Hybrid Microgrid Testbed Involving Wind/Solar/Fuel Cell ... - IEEE Xplore

3rd International Conference on Renewable Energy Research and Applications

Milwakuee, USA 19-22 Oct 2014

Hybrid Microgrid Testbed Involving Wind/Solar/Fuel Cell Plants A desing and analysis testbed Ersan Kabalci1 Ramazan Bayindir2 Eklas Hossain3 [email protected]; [email protected]; [email protected] Nevsehir University, Faculty of Engineering, Department of Electrical & Electronics Engineering, Nevsehir-Turkey 2 Gazi University, Faculty of Engineering, Department of Electrical & Electronics Engineering, 06500, Ankara-Turkey 3 University of Wisconsin-Milwaukee, Department of Mechanical Engineering, Milwaukee, WI-53211, USA 1

Abstract—This study introduces design, integration, control, and analysis of a hybrid microgrid (MG) testbed including a 300 kW wind energy plant of three 100-kW permanent magnet synchronous generators (PMSGs), 50 kW solar energy plant of 200 solar panels, and a 50 kW fuel cell stack. Generation side of the hybrid plant coupled on 120V dc bus where wind energy plant consists of uncontrolled diode rectifier and dc-dc buck converter rail while the solar and fuel cell plants are just controlled by dc-dc buck converters. The energy conversion of solar energy plant is operated by using proportional-integral (PI) controller assisted perturb and observe (P&O) maximum power point tracking (MPPT) algorithm. The dc-dc converters of the left generators are operated with PI controllers. All the dc-dc converters are constituted with mosfets that are switched at 30 kHz. The islanded AC grid of the plant is built by three-phase inverter that generates 480 V 60 Hz industrial voltages of the U.S. The inverter is operated by a sinusoidal pulse width (SPWM) modulator where the modulation index is set to 0.8 and switching frequency to 5 kHz. The proposed testbed is assumed essential to test and validate several case studies in terms of MG and renewable energy source integration issues. The most recent power control methods (PQ) and amplitude controls (V-f) can also be implemented by using the presented testbed. Keywords-component; microgrid; wind energy; solar energy; fuel cell, renewable energy source; distributed generation.

I.

INTRODUCTION

The international regulations and agreements advising to decrease the carbon emission steered researchers to detect environment friendly sources in electricity generation. The improvements seen on power electronics enabled producers to integrate several different sources to construct islanded microgrids (MGs). On the other hand, many customers rapidly adapted their habits to distributed energy resource (DER) utilization particularly including solar and wind energy sources. The MG could be defined as an integrated variation of sources and load themselves where each one can be operated autonomously and robustly. This novel grid type allows coupling the generators not only over alternative current (ac) bus but also over direct current (dc) bus owing to islanded freedom of distributed generation (DG) structure [1-3]. Moreover, the dc-bus islanding can be easily joined to ac-grids

such as utility grid, diesel generator or combined heat-power (CHP) plant based islanded grids [2]. The researches on MG cover a wide variety of DER integration, control strategies, and power electronics implementation. Lopes et al. defines the control strategies of MG in [4] where a low voltage (LV) test network was also proposed. In another research [5], Majumder improved a testbed to analyze the back-to-back converters that uses droop control for power flow management. On the other hand, Belvedere et al. proposed a laboratory setup with the limited power of 9.3 kW to analyze power management system with microcontroller aided infrastructure [6]. Since the ac and dc subgrids can be built along within a MG, the power electronic converters require essential attention to achieve the highest efficiency from DERs. The dc-dc and/or dc-ac converters should precisely convert the generated energy that are extensively researched in terms of bidirectional [7], autonomous [8], or parallel connection aspects [9-12]. Another MG research area is related to grid-tie of islanded operation where the frequency and VAR compensations are particularly analyzed [13-15]. These infrastructures involve robust controllers and high-precise decision algorithms that are operated by floating point microprocessors. The preliminary modeling studies of fault detection and control algorithms can be found in [16-18] that are either islanded or grid-tied operation of MGs. This paper proposes a hybrid and islanded MG testbed including wind turbines (WTs), photovoltaic (PV) panels, and fuel cell stack that the rated powers of each plant are 300 kW, 50 kW, and 50 kW respectively. The WTs all consist of 100 kW PMSG. The solar plant is modeled with 20 PV arrays connected in parallel of series arrays including 10 panels in each group. The fuel cell stack is also modeled according to parameters of 50 kW rated power. The WTs are operated according to various wind speed by assuming that are installed in different geographical sites. The irradiation of solar plant are planned to oscillate between 600 W/m2 and 1400 W/m2 to validate the MPPT algorithm. The fuel cell stack with 50 kW rated power is modeled by 900 fuel cells instantly generating 430 V and 280 A at maximum operating point. The nominal operating voltage of the stack is 625 Vdc.

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In the modeled testbed, it could be beneficial to replace the ac grid with dc subgrids owing to widely known advantages of dc networks. Furthermore, dc network provides simplified power converters by eliminating the reactive power (Q) components for all generators operating in dc waveforms such as PV panels, fuel cell and others [19,20]. In MGs containing dc subgrids, it is possible to obtain high power quality of dc network. The proposed testbed is capable to meet plug-andplay (PP) requirements of any additional source since it is not managed by a centralized management control. Each converter has autonomous controller that are mostly based on PI controllers. This feature enables all converters to operate without regarding other sources connected to or disconnected from the MG where the sustainable system operation is assured. The hybrid MG model and its equipments are introduced in the Section II while the control strategies are analyzed in Section III. Some case studies of dc and ac MG are presented in Section IV, and the conclusions are finally made in Section V. II.

HYBRID MICROGRID MODEL

The schematic diagram of the MG model that is designed using Simulink is illustrated in Fig. 1. The dc busbar is shown in the middle of figure that is fixed to 120 V. The generation plants of the model are depicted in colored boxes on the right and left hand sides of dc busbar. The wind energy plant that is seen on the left hand side of diagram consists of three 100 kW wind turbines. The initial magnetizing currents of WTs are provided by capacitor banks that are coupled to generator before uncontrolled rectifiers. The rectified voltages of each WTs are coupled on a dc bus where the dc-dc converter is supplied by using the first dc bus to stabilize wind plant voltage to 120V of dc busbar. The solar energy plant that is seen on low upper right hand side includes 20 parallelconnected PV arrays where each array consists of serial connected 10 PV panels with 250 Wp rated power for each. The last generation plant seen in green box includes a proton exchange membrane (PEM) fuel cell stack with 50 kWp rated power. The parameters of each generator and plant are given in Table 1 where the modelling studies of each are explained in this section. A. Wind Turbine Dynamics and Modelling The wind turbine (WT), an electromechanical system itself, generates the power owing to kinetic energy of the wind. The air density of wind crossing through the blades gets the rotor to move and magnetizes the stator windings that generate the output power [21]. The output power of a wind turbine is calculated by using (1); 1 Pm C p (O , E ) U A v 3 (1) 2 where, P m = mechanical output power of wind turbine, C p = Coefficient of performance (the rotor efficiency), Ȝ= Pitch angle (degree),

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Figure 1. Schematic diagram of the hybrid MG testbed

ȕ= Peak velocity ratio, ȡ= Air density (kg/m3), A= Rotor swept area (m2), v= Velocity of wind (m/s) TABLE I.

PARAMETERS OF PLANTS AND GENERATORS

Wind Energy Plant PMSG 3 1800 Nm 960 V 60 rpm 100 kW 120 kW 4.5 m/s 13 m/s 0.06ȍ 525ȝH Solar Energy Plant Rated Power of each PV oanel 250 W Open Circuit Voltage (Voc) 43.1 V Maximum Power Voltage (Vpm) 34.9 V Short Circuit Current (Isc) 7.74 A Maximum Power Current (Ipm) 7.18 A Series Panel in each array 10 Number of Parallel Array 20 Fuel Cell Energy Plant DC Voltage at 80A 625 V Number of Cell 900 Maximum Current 280 A Vnom at Max. operating point 430 V Nominal stack efficiency (%) 85 Nominal supply pressure [1.5, 1] [Fuel (bar), Air (bar)] Nominal Air flow rate (lpm) 2100 Fuel composition Hydrogen, nitrogen blend 80% H 2 Generator Type WT number Torque Nominal Voltage Max Generator Speed Rated Power Max Power Startup wind speed Rated speed Stator phase resistance Phase inductance

The efficiency of C p varies with the tip-speed ratio (TSR) defining as follows for a given wind speed, ZR TSR (2) v

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where, R= rotor radius, Ȧ= angular speed (rad/s) The Type D variable speed wind turbines commonly includes PMSG generators that has several advantages as gearless operation, entirely controllable power electronics, not requiring external dc excitation, and fault-ride through control. On the other hand, the reliability and efficiency indexes of the inverter used in a PMSG are greater than the other used in a doubly fed induction generator (DFIG) [21]. The electromagnetic torque of a PMSG and its reduced expression for cylindrical PMSG are presented respectively,

Te Te

3P ª Ld Lq iq id Omiq º ¼ 22¬

(3)

3P Omiq 22

(4)

where; V q = voltage in quadrature axis, R s = rotor resistance, L q = inductance in quadrature axis, i q = current in quadrature axis, Ȧ r = rotor speed, L d = inductance in direct-axis, i d = current in direct-axis, Ȝ m = magnetic flux, P= the number of poles of the generator

in where; I o : output current of panel, I L : current generated by irradiation value, I Rs : current of output resistor, V T : thermal voltage, V o : output voltage, Ș I : diode ideality factor

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C. Fuel Cell Stack and Power System The fuel cell stack is integrated by using preset model of 50-kWp proton exchange membrane (PEM) fuel cell in Simulink. The stack design has the parameters shown in Table 1. A PEM fuel cell converts the chemical energy to electrical energy during an electrochemical reaction of hydrogen and oxygen. The reaction of anode, cathode, and total electrical energy generated by a cell are calculated as shown in (6)-(8) respectively [11-13].

0V

O2 2 H 2e o H 2O ? E 0 1.229V 2

H2

B. Solar Plant Planning and Modelling The solar plant is planned to generate 50 kWp rated power regarding to conditions of 25°C ambient temperature and 1600 W/m2 irradiation. The PV module is designed by using parameters of a 245±5W panel of Panasonic [22]. However, the parameters given in Table I can be changed to any commercial PV module. The modeling studies are performed regarding to analytical expression of output current that is shown in (5). §ª §V I R · º V I R I L I Rs ¨ «exp ¨ O o s ¸ 1» O O S ¨ Rsh © K IVT ¹ ¼ ©¬

The output power of a PV cell is generated by the shortcircuit current (I sc ), the ideality factor of diode (Ș I ), the shunt resistance (R sh ), and the series resistance (R S ) parameters [21]. The built-in PV panel with 250 Wp rated power are connected serial to construct a PV array that generates 2500 Wp itself. Each PV array are controlled by an autonomous MPPT to minimize power oscillations and losses where MPPT output is used to adjust duty cycle of dc-dc converter. The PV arrays are then connected to each other in parallel to increase the current capacity of the plant where the parallel group includes 20 arrays.

H 2 o 2 H 2e ? E 0

The wind turbines in the modeled plant are operated with various wind speeds to optimize the controller of dc-dc converter. The PI parameters are tuned to exact values to track the reference value of 120 V in the allowed wind speed limits. The related measurements and analysis results are given in the next section.

Io


· ¸ ¸ ¹

(5)

O2 o H 2O ? E 0 1.229V 2

(6) (7) (8)

The configured stack generates 430V nominal output while supplying maximum 280A to the load. The maximum output voltage is 625V at 80A for the transient peak times. The output voltage is applied to a dc-dc buck converter that adjusts the dc line voltage to 120 V owing to its PI feedback controller. III.

CONTROL STRATEGIES

The control strategy of a MG is implemented regarding to traditional centralized control of grid or decentralized control of islanding mode that any of them is used to increase the efficiency and stability of the entire MG. The decentralized control that is also preferred in this study makes the MG more flexible and resistant to variations of sources. Besides, it enables the MG to support the PP structure. The power control of a MG can be managed by performing one of the several methods such as voltage-frequency regulation, active-reactive power (P-Q) flow control, synchronisation, and energy management between DERs and grid [3, 23-25]. The control strategies used in MG are arranged according to stages and their hierarchical structures among others. The primary level control involves voltage and/or current control loops, droop controls, or impedance controls where it is preferred to perform with voltage control loops owing to its dynamic and simple structure in dc subgrids. Moreover, the primary level control entirely deals with power control process, and it does not require any communication infrastructures where the secondary level control does [23-25].

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The control strategy of wind energy plant that is based on voltage control loop for WTs is shown in Fig. 2. Uncontrolled diode rectifiers convert the output voltages of WTs to dc that constitute the dc bus. A unique dc-dc converter is used to integrate dc bus of wind plant to dc busbar of MG as seen on the right hand side of Fig.2. A PI controller operates the voltage control loop of dc-dc converter where the output of PI block defines switching angle of SPWM modulator that adjusts duty cycle and fixes dc bus voltage to 120 V. The control strategy used in WTs is also applied in PEM fuel cell stack. Another PI controller autonomously fixes the dc output voltage of fuel cell to 120 V dc voltage. The solar plant includes an additional controller to PI voltage control loop. The MPPT controller that operates a P&O algorithm builds the power control loop since irradiation and number of PV panel are considerably effective on output power. Therefore, the PI controller assists the power control loop of solar plant. The P&O algorithm modifies the voltage difference of PV array and the converter by perturbing the duty cycle. The flowchart of P&O algorithm is shown in Fig. 4 where the voltage and current variations of PV array are instantly measured. The last perturbation of the obtained Fig. 4. Flowchart of P&O MPPT algorithm

power is compared to the previous perturbation to determine the increment or decrement ratio in this control method. In the next step, the determined difference is used to indicate the next duty cycle rate of SPWM signal. The implemented algorithm seeks the increment of power perturbation and keeps the duty cycle in the same direction in order to reach the MPP, and if there is a decrement occurs in the power then the algorithm reverses the perturbation. The algorithm repeats this operation until the MPP is reached, and then generates fixed duty cycle when the system catches the MPP [21]. IV.

Fig. 2. Current and voltage waveforms of wind turbine.

CASE STUDIES

The designed hybrid MG testbed is operated according to various wind speed and solar irradiation conditions. In the case studies, many variations of wind speeds are applied to each WT at 0, 0.1, 0.2, and 0.3 s in order to indicate transient response of the wind energy plant. On the other hand, the solar irradiation values are also shifted at 0, 0.01, 0.1, 0.25, and 0.35 s to evaluate the response of solar energy plant. The variations of wind speed, rotor speed, solar irradiation, and utilization factor of fuel cell are shown in Fig. 5.a. The wind speeds applied to first WT are 5 m/s, 7 m/s, 9 m/s, 11 m/s, and 9 m/s regarding to analyze intervals of 0s, 0.01 s, 0.1 s, 0.25 s, and 0.35 s where it is shown with blue line in the first curve of Fig. 5.a. The second wind turbine is operated with 9 m/s, 10 m/s, 12 m/s, 15 m/s, and 10 m/s at the seconds of 0 s, 0.1 s, 0.2 s, 0.3 s, and 0.6 s that is indicated with black line in the same curve with first one. The last wind turbine is operated at 6 m/s, 7 m/s, 9 m/s, and 10 m/s corresponding to 0, 0.1, 0.2, 0.3, and 0.6 s which is shown with red line in the first curves of Fig.5.a. The second curve of the same figure illustrates the rotor speed of each wind turbine in terms of rpm that is driven by the wind speeds.


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The solar irradiation values that are applied as 100 W/m2, 500 W/m2, 750 W/m2, 1400 W/m2, and 1200 W/m2 at the

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intervals of 0, 0.01, 0.1, 0.25, and 0.35 s are shown in the third curve of Fig. 5.a. The oxygen and hydrogen utilization ratios of the fuel cell during energy generation are shown in the last curve of Fig. 5.a where they did not changed by any factor.

seen in the first curve illustrates around 280 kW power supplied to loads. It should be noted that the output power pursues its level against the variations of wind speed and output voltages caused by unstable wind speed of each WT.

The generated power of each energy plant are analysed in Fig. 5.b that are obtained by using minimum loads to express capacity of MG. The output power of wind energy plant that is

In another case study where the irradiation values are shifted to different values seen in the third curve of Fig. 5.a, the solar energy plant output is also continue to track its level around 38 kW as seen in the second curve of Fig. 5.b. In another analysis, it is seen from the third curve that the fuel cell plant provides output power around 32 kW. The per-unit (p.u.) values of each energy plant are depicted in the last curve where the black line indicates wind energy plant, green indicates the solar, and blue shoes the fuel cell. The maximum power transfer conditions and operations of the MG could also be done by applying the well-known theories. However, it is not needed to repeat at this point.

(a) (b) Fig. 5. Output power vs. variable input parameters, (a) variations of wind speed, rotor speed, solar irradiation, and utilization factor of fuel cell, (b) output power of each plant in watt and p.u.

(a)

Fig. 6 shows inverter output voltage; phase and line voltages of the inverter that is constituted in the full-bridge three-phase topology with Insulated Gate Bipolar Transistor (IGBT) switches. The dc busbar voltage is applied to inverter that is switched by using SPWM modulation with 0.8 modulation index and 5 kHz switching frequency. The output voltage of the inverter seen in Fig. 6.a is around 95 V and naturally in square waveform. The output transformer of inverter is configured in three-phase delta-wye connection to increase output phase voltage to 277 V and line voltage to 480 V as seen in Fig. 6.b and Fig. 6.c, respectively. All the voltages have 60 Hz line frequency owing to modulating signal frequency of SPWM modulator that is adjusted according to standard industrial transmission lines in the U.S. [26]. The three-phase voltages at the end of the 10 km transmission line are shown in Fig. 7. The positive-sequence and zero-sequence resistance, inductance, and capacitance parameters of transmission line per km are fixed to [0.01273 Ohms/km, 0.03864 Ohms/km], [0.9337 10-9 H/km, 4.1264 10-9 H/km], and [12.74 10-9 F/km, 7.751 10-9 F/km], respectively. V.

(b) (b) Fig. 6. Voltage waveforms (a) Inverter output phase voltage, (b) phase voltage at the transmission line, (c) line voltage at the transmission line


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CONCLUSIONS

This paper deals with design of a hybrid MG including 300 kW wind energy plant, 50 kW solar energy plant, and 50 kW fuel cell energy plant. The energy plants are modeled using parameters of commercial and industrial generators. The wind energy plant consists of three PMSGs where each WT has 100 kW rated power. The solar energy plant is constituted using 250 Wp PV panels, while the fuel cell stack includes 900 fuel cells to generate 50 kWp power. The output voltage of each energy plant is adjusted to 120 V dc and is coupled over dc busbar. The control strategy is based on decentralized control that enables to achieve plug-and-play type MG where any plant can be disconnected or connected to the grid autonomously. The case studies depending on various physical parameters for plants are performed to analyze the validation of control strategies and algorithms used in each generator. The wind speeds are shifted to independent values for each wind turbine while the output voltages are coupled on the same dc subgrid. Nevertheless, the controller of dc-dc converter managed coped with this issue to track the reference dc busbar voltage (120V). Moreover, the solar irradiation values are also changed in a

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wide range to test the capability of MPPT and PI algorithms operated in solar energy plant. Although the irradiation changes varied the output voltage of solar arrays, the total output voltage is precisely tracked the reference dc busbar voltage owing to algorithms. Despite the tests are performed for longer times, the analysis results are illustrated up to 0.4s in order to depict the transients clearly. The hybrid microgrid testbed presented in this study is intended to be improved to implement several other control strategies such as second and third level hierarchical control including reactive power control and droop control. Authors anticipate that the developed and introduced testbed in detail contribute researchers’ studies on microgrid. REFERENCES [1]

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