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Title : Dashboard design for management of smart electricity grids.

Submitted by : Jatin Pherwani (B. Des, final year)
Guide : Prof. Pradeep Yammiyavar (Head, CET IIT Guwahati)
Acknowledgements
I express my sincere gratitude to the following people and
authorities for their cooperation in my project’s field study and
expert consultation.
•

Assistant professor (Dr.) Praveen Kumar
(Dept. of Electrical Engineering IIT Guwahati).

•

The Assam State Electricity Board ASEB substation (IIT Gate).

•

Engineers at State Load Dispatch Centre (SLDC), Assam.

•

Mr. H.C. Phukan, Chief General Manager(SLDC)
Mr. Jatinder Baishya, Assistant General Manager(LDC)
Aim
The aim of this project is to allow efficient management of
smart electricity grids in rural as well as urban areas primarily
by catering the needs of engineers and technicians involved in
monitoring and controlling the grid parameters for a huge and
self sustaining electricity network.

Objectives
•

To study the smart electricity grid phenomena holistically.

•

Perform a thorough need analysis at the management end
of the grid network.

•

Visualize the data generated from the use of smart grid (by
making comprehensible dynamic info graphs).

•

Design a wholesome dashboard which can facilitate easy
and quick decision making.

•

Test the proposed concept with the users in their work
environment.

•

Explore new visualization mediums to depict the same
information.
Methodology
The methodology that the project followed can broadly be
grouped into three phases. The first being a basic background
study and analysing expert inputs on the subject matter of
smart electricity grids. The second phase is about following a
user centred approach to design a solution that enables easy
and quick decision making at the management end of the
network. The third phase will comprise of various explorations
in data visualization to set a different grammar in the visual
domain of information representation.
1. Background Study and Secondary research
1.1 Literature Review
1.2 Subject Matter Expert (SME) interviews
2. User requirement analysis
2.1 Workstation observations
2.2 Contextual Interviews
2.3 Task analysis and Card sorting
2.4 User personas
2.5 Use case scenarios
3. Design
3.1 Data gathering / analysis
3.2 Ideate visualizations
3.3 Prototype concepts
3.4 Wireframe dashboard structure.
Literature Review
The literature review consists majorly of two parts, the first to
acquaint myself with the important concepts of a smart
electricity grid system and the second about various techniques
used to visualize the grid parameters.
What is a smart grid ?
Smart grid is a modernized electrical grid that uses
information and communications technology to gather and act
on information, such as information about the behaviours of
suppliers and consumers, in an automated fashion to improve
the efficiency, reliability, economics, and sustainability of the
production and distribution of electricity.[1] The term smart grid
has been in use since at least 2003, when it appeared in the
article "Reliability demands will drive automation investments"
by Michael T. Burr[2]. A common element to most definitions is
the application of digital processing and communications to the
power grid, making data flow and information management
central to the smart grid.
Features of smart grid :
1.Reliability
2.Flexibility in network topology
3.Efficiency
4.Load adjustment
5.Peak curtailment/levelling and time of use pricing
6.Market-enabling
7.Demand Response
Visualizations of smart Grid parameters
To determine how power moves through a transmission
network from generators to loads, it is necessary to calculate
the real and reactive power flow on each and every
transmission line or transformer, along with associated bus
voltages (in other words, the voltages at each node). With
networks containing tens of thousands of buses and branches,
such calculations yield a lot of numbers. Traditionally they were
presented either in reams of tabular output showing the power
flows at each bus or else as data in a static so-called one-line
diagram. (One-line diagrams are so named because they
represent the actual three conductors of the underlying threephase electric system with a single equivalent line.)[3]
The visualization challenge is to make these concepts intuitive.
One simple yet effective technique to depict the flow of power
in an electricity network is to use animated line flow. Here, the
size, orientation, and speed of the arrows indicate the direction
of power flow on the line, bringing the system almost literally
to life. Dynamically sized pie charts are another visualization
idea that has proven useful for quickly detecting overloads in a
large network. On the one-line, the percentage fill in each pie
chart indicates how close each transmission line is to its
thermal limit. When thousands of lines must be considered,
however, checking each and every value is not an option. Of
course, tabular displays can be used to sort the values by
loading percentage, but with a loss of geographical relevance.
Visualizations of Smart Grid parameters
Because engineers and traders are mostly concerned with
transmission lines near or above their limits, low-loaded lines
can be eliminated by dynamically sizing the pie charts to
become visible only when the loading is above a certain
threshold.
Contouring the grid
Using pie charts to visualize these values is helpful, unless a
whole host of them appear on the screen. Here, an entirely
different visualization approach is useful--contouring.
Contours are a familiar way of displaying continuous, spatially
distributed data. The equal-temperature contours provided in a
newspaper's weather forecast form a well-known example. The
trouble with contouring power system data is that it is not
spatially continuous. Bus voltage magnitudes exist only at
buses, and power only as flows on the lines, yet the spaces
between buses and lines appear in contour maps as continuous
gradients, not as gaps. In practice the artificially blended spaces
between nodes and lines do not matter much, as the main
purpose of a contour is to show trends in data. [4] Values are
exact only at the buses or on the lines. Colors can be used to
represent a weighted average of nearby data-points. This color
gradation brings out the spatial relationships in the data.
Subject Matter Expert (SME) interviews
In the process of taking an expert’s take on the visualizations of
Electricity Grids’ network and monitoring, two subject matter
interviews were done.
1.Assistant professor (Dr.) Praveen Kumar at the Dept. of
Electrical Engineering IIT Guwahati. The following observations
were made, a few proved to be very insightful in the further
design.

• The basic parameters for management of any electrical
network can be done with the following 5 parameters :
Voltage, Current, Frequency, Power Factor, Transformer
temperature.
• In addition to these, each decentralized power generation
site can be said to inject power into the smart grid along
these five parameters only.
• Topological and geographical representations are of
secondary importance.
• Once the pricing off power units gets to be volatile, it can be
of use to show it in the dashboard.
• Power distribution policies vary with time too and once the
smart devices and more renewable energy producing sites
jump in, a visualization would of a lot more use.
2. Mr. H.C. Phukan, Chief General Manager at the State Load
Dispatch Center(SLDC), Kahilipara Assam. With the theoretical
concepts of electricity transmission clear, visits were paid to the
stat’s grid management center. The functions at the
management end for a grid were thoroughly understood and
reflected upon with discussions giving the following points.
The major objectives of any grid management center shall
comprise of the following:
•

Demand Estimation for operational purpose.

•

Scheduling for Merit Order Dispatch.

•

Regulating Generation Load Balance.

•

Schedule for Central Sector Drawl & Regulate it.

•

Monitor bilateral power supply agreement.

•

Maintaining system frequency in 49.0Hz to 50.5Hz.

•

Restoration procedure planning and implementation.

•

Load Shedding implementation.

•

Coordination with RLDC and other constituents.
Observations (Workstation)
The field visits covered observation sessions at the users actual
workplace, in our case being the State Load Dispatch center,
Kahilipara in Guwahati, Assam (Figure1). The center is one of
the 18 present in the entire nation and is responsible for all
electricity transmission, distribution and electric grid related
regulations of the North-East Grid, one of the 5 major grids of
the country.

Figure 1

Figure 2

Figure 3

The office has about 25 employees with 5 main engineers,
taking the important decisions at the management end of the
system. On the first floor of the building takes place the
monitoring and control of the electricity grids. With at least 8
CRT monitors and one huge LCD display (Figure3), the
visualizations and all other grid related information are visible
to the engineers and staff. Other than the conventional screens,
which consisted of LCDs and CRTs, a digital display is included in
the main room to show the grid’s frequency, which is a number
close to 50, but of utmost importance to monitor any grid’s
health (see Figure 2).

10
Contextual Interviews
Mr. Jatinder Baishya is the Assistant General Manager at the
Load Dispatch division in SLDC Assam. Other than holding a
bachelors degree in electrical engineering, he has had a 3
month training for handling SCADA systems used in huge
electrical networks. Most of his days at the office are busy,
however he agreed to give us a couple of appointments to
discuss the his role in the management of the electricity grids
in the state.

Figure 4

Mr. Baishya’s regular days involves decision making activities
mostly done by looking at a SCADA system deployed to
reflect the grid’s state. His job is to make sure that the
objective set by the SLDC are met so that an efficient and
‘disciplined’ electricity network is established across the
North East region of the country. The following three are
primary objectives assigned to him :
• Matching of forecasted & Actual Demand
• Meticulous Planning for Day Ahead Schedule
• Regulation of own generation according to frequency
profiles
For all the above regulations to come to action, it must take
Jatinder to follow a few protocols and take decisions at his
own discretion but all mainly supported by the information
he receives through visualizations of the SCADA’s manmachine interface(MMI) as they call it.
11
An evaluation of the current SCADA visualizations used by
engineers was done at the SLDC Assam and otherwise as a
part of desktop research. A few prominent screens appear to
the picture at every stage of decision making for the Load
distribution engineers.

Figure 5

Figure 6

Line Diagrams : These are reduced versions of the electricity
grid’s physical components like transmission lines,
transformers, substations and power stations(see Figure 5).
The screen visualizations are very heavy in terms of the
information they depict, the segregation has been done by
color and thickness of the lines, otherwise mostly textual
information is presented to depict the name of places and
corresponding elements. Looking at so much of coded
information, taking a decision a quick glance is difficult for the
engineers. The overlapping of lines and excess chart junk
present in these diagrams, increase the cognitive workload of
the users resulting in errors and taking more time to act on a
situation.
Import – Export Power Tables : The column charts depict the
state of generation and supply of power to various end
points(see figure 6). The information has three basic
components, the power incoming the grid and the power
leaving it for consumption. Also mentioned are the grid
frequencies and Unscheduled Interchange (UI) rate of power
at this time and frequency. Also represented are the name and
KiloVoltage of the transmission lines carrying that load.
Frequency Regulation : The grid at any point has a specific
frequency at which the power is being generated and injected
into it. This is the average of generating frequencies of all the
power stations in the grid. Interestingly, this frequency is the
most important parameter to be monitored at regular
intervals by the engineers. It depicts the demand response
activities and overall grid health. The frequency of power
generation allotted in Indian scenario is 50.00 Hz. Any
deviation from this number means some sort of deviation in
the scheduled power distribution. It also affects the decision
to buy electricity from other sources as the Availability Based
Tariffs of electricity are a function of frequency of your grid.
The grid frequency can judge the load shedding patterns of a
region too. Since this number is of utter importance to the
engineers, it is depicted in all the SCADA’s screen visualizations
as a number value up to two or three decimal places. To keep
more check, a digital display gives real time value of the grid
frequency at an interval of one second (see Figure 7).

Figure 7

Data Charts and Archives : The power distribution authorities
have a set of protocols, which every grid managing organization
must follow. These mainly include the order of grid recovery, the
archiving of all abnormal grid states and load shedding
instructions. Huge amount of data is stored in big hard drives of
terabyte capacities and mediated with the cloud.
13
Ideation (Crude Concepts)
The visits to the SLDC office and coming across the various
parameters important in grid management allowed many
insights to trigger in the visualization paradigm. As the next
step in project I chose to give a first attempt at the novel
visualization methods that might prove to be more useful and
less cumbersome for engineers. As the ‘smartness’ of the grid
increases, more parameters might join in the picture to be
monitored at an authority. Here in this report, the first
iterations of the three chosen platforms is presented.

Figure 8

Line Diagram Abstraction : The line diagram as we had seen
was a very heavy and dense info graphic to absorb. As an
attempt to simplify it and add the geographical position of the
stations and lines, the proposed diagram is presented.
The basic approach in reducing this visualization was centered
around the idea of reducing the cognitive workload on the
viewers’ minds. Inspired from the London Tube’s map, the
visualization uses lines only at ninety and forty –five degrees,
reducing the excess computation for the brain. Overlapping
has been tried to be kept at a minimum however the data
cannot be challenged. A different and supposedly simpler
grammar is introduced to reduce the chart junk due to the
earlier used symbols. A much simpler version of transformer
symbol and power stations is used. Geography is the main
addition to this line diagram, which makes it comprehendible
for a layman, however it is a distorted map and the places and
boundaries are not accurate.
14
Ideation (Crude Concepts)
Input and Output power : The power input-output screen has
been converted for a table with just textual information, to a
radial spider visualization. The 17 end points are taken as the
vertices of the polygon, and radial lines outward are the
transmission line carrying that load. The red area peak gives
the demand in that region. Where as the blue region shows
the amount of electricity allotted if the schedule was to be
followed. Unlike the earlier table, this visualization
additionally shows the shortage and load shedding
possibilities in the region. The actual values are also depicted
so there is hardly any trade off in terms of the figurative
display of information.

Figure 9

15
Ideation (Crude Concepts)

Figure 10

Frequency Regulation: A discussed before, the frequency
monitoring is of prime importance to ant grid managing
authority. The regional grid frequency, cumulatively makes up
the grid’s total frequency. The different power stations across
a region experience a change in frequency due to the load
patterns of that area. The individual ‘area’ in the grid ends up
having the same frequency, as it spreads to the nearest power
station. Hence making the regional information of power
generation frequency of interest to the engineers. The
frequency of entire grid is represented over a gradation scale
with it’s mid point being 50.00 Hz. Any fluctuation is evident in
the map as the color of the region is altered accordingly. The
mid value is chosen to be green and going off it shall produce
noticeable changes in the color values. To depict drastic
changes in frequency, which can be fatal for the grid (and
cause blackouts) highly contrasting red color is used, this
should invite immediate attention of the engineers and allow
them to act accordingly. Another idea being to display callouts
with regions frequencies but only higher than normal values.
The entire area is again an abstracted version of the actual
map.

16
Discussion
The project aims at visualizing electricity grid parameters
important at the management end, and making them
comprehensively into a dashboard. While the semester long
work mostly included field research and user requirement
analysis, the generated concepts are of potential to cater the
existing problems pertaining to data visualization. All the
concepts are prototyped in Flash CS5 software for inclusion in
the dashboard's design. More interactive visualizations shall
join the existing ones as an explorations, given the scenarios of
an implemented smart grid. A few interesting domains left to
be explored are the visualization of price management in a
smart grid and three-dimensional info graphics of the smart
grid parameter. Visualization software packs a large amount of
information into a single computer-generated image, enabling
viewers to interpret the data more rapidly and more
accurately than ever before. This kind of software will become
still more useful, even indispensable, as electricity grids are
integrated over ever-larger areas, as transmission and
generation become competitive markets, and as transactions
grow in number and complexity. Thus, In the new world of
competition, power traders, grid managers, public service
boards, and the public itself all need to take in what’s
happening at a glance.

17
References
[1] U.S. Department of Energy. "Smart Grid / Department of
Energy". Retrieved August 20, 2013.

[2]Jump up "Smart Grids European Technology Platform |
www.smartgrids.eu". smartgrids.eu.
2011 [last update]≤. Retrieved August 11, 2013.
[3] Visualizing the Electric Grid BY THOMAS J . OVERBYE
University of Illinois at Urbana-Champaign & JAMES D. WEBER
PowerWorld Corp.
Smart Grid Analytics Aaron DeYonker VP of Products @ eMeter,
a Siemens Business
LiveData SmartGrid Manager report
Exploring the imperative of revitalizing America’s electric
infrastructure. (prepared for the U.S. Department of Energy by
Litos Strategic Communication under contract No. DE-AC2604NT41817, Subtask 560.01.04)

Visualizing Energy Resources Dynamically on Earth Arjun
Shankar1, PhD John Stovall2, Steve Fernandez1, Rangan
Sukumar1, Alex Sorokine1, and Femi Omitaomu1 1Computational
Sciences; 2Energy Sciences Oak Ridge National Laboratory
18

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Final Year Project Report. (Management of Smart Electricity Grids)

  • 1. Title : Dashboard design for management of smart electricity grids. Submitted by : Jatin Pherwani (B. Des, final year) Guide : Prof. Pradeep Yammiyavar (Head, CET IIT Guwahati)
  • 2. Acknowledgements I express my sincere gratitude to the following people and authorities for their cooperation in my project’s field study and expert consultation. • Assistant professor (Dr.) Praveen Kumar (Dept. of Electrical Engineering IIT Guwahati). • The Assam State Electricity Board ASEB substation (IIT Gate). • Engineers at State Load Dispatch Centre (SLDC), Assam. • Mr. H.C. Phukan, Chief General Manager(SLDC) Mr. Jatinder Baishya, Assistant General Manager(LDC)
  • 3. Aim The aim of this project is to allow efficient management of smart electricity grids in rural as well as urban areas primarily by catering the needs of engineers and technicians involved in monitoring and controlling the grid parameters for a huge and self sustaining electricity network. Objectives • To study the smart electricity grid phenomena holistically. • Perform a thorough need analysis at the management end of the grid network. • Visualize the data generated from the use of smart grid (by making comprehensible dynamic info graphs). • Design a wholesome dashboard which can facilitate easy and quick decision making. • Test the proposed concept with the users in their work environment. • Explore new visualization mediums to depict the same information.
  • 4. Methodology The methodology that the project followed can broadly be grouped into three phases. The first being a basic background study and analysing expert inputs on the subject matter of smart electricity grids. The second phase is about following a user centred approach to design a solution that enables easy and quick decision making at the management end of the network. The third phase will comprise of various explorations in data visualization to set a different grammar in the visual domain of information representation. 1. Background Study and Secondary research 1.1 Literature Review 1.2 Subject Matter Expert (SME) interviews 2. User requirement analysis 2.1 Workstation observations 2.2 Contextual Interviews 2.3 Task analysis and Card sorting 2.4 User personas 2.5 Use case scenarios 3. Design 3.1 Data gathering / analysis 3.2 Ideate visualizations 3.3 Prototype concepts 3.4 Wireframe dashboard structure.
  • 5. Literature Review The literature review consists majorly of two parts, the first to acquaint myself with the important concepts of a smart electricity grid system and the second about various techniques used to visualize the grid parameters. What is a smart grid ? Smart grid is a modernized electrical grid that uses information and communications technology to gather and act on information, such as information about the behaviours of suppliers and consumers, in an automated fashion to improve the efficiency, reliability, economics, and sustainability of the production and distribution of electricity.[1] The term smart grid has been in use since at least 2003, when it appeared in the article "Reliability demands will drive automation investments" by Michael T. Burr[2]. A common element to most definitions is the application of digital processing and communications to the power grid, making data flow and information management central to the smart grid. Features of smart grid : 1.Reliability 2.Flexibility in network topology 3.Efficiency 4.Load adjustment 5.Peak curtailment/levelling and time of use pricing 6.Market-enabling 7.Demand Response
  • 6. Visualizations of smart Grid parameters To determine how power moves through a transmission network from generators to loads, it is necessary to calculate the real and reactive power flow on each and every transmission line or transformer, along with associated bus voltages (in other words, the voltages at each node). With networks containing tens of thousands of buses and branches, such calculations yield a lot of numbers. Traditionally they were presented either in reams of tabular output showing the power flows at each bus or else as data in a static so-called one-line diagram. (One-line diagrams are so named because they represent the actual three conductors of the underlying threephase electric system with a single equivalent line.)[3] The visualization challenge is to make these concepts intuitive. One simple yet effective technique to depict the flow of power in an electricity network is to use animated line flow. Here, the size, orientation, and speed of the arrows indicate the direction of power flow on the line, bringing the system almost literally to life. Dynamically sized pie charts are another visualization idea that has proven useful for quickly detecting overloads in a large network. On the one-line, the percentage fill in each pie chart indicates how close each transmission line is to its thermal limit. When thousands of lines must be considered, however, checking each and every value is not an option. Of course, tabular displays can be used to sort the values by loading percentage, but with a loss of geographical relevance.
  • 7. Visualizations of Smart Grid parameters Because engineers and traders are mostly concerned with transmission lines near or above their limits, low-loaded lines can be eliminated by dynamically sizing the pie charts to become visible only when the loading is above a certain threshold. Contouring the grid Using pie charts to visualize these values is helpful, unless a whole host of them appear on the screen. Here, an entirely different visualization approach is useful--contouring. Contours are a familiar way of displaying continuous, spatially distributed data. The equal-temperature contours provided in a newspaper's weather forecast form a well-known example. The trouble with contouring power system data is that it is not spatially continuous. Bus voltage magnitudes exist only at buses, and power only as flows on the lines, yet the spaces between buses and lines appear in contour maps as continuous gradients, not as gaps. In practice the artificially blended spaces between nodes and lines do not matter much, as the main purpose of a contour is to show trends in data. [4] Values are exact only at the buses or on the lines. Colors can be used to represent a weighted average of nearby data-points. This color gradation brings out the spatial relationships in the data.
  • 8. Subject Matter Expert (SME) interviews In the process of taking an expert’s take on the visualizations of Electricity Grids’ network and monitoring, two subject matter interviews were done. 1.Assistant professor (Dr.) Praveen Kumar at the Dept. of Electrical Engineering IIT Guwahati. The following observations were made, a few proved to be very insightful in the further design. • The basic parameters for management of any electrical network can be done with the following 5 parameters : Voltage, Current, Frequency, Power Factor, Transformer temperature. • In addition to these, each decentralized power generation site can be said to inject power into the smart grid along these five parameters only. • Topological and geographical representations are of secondary importance. • Once the pricing off power units gets to be volatile, it can be of use to show it in the dashboard. • Power distribution policies vary with time too and once the smart devices and more renewable energy producing sites jump in, a visualization would of a lot more use.
  • 9. 2. Mr. H.C. Phukan, Chief General Manager at the State Load Dispatch Center(SLDC), Kahilipara Assam. With the theoretical concepts of electricity transmission clear, visits were paid to the stat’s grid management center. The functions at the management end for a grid were thoroughly understood and reflected upon with discussions giving the following points. The major objectives of any grid management center shall comprise of the following: • Demand Estimation for operational purpose. • Scheduling for Merit Order Dispatch. • Regulating Generation Load Balance. • Schedule for Central Sector Drawl & Regulate it. • Monitor bilateral power supply agreement. • Maintaining system frequency in 49.0Hz to 50.5Hz. • Restoration procedure planning and implementation. • Load Shedding implementation. • Coordination with RLDC and other constituents.
  • 10. Observations (Workstation) The field visits covered observation sessions at the users actual workplace, in our case being the State Load Dispatch center, Kahilipara in Guwahati, Assam (Figure1). The center is one of the 18 present in the entire nation and is responsible for all electricity transmission, distribution and electric grid related regulations of the North-East Grid, one of the 5 major grids of the country. Figure 1 Figure 2 Figure 3 The office has about 25 employees with 5 main engineers, taking the important decisions at the management end of the system. On the first floor of the building takes place the monitoring and control of the electricity grids. With at least 8 CRT monitors and one huge LCD display (Figure3), the visualizations and all other grid related information are visible to the engineers and staff. Other than the conventional screens, which consisted of LCDs and CRTs, a digital display is included in the main room to show the grid’s frequency, which is a number close to 50, but of utmost importance to monitor any grid’s health (see Figure 2). 10
  • 11. Contextual Interviews Mr. Jatinder Baishya is the Assistant General Manager at the Load Dispatch division in SLDC Assam. Other than holding a bachelors degree in electrical engineering, he has had a 3 month training for handling SCADA systems used in huge electrical networks. Most of his days at the office are busy, however he agreed to give us a couple of appointments to discuss the his role in the management of the electricity grids in the state. Figure 4 Mr. Baishya’s regular days involves decision making activities mostly done by looking at a SCADA system deployed to reflect the grid’s state. His job is to make sure that the objective set by the SLDC are met so that an efficient and ‘disciplined’ electricity network is established across the North East region of the country. The following three are primary objectives assigned to him : • Matching of forecasted & Actual Demand • Meticulous Planning for Day Ahead Schedule • Regulation of own generation according to frequency profiles For all the above regulations to come to action, it must take Jatinder to follow a few protocols and take decisions at his own discretion but all mainly supported by the information he receives through visualizations of the SCADA’s manmachine interface(MMI) as they call it. 11
  • 12. An evaluation of the current SCADA visualizations used by engineers was done at the SLDC Assam and otherwise as a part of desktop research. A few prominent screens appear to the picture at every stage of decision making for the Load distribution engineers. Figure 5 Figure 6 Line Diagrams : These are reduced versions of the electricity grid’s physical components like transmission lines, transformers, substations and power stations(see Figure 5). The screen visualizations are very heavy in terms of the information they depict, the segregation has been done by color and thickness of the lines, otherwise mostly textual information is presented to depict the name of places and corresponding elements. Looking at so much of coded information, taking a decision a quick glance is difficult for the engineers. The overlapping of lines and excess chart junk present in these diagrams, increase the cognitive workload of the users resulting in errors and taking more time to act on a situation. Import – Export Power Tables : The column charts depict the state of generation and supply of power to various end points(see figure 6). The information has three basic components, the power incoming the grid and the power leaving it for consumption. Also mentioned are the grid frequencies and Unscheduled Interchange (UI) rate of power at this time and frequency. Also represented are the name and KiloVoltage of the transmission lines carrying that load.
  • 13. Frequency Regulation : The grid at any point has a specific frequency at which the power is being generated and injected into it. This is the average of generating frequencies of all the power stations in the grid. Interestingly, this frequency is the most important parameter to be monitored at regular intervals by the engineers. It depicts the demand response activities and overall grid health. The frequency of power generation allotted in Indian scenario is 50.00 Hz. Any deviation from this number means some sort of deviation in the scheduled power distribution. It also affects the decision to buy electricity from other sources as the Availability Based Tariffs of electricity are a function of frequency of your grid. The grid frequency can judge the load shedding patterns of a region too. Since this number is of utter importance to the engineers, it is depicted in all the SCADA’s screen visualizations as a number value up to two or three decimal places. To keep more check, a digital display gives real time value of the grid frequency at an interval of one second (see Figure 7). Figure 7 Data Charts and Archives : The power distribution authorities have a set of protocols, which every grid managing organization must follow. These mainly include the order of grid recovery, the archiving of all abnormal grid states and load shedding instructions. Huge amount of data is stored in big hard drives of terabyte capacities and mediated with the cloud. 13
  • 14. Ideation (Crude Concepts) The visits to the SLDC office and coming across the various parameters important in grid management allowed many insights to trigger in the visualization paradigm. As the next step in project I chose to give a first attempt at the novel visualization methods that might prove to be more useful and less cumbersome for engineers. As the ‘smartness’ of the grid increases, more parameters might join in the picture to be monitored at an authority. Here in this report, the first iterations of the three chosen platforms is presented. Figure 8 Line Diagram Abstraction : The line diagram as we had seen was a very heavy and dense info graphic to absorb. As an attempt to simplify it and add the geographical position of the stations and lines, the proposed diagram is presented. The basic approach in reducing this visualization was centered around the idea of reducing the cognitive workload on the viewers’ minds. Inspired from the London Tube’s map, the visualization uses lines only at ninety and forty –five degrees, reducing the excess computation for the brain. Overlapping has been tried to be kept at a minimum however the data cannot be challenged. A different and supposedly simpler grammar is introduced to reduce the chart junk due to the earlier used symbols. A much simpler version of transformer symbol and power stations is used. Geography is the main addition to this line diagram, which makes it comprehendible for a layman, however it is a distorted map and the places and boundaries are not accurate. 14
  • 15. Ideation (Crude Concepts) Input and Output power : The power input-output screen has been converted for a table with just textual information, to a radial spider visualization. The 17 end points are taken as the vertices of the polygon, and radial lines outward are the transmission line carrying that load. The red area peak gives the demand in that region. Where as the blue region shows the amount of electricity allotted if the schedule was to be followed. Unlike the earlier table, this visualization additionally shows the shortage and load shedding possibilities in the region. The actual values are also depicted so there is hardly any trade off in terms of the figurative display of information. Figure 9 15
  • 16. Ideation (Crude Concepts) Figure 10 Frequency Regulation: A discussed before, the frequency monitoring is of prime importance to ant grid managing authority. The regional grid frequency, cumulatively makes up the grid’s total frequency. The different power stations across a region experience a change in frequency due to the load patterns of that area. The individual ‘area’ in the grid ends up having the same frequency, as it spreads to the nearest power station. Hence making the regional information of power generation frequency of interest to the engineers. The frequency of entire grid is represented over a gradation scale with it’s mid point being 50.00 Hz. Any fluctuation is evident in the map as the color of the region is altered accordingly. The mid value is chosen to be green and going off it shall produce noticeable changes in the color values. To depict drastic changes in frequency, which can be fatal for the grid (and cause blackouts) highly contrasting red color is used, this should invite immediate attention of the engineers and allow them to act accordingly. Another idea being to display callouts with regions frequencies but only higher than normal values. The entire area is again an abstracted version of the actual map. 16
  • 17. Discussion The project aims at visualizing electricity grid parameters important at the management end, and making them comprehensively into a dashboard. While the semester long work mostly included field research and user requirement analysis, the generated concepts are of potential to cater the existing problems pertaining to data visualization. All the concepts are prototyped in Flash CS5 software for inclusion in the dashboard's design. More interactive visualizations shall join the existing ones as an explorations, given the scenarios of an implemented smart grid. A few interesting domains left to be explored are the visualization of price management in a smart grid and three-dimensional info graphics of the smart grid parameter. Visualization software packs a large amount of information into a single computer-generated image, enabling viewers to interpret the data more rapidly and more accurately than ever before. This kind of software will become still more useful, even indispensable, as electricity grids are integrated over ever-larger areas, as transmission and generation become competitive markets, and as transactions grow in number and complexity. Thus, In the new world of competition, power traders, grid managers, public service boards, and the public itself all need to take in what’s happening at a glance. 17
  • 18. References [1] U.S. Department of Energy. "Smart Grid / Department of Energy". Retrieved August 20, 2013. [2]Jump up "Smart Grids European Technology Platform | www.smartgrids.eu". smartgrids.eu. 2011 [last update]≤. Retrieved August 11, 2013. [3] Visualizing the Electric Grid BY THOMAS J . OVERBYE University of Illinois at Urbana-Champaign & JAMES D. WEBER PowerWorld Corp. Smart Grid Analytics Aaron DeYonker VP of Products @ eMeter, a Siemens Business LiveData SmartGrid Manager report Exploring the imperative of revitalizing America’s electric infrastructure. (prepared for the U.S. Department of Energy by Litos Strategic Communication under contract No. DE-AC2604NT41817, Subtask 560.01.04) Visualizing Energy Resources Dynamically on Earth Arjun Shankar1, PhD John Stovall2, Steve Fernandez1, Rangan Sukumar1, Alex Sorokine1, and Femi Omitaomu1 1Computational Sciences; 2Energy Sciences Oak Ridge National Laboratory 18