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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).

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 a hybrid electric power system for a real surface tramway. The hybrid system consists of two electrical energy sources integrating a single dc/dc converter to provide the power demanded by the tramway loads (four electric traction motors and auxiliary services): (1) a Polymer Electrolyte Membrane (PEM) fuel cell (FC) as the primary and (2) a rechargeable Ni–MH battery as electrical energy storage to supplement the FC over the driving cycle. According to the requirements of the real driving cycle of the tramway, it was considered a 200 kW PEM FC system with two FCs connected in parallel and a 34 Ah Ni–MH battery. The PEM FC and Ni–MH battery models were designed from commercially available components. The power conditioning system provides the appropriate power for the tramway. It is composed of: (1) a unique dc/dc boot converter which adapts the FC output voltage to the 750 V traction standard dc bus; (2) three phase inverters to drive properly each electric motors; and (3) a braking chopper to dissipate excess of regenerative braking energy. Suitable state machine control architecture is presented for the hybrid system, its objective being to provide demanded power by the driving cycle, optimizing the energy generated. Following this objective, a new state machine control strategy based on eight states decides the operating point of each component of the system and a cascade control structure allows achieving the operating points determined by the strategy. Simulation results of the real driving cycle of the tramway check the adequacy of the hybrid electric power system.

As a result of the increasing wind farms penetration on power systems, the wind farms begin to influence power system, and therefore the modeling of wind farms has become an interesting research topic. In this paper, new equivalent models of wind farms equipped with wind turbines based on squirrel-cage induction generators and doubly-fed induction generators are proposed to represent the collective behavior on large power systems simulations, instead of using a complete model of wind farms where all the wind turbines are modeled. The models proposed here are based on aggregating wind turbines into an equivalent wind turbine which receives an equivalent wind of the ones incident on the aggregated wind turbines. The equivalent wind turbine presents re-scaled power capacity and the same complete model as the individual wind turbines, which supposes the main feature of the present equivalent models. Two equivalent winds are evaluated in this work: (1) the average wind from the ones incident on the aggregated wind turbines with similar winds, and (2) an equivalent incoming wind derived from the power curve and the wind incident on each wind turbine. The effectiveness of the equivalent models to represent the collective response of the wind farm at the point of common coupling to grid is demonstrated by comparison with the wind farm response obtained from the detailed model during power system dynamic simulations, such as wind fluctuations and a grid disturbance. The present models can be used for grid integration studies of large power system with an important reduction of the model order and the computation time.