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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.

This study presents a new energy management system (EMS) for the optimised operation of power sources of a hybrid charging station for electric vehicles and fuel cell vehicles. It is composed of a photovoltaic (PV) system, a battery and a hydrogen system as energy storage systems (ESSs), a grid connection, six fast charging units and a hydrogen supplier. The proposed EMS is designed to reduce the utilisation costs of the ESS and make them work, as much as possible, around their maximum efficiency points. The optimisation function depends on a cost prediction system that calculates the net present cost of the components from their previous performance and a fuzzy logic system designed for improving their efficiency. Finally, a particle swarm optimisation algorithm is used to solve the optimisation function and obtain the required power for each ESS. The proposed EMS is checked under Simulink environment for long-term simulations (25 years). By comparing the EMS with a simpler one that optimises only the costs, it is proved that the proposed EMS achieves better efficiency of the charging station (+7.35%) and a notable reduction in the loss of power supply probability (-57.32%) without compromising excessively its average utilisation cost (+1.81%).

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.

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.

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

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.

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.

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).

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

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

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

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

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

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

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

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.

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

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

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

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

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

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

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

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

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 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.

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 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.

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.

This paper presents a sizing method and different control strategies for the suitable energy management of a stand-alone hybrid system based on photovoltaic (PV) solar panels, hydrogen subsystem and battery. The battery and hydrogen subsystem, which is composed of fuel cell (FC), electrolyzer and hydrogen storage tank, act as energy storage and support system. In order to efficiently utilize the energy sources integrated in the hybrid system, an appropriate sizing is necessary. In this paper, a new sizing method based on Simulink Design Optimization (SDO) of MATLAB was used to perform a technical optimization of the hybrid system components. An analysis cost has been also performed, in that the configuration under study has been compared with those integrating only batteries and only hydrogen system. The dynamic model of the designed hybrid system is detailed in this paper. The models, implemented in MATLAB-Simulink environment, have been designed from commercially available components. Three control strategies based on operating modes and combining technical-economic aspects are considered for the energy management of the hybrid system. They have been designed, primarily, to satisfy the load power demand and, secondarily, to maintain a certain level at the hydrogen tank (hydrogen energy reserve), and at the state of charge (SOC) of the battery bank to extend its life, taking into account also technical-economic analysis. Dynamic simulations were performed to evaluate the configuration, sizing and control strategies for the energy management of the hybrid system under study in this work. Simulation results show that the proposed hybrid system with the presented controls is able to provide the energy demanded by the loads, while maintaining a certain energy reserve in the storage sources.

This paper evaluates the option of using a new powertrain based on fuel cell (FC), battery and supercapacitor (SC) for the Urbos 3 tramway in Zaragoza, Spain. In the proposed powertrain configuration, a hydrogen Proton-Exchange-Membrane (PEM) FC acts as main energy source, and a Li-ion battery and a SC as energy support and storage systems. The battery supports the FC during the starting and accelerations, and furthermore, it absorbs the power generated during the regenerative braking. Otherwise, the SC, which presents the fastest dynamic response, acts mainly during power peaks, which are beyond the operating range of the FC and battery. The FC, battery and SC use a DC/DC converter to connect each energy source to the DC bus and to control the energy exchange. This configuration would allow the tramway to operate in an autonomous way without grid connection. The components of the hybrid tramway, selected from commercially available devices have been modeled in MATLAB-Simulink. The energy management system used for controlling the components of the new hybrid system allows optimizing the fuel consumption (hydrogen) by applying an equivalent consumption minimization strategy. This control system is evaluated by simulations for the real driving cycle of the tramway. The results show that the proposed control system is valid for its application to this hybrid system.

This paper presents a comparative study of Proton Exchange Membrane (PEM) Fuel Cell (FC) models for integration in hybrid propulsion systems, based on a commercial FC from Nuvera, which is especially manufactured for this application. An existing model is used as a reference in order to build dynamical mathematical models which describe its dynamical behavior in the time domain. These mathematical models are obtained by applying system identification techniques to the reference model. The proposed FC models have been tested through simulations for the real drive cycle of the existing Metro Centro tramway in Seville.

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.

Steady state and dynamic simulations are performed in order to compare the models. Considering the external response of FC system integrated in the tramway hybrid system, both reduced models show similar results with an important reduction of computation time with respect to the complete model. However, the reduced model 1 shows better results than the reduced model 2 when representing the internal behaviour of FC system, so that this model is considered the most appropriate for propulsion system applications.

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.

This paper describes a comparative study of two control schemes for the energy management system of a hybrid tramway powered by a Polymer Electrolyte Membrane (PEM) Fuel Cell (FC) and an Ni-MH battery. The hybrid system was designed for a real surface tramway of 400 kW. It is composed of a PEM FC system with a unidirectional dc/dc boost converter (FC converter) and a rechargeable Ni-MH battery with a bidirectional dc/dc converter (battery converter), both of which are coupled to a traction dc bus. The PEM FC and Ni-MH battery models were designed from commercially available components. The function of the two control architectures was to effectively distribute the power of the electrical sources. One of these control architectures was a state machine control strategy, based on eight states. The other was a cascade control strategy which was used to validate the results obtained. The simulation results for the real driving cycle of the tramway reflected the optimal performance of the control systems compared in this study.

The present document explores how air pollution can be assessed from a multiagent point of view. In order to do so, a traffic system was simulated using agents as a way to measure if air pollution levels go down when the traffic lights employ a multigent cooperative system that negotiates the green light duration of each traffic light, in order to minimize the time a car has to wait to be served in an intersection. The findings after running some experiments where lanes of each direction are congested incrementally showed, that using this technique, there is a significant decrease in air pollution over the simulated area which means that traffic lights controlled by the multiagent system do improve the levels of air pollution.

This paper describes the application of a solid-oxide fuel cell for compensation of distribution system voltage. A grid-connected fuel-cell plant consists of the fuel cell and the voltage-source inverter. The flux-vector control is used very effectively for the control of this inverter, where artificial neural networks implement the space-vector pulse width modulation. Comprehensive results are presented to assess the performance of this device to alleviate power quality problems present on the ac grid.