1

Abstract— With the deregulation of U.S. electricity market and

blooming of smart grid, system operators are expected to process and analyze larger amount of information than before. They also have to make vital decisions based on their analysis that can have significant impacts on the network. During the past few decades, there have been many advanced techniques in gathering information, mainly data. However, there has not been much investment in visualizing this data. GridLAB-D, which is an example of the mentioned situation, is a promising power distribution system simulation and analysis tool. Although it is equipped with advanced algorithms to simulate distribution network and residential customers, it does not provide a user-friendly graphical interface, and the output of the program is plain text. MATLAB Guide toolbox can be applied to develop a graphical user interface (GUI) that integrates the simulation capabilities of GridLAB-D with visualization tools of MATLAB. Contouring has been proved to be an efficient algorithm for visualizing power system parameters. This paper provides a friendly and interactive user interface for a modified IEEE 13 Node Test Feeder model, and uses contour plots for visualizing the output data computed by GridLAB-D.

Index Terms—GridLAB-D, Matlab GUI, visualization, voltage coutour.

I. INTRODUCTION RADITIONALLY, most of the research on power system was focused on developing and improving algorithms of

providing data for system operators. However, with the development of the most advanced measurement tools such as phasor measurement units (PMUs), microcomputer equipment can produce massive data in a short amount of time. Power system engineers are facing a new challenge which is analysis of vast amount of data in a timely manner.

Although the act of visualization has a history as old as the paintings of cavemen, visualization in scientific computing (ViSC) went back to 1980s with the production of a key report for the US National Science Foundation (NSF) [1]. Visualization makes it easy for the users to interpret and analyze the data, and as a consequence, decisions can be made better and faster [2].

Generally, parameters of the power system are presented in tables or in numerical forms next to their associated system component [3]. However, with the deregulation of U.S. electricity market, vertically integrated utilities were

R. Fu, M. Sheikholeslami, and H. Oh are with the Department of Electrical

Engineering, State University of New York at Buffalo, Buffalo, NY 14260

disintegrated and the control of the power grid was handed to non-profit organizations such as independent system operators (ISOs) or regional transmission organizations (RTOs) [4]. The result was that system operators had to monitor and control more buses compared to before [3]. Also, operators would have to make quick decisions that could tremendously impact the electricity market. All of these factors proved that the traditional methods of presenting power system information were inadequate for the restructured system [3].

Over the past few years, many visualization methods were developed to address this issue [5-12]. Some examples of the developed methods are: Contouring bus data such as voltage magnitude or locational marginal price (LMP), contouring line data such as line loading parameter or transmission line power transfer distribution factors (PTDF), visualization of line flows with directional arrows and 3D visualization [12].

Most of the papers in this research area focus on the visualization of transmission grid. The core concept of transmission grid and distribution grid is the same; transferring electrical energy from one point to another. However, they have fundamental differences. These differences also determine the differences in the way the two are analyzed.

II. DISTRIBUTION SYSTEM ANALYSIS AND GRIDLAB-D The distribution network structure is large and complex. The

network configuration often changes due to opening or closing the switches for the faults or load transfer.

Fig. 1. Typical distribution feeder [13].

Distribution systems are actually mesh networks but they are treated as radial systems by keeping some of the lines offline. The reason is that whenever a fault happens within the system, by changing the status of the lines and taking out the faulted line and switching on some the lines that were not active, the system will be restored. This is called network reconfiguration [14]. Other objectives of network reconfiguration can be for reducing the cost and line losses [15-17] or load balancing [18,

USA. (e-mail: raofu@buffalo.edu; mehrdads@buffalo.edu; hso1@buffalo. edu).

Visual Computation for the Operation of Distribution Systems

Rao Fu, Mehrdad Sheikholeslami, Student Member, IEEE and HyungSeon Oh, Member, IEEE

T

2

19]. Recently with the introduction of distributed energy resources (DER) optimal allocation and sizing of these unit is also considered in network reconfiguration [20, 21]

GridLAB-D™ is being widely used by many utilities and distribution system designers and operators which are taking advantage of the latest energy technologies [22]. This open source software package is considered cutting edge simulation and analysis tool with the most advanced modeling techniques in distribution system. Its high-performance algorithms deliver the best end-use modeling.

Fig. 2. Multiple disciplines are combined in GridLAB-D: Power System, Control System, Buildings, and Markets.

After a period of development, GridLAB-D is now more and more popular with researchers and utilities. States News Service rated it as “A one-of-a-kind, high-tech modeling tool”[23]. American Electric Power (AEP) Ohio used GridLAB-D to calculate the power flow for each circuit modeled in a demand response project. “It supported incredibly detailed analysis of the project: everything from the substation to the meter and everything in the house, including appliances, lighting load, plug load, heating, cooling and more.” [24]

However, GridLAB-D does not provide a user-friendly graphical interface. Fig. 3 and Fig. 4 has fully explained this.

Fig. 3. GridLAB-D using command line to run its program.

Fig. 4. Output XML file from GridLAB-D

With an effective user interface, GridLAB-D will perform its powerful strength in the distribution network analysis.

III. SOFTWARE DESIGN Matlab GUI development environment (GUIDE) provides

tools to design user interfaces for custom apps. With the GUIDE Layout Editor, users can design graphically user interfaces. GUIDE will automatically generate the MATLAB code for constructing the UI, which users can then program the behaviors of the app [25, 26].

Fig. 5. The GUIDE interface.

A. Interface Structure GridLAB-D has a unique programming language within its

own ‘glm’ file. Our UI design is mainly to achieve the interaction between glm file and Matlab GUI. After loading the input excel file which contains all the distribution network information, a one-line diagram will be created. In the meantime, a glm file will also be written with the data from the input file for GridLAB-D. The ‘Plot Voltage Contour’ button will generate a voltage contour plot for the system.

Fig. 6. Our user interface design using Matlab GUI. Users can easily modify network information by adding or deleting lines, or changing bus parameters in our UI;

This interface has the ability that users can do graphically operation on the one-line diagram to obtain different network

3

configurations, and then receive new voltage contour plots.

Fig. 7. Block-diagram for our software structure.

IV. CASE STUDY On the evening of December 20, 2016, a large area of power

outages occurred in the Clarence Centre and East Amherst areas. According to NYSEG spokesman, 860 local users were affected and lost power. The cause of the power outages were the winds that led to the power lines coming down. [27]

The power outages lasted about five to six hours. As of 10:15 that night, a NYSEG official said that the vast majority of the power supply had been restored, but there were still 10 users had not yet recovered.

It was fortunate that these winds were accompanied by mild temperatures, which remained well above 0°C on that day [28]. There were no reports of the loss of life, and only minimal property damage occurred. Even so, we should still point out that if such an outage occurs on a colder day, the consequences could be very serious.

The area where the outage occurred was covered by a residential distribution network. Using our software package, we can quickly simulate the distribution network failure situation, and find the best solution based on different system conditions.

A. Case Description For research purposes, a modified IEEE 13 Node Test Feeder

model [29] is applied here to simulate the electric power grid in Clarence Center and East Amherst area where the fault happened. IEEE 13 Node Test Feeder model is a typical radial distribution network connection without any spare lines. This is the cheapest to build, and is widely used in lightly populated areas, such as Clarence and East Amherst. A radial system has only one power source for its group of customers. A power failure, short-circuit, or a downed power line would interrupt power in the entire line which must be fixed before power can be restored. The one-line diagram configuration of the system has shown in Fig. 8.

Fig. 8. One-line diagram of modified IEEE 13 Node Test Feeder model, Phase C. Bus 650 is the only power source. On Bus 630, there is a voltage regulator which is used to maintain the voltage level for the rest of the grid. Main load is on Bus 634, 671 and 675. Bus 611 and 692 also has a certain amount of load on their Phase C.

Considering the characteristics of the radial system, Line 632-692 is back-up connection when fault happens on Line 632-671. Different from the original model, there is no Bus 652 in this configuration which should be connected with Bus 684, because we only monitor voltage changes in our case study, yet the system is highly unbalanced. Single-phase loads are located in distribution system and not all phases are distributed to each bus. Therefore, each phase of distribution has to be visualized individually. In our cases, we use Phase C as our research object, where there is no Phase C in Bus 652.

Before the fault happened, the voltage contour map of the grid is shown in Fig. 9.

Fig. 9. Voltage (in per unit) contour plot of the model before the fault happened. During the winter, power consumption on the load will be much greater than other seasons in Buffalo area. Therefore, most load buses in this area are experiencing significant voltage drops than at other times of the year.

Assume the fault happened on Line 632-671, which was a critical strike to this local electric grid, nearly 90 percent of the load lost the power.

GLM File

GridLAB-D

Network Information

System Status/ Monitoring

Matlab GUI

4

Fig. 10. Voltage contour plot of the model after the fault happened. We can see that the lower half of the grid which contains the main load went completely dark after the fault.

In this case, local power authority would close Line 632-692 immediately in order to restore the power to the outage area as quick as possible. However, reconnecting the disconnected area due to power outage can cause severe voltage problem. Moreover, cascading effect can occur. Under this operating condition, the voltage problem may trigger some circuit breakers to trip, which will cause a larger area to lose power.

Fig. 11. Voltage contour plot of the model after close Line 632-692. Compared with Fig. n+1, the voltage drop is now more critical than before the fault.

To solve this severe voltage problem, we simulated four different additional system conditions.

B. Network Reconfiguration Network reconfiguration can be applied as a corrective

mechanism when there is line overloading or voltage violations [18]. Using network reconfiguration, we can also have better topology of the network with better power flow result and voltage reliability.

Assume there is another back-up line coming out from Bus 630 to Bus 692, since the voltage regulator on Bus 630 brings the highest voltage in the system. To avoid loop connection of the network, we should open Line 630-632. Fig. 12 shows the effect of the proposed network configuration.

Fig. 12. Voltage contour plot of the model after line switching. Major voltage problem has been solved. Only voltage on Bus 634 is lower than the rest of the area yet no smaller than 0.99 per unit.

As we are aware of, network reconfiguration operations will surely be accompanied by certain risks and costs. Besides that, we first need this Line 630-692 to be already built, which apparently requires more capital investment, as well as time and management input. [30] indicates that, for new 69 kV overhead single-circuit line, installation costs are approximately $285,000 per mile. The operation cost is $203 per line switching [31].

C. Back-up Generator Back-up generator can be used during power outage to

reduce losses and speed the recovery process. Critical infrastructure facilities such as hospitals and emergency response agencies will be able to maintain normal operation.

Based on our simulation, if connected to any one of the four load buses, 500 kVA generator can provide sufficient power supply to this grid. In this way, the output power of the generator can balance the pressure by the load to the local grid. Result shows that, a generator with 100 kW and 165 kvar output power on Bus 611 can meet the minimum requirement to solve the voltage problem in our case. This means, we can install a generator as small as 200 kVA and 100 kW output power.

Fig. 13. Voltage contour plot of the model after adding back-up generator on Bus 611. Major voltage problem has been solved on all the buses. Only voltage on Bus 675 is very close to 1 per unit.

In comparison, the investment of installing one back-up generator is much smaller. For instance, if we search for 500 kVA generator on Alibaba (a B2B sales services website), we can find that, for some relatively high quality diesel generators, the prices are even lower than $10,000 [32]. Operation cost of

5

this plan is the highest if we choose diesel or gas generators which are the most common back-up generators at present. They all have quite impressive fuel cost when generating power to the system. Take [33] as an example. Fuel consumption at 100% load C = 226 g/kWh. The diesel fuel price was P = $2.60/gallon. One gallon of diesel fuel weighs W = 3.2245 kilograms. Therefore, the operation cost O is 𝑂 = 𝐶×𝑃×𝑊

= 0.226 𝑘𝑔 𝑘𝑊ℎ×$2.60

𝑔𝑎𝑙𝑙𝑜𝑛 ×3.2245𝑔𝑎𝑙𝑙𝑜𝑛/𝑘𝑔

= $1.9/𝑘𝑊ℎ For 100 kW generator, the cost would be $190 per hour.This

will cause the electric price to be higher.

D. Back-up Batteries Back-up batteries are now more and more popular, as are

their abilities in managing peak load [34]. For example, when consumption is less than supply, the excess energy is stored; this stored energy will then be used as necessary to meet increasing consumption. Different types of back-up batteries can be used in our case, including compressed air energy storage(CAES), Cryogenic energy storage (CES), Battery storage power station etc.[35] Compared with back-up generator, back-up batteries may not inject as much power into the grid, yet the size of back-up batteries is relatively smaller than a back-up generator, and the installation is also easier, so the local power grid can have more than one back-up battery.

In our research, two batteries have been placed on Bus 611 and 675. The battery on Bus 611 has 85 percent output power of the load on Bus 611, while Bus 675 has 20 percent of the load on its own bus.

Fig. 14. Voltage contour plot of the model after adding back-up batteries on Bus 611 and 675. Major voltage problem has been solved on all the buses. Only voltage on Bus 675 is very close to 1 per unit.

Fig. 14 shows the effect of adding batteries on the voltage profile of the system, similar as adding a generator. Yet since batteries do not have the same capability of generating reactive power like generators, the voltage drop is slightly more than Fig. 13 in this scenario.

Use lithium-ion batteries in electric vehicles as an example to discuss the cost of back-up batteries [36]. In 2014, EV batteries’ cost was between $310 and $400per kWh. By 2020, we will have it at $100/kWh. Operation cost of this batteries type is the charging cost. Usually we charge the batteries during the night when the electric price is lower than in the daytime.

Assume the price is $0.07/kWh in the night. For a 50 kWh battery, the total cost is $3.5 if fully charged.

E. Demand Side Management(DSM) When there are no switching lines, back-up generators or

batteries, demand side management will be the most effective and most viable solution above all. DSM is the process of seeking balance between the power supply and the power load [37]. Instead of adjusting the output of the power plant, it directly adjusts or controls the load [38]. By reducing certain amount of load during peak hours, we can adjust and stabilize the voltage in a specified range for the system.

Here we compare the results by load reduction percentage. It is obvious that, the more load is reduced, the better the voltage performances. However, local customers would not likely support a limitation to their own electric power consumption. In other words, best solution is to fulfill the lowest voltage requirement with the minimum load reduction. Contour plots in Fig. 15 suggests that, even up to 15% of load reduction, the voltage performance may still not as good as previous proposals.

Fig. 15. Voltage contour plots of the model after load reduction. The top two figures are 1% and 5% load reduction. The bottom two figures are 10% and 15% load reduction. In our case, load reduction may not be a good option to eliminate the voltage issue.

Remote load control devices can now be provided by utility companies for free. Yet there will be installation cost, approximately $300 per device according to Google Search. The operation of the device can be done by operators remotely, and it will be normal operation cost.

V. PARALLEL COORDINATES Unlike the Cartesian coordinates which use perpendicular

axes, parallel coordinates use parallel axes as a representation of dimensions of multidimensional data set. Dimensions are presented by vertical lines and minimum and maximum of each dimension is scaled to the upper and lower boundaries of these vertical lines. Then, a polyline consisting of n-1 line segments connects the axes to visualize the data appropriately [39, 40].

Parallel coordinates are a good visualization tool to enable the user to find trends within the system parameters. The voltage magnitudes of the normal system operation as well as the countermeasures to restore the system are plotted in the parallel coordinates and is shown in Fig. 16. This parallel

6

coordinates helps the user to track the effects of each change to the system. For example, in the network reconfiguration case (yellow line), by connecting the line connecting bus 630 to 692, we are hypothetically swapping the places of the horizontal branches. As a result, compared to other countermeasures, this action causes voltage drop at the top horizontal branch (buses 632-646). Also, it can be seen that voltage at buses 630 and 650 is always fixed. This is due to the fact that there is a voltage regulator located at bus 630 and bus 650 is selected as the slack bus. As it can be seen, visualizing system parameters with parallel coordinates is very informative and enables the system operator to easily track the changes in the system and find trends.

Fig. 16. Parallel coordinates of all buses’ voltages.

VI. CONCLUSION This paper analyzed a modified IEEE 13 Nodes Test Feeder

model with the help of our developed visualization tool. Four different approaches were proposed to solve the voltage issue in the model, simply by adding or deleting lines, or changing bus parameters in our user interface. From the figures we can see that, voltage contour plots are very intuitive to show the state of the system voltage under different conditions, contrasts are also obvious among figures; Using parallel coordinates can also track the changes in the system and find trends. Such result is undoubtedly effective for decision makers. The next step will be to further refine the UI, making it a more versatile, complete and operational UI.

REFERENCES [1] H. Wright, Introduction to scientific visualization.

Springer Science & Business Media, 2007. [2] T. Munzner, Visualization analysis and design. CRC

Press, 2014. [3] T. J. Overbye, D. A. Wiegmann, A. M. Rich, and Y.

Sun, "Human factors aspects of power system voltage contour visualizations," IEEE Transactions on Power Systems, vol. 18, no. 1, pp. 76-82, 2003.

[4] R. J. Michaels, "Vertical integration and the restructuring of the US electricity industry," 2004.

[5] P. M. Mahadev and R. D. Christie, "Envisioning power system data: concepts and a prototype system state representation," IEEE Transactions on Power Systems, vol. 8, no. 3, pp. 1084-1090, 1993.

[6] M. Anderson, H. Pottinger, C. Schroeder, and R. Adapa, "Advanced graphics zoom in on operations,"

IEEE Computer Applications in Power, vol. 6, no. 2, pp. 25-28, 1993.

[7] K. Ghoshal and L. D. Douglas, "GUI display guidelines drive winning SCADA projects," IEEE Computer Applications in Power, vol. 7, no. 2, pp. 39-42, 1994.

[8] G. P. De Azevedo, C. S. De Souza, and B. Feijó, "Enhancing the human-computer interface of power system applications," in Power Industry Computer Application Conference, 1995. Conference Proceedings., 1995 IEEE, 1995, pp. 115-120: IEEE.

[9] J. Gronquist, W. Sethares, F. Alvarado, and R. Lasseter, "Animated vectors for visualization of power system phenomena," in Power Industry Computer Application Conference, 1995. Conference Proceedings., 1995 IEEE, 1995, pp. 121-127: IEEE.

[10] P. M. Mahadev and R. D. Christie, "Minimizing user interaction in energy management systems: Task adaptive visualization," IEEE Transactions on Power Systems, vol. 11, no. 3, pp. 1607-1612, 1996.

[11] H. Mitsui and R. D. Christie, "Visualizing voltage profiles for large scale power systems," IEEE Computer Applications in Power, vol. 10, no. 3, pp. 32-37, 1997.

[12] T. J. Overbye and J. D. Weber, "New methods for the visualization of electric power system information," in Information Visualization, 2000. InfoVis 2000. IEEE Symposium on, 2000, pp. 131-16c: IEEE.

[13] G. M. Masters, Renewable and efficient electric power systems. John Wiley & Sons, 2013.

[14] N. Sarma, V. Prasad, K. P. Rao, and V. Sankar, "A new network reconfiguration technique for service restoration in distribution networks," IEEE Transactions on Power Delivery, vol. 9, no. 4, pp. 1936-1942, 1994.

[15] A. Y. Abdelaziz, R. Osama, and S. El-Khodary, "Reconfiguration of distribution systems for loss reduction using the hyper-cube ant colony optimisation algorithm," IET generation, transmission & distribution, vol. 6, no. 2, pp. 176-187, 2012.

[16] D. Shirmohammadi and H. W. Hong, "Reconfiguration of electric distribution networks for resistive line losses reduction," IEEE Transactions on Power Delivery, vol. 4, no. 2, pp. 1492-1498, 1989.

[17] Q. Zhou, D. Shirmohammadi, and W.-H. Liu, "Distribution feeder reconfiguration for operation cost reduction," IEEE Transactions on Power Systems, vol. 12, no. 2, pp. 730-735, 1997.

[18] M. Kashem, V. Ganapathy, and G. Jasmon, "Network reconfiguration for load balancing in distribution networks," IEE Proceedings-Generation, Transmission and Distribution, vol. 146, no. 6, pp. 563-567, 1999.

[19] Q. Zhou, D. Shirmohammadi, and W.-H. Liu, "Distribution feeder reconfiguration for service restoration and load balancing," IEEE Transactions on Power Systems, vol. 12, no. 2, pp. 724-729, 1997.

[20] F. Abbasi and S. M. Hosseini, "Optimal DG allocation and sizing in presence of storage systems considering network configuration effects in distribution systems,"

7

IET Generation, Transmission & Distribution, vol. 10, no. 3, pp. 617-624, 2016.

[21] P. M. de Quevedo, J. Contreras, M. J. Rider, and J. Allahdadian, "Contingency assessment and network reconfiguration in distribution grids including wind power and energy storage," IEEE Transactions on Sustainable Energy, vol. 6, no. 4, pp. 1524-1533, 2015.

[22] D. P. Chassin, K. Schneider, and C. Gerkensmeyer, "GridLAB-D: An open-source power systems modeling and simulation environment," in Transmission and distribution conference and exposition, 2008. t&d. IEEE/PES, 2008, pp. 1-5: IEEE.

[23] A. Haas and F. White. (2012, Oct 1). GRIDLAB-D: A ONE-OF-A-KIND ENERGY GRID SIMULATOR. Available: http://www.pnnl.gov/news/release.aspx?id=948

[24] B. Schell. (2014, Feb 10). AEP Visualizes the Future of the Grid. Available: http://www.tdworld.com/distribution/aep-visualizes-future-grid

[25] MathWorks. (2017). MATLAB GUI. Available: https://www.mathworks.com/discovery/matlab-gui.html

[26] S. T. Smith, MATLAB: advanced GUI development. Dog ear publishing, 2006.

[27] WGRZ. (2016, Dec 20). About 860 customers without power in WNY. Available: http://www.wgrz.com/news/local/about-2769-customers-without-power-in-wny/375511238

[28] G. B. I. Airport. (2016, Dec 20). East Amherst NY December 20 2016 Weather History. Available: http://www.friendlyforecast.com/usa/archive/archive.php?region=NY&id=212839&date=20161220000000&sort=hour

[29] W. H. Kersting, "Radial distribution test feeders," in Power Engineering Society Winter Meeting, 2001. IEEE, 2001, vol. 2, pp. 908-912: IEEE.

[30] F. ALONSO and C. A. GREENWELL. (2013, Feb 01). Underground vs. Overhead: Power Line Installation-Cost Comparison and Mitigation. Available: http://www.elp.com/articles/powergrid_international/print/volume-18/issue-2/features/underground-vs-overhead-power-line-installation-cost-comparison-.html

[31] S.-A. Yin and C.-N. Lu, "Distribution Feeder Scheduling Considering Variable Load Profile and Outage Costs," IEEE Transactions on Power Systems, vol. 24, no. 2, pp. 652-660, 2009.

[32] Alibaba.com. (2017, Mar 3). 300kva generator price. Available: https://www.alibaba.com/showroom/300kva-generator-price.html

[33] L. Jianghao Generator Co. (2016). 300kva diesel power generator with UK engine 1606A-E93TAG5,300kva generator price. Available: https://www.alibaba.com/product-detail/300kva-diesel-power-generator-with-

UK_60285880060.html?spm=a2700.7724838.0.0.9Xr3uV&s=p

[34] B. Dunn, H. Kamath, and J.-M. Tarascon, "Electrical energy storage for the grid: a battery of choices," Science, vol. 334, no. 6058, pp. 928-935, 2011.

[35] D. Akinyele and R. Rayudu, "Review of energy storage technologies for sustainable power networks," Sustainable Energy Technologies and Assessments, vol. 8, pp. 74-91, 2014.

[36] R. Naam. (2015). Why Energy Storage is About to Get Big - and Cheap. Available: http://rameznaam.com/2015/04/14/energy-storage-about-to-get-big-and-cheap/

[37] A. Mohsenian-Rad, V. W. S. Wong, J. Jatskevich, R. Schober, and A. Leon-Garcia, "Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid," IEEE Transactions on Smart Grid, vol. 1, no. 3, pp. 320-331, 2010.

[38] D. S. Loughran and J. Kulick, "Demand-side management and energy efficiency in the United States," The Energy Journal, pp. 19-43, 2004.

[39] A. Inselberg, "Parallel Coordinates," ed, 2009, pp. 2018-2024.

[40] G. Grinstein, M. Trutschl, and U. Cvek, "High-dimensional visualizations," Citeseer.

Rao Fu Received B.Sc. degree in Electrical Engineering from Chongqing University, Chongqing, China, in 2009 and M.S. degree in Electrical Engineering from State University of New York at Buffalo, Buffalo, NY, USA, in 2013.He is currently pursuing Doctor’s degree in State University of New York at Buffalo. His research interest are power systems computation, visualization in power system. He has previously worked as an electrical engineer and project group leader in Shandong Electric Power T&T Engineering Company, State Grid Corperation of China, Jinan, China. Mehrdad Sheikholeslami Received B.Sc. degree in Electrical Engineering from Tehran Azad University, Tehran, Iran, in 2013 and M.B.A from University of Tehran, Tehran, Iran in 2015. He is currently pursuing Master’s degree in State University of New York at Buffalo. His research interests are power systems and power system visualization. HyungSeon Oh received the Ph.D. degree in electrical and computer engineering from Cornell University, Ithaca, NY, in 2005.He is an Assistant Professor at the State University of New York at Buffalo. Before the appointment, he was a staff engineer at the National Renewable Energy Laboratory. His research interests include: energy systems economics, power system planning, smart grid, storage, computer simulation, and nonlinear dynamics.