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Typically, permanent magnet synchronous generator (PMSG)-driven wind turbines (WTs) present a two-stage power converter topology based on a DC/DC boost converter and voltage source inverter. In this study, this configuration is substituted by a quasi-Z-source inverter (qZSI), which is an attractive solution for boosting and converting the voltage from DC to AC in a single stage. A 2 MW PMSG WT with qZSI was studied herein. A switched dynamic model (SDM) of the qZSI (including the modeling of all switches and firing pulses) is not recommended for steady-state stability studies, long-term simulations, or large electric power systems. For such studies, two averaged dynamic models are proposed in this work. Both models present the same control system as the SDM, except for the generation of firing pulses, which is not necessary in the averaged models. The two proposed models were evaluated and compared with the SDM in the large-scale WT under different operating conditions, such as wind speed fluctuations, variable power references, and grid disturbances (voltage sag and 3rd and 5th order harmonics injection), in order to demonstrate their adequacy to represent the system response with a high reduction in the simulation time and computational efforts.

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.

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.

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.

Grid connection of permanent magnet synchronous generator -based wind turbines can be performed through a three-stage power conversion system: 1) uncontrolled rectifier, 2) DC boost converter, and 3) voltage source inverter. In this paper, this topology is modified by replacing the two intermediate stages by a quasi-Z-source inverter (qZSI). Additionally, a battery is integrated with the qZSI without any additional DC/DC converter. This battery will allow reducing the intermittence of the generation and improving the operability of the wind turbine. The application and control of qZSI for large-scale wind turbine with energy storage systems is illustrated in this work. An energy management system is implemented for the proper operation of the wind turbine and the battery. The qZSI is controlled through a Z-Space Vector Modulation (ZSVM) technique to achieve maximum power point tracking in the wind turbine, reactive power control, and battery charge/discharge depending on the operating conditions and the battery state-of-charge. The simulations show the proper behavior of the system under study.

Typically, grid-connected wind turbines equipped with permanent magnet synchronous generator (PMSG) present a back-to-back power converter based on voltage-source converters. In this paper, this structure is modified using an uncontrolled rectifier for the AC/DC conversion in the generator, and a quasi-Z-source inverter (qZSI) at the grid side. The qZSI is an attractive solution to boost and convert DC voltage to AC in a single stage. The main objective of the paper is the application of a qZSI for large grid-connected wind energy systems. In this configuration, active and reactive powers can be controlled through a Space Vector Modulation technique adapted for Z-source converter. Simulations were performed using MATLAB/Simulink® with wind and load variations to confirm the proper functioning of the proposed controllers. The results showed satisfactory responses of the proposed 5 MW PMSG-driven grid-connected wind turbine.

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.

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).

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.

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.

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.

Due to its high-efficiency characteristics, the three-level boost DC-DC converter is widely used as a PV fed converter to connect PV panels to the DC bus. In our previous work [13], a decentralized energy management system for EV fast charging station was proposed where a conventional two-level boost DC-DC converter was used as a PV fed converter. In this paper, this converter is replaced by a three-level one. MPPT, output voltage and capacitors voltage balance are carried out, which facilitate the converter implementation on the charging station.

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.

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 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.

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.