Design and Active Control of a Microgrid Testbed - IEEE Xplore

IEEE TRANSACTIONS ON SMART GRID, VOL. 6, NO. 1, JANUARY 2015

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Design and Active Control of a Microgrid Testbed Greg Turner, Member, IEEE, Jay P. Kelley, Student Member, IEEE, Caroline L. Storm, David A. Wetz, Jr., Senior Member, IEEE, and Wei-Jen Lee, Fellow, IEEE

Abstract—Researchers at the University of Texas at Arlington (UTA) have installed a novel Microgrid testbed architecture that will be used for educational and research purposes. This paper documents the architecture of the UTA grid, the development of the national instruments based active control system, and some of the research progress made thus far. The specific commercial off the shelf components that make up the hardware in the system, which is actually broken up into three independent smaller grids, is described. A custom interconnection architecture will also be discussed that enables the three individual Microgrids to operate independently or in an actively interconnected mode of operation. The implementation of system control software will be presented in terms of the state machine used to develop the software. Finally, experimental data gathered from the dynamic performance of the Microgrid will be presented. Index Terms—Microgrid, renewable energy, smart grid.

I. I NTRODUCTION HE UNITED States (U.S.) Department of Energy (DoE) has a goal to upgrade the nation’s electrical grid to a more intelligent, efficient, robust, and reliable architecture by 2030 [1]. This architecture is commonly referred to as the smart grid due to the integration of a sensor and communication network capable of monitoring and controlling the flow of energy in real-time. As stated on the DoE’s website, the smart grid can be thought of as the computerizing of the electric utility grid. Part of this involves having real time communication among the various utility operation centers, sensor networks, and the electricity consumers [2], [3]. The smart grid architecture integrates many different energy generation technologies which can include solar panels, fuel cells, wind turbines, batteries, coal fired, hydro, nuclear, and natural gas power generation facilities, among others. The variety of distributed generation sources enables energy to be generated and consumed in the most efficient, affordable, and reliable manner at all times. Energy storage systems are also distributed throughout the grid to ensure that energy is always available to serve critical loads in the event of an outage or for use in mitigating the intermittence of the renewable energy resources such as wind and solar.

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Manuscript received December 19, 2013; revised April 14, 2014 and May 29, 2014; accepted July 12, 2014. Date of publication August 7, 2014; date of current version December 17, 2014. This work was supported by the U.S. Department of Energy’s National Energy Technology Laboratory under Contract DE-OE000036. Paper no. TSG-00926-2013. The authors are with the Electrical Engineering Department, University of Texas at Arlington, Arlington, TX 76019-0016 USA (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TSG.2014.2340376

The large component count, the complex control architecture, and the development and implementation of safety standards makes implementing the smart grid an extremely large undertaking. Therefore, the DoE continues to sponsor many research efforts at universities, national laboratories, and industrial facilities within the U.S. in order to make the smart grid concept a reality. Many of these research efforts are purely software based simulations of Microgrid operation and control theory [4], [5] while others have developed hardware platforms on which to evaluate how the hardware and control systems behave in complex scenarios [6], [7]. Each of the hardware platforms developed are unique with each having their own strengths and weaknesses. Researchers at the University of Texas at Arlington (UTA) have utilized their expertise in power system and control system design to develop a three-tiered hierarchical structure to describe the Microgrid control system [8]. The primary and secondary tiers of this hierarchy deal with the actual control of power electronic elements in the Microgrid for the purpose of producing ac power from dc and also maintaining proper voltage and frequency stability. The testing of these control schemes are most easily performed in MATLAB or other simulation environment. In the third, or tertiary, level of this hierarchy the control system is concerned with optimal performance of power flow of the Microgrid when it is connected to a utility grid and possibly interconnected to other Microgrids. The testing of optimal high level performance control schemes is difficult to test in a MATLAB simulation environment and is better suited for testing in an actual Microgrid with real hardware. A flexible architecture that enables multiple Microgrid to be connected together and while serving multiple arbitrary loads is the goal of the UTA Microgrid testbed. Universities such as Santa Clara [9] and Illinois Institute of Technology (IIT) [10] have transformed parts of the power system of their respective universities into a Microgrid. This type of sustainable infrastructure enables each university’s respective researchers to use their grid as an educational tool as well as a research platform. Because the grid is actually being used by the university for its electricity service, it enables researchers to observe how this type of grid responds to real life events. While this is very beneficial, it is also restrictive in a research setting since the grid’s loads can no longer be readily altered for the purpose of laboratory experimentation. Power interruption in these grids may have severe consequences to the users. This decreases the university’s ability to focus on design and development research in exchange for a higher focus on applications based studies. West Virginia

c 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. 1949-3053 See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

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University has implemented their Microgrid in a community setting so the operational use of a Microgrid by ordinary people, in what they call an ETOWN, can be studied [11]. For those unfamiliar, an ETOWN is a small city that is actually a living laboratory. Research performed in the ETOWN is used to determine the success of a certain technology in a limited setting before integrating it into the larger environment. The technologies and human practices necessary for a city to leave a low carbon footprint are being developed. Other universities and industrial researchers have developed isolated testbeds to be used for architecture and control system development. The consortium for electric reliability technology solutions (CERTS) was founded in 1999 as a way to get a team of researchers at four national laboratories, nine universities, and eight industrial organizations working together to solve these complex challenges. The university members include Arizona State, UC Berkeley, Cornell, Georgia Tech, University of Illinois, Iowa State, Texas A&M, University of Wisconsin, and Washington State. The main CERTS Microgrid testbed is located at the Walnut Test facility in Columbus, Ohio and it is primarily operated by American Electric Power. The CERTS Microgrid was created for the development a semi-universal Microgrid control architecture that can be widely used rather than each installation requiring a custom engineered control system [12], [13]. Along the same lines, researchers at the University of Texas at Arlington (UTA), with support from the DoE’s NETL, have installed a Microgrid testbed on campus for educational and research purposes. The unique features of the UTA grid include the use of a national instruments (NI) CompactRio embedded control system and the novel system architecture which consists of three Microgrids operating independently or in an interconnected manner. This design allows the study of novel architectures and peer based communication strategies to be carried out. Solar, wind, and fuel-cell renewable energy sources have been installed and integrated with the legacy electrical grid, a diesel generator, and energy storage modules. A description of the grid capabilities and research benefits will be discussed in the sections that follow. II. H ARDWARE A RCHITECTURE UTAs Microgrid testbed is divided into three sub-grids. A block diagram of the current setup is shown in Fig. 1. Each sub-grid has its own distributed energy source (DES), dynamically reconfigurable real-time control system, and dedicated loads. This enables each of the sub-grids to function as an independent Microgrid. A central ring architecture enables each sub-grid to service or source either of the other two via a central ac bus. This architecture was chosen for two reasons. First, the DoEs smart grid concept involves the communication and interconnection of several independent Microgrids, in what could be considered smart nodes, to promote system reliability and efficiency. Second, the Department of Defense’s (DoDs) planned electrical architectures involve the communication of several different DESs configured in various smaller grid configurations throughout the platform or base installation [14]. Therefore, the current UTA configuration supports both of these agencies’ missions.

Fig. 1.

Block diagram of the UTA Microgrid testbed.

The UTA Microgrid incorporates several different renewable energy sources along with a conventional diesel generator and the legacy electrical grid to power the testbed loads. Each of the three sub-grids harnesses wind energy through the installation of 300 W HiVAWT vertical axis wind turbines [15] and solar energy via 230 W Schott solar panels [16]. A total of 1.2 kW of wind turbines and 2.76 kW of solar panels is installed. Each of the three sub-grids has four, ∼30 V, 230 W solar panels installed that are connected in a 2 series/2 parallel configuration. The solar panels installed on sub-grid one and three are fed into each grid’s own dedicated FLEXmax 60 maximum-power-point-tracking (MPPT) charge controller manufactured by Outback Power [17]. Each of the series stacked solar panels connected on sub-grid two is fed into its own Xantrex C40 pulse width modulated (PWM) charge controller [18]. The different charge controllers were chosen so that researchers and students could compare the difference between the MPPT and PWM charging technologies. The MPPT charge controller is more sophisticated than the PWM controller in that it actively monitors the renewable source voltage and the battery voltage. The voltage from the renewable source is then converted to the best voltage that will maximize the current into the battery. The overall effect is that maximum power is always transferred to the battery. Microgrid 1 has two DS300 wind turbines installed while the other two only have one. Each of the wind turbines are fed directly into its own 400 W MPPT charge controller manufactured by HiVAWT. In addition to the renewable DESs discussed above, subgrid one has a NEXA 1.2 kW proton exchange membrane (PEM) fuel cell manufactured by Ballard Power Systems [19] installed. The working voltage of the fuel cell starts out at 45 V and decreases down to 26 V as the output power approaches 1.2 kW. A 5 kW dc-dc buck converter developed by Zahn Electronics conditions the fuel cell’s unregulated power and ties it into the grid’s 24 V dc bus [20]. Each sub-grid has two series connected 12 V—1150 CCA DieHard Platinum gelcell batteries for energy storage [21]. The regulated dc output

TURNER et al.: DESIGN AND ACTIVE CONTROL OF A MICROGRID TESTBED

voltage from each charge controller ties into the batteries which provide energy when the ac grid is unavailable or the cost of energy from the legacy grid is prohibitive. While gel-cell batteries are currently installed, future plans include installing other forms of electrochemical energy storage, such as lithium-ion batteries, lithium-ion capacitors, and supercapacitors in order to understand the benefits these new technologies offer for driving both conventional and high power Smart loads. The 24 V dc bus on each of the sub-grids connects directly to a dedicated dc/ac sine wave grid-tie inverter manufactured by Outback Power Systems. A GTFX1424 [22], 1400 W inverter is installed on sub-grid’s two and three while a GTFX2524 [23] 2500 W inverter is installed on sub-grid one due to the higher input power available. Each inverter can operate in an islanded or grid-tied mode of operation and outputs a 60 Hz, single phase, 120 V ac sine wave. The output of each inverter goes through a no-fuse-breaker (NFB) first that automatically opens in an overcurrent event for safety. Second, the current feeds into a digitally controlled solid state relay (SSR) [24], which provides controllability using a real time control system. Finally, each SSRs output feeds energy to its respective sub-grid’s main ac bus. Each grid has dedicated loads connected to its respective central ac bus via a series connection of a NFB and a SSR to ensure safety, controllability, reliability, and flexibility. A 3.6 kW, ac/dc, 63803 programmable load manufactured by Chroma Systems [25] is connected on each grid as a means to simulate a realistic load profile and implement a transient loading capability. In addition to the programmable loads, conventional loads such as light bulbs, fans, etc. can also be installed on each grid and controlled using the load’s dedicated SSR. The digital controllability enables researchers to develop load shedding algorithms which ensure that the most critical loads will have power in the event of a shortage. In the normal mode of operation, the inverters operate in a grid-tied fashion, connected to the central ac bus, using the legacy electrical grid as a reference. This mode of operation allows the inverters to use the legacy grid to power loads and charge energy storage devices. Once charged, the energy storage system can be used to serve the loads. This mitigates the intermittence of the renewable generation (RG) resources and provides ancillary services to the grid. In the event of a legacy grid outage, the inverters automatically convert over to an islanded configuration, using the batteries to source the loads. If any particular grid’s energy storage runs low and critical loads are still in need of power, the ac output bus from any of the other Microgrids can connect to the central ac bus, acting as the new reference grid, and source power to the grid(s) in need. Finally, in addition to all of the other DESs already mentioned, a DuroStar DS7200Q, 6 kW diesel generator is also installed [26]. If the energy from the legacy grid is too expensive or otherwise unavailable and the energy storage runs low, the generator can source the ac input of any sub-grid’s inverter via its connection to the central ac bus. The type, quantity, and power rating of each of the DESs installed is listed in Table I and that of the different charge controllers is found in

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TABLE I D ISTRIBUTED E NERGY S OURCE (DES) S PECIFICATIONS

TABLE II C HARGE C ONTROLLER AND I NVERTER S PECIFICATIONS

Table II. A simple connection diagram of the entire testbed just described is shown in Fig. 2 and photographs of various portions of the testbed are shown in Figs. 3–6. In its current configuration, each Microgrid has four load lines, each of which is connected to the ac bus via its own NFB and SSR for safety and independent controllability, respectively. The first two load lines of each Microgrid are connected in parallel to a Chroma model 63803 programmable ac/dc Load. This type of load is programmable in constant current, constant resistance, constant voltage, constant power, and rectified load modes of operation. It can dissipate 3.6 kW with a peak operating voltage of 350 VRMS and current of 108 Apeak/36ARMS . The programmable load will be used to simulate normal household and industrial load profiles and induce transient loads onto the grid. The other two load lines on each grid are currently configured to power conventional 120 VAC, 60 Hz loads via a standard U.S. plug connections. In the future, it is hoped that more Smart loads will be developed commercially and installed on the grid. In addition to conventional ac loads, the grid is configured to test how transient dc loads and faults impact the quality of the voltage on the main ac bus. This is accomplished by connecting two insulated-gate-bipolar-junction (IGBT) transistors in parallel between the dc energy storage and a high power adjustable impedance load. Each switch can hold off 1.7 kV, conduct 2.4 kA continuously, as long as the collector temperature stays below 80◦ C, and can conduct a peak surge current of

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Fig. 2.


Schematic of Microgrid testbed installed at UTA.

Fig. 3. Photograph of the solar panels, wind turbines, and diesel generator (housed beneath the solar panels to shield it from the weather) on the roof of UTAs engineering laboratory building (ELB). Fig. 5. Close-up photograph of the NFB and SSR relays used to control Microgrid 1 (MG1) and the legacy electrical grid (LG).

Fig. 4.

Laboratory photograph of the Microgrid testbed.

20 kA for roughly 10 ms [27]. Initially a low impedance load that is almost purely resistive is being used. A series/parallel connection of up to 20–100 m high energy disk resistors

enable the load to vary easily from roughly 8 m up to 2 Ohms. Finally, each sub-grid has its own real-time digital acquisition and control system installed, as seen in Fig. 7. A dedicated NI CompactRIO has been installed on each subgrid. The CompactRIO’s hardware architecture incorporates a reconfigurable field-programmable gate array (FPGA) chassis, an embedded controller, and the ability to easily swap out different I/O modules as needed [28]. The CompactRIO is programmed using NIs LabVIEW graphical programming tools and can be used in a variety of embedded control and monitoring applications. Each grid’s CompactRIO has eight I/O modules installed that give it digital output, digital input, analog output, and analog input capability. The digital and analog output signals are primarily used for relay and device


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TABLE III NI C OMPACT RIO DATA ACQUISITION AND C ONTROL S YSTEM S PECIFICATION

Fig. 6.

NEXA 310-0027 Ballard 1.2 kW PEM fuel cell.

Fig. 7. National instruments CompactRIO data acquisition and digital control system.

control. The digital and analog input channels are used for real-time monitoring, data collection, and feedback control. Each CompactRIO can be controlled using a single, remotely accessible, visual user interface. The voltage and current at nearly every input and output in each of the different subgrids is monitored using voltage probes and Hall effect current sensors whose outputs are fed directly to the analog input channels on the sub-grid’s respective control system. The name, quantity, and specifications of all the hardware that makes up each sub-grid’s CompactRIO is listed in Table III below. III. C ONTROL S OFTWARE D EVELOPMENT The design of the software used to control the Microgrid is easy to conceptualize in terms of a state machine. A state machine is a mathematical model used to develop a logical process or in this case, design a software application. It can be thought of as a machine with a finite number of operational conditions called states. The machine can be in only one state at a time and can transition to another state based on some event or trigger. Therefore, designing the software is a matter of defining the states and deciding on the events which cause the states to transition from the current state to the next state. The specific states a Microgrid may need to transition into could vary widely depending on the application for which it is being used. In any case, there are almost always critical loads which must be powered at all times and loads which can be shed in the event of a power shortage. The state machine must transition between states as efficiently as possible ensuring that first the critical loads are serviced and second that the grid is operating in the most efficient and reliable manner possible at any given time. With this later demand in mind, it can be imagined that the state machine would prioritize use of the

renewable sources as much as possible before switching to the utility or other backup for power. The inverters in this Microgrid are capable of sourcing power simultaneously from a local dc bus and an external ac bus, but there is very limited programmatic control of this behavior because they are off-theshelf components and not custom built for this application. This limitation on the control of the inverters is actually a benefit to the researchers interested in demonstrating a control system capable of switching sources while maintaining continuous service to local loads. In residential applications, efficiency will often be given the highest priority as this is an application where few critical loads may exist. On the other hand if a Microgrid is employed in a military forward operating base (FOB), higher priority would be given to ensuring that essential needs such as the communications headquarters, medical station, and radar/weapons systems are always powered. This will often require the grid to automatically shed any unnecessary loads in order to always ensure critical loads are being powered. In either residential or FOB applications, it can be envisioned that multiple Microgrids may be located in close proximity and the ability of the state machine to seamlessly interconnect them as individual grids fall short of the power demanded from them is critical. The first step in the creation of a state machine is to decide on the possible operational states of the Microgrid. These states used in the machine developed here include RG, RG charging, battery backup, grid tied, and dark. RG is the state where the system is being powered by renewable resources and the batteries are fully charged. This would be the most desirable state for the system. This state would also include the sell-back of any excess power to the legacy grid. In the RG Charging state, the system is running off of RG, but the batteries are also being recharged. The Battery Backup state is for when the system is running off of

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TABLE IV M ICROGRID S TATE M ACHINE

the batteries. In this state, RG is not available for some reason. Grid Tied is the state where the system has switched to being powered primarily from an external ac source. The peer to peer network connecting all the Microgrids allows each one to request power from an adjacent Microgrid or switch to the legacy grid. The power fed into each inverter’s utility grid connection can either come from the legacy electrical grid, the backup diesel generator, or even another Microgrid. In this state, renewable energy is insufficient for the local loads and the batteries have been depleted beyond some predetermined amount. Also, in this state the batteries may be recharging from the external source. Finally, if all possible sources are unavailable there is a Dark state where all loads are left unpowered. Within each state, the objectives of the overall system must be considered. For instance, within the RG state there are considerations for the threshold at which the system will make a transition. For the system presented here the only consideration is for the servicing of the local loads. But the control model is capable of being extending to include other objectives. Economic objectives could include load shedding that would take advantage of fluctuating electricity prices. To complete the state machine, a set of transition events must be defined for each state. For each transition event, one of other states must be chosen as the next state. Each transition event causes the state of the system to move from the current state to a different or next state. Table IV describes the states for a general purpose Microgrid. Fig. 8 provides a graphical representation of Table IV. The legend at the bottom of the figure describes some numerical values to conditions that define the state. For example, the Battery State condition is described with four numerical values to represent the four conditions of the battery (charged, charging, supplying, or depleted). The actual implementation of the state machine is achieved using LabVIEW software embedded in each CompactRIO’s FPGA. Utilizing the real time data monitoring and input/output capability, the

Fig. 8.

Microgrid state machine.

CompactRIO acts as an autonomous control system based on the state machine just discussed. Resources on creating software from an abstract state machine design are found in [29]. The LabVIEW application used to run the Microgrid can also use preestablished use cases. One useful property of the application is to set the battery level to a depleted state in order to affect the state machine. This allows test case transitions to be triggered without actually waiting for the batteries to discharge. IV. E XPERIMENTAL DATA The data presented here demonstrates the dynamic Microgrid operation. The data measurements are sampled at a rate of 50 ms or about every three cycles of the 60 Hz ac waveform. It is possible to adjust this sample rate from the Microgrid LabVIEW application. In Fig. 9, the dc bus currents are shown in a steady state operation for about five seconds in time along the x-axis. At this particular moment in time, the wind is calm and no usable energy is provided by the wind turbines (orange line). There is no load connected to the ac bus, but the inverter (gray line) is still drawing some positive current from the dc bus for standby operation. This current is coming mostly from the solar panels (blue line). The solar panel charge controller is operating as to provide just enough current to the inverter. The battery current (yellow line) shows that it follows precisely the current from the solar panels. When the solar panel current is above the required inverter current, the battery current is also positive indicating that the battery is in a charging state (positive current going into the battery). In the areas where the solar panel current is below the inverter current, the battery


Fig. 9.

Fig. 10.

Microgrid steady state.

Microgrid load dynamics.

current becomes negative indicating that it supplying current to the inverter. In either case, this plots shows a renewable source and energy storage elements working together to support the overall system operation. Fig. 10 starts out similar to Fig. 9 in that it shows how the solar panel charge controller is supplying current to the inverter with excess current going to charge the batteries. One additional plot line has been added to show the ac current output of the inverter (dark blue line). The connection of two large loads on the ac bus produce a current draw from the inverter which cannot be satisfied by the solar panels. The batteries supplement the solar panels on the initial spike and also on the subsequent steady current draw. The loads on the ac bus operate normally and there is no disruption of power service to the loads. When the loads are turned off, the system returns to the initial operating mode. This dc bus current values have been scaled so that the ac currents measured on the inverter output can be visually compared to the dc bus current going into the inverter. The ac currents are measured as RMS values. Fig. 11 shows longer term dynamics in the system (about one minute of time along the x-axis). In this plot, there is a constant ac load on the ac bus indicated by the steady draw of dc current by the inverter and the steady ac current output of the inverter. This particular plot was captured to show the effect of clouds going past the solar panels. For about the first 80% of this plot the current from the solar panels it not sufficient to supply the inverter due to clouds passing over the panels. During this time period, the battery provides

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Fig. 11.

Microgrid long term solar operation.

Fig. 12.

Microgrid operation on loss of solar power.

the additional current needed to keep the dc current into the inverter constant. The sharp dip in the middle of the graph is the location where the solar panel charge controller switches modes from normal operation to a stand-by mode where the controller focuses on supporting the voltage level of the dc bus while the battery supports the extra current needed to keep the system running. The sharp upward spike of the solar panel current near the end of the plot is the point where the clouds have passed. The controller returns to normal operation supplying current to the inverter and charging the battery. The previous plots show that the batteries do a fine job of providing current any time that the renewable sources are unable to provide enough current to the inverter. This is exactly the type of operation needed for an islanded grid operation. But what about the dynamic performance of the grid when power needs to come from outside the grid. Fig. 12 shows the performance of the system when the grid experiences an unexpected outage of the renewable resources. The application has been designed to sense the outage and switch to an alternate source. In this case, power is connected into the ac input of the inverter from the ring bus which is being supplied from another Microgrid. The inverter is designed to automatically switch to its ac input when the dc supply drops. A small delay occurs while the inverter syncs to the new source. This delay is an adjustable setting in the inverter itself. For this example, it has been set to its minimum value and results in the delay shown of about 12 s. The plot shows the initial outage of the solar panel at about the 12 s

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mark and the subsequent response of the batteries to support the inverter current. The complete loss of power from the solar panels triggers the inverter to switch from the dc bus (gray line) to the alternate source (green line). The transition is seamless to the ac bus of the Microgrid and service is uninterrupted (dark blue line). In this case the power is coming from an adjacent Microgrid which happens to have excess power to spare. As power from the solar panel is restored, the system makes a transition back to using power from the dc bus. V. C ONCLUSION In order to successfully operate, maintain, and upgrade the future smart grid, it is crucial to ensure that the next generation of scientists and engineers are experienced and educated in this critical technological area. With the support of DoE, UTA has built a Microgrid testbed to be used for research and educational purposes. Having real off-the-shelf hardware enables high level control system designs, defined here as tertiary control concerned with the long term power flow in an interconnected Microgrid system. The Microgrid testbed is divided into three sub-grids. In the normal mode of operation, each of three subgrids function as an independent Microgrid. When needed, the sub-grids can also operate in an interconnected fashion to source each other’s critical loads. This architecture was chosen to increase system reliability, efficiency, and robustness. The testbed will be a vital tool for designing and testing control systems that do not lend themselves to traditional simulation techniques. ACKNOWLEDGMENT The authors would like to thanks the DoE and NETL for their continued support. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors, and do not necessarily reflect the views of the sponsor. R EFERENCES [1] (2007, Jan. 4). “United States Energy Independence and Security Act of 2007,” 110th U.S. Congress [Online]. Available: http://www. gpo.gov/fdsys/pkg/BILLS-110hr6enr/pdf/BILLS-110hr6enr.pdf [2] F. Li et al., “Smart transmission grid: Vision and framework,” IEEE Trans. Smart Grid, vol. 1, no. 2, pp. 168–177, Sep. 2010. [3] U.S. Department of Energy. (2009, Jul. 29). The Smart Grid: An Introduction [Online]. Available: http://energy.gov/oe/downloads/smartgrid-introduction-0 [4] A. Bidram, A. Davoudi, F. L. Lewis, and J. M. Guerrero, “Distributed cooperative secondary control of microgrids using feedback linearization,” IEEE Trans. Power Syst., vol. 28, no. 3, pp. 3462–3470, Aug. 2013. [5] G. Dehnavi and H. L. Ginn, “Distributed control of orthogonal current components among converters in an autonomous microgrid,” in Proc. IEEE Energy Convers. Congr. Expo. (ECCE), Raleigh, NC, USA, 2012, pp. 974–981. [6] (2013, Nov.). The University of Texas at Austin Center for Electromechanics [Online]. Available: http://www.utexas.edu/research/ cem/smartgrid.html [7] (2013, Nov.). Office of Naval Research Science and Technology [Online]. Available: http://www.onr.navy.mil/Media-Center/Fact-Sheets/ Greens-Solar-Energy-Battery.aspx [8] A. Bidram and A. Davoudi, “Hierarchical structure of microgrids control system,” IEEE Trans. Smart Grid, vol. 3, no. 4, pp. 1963–1976, Dec. 2012.

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Greg Turner (M’94) is from Dallas, TX, USA. He received the B.S. and M.S. degrees in electrical engineering from the University of Texas at Arlington, Arlington, TX. He has been involved in the industry for many years as a Software Developer primarily developing software for embedded systems. His dissertation topic is “Control System Designs for Autonomous Microgrid Systems.”


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Jay P. Kelley (S’11) was born in Grand Prairie, TX, in 1989. He received the B.Sc. degree in electrical engineering from the University of Texas at Arlington, Arlington, TX, in 2012. He is currently pursuing the M.S. degree in electrical engineering from the University of Texas at Arlington. His thesis is focused on understanding the impact that high pulsed power loading has on a Microgrid’s dc or ac bus.

Wei-Jen Lee (S’85–M’85–SM’97–F’07) received the B.S. and M.S. degrees from National Taiwan University, Taipei, Taiwan, and the Ph.D. degree from the University of Texas at Arlington, Arlington, TX, USA, in 1978, 1980, and 1985, respectively, all in electrical engineering. In 1985, he joined the University of Texas at Arlington, where he is currently a Professor with the Electrical Engineering Department and the Director of the Energy Systems Research Center. His current research interests include utility deregulation, renewable energy, smart grid, Microgrid, arc flash, load forecasting, power quality, distribution automation and demand side management, power systems analysis, online real time equipment diagnostic and prognostic system, and microcomputer based instrument for power systems monitoring, measurement, control, and protection. He has served as the primary investigator (PI) or Co-PI for over ninety funded research projects. He has published over 240 journal papers and conference proceedings. He has provided onsite training courses for power engineers in Panama, China, Taiwan, Korea, Saudi Arabia, Thailand, and Singapore. He has refereed numerous technical papers for IEEE, Institution of Engineering and Technology (IET), and other professional organizations. He is a Registered Professional Engineer in the State of Texas.

Caroline L. Storm was born in Austin, TX, USA. She is currently pursuing the B.Sc. degree in electrical engineering from the University of Texas at Arlington, Arlington, TX.

David A. Wetz, Jr. (S’01–M’06–SM’13) was born in El Paso, TX, USA, in 1982. He received the B.Sc. degree in electrical engineering, the B.Sc. degree in computer science, and the M.Sc. and Ph.D. degrees in electrical engineering from Texas Tech University, Lubbock, TX, in 2003, 2004, and 2006, respectively. He was a Post-Doctoral Fellow with the Institute for Advanced Technology (IAT) from 2006 to 2007 and a Research Associate from 2007 to 2010. While at IAT, his research was concentrated on improving the basic understanding and fieldability of electromagnetic launchers for the U.S. Army, Navy, and Air Force. He joined the Electrical Engineering Faculty at the University of Texas at Arlington, Arlington, TX, as an Assistant Professor in 2010. His current research interests include pulsed power, power electronics, energy storage, and power system analysis. Dr. Wetz won an Office of Naval Research Young Investigator Award in 2011.