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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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

This paper presents a “full green” version of a rubber tyre gantry (RTG) crane with a fuel cell (FC) unit and supercapacitors (SCs) as energy storage system (ESS), instead of using the conventional RTG powered by a diesel engine. The SCs provide the required high current peaks and power demands when accelerating the load in the hoisting-up movement. Once the power demand reaches a steady level (hoist up constant speed), the FC provides the energy needed for the rest of the movements. The SCs are also charged when the hoist down movement is taking place. In this case, the regenerative energy can be stored in the SCs instead of being burnt in the braking resistors as in the conventional RTG crane. The new hybrid powertrain based on FC and SCs is designed and evaluated from the real driving cycle of the RTG crane. Simulation results, which include a comparative study with the current configuration of the RTG crane (powered only by diesel engine), demonstrate the technical viability of the RTG crane powered by FC and SCs. This hybrid powertrain is more expensive than the diesel powertrain, but more energy-efficient, and a better solution from the environmental point of view.

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.

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.

This paper deals with the modeling and control of a hybrid system integrating a doubly-fed induction generator (DFIG) wind turbine and batteries as energy storage system (ESS). The modeling of the mechanical and electrical main components of a 1.5 MW wind turbine is described. Specific focus is to be taken on the power converter of the DFIG, since it allows the interconnection of the ESS to the generator and a proper energy management. A lead-acid battery is used as energy storage device, which is connected through a bidirectional DC/DC converter to the DC bus of the DFIG power converter. A new supervisory control system, responsible for the coordinate operation of power sources (DFIG wind turbine and ESS), is described and evaluated by simulation under wind speed fluctuations and grid demand changes. It is based on using the wind turbine as primary power source and the ESS as auxiliary power source, providing or storing the power mismatching between the actual wind power and grid demand, whenever the battery state-of-charge (SOC) remains within the recommended limits. This configuration increases the generation capability and smooths the output power fluctuations caused by the wind speed variability, and therefore, improves the grid integration of wind turbines.

This paper focuses on describing a control strategy for a real surface tramway powered by a hybrid system based on fuel cell and battery. This tramway, called Metro Centro , serves the centre of Seville, a city in Spain. Currently, it operates as catenary-powered tramway. The configuration and modeling of all principal components of the hybrid system are briefly described. The models, implemented in MATLAB-Simulink environment, have been designed from commercially available components. The implemented control is based on an equivalent consumption minimization strategy. It allows a suitable energy management of the hybrid system, minimizing the hydrogen consumption.

This paper presents a model for a fuel cell (FC)-battery powered hybrid system for the Metro-Centro tramway (400kW) from Seville, Spain. Modeling of each component, implemented in MATLAB/SIMULINK environment, is briefly presented. Polymer Electrolyte Membrane (PEM) FC and Ni-MH battery models are designed from commercial available components. Two control strategies are presented and tested for the energy management of the hybrid system: cascade and fuzzy logic. The objective of both strategies is to manage the primary (PEM FC) and secondary (battery) energy source to supply the power requirements of the tramway forcing the FC to work around its maximum efficiency point and maintaining the battery state of charge (SOC) in a desired level.

This paper describes the configuration, modeling, and control of a fuel cell (FC)-battery-powered hybrid system for the Metro Centro tramway in Seville, Spain. The proposed hybrid system presents a polymer electrolyte membrane FC as the primary energy source and a nickel-metal hydride cell battery as the secondary energy source, which supplements the output of the FC during tramway acceleration or whenever else needed and cruise and for energy recovery during braking. The tramway presents a traction system which is composed of four traction induction motor drives. The hybrid system also supplies the power for the auxiliary services. The power conditioning system is composed of two converters: 1) a boost-type unidirectional dc/dc converter for the FC and 2) a boost-type bidirectional dc/dc converter for the battery. The energy management system (EMS) of the hybrid tramway determines the reference signals for the electric motor drives, FC, and power converters in order to regulate accurately the power from the two electrical sources. EMS also determines the reference signal for energy dissipation in braking chopper when required during regenerative braking. In this paper, the proposed hybrid system is evaluated for the real driving cycle of the tramway. The results demonstrate the hybrid system capability to meet appropriate driving cycle.

In this study, the energy management and switching control of plug-in hybrid electric vehicles (PHEVs) in a hybrid smart micro-grid system was designed. The charging station in this research consists of real market PHEVs of different companies with different sizes. The rate of charging of PHEVs is managed via switching control to receive maximum benefits from renewable energy sources and reduce the consumption of electricity from the grid. To support the optimum utilization of sustainable power, charging time and network stability, seven scenarios were developed for different interaction among the proposed micro-grid system and PHEVs. The proposed hybrid smart micro-grid system consists of three renewable energy sources: photovoltaic (PV) array controlled via an intelligent fuzzy control maximum power point subsystem, a fuel cell stack and a microturbine set controlled by proportional integral differential/proportional integral subsystems. A hybrid energy storage system (super-capacitor, battery storage bank and hydrogen) and residential load are also included in the proposed architecture. The hybrid smart micro-grid system is checked in terms of voltage regulation, frequency deviation and total harmonic distortion (THD). It was found that these are in limits according to the international standards. The simulations verify the feasibility of the proposed system and fulfill the requirement of vehicle-to-grid and grid-to-vehicle operations in a smart grid environment.

Since energy consumption and pollution levels are increasing worldwide, the traditional fossil fuel-based generation methods are open to debate. A considerable number of countries are promoting the use of renewable resources in order to set a new energetic scenario. Wind power generation needs to improve some specific aspects that hinder its development. Several devices have been designed and are currently in use to solve the problem of energy volatility, since they are able to store energy in different forms for a given period of time. All these devices have different characteristics; therefore, they behave dissimilarly under certain operating conditions. Lead-acid batteries are being used either for energy or power applications. However, there exist other devices that may provide better performance. These devices are here described and compared, coming to the conclusion that some of them will substitute current technology in the near future if they are able to improve several indicated aspects.

As a result of the increasing wind power penetration on power systems, the wind farms are today required to participate actively in grid operation by an appropriate generation control. This paper presents a comparative study on the performance of three control strategies for DFIG wind turbines. The study focuses on the regulation of the active and reactive power to a set point ordered by the wind farm control system. Two of them (control systems 1 and 2) are based on existing strategies, whereas the third control system (control system 3) presents a novel control strategy, which is actually a variation of the control system 2. The control strategies are evaluated through simulations of DFIG wind turbines, under normal operating conditions, integrated in a wind farm with centralized control system controlling the wind farm generation at the connection point and computing the power reference for each wind turbine according to a proportional distribution of the available power. The three control systems present similar performance when they operate with power optimization and power limitation strategies. However, the control system 3 with down power regulation presents a better response with respect to the reactive power production, achieving a higher available reactive power as compared with the other two. This is a very important aspect to maintain an appropriate voltage control at the wind farm bus.

This paper presents a new Energy Management System (EMS) for a microgrid based on four energy sources: a wind turbine (WT), photovoltaic (PV) solar panels, a battery, and a hydrogen system, which is composed of a fuel cell (FC) and an electrolyzer. This novel control strategy optimizes the total cost of the hybrid system (generation and reposition costs) through lifetime estimations calculated hourly for each energy storage device (the battery and hydrogen system). This control strategy links the expected lifespans of the energy sources to their generation costs, i.e., when the lifespan is low, the generation cost increases and, consequently, this energy source will start to be used less. The performance of the novel EMS, including these estimations, was tested for an isolated load located in Alora (Spain) and compared with two simpler EMSs: EMS-1, which considered fixed generation costs and lifetimes, and EMS-2, which prioritized the use of the battery. Simulation results show the appropriate behavior for the novel EMS to optimize the generation costs and the number of required elements throughout the expected lifetime of the hybrid system (25 years).