and weather parameters. The performance of existing neuro-fuzzy scheme is improved further using a Hermite wavelet in the proposed architecture. The parameters of the controller are trained adaptively online via backpropagation algorithm. The proposed control law adopts the free direct control model which shorten the weight of the lengthy pre-learning, and memory requirements for real time application. Various computer simulation results and performance comparison indexes are given to show that the proposed control law is better in terms of efficiency, output power and steady-state performance over the existing state-of-the-art.

Forecasting the power production from renewable energy sources (RESs) has become fundamental in microgrid applications to optimize scheduling and dispatching of the available assets. In this article, a methodology to provide the 24 h ahead Photovoltaic (PV) power forecast based on a Physical Hybrid Artificial Neural Network (PHANN) for microgrids is presented. The goal of this paper is to provide a robust methodology to forecast 24 h in advance the PV power production in a microgrid, addressing the specific criticalities of this environment. The proposed approach has to validate measured data properly, through an effective algorithm and further refine the power forecast when newer data are available. The procedure is fully implemented in a facility of the Multi-Good Microgrid Laboratory (MG L a b 2 ) of the Politecnico di Milano, Milan, Italy, where new Energy Management Systems (EMSs) are studied. Reported results validate the proposed approach as a robust and accurate procedure for microgrid applications.

Hybrid configurations involving renewable energies and storage devices pose certain challenges regarding their energy management strategies, such as the intermittent and fluctuating power generation from renewable sources, the time-varying available energy in the storage systems, or their maximum charge and discharge limitations. Observing these aspects is mandatory in order to develop a smart energy management strategy within the hybrid system. This chapter presents a control strategy for the coordinated operation of a wind power generator and two different energy storage devices. The proposed control scheme is based on fuzzy logic to monitor the state of charge of the storage systems, while defining their power references to comply with an imposed grid demand. The control strategy has been evaluated through simulation under different operating conditions, proving a satisfactory regulation of the monitored parameters and an adequate supply of the grid requirements.

Solar energy has been consolidated as one of the main renewable energy sources capable of contributing to supply global energy demand. However, the solar resource has intermittent feature in electricity production, making it difficult to manage the electrical system. Hence, we propose the application of Deep Learning (DL), one of the emerging themes in the field of Artificial Intelligence (AI), as a solar predictor. To attest its capacity, the technique is compared with other consolidated solar forecasting strategies such as Multilayer Perceptron, Radial Base Function and Support Vector Regression. Additionally, integration of AI methods in a new adaptive topology based on the Portfolio Theory (PT) is proposed hereby to improve solar forecasts. PT takes advantage of diversified forecast assets: when one of the assets shows prediction errors, these are offset by another asset. After testing with data from Spain and Brazil, results show that the Mean Absolute Percentage Error (MAPE) for predictions using DL is 6.89% and for the proposed integration (called PrevPT) is 5.36% concerning data from Spain. For the data from Brazil, MAPE for predictions using DL is 6.08% and 4.52% for PrevPT. In both cases, DL and PrevPT results are better than the other techniques being used.

This paper provides the development of a monitoring system for a hybrid power supply which consists of different energy resources, namely, a photovoltaic array controlled via maximum power point subsystem and a fuel cell stack controlled via Proportional Integral Differential/Proportional Integral (PID/PI) subsystems and operating in ON and OFF grid modes. An energy storage system (super-capacitor, battery bank and hydrogen) controlled through DC-DC power converter embedded with PID/PI controllers is also integrated in the proposed test-bed. The proposed system is designed in two stages. In the stage one, real time weather pattern, such as solar irradiance and temperature, are measured online through the internet from the weather station using LabVIEW. In the stage two, the modelling, control and energy management of the hybrid power supply are performed in MATLAB. The overall scheme of the proposed system is performed through classical-based monitoring switching control system which maximizes the use of alternative energy resources and reduces stress on the utility for 24 hours. The proposed system uses real weather data and load demand. Simulation work concludes the effectiveness of the proposed test-bed.

Large mammal communities, integral components of Neotropical ecosystems, are increasingly threatened by habitat fragmentation and degradation due to anthropogenic pressures and climate change. Lowland Neotropical palm forests harbor diverse mammal communities, including threatened and endangered species. We monitored three transects in the Barra del Colorado Wildlife Refuge in Northeast Costa Rica from February 2014 until April 2015 documenting large terrestrial mammal and primate detections. Following established large mammal survey methodology, each transect was walked weekly alternating between early morning and late afternoon to maximize detection. The three transects represent three levels of anthropogenic disturbance with highest disturbance at the Cerro, followed by Caño Palma Biological Station (CPBS), and lowest levels in Tortuguero National Park (TNP). Overall we found higher diversity and increased presence of endangered species in TNP and the lowest diversity and lower presence of those species in the Cerro. Overall we detected 25 species; 19 at the Cerro, 22 in TNP and 23 at CPBS. TNP had the highest diversity as calculated by Simpson index (28.3) followed by CBPS (27.9), and the Cerro had the lowest diversity of mammals (2.27). The most influential species driving the differences of communities between the three transects included the white-lipped and collared peccaries, which were both present in higher densities in transects with lower disturbance and higher levels of protection, and domestic dogs which were present in higher densities at the Cerro. This study has documented baseline densities of large mammal communities in the wildlife refuge which can be used as preliminary data for future comparisons. As development is continuing in the region, including construction of a tourist trail at the Cerro, a continued monitoring is recommended to understand the effect of development on mammal communities.

The most important nesting site for the green turtle, Chelonia mydas, in the Atlantic basin is Costa Rica’s Tortuguero National Park (TNP). An important portion of this population nests on Playa Norte, an adjacent beach located to the north of TNP. Using 10 years (2006–2015) of mark-recapture data, we estimated demographic parameters for green turtles nesting at Playa Norte. During the 10-year study period, 1573 nesting encounters were recorded, representing 1101 distinct individuals. Open-population models estimated an abundance of 6004 (95% CI 5037–6970), representing the contingent of adult females that nested on Playa Norte at least once during the study period. This estimate includes females that died did not use Playa Norte as nesting site or became post-reproductive after the first nesting encounter as well as nesting and non-nesting adult females in a given year. This open-population abundance estimate and related demographic rates are also assumed to be robust against inter-nesting occurrences between Playa Norte and TNP, and between Playa Norte and other unmonitored and minor beaches. The mean annual survival rate was 0.85 (95% CI 0.76–0.91) and the realized annual population growth from 2006 to 2015 was 1.0179 (95% CI 0.7984–1.2978), indicating a positive trend characterized by an average 1.79% annual increase. The results presented here concur with results from studies of nesting females at TNP and highlight the importance of Playa Norte as a nesting ground for Atlantic green turtles. In addition, our study demonstrated that reliable demographic information can be derived from nesting sites at the edge of a main nesting area if proper survey designs and analytical methods are used.

The use of distributed charging stations based on renewable energy sources for electric vehicles has increased in recent years. Combining photovoltaic solar energy and batteries as energy storage system, directly tied into a medium voltage direct current bus, and with the grid support, results to be an interesting option for improving the operation and efficiency of electric vehicle charging stations. In this paper, an electric vehicle charging station supplied by photovoltaic solar panels, batteries and with grid connection is analysed and evaluated. A decentralized energy management system is developed for regulating the energy flow among the photovoltaic system, the battery and the grid in order to achieve the efficient charging of electric vehicles. The medium voltage direct current bus voltage is the key parameter for controlling the system. The battery is controlled by a model predictive controller in order to keep the bus voltage at its reference value. Depending on the state-of-charge of the battery and the bus voltage, the photovoltaic system can work at maximum power point tracking mode or at bus voltage sustaining mode, or even the grid support can be needed. The results demonstrate the proper operation and energy management of the electric vehicle charging station under study.

We introduce para-S-manifolds and obtain some results concerning the curvature of these manifolds. In particular, we prove that there does not exist Einstein para-S-manifold, and consequently, we investigate \(\eta \)-Einstein para-S-manifolds and the conditions for them to be \(\xi \)-conformally flat.

The continuous progress on the expansion of renewable energies leads to the development of hybrid power systems, where several power sources contribute to provide a clean and reliable alternative to traditional fossil fuels. The hydrogen technology is viewed with particular interest in this regard. Hydrogen is an outstanding energy carrier that can be exploited for various applications, including electricity generation. Hence, production of hydrogen from renewable sources has received the attention of many researchers lately. With this purpose, this paper deals with the coupled operation of electrolyzer (EZ) and wind turbine. Four different EZ models are presented and evaluated in this work. These models are aggregated to a variable speed wind turbine model using MATLAB/Simulink. The four configurations are evaluated, and their responses compared, under variable wind speed and grid demand.