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In this paper, passive reinforcement learning (RL) solved by particle swarm optimization policy (PSO–P) is used to handle an adaptive neuro-fuzzy inference system (ANFIS) type-2 structure with unsupervised clustering for controlling the pitch angle of a real wind turbine (WT). The proposed control scheme is based on gain-scheduled reinforcement learning recurrent ANFIS type 2 (GS-RL-RANFIST2) pitch angle controller to maintain the rotor speed at its rated value while smoothing the output power and the performance of the pitch angle system. The practical application of the proposed controller is evaluated by using FAST tool for a real 600 kW WT equipped with a synchronous generator with a full-size power converter (CART3, located at the National Renewable Energy Laboratory, NREL), whose results are compared with those obtained by a gain corrected proportional integral (GC-PI) controller. The results demonstrate that the GS-RL-RANFIST2, which sets the nonlinear characteristics of the system automatically and waves more uncertainties in the windy conditions, allows to increase the energy capture and smooth the output power fluctuation, and therefore, to improve the control and response of the WT.

The existing model predictive control algorithm based on continuous control using quadratic programming is currently one of the most used modern control strategies applied to wind turbines. However, heavy computational time involved and complexity in implementation are still obstructions in existing model predictive control algorithm. Owing to this, a new switched model predictive control technique is developed for the control of wind turbines with the ability to reduce complexity while maintaining better efficiency. The proposed technique combines model predictive control operating on finite control set and artificial intelligence with reinforcement techniques (Markov Chains, MC) to design a new effective control law which allows to achieve the control objectives in different wind speed zones with minimization of computational complexity. The proposed method is compared with the existing model predictive control algorithm, and it has been found that the proposed algorithm is better in terms of computational time, load mitigation, and dynamic response. The proposed research is a forward step towards refining modern control techniques to achieve optimization in nonlinear process control using novel hybrid structures based on conventional control laws and artificial intelligence.

This work presents a new configuration for a hybrid charging station of electric vehicles (EV) based on Z-source converters (ZSC) in a medium voltage direct current (MVDC) grid. A photovoltaic (PV) system, a battery (BAT), a hydrogen system with fuel cell (FC), electrolyzer (LZ) and hydrogen tank as energy storage system (ESS), a local grid connection and two units of fast charging for EV are the main components of this system. Thanks to the proposed configuration the output voltages of the components can be adapted to MVDC to control their output power and reduce the number of power converters compared with the common configuration without ZSC. In addition, a new supervisory control system (SCS) is designed to keep the power balance in the hybrid charging station and control the level of energy in the ESS. The behavior of the charging station and SCS are proven under variable sun irradiance and considering the connection of three EV to the charging station. The simulation results show that the proposed system (ZSC-based configuration and SCS) is perfectly valid for charging stations.

Renewable energy systems based on photovoltaic (PV) power plants are usually connected to the grid through a DC-DC boost converter and a voltage source inverter. In this paper, this topology is modified by replacing this conversion system consisting of two stages for one of single stage, employing a quasi-Z-source inverter (qZSI), which allows to achieve a medium voltage direct current (MVDC) at the output of the Z source. In addition, a battery is integrated directly into the qZSI, without any additional DC/DC converter, which is used to support the intermittence of the PV generation and improve the operability of the system. The PV power plant operates at maximum power point tracking (MPPT) and the active and reactive power delivered by the PV power plant through the qZSI are controlled by a Z-Space Vector Modulation (ZSVM) technique. An energy management system (EMS) based on a fuzzy logic controller is implemented for the supervisory control of the PV plant and battery. The EMS decides to charge or discharge the battery depending on the power generated by PV power plant, the battery state-of-charge (SoC), and the grid energy price. The main advantages of the proposed system are: (i) the use of a single stage conversion; (ii) the battery connection directly to the Z source, which allows not to use an additional DC/DC converter; (iii) the application of fuzzy logic control for the EMS, while maintaining the battery within secure SoC limits and with a smooth response. The results illustrate the proper behavior of the PV power plant under different operating conditions.

Solar energy is one of the main renewable energy sources capable of contributing to global energy demand. However, the solar resource is intermittent, making its integration into the electrical system a difficult task. Here, we present and compare two machine learning techniques, deep learning (DL) and support vector regression (SVR), to verify their behavior for solar forecasting. Our testing from Spain showed that the mean absolute percentage error for predictions using DL and SVR is 7.9% and 8.52%, respectively. The DL achieved the best results for solar energy forecast, but it is worth mentioning that the SVR also obtained satisfactory results.

Hybrid energy storage systems (HESSs) help mitigating the fluctuations and variable availability of certain renewable sources, such as wind power, as they can provide support in different time scales. Therefore, regulating their state-of-charge (SOC) becomes crucial to ensure that the hybrid system complies with generation commitments agreed in time-ahead markets despite subsequent unexpected wind speed variations. So far, research has been mainly targeted at avoiding extreme SOC situations in the storage devices, whereas the regulation of this parameter to specific values has often been disregarded. A novel approach is proposed in this work, where model predictive control (MPC) is used to regulate the SOC of a HESS under variable wind and grid demand scenarios. The MPC-based supervisory controller developed for the hybrid system has been implemented and simulated under different situations. This controller monitors the future variation of the SOC with the aim of having the HESS available to develop its assigned functions successfully. The results show that a proper regulation of the SOC in the HESS increases the capacity to manage the active power supplied to the grid by the hybrid system based on wind power, as well as the level of compliance with generation commitments established time ahead.

Finding and tracking maximum power point are two important dynamics in the control of variable-speed wind turbines since they determine the efficiency of wind turbines. The conventional hill climbing possesses the problems of wrong directionality and low performance since it does not take the inertial effect into account. In this paper, a novel hill climbing method is proposed by considering the inertial effect to solve these problems. Besides, employing the exact model knowledge of the generator in the maximum power tracking control deteriorates the efficiency considerably; therefore, it is required to design a parameter independent and robust control system if possible. Thus, the third-order super-twisting sliding mode and continuous integral sliding mode controllers are designed for the control of generator and grid-side converters to track the maximum power trajectory accurately, and they are compared to each other for the chattering in experimental results. A comparison is also performed between the conventional and proposed hill climbing methods based on the captured energy from the wind. Experimental results, with a wind turbine emulator, demonstrate that the proposed hill climbing method relaxes the wrong directionality and sluggish performance of the conventional one.

Renewable energies are reaching the maturity of their technological development, but before they are ready to become the main or even the only power sources in the energy mix, they must become profitably manageable. Currently there are many research topics regarding the integration of renewable energies in power systems, such as the combination of two or more sources into hybrid systems, the addition of energy storage systems, etc. The present paper studies the economic feasibility of converting an existing pumped-storage hydro power plant into a hybrid hydro-wind power plant through the integration of a wind farm in its surroundings. For this purpose, the estimated economic benefits of the existing pumped-storage hydropower plant are compared with the potential benefits of the proposed hybrid hydro-wind configuration. Furthermore, two energy management systems are conceived in order to estimate the energy generated and consumed by the hybrid hydro-wind plant, as well as the income and expenses resulting from the energy purchase-sale. The results point out the economic feasibility of the project, as well as an increased participation of the hybrid plant in the power system.

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.

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.

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

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).

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.

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.

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.

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.

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.

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.

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.

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.

This paper is focused on the study of particle swarm optimization (PSO)-based PI controllers for the power control of a grid-connected inverter supplied from a hybrid renewable energy system. It is composed of two renewable energy sources (wind turbine and photovoltaic – PV – solar panels) and two energy storage systems (battery and hydrogen system, integrated by fuel cell and electrolyzer). Three PSO-based PI controllers are implemented: (1) conventional PI controller with offline tuning by PSO algorithm based on the integral time absolute error (ITAE) index; (2) PI controllers with online self-tuning by PSO algorithm based on the error; and (3) PI controllers with online self-tuning by PSO algorithm based on the ITAE index. To evaluate and compare the three controllers, the hybrid renewable energy system and the grid-connected inverter are simulated under changes in the active and reactive power values, as well as under a grid voltage sag. The results show that the online PSO-based PI controllers that optimize the ITAE index achieves the best response.

This paper presents a novel energy dispatching based on Model Predictive Control (MPC) for off-grid photovoltaic (PV)/wind turbine/hydrogen/battery hybrid systems. The renewable energy sources supply energy to the hybrid system and the battery and hydrogen system are used as energy storage devices. The denominated “hydrogen system” is composed of fuel cell, electrolyzer and hydrogen storage tank. The MPC generates the reference powers of the fuel cell and electrolyzer to satisfy different objectives: to track the load power demand and to keep the charge levels of the energy storage devices between their target margins. The modeling of the hybrid system was developed in MATLAB-Simulink, taking into account datasheets of commercially available components. To show the proper operation of the proposed energy dispatching, a simpler strategy based on state control was presented in order to compare and validate the results for long-term simulations of 25 years (expected lifetime of the system) with a sample time of one hour.

Hydrokinetic river (HKT) and photovoltaic (PV) panels systems are of the promising technologies to be used for remote rural electrification. In rural areas with access to water and solar resources, renewable generation is a promising option for electrification. This paper presents a study by two sizing methods for a stand-alone hybrid generation system integrating renewable energies (PV panels and hydrokinetic) and storage system based on battery and backup generator diesel. In the first case, optimal technical sizing is achieved by using basic equations and Simulink Design Optimization (SDO). The other method perform an optimal techno-economical sizing by using the hybrid system optimization software HOMER. These methods have been applied to design a stand-alone hybrid system that supplies the load energy demand for one year, minimizing the use of the diesel generator. The results are reported and discussed in the paper.

This paper presents a comparative study of four sizing methods for a stand-alone hybrid generation system integrating renewable energies (photovoltaic panels and wind turbine) and backup and storage system based on battery and hydrogen (fuel cell, electrolyzer and hydrogen storage tank). Two of them perform a technical sizing. In one case, the sizing is based on basic equations, and in the other case, an optimal technical sizing is achieved by using Simulink Design Optimization. The other two methods perform an optimal techno-economical sizing by using the hybrid system optimization software HOMER and HOGA, respectively. These methods have been applied to design a stand-alone hybrid system which supplies the load energy demand during a year. A MATLAB-Simulink model of the hybrid system has been used to simulate the performance of hybrid system designed by each method for the stand-alone application under study in this work. The results are reported and discussed in the paper.

This paper presents two novel hourly energy supervisory controls (ESC) for improving long-term operation of off-grid hybrid systems (HS) integrating renewable energy sources (wind turbine and photovoltaic solar panels), hydrogen system (fuel cell, hydrogen tank and electrolyzer) and battery. The first ESC tries to improve the power supplied by the HS and the power stored in the battery and/or in the hydrogen tank, whereas the second one tries to minimize the number of needed elements (batteries, fuel cells and electrolyzers) throughout the expected life of the HS (25 years). Moreover, in both ESC, the battery state-of-charge (SOC) and the hydrogen tank level are controlled and maintained between optimum operating margins. Finally, a comparative study between the controls is carried out by models of the commercially available components used in the HS under study in this work. These ESC are also compared with a third ESC, already published by the authors, and based on reducing the utilization costs of the energy storage devices. The comparative study proves the right performance of the ESC and their differences.

Improving grid connection of wind farms is a relevant issue to be addressed, especially for fixed-speed wind turbines. Certain elements, such as FACTS (Flexible AC Transmission Systems), are able to perform voltage and reactive power regulation in order to support voltage stability of wind farms, and compensate reactive power consumption from the grid. Several devices are grouped under the name of FACTS, which embrace different technologies and operating principles. Here, three of them are evaluated and compared, namely STATCOM (Static Synchronous Compensator), SVC (Static Var Compensator) and SSSC (Static Synchronous Series Compensator). They have been modeled in MATLAB / Simulink, and simulated under various scenarios, regarding both normal operation and grid fault conditions. Their response is studied together with the case when no FACTS are implemented. Results show that SSSC improves the voltage stability of the wind farm, whereas STATCOM and SVC provide additional reactive power.

This paper presents and evaluates three energy management systems (EMSs) based on Particle Swarm Optimization (PSO) for long-term operation optimization of a grid-connected hybrid system. It is composed of wind turbine (WT) and photovoltaic (PV) panels as primary energy sources, and hydrogen system (fuel cell –FC–, electrolyzer and hydrogen storage tank) and battery as energy storage system (ESS). The EMSs are responsible for making the hybrid system produce the demanded power, deciding on the energy dispatch among the ESS devices. The first PSO-based EMS tries to minimize the ESS utilization costs, the second one to maximize the ESS efficiency, and the third one to optimize the lifetime of the ESS devices. Long-term simulations of 25 years (expected lifetime of the hybrid system) are shown in order to demonstrate the right performance of the three EMSs and their differences. The simulations show that: 1) each EMS outperforms the others in the designed target; and 2) the third EMS is considered the best EMS, because it needs the least ESS devices, and presents the lowest total acquisition cost of hybrid system, whereas the rest of parameters are similar to the best values obtained by the other EMSs.

Hybrid systems comprising battery energy storage systems (BESSs) and wind power generation entail considerable advances on the grid integration of renewable energy. Doubly fed induction generators (DFIGs) stand out among different wind turbine (WT) technologies. On the other hand, electrochemical batteries have proved to be valid for these purposes. In this paper, a comparative analysis is carried out between two alternative configurations for hybrid WT-BESS systems, where the BESS is connected either outside or inside the DFIG. The modeling of these two configurations and the control systems applied for achieving the coordinate operation of the energy sources (DFIG and batteries) are illustrated. The hybrid systems under study are evaluated by simulation under normal operation (wind speed fluctuations and grid demand changes) and grid faults. Simulation results show that both configurations improve the grid integration capability of the WT, although the configuration with external BESS presents better results since it can provide additional active/reactive power injection. Copyright © 2014 John Wiley & Sons, Ltd.

Energy storage systems (ESSs) appear as a viable solution to some of the stability and intermittency problems of wind power generation. As a consequence, it is crucial to develop adequate control strategies that allow the coordinate operation of both energy sources. Moreover, in order to minimize the impact of large wind farms on the power system, many countries have set strict grid codes that wind power generators must accomplish. Hence, it is also necessary to pay due attention to the fault ride through capabilities of these hybrid systems. In this paper two different hybrid configurations are modeled in MATLAB/Simulink, consisting on a doubly fed induction generator driven wind turbine and electrochemical batteries as ESS. They are simulated and compared under various operating conditions (i.e. real fluctuating wind speed input with variable active and reactive power grid demand, voltage sags, three-phase and single-phase fault to ground, and overvoltage). A conventional wind turbine without ESS is also considered as a base-case in order to highlight the main benefits of the hybrid schemes. The results show that by implementing one of the presented control strategies, it is possible to enhance the response to faults of the hybrid systems, achieving higher active power injection and helping the recovery to steady-state, thus improving the grid connection capabilities of hybrid wind farms.

Integrating energy storage systems (ESS) with wind turbines results to be an interesting option for improving the grid integration capability of wind energy. This paper presents and evaluates a wind hybrid system consisting of a 1.5 MW doubly-fed induction generator (DFIG) wind turbine and double battery-ultracapacitor ESS. Commercially available components are used in this wind hybrid system. A novel supervisory control system (SCS) is designed and implemented, which is responsible for setting the active and reactive power references for each component of the hybrid system. A fuzzy logic controller, taking into account the grid demand, power generation prediction, actual DFIG power generation and state-of-charge (SOC) of the ESSs, sets the active power references. The reactive power references are proportionally delivered to each element regarding their current limitations in the SCS. The appropriate control of the power converters allows each power source to achieve the operation defined by the SCS. The wind hybrid system and SCS are assessed by simulation under wind fluctuations, grid demand changes, and grid disturbances. Results show an improved performance in the overall response of the system with the implementation of the SCS.

This paper describes and evaluates an adaptive neuro-fuzzy inference system (ANFIS)-based energy management system (EMS) of a grid-connected hybrid system. It presents a wind turbine (WT) and photovoltaic (PV) solar panels as primary energy sources, and an energy storage system (ESS) based on hydrogen (fuel cell -FC-, hydrogen tank and electrolyzer) and battery. All of the energy sources use dc/dc power converters in order to connect them to a central DC bus. An ANFIS-based supervisory control system determines the power that must be generated by/stored in the hydrogen and battery, taking into account the power demanded by the grid, the available power, the hydrogen tank level and the state-of-charge (SOC) of the battery. Furthermore, an ANFIS-based control is applied to the three-phase inverter, which connects the hybrid system to grid. Otherwise, this new EMS is compared with a classical EMS composed of state-based supervisory control system based on states and inverter control system based on PI controllers. Dynamic simulations demonstrate the right performance of the ANFIS-based EMS for the hybrid system under study and the better performance with respect to the classical EMS.

This paper evaluates a hybrid powertrain based on fuel cell (FC), battery, and supercapacitor (SC) for the “Urbos 3” tramway, which currently operates powered by SC in the city of Zaragoza, Spain. Due to the dynamic limitations of the main energy source, a proton-exchange-membrane (PEM) FC, other energy secondary sources (ESSs), battery and SC, are needed to supply the vehicle power demand. Moreover, these energy sources allow the energy recovery during regenerative braking. The different sources are connected to a dc bus through dc-dc converters which adapt their variable voltages to the bus voltage and allow the control of energy flow between the sources and the load. The components of the hybrid tramway have been modeled in Matlab/Simulink and are based on commercially available devices. The energy management system used in this work to achieve a proper operation of the energy sources of the hybrid powertrain is based on predictive control. Simulations for the real cycle of the tramway show the suitability of the proposed powertrain and control strategy.

The entry into force of Royal Decree-Law 1/2012 has led to the elimination of the support schemes to promote the use of the energy from renewable energy sources existing to date in Spain, and thus, certain standstill of this sector activity. In this context, some renewable technologies, mainly photovoltaic solar, can find in self-consumption an alternative for promoting their development and use. The self-consumption in the form of net-metering is mainly characterized by the possibility that the consumer can generate in situ part of the consumed electricity and use the network as storage or supply system. This option is still under study by the Government, so that there is a draft of Royal Decree that has not yet been approved. The aim of this paper is to analyze the economic viability of the net-metering mode in dwellings with small power photovoltaic facilities, considering those options that can contribute to make them viable.

This paper presents a novel hourly energy management system (EMS) for a stand-alone hybrid renewable energy system (HRES). The HRES is composed of a wind turbine (WT) and photovoltaic (PV) solar panels as primary energy sources, and two energy storage systems (ESS), which are a hydrogen subsystem and a battery. The WT and PV panels are made to work at maximum power point, whereas the battery and the hydrogen subsystem, which is composed of fuel cell (FC), electrolyzer and hydrogen storage tank, act as support and storage system. The EMS uses a fuzzy logic control to satisfy the energy demanded by the load and maintain the state-of-charge (SOC) of the battery and the hydrogen tank level between certain target margins, while trying to optimize the utilization cost and lifetime of the ESS. Commercial available components and an expected life of the HRES of 25 years were considered in this study. Simulation results show that the proposed control meets the objectives established for the EMS of the HRES, and achieves a total cost saving of 13% over other simpler EMS based on control states presented in this paper.

This paper focuses on describing a control strategy for a real tramway, in Zaragoza (Spain), whose current propulsion system is to be replaced by a hybrid system based on fuel cell (FC) as primary energy source and batteries and ultracapacitors (UCs) as secondary energy sources. Due to its slow dynamic response, the FC needs other energy sources support during the starts and accelerations, which are used as energy storage devices in order to harness the regenerative energy generated during brakings and decelerations. The proposed energy management system is based on an operation mode control, which generates the FC reference power, and cascade controls, which define the battery and UC reference powers in order to achieve a proper control of the DC bus voltage and states of charge (SOC) of battery and UC. The simulations, performed by using the real drive cycle of the tramway, show that the proposed hybrid system and energy management system are suitable for its application in this tramway.

Problems relating to oil supply, pollution, and green house effects justify the need for developing of new technologies for transportation as a replacement for the actual technology based on internal combustion engines (ICE). Fuel cells (FCs) are seen as the best future replacement for ICE in transportation applications because they operate more efficiently and with lower emissions. This paper presents a comparative study performed in order to select the most suitable control strategy for high-power electric vehicles powered by FC, battery and supercapacitor (SC), in which each energy source uses a DC/DC converter to control the source power and adapt the output voltage to the common DC bus voltage, from where the vehicle loads are supplied. Five different controls are described for this kind of hybrid vehicles: a basic control based on three operation modes of the hybrid vehicle depending on the state of charge (SOC) of the battery (operation mode control); a control strategy based on control loops connected in cascade, whose aim is to control the battery and SC SOC (cascade control); a control based on the technique of equivalent fuel consumption, called equivalent consumption minimization strategy (ECMS); and two based on control techniques very used nowadays, the first one of them is a fuzzy logic control and the second one is a predictive control. These control strategies are tested and compared by applying them to a real urban street railway. The simulation results reflect the optimal performance of the presented control strategies and allow selecting the best option for being used in this type of high-power electric vehicles.

Electric vehicles are the most promising sustainable transport technology for solving the environmental, economic and social problems associated to the internal combustion engine vehicles. The research and development of new charging methods is necessary for a further integration of electric vehicles in transport sector. Inductive power transfer is the most promising technology for dynamic wireless charging of electric vehicles, which can be used to supply the motors and/or charge the battery of the electric vehicle while it is moving. This charging system can help to reduce the size and cost of the battery, increase the driving range and expand the use of electric vehicles. This paper presents an analysis methodology of a Dynamic Wireless Charging system applied to evaluate the power and energy requirements for charging electric vehicles moving along a stretch of the A-381 Highway in Cadiz (Spain). The study shows the great dependency of the energy transferred to the electric vehicles with the speed and the great variability of power and energy requirements related to the daily traffic.

Currently, electric vehicles (EV) are going through an important emergence, nevertheless, the technology associated to them is still under development. This paper presents a fast charging station (FCS) for EVs supplied by renewable energy with a novel decentralized control of the system. The fast CS is composed of a photovoltaic (PV) system, an energy storage system (ESS), and a connection with the local grid. Thanks to this configuration, the FCS is able to work as a stand-alone system most of the time, with occasional grid support. The proposed control is based on the voltage control of the common medium voltage DC (MVDC) bus, to which all the energy sources are connected. Thus, depending on its voltage, the PV system, the ESS or the grid are used to supply the energy demanded by the EVs. To show the viability of this control, the charging of two EVs is simulated under different operating conditions. Simulation results show the proper operation of the FCS.

Electric power systems will need to accommodate large amounts of renewables and a growing number of DC loads with reduced costs and losses. Medium-voltage DC distribution lines can play a key role. Moreover, a higher availability and reliability of the power supply can be achieved with energy storage systems (ESSs). In this paper, a hybrid configuration merging both concepts is evaluated. A medium-voltage DC distribution system with an integrated ESS has been proposed, modelled, and simulated. Voltage source converters have been used for the AC/DC conversions. The ESS is connected to the DC bus through a dual active bridge enabling bidirectional power flow while covering a large voltage gap. The controllers for all converters and a supervisory control scheme have been implemented. Simulations show the capability of the hybrid configuration to fulfill variable grid demand regardless of the wind power generation, thus transforming a fluctuating source into a dispatchable supply.

SUMMARY This article presents a comparative study among four control strategies for the energy management of a fuel cell (FC) battery–powered hybrid system applied to a surface tramway of 400 kW, which, at present, operates as catenary-powered tramway in the center of Seville, Spain. The proposed hybrid system is composed of a polymer electrolyte membrane FC with a unidirectional dc/dc boost converter and a rechargeable nickel–metal hydride battery with a bidirectional dc/dc converter, both of which are coupled to a traction dc bus. This configuration allows a suitable control of the energy of the hybrid tramway and the traction dc bus voltage. The polymer electrolyte membrane FC and nickel–metal hydride battery models are designed from commercially available components. The control strategies presented in this work are as follows: (i) control strategy based on operating states of the tramway (state machine control), (ii) control strategy based on cascade control loops (cascade control), (iii) control strategy based on fuzzy logic (fuzzy logic control), and (iv) control strategy based on the fuel consumption minimization (equivalent consumption minimization strategy. The control strategies are tested and compared for the real driving cycle of the tramway. The simulation results reflect the optimal performance of the presented control systems. Copyright © 2012 John Wiley & Sons, Ltd.