a gis based decision and suitability model: solving …a gis based decision and suitability model:...

A GIS BASED DECISION AND SUITABILITY MODEL: SOLVING THE “TOWER LOCATION PROBLEM” IN SUPPORT OF ELECTRIC POWER SMART GRID

INITIATIVES

A THESIS PRESENTED TO THE DEPARTMENT OF HUMANITIES AND SOCIAL SCIENCES

IN CANDIDACY FOR THE DEGREE OF MASTER OF SCIENCE

By CHAD BENHAM

NORTHWEST MISSOURI STATE UNIVERSITY MARYVILLE, MISSOURI

June 2012

A GIS BASED DECISION AND SUITABILITY MODEL

A GIS Based Decision and Suitability Model:

Solving the “Tower Location Problem” in Support of Electric Power Grid Initiatives

Chad Benham

Northwest Missouri State University

THESIS APPROVED

Thesis Advisor, Dr. Yi-Hwa Wu Date

Dr. Ming-Chih Hung Date

Dr. Patricia Drews Date

Dean of Graduate School Date

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A GIS Based Decision and Suitability Model: Solving the “Tower Location

Problem” in Support of Electric Power Smart Grid Initiatives

ABSTRACT

Georgia Power Company (GPC) is in the midst of improving its electrical

power grid by upgrading equipment in the field. It is working towards building a

version of the “Smart Grid”, a more efficient, secure, and environmentally sound

way of providing electricity to the State of Georgia. A big part of this effort is

focused on replacing mechanical power meters at commercial and residential

customers with Automated Metering Infrastructure (AMI) power meters which can

communicate power usage information back to the company wirelessly. These

AMI meters rely on radio frequency communication channels similar to

broadband and WiFi to send signals wirelessly back to a communication tower

which collects then forwards the data to an operating center.

In most parts of central and southern Georgia these communication

towers are widespread and can easily receive a signal from the meters. The

northern parts of Georgia present a challenge to the transmission of radio

signals. Mountains, valleys and other topographically varied features hinder the

transmission of signals sent in the portion of the electromagnetic spectrum in

which the AMI meters operate. Finding ways to get a signal from the meter back

to a communication tower becomes a spatial problem involving line-of-sight

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between the meter and tower, and occasionally there is a need to build new

towers to handle the AMI meter demand.

A suitability model was built that took land features along with other data

inputs to help narrow down potential building sites for new radio communication

towers. The main variables taken into account by this model were elevation,

slope, the distance between one tower to the next, and the distance between the

meters and each tower. After the model identified appropriate sites for building a

tower, additional location-allocation methods were employed which further

reduced the potential tower construction sites to the most optimal locations.

This model was tested in one of the most rugged and mountainous

sections of the State of Georgia, and it produced enough tower locations so that

all the AMI meters under study were able to “see” a communication tower and

send a radio signal. This model should be helpful in reducing the research

expenditures of GPC by narrowing down the possible locations the company will

look at when new communication towers are needed.

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TABLE OF CONTENTS

ABSTRACT ....................................................................................................... iii

LIST OF FIGURES ........................................................................................... vii

LIST OF TABLES .............................................................................................. ix

CHAPTER 1: INTRODUCTION ......................................................................... 1

1.1 The Power Grid ......................................................................................... 1

1.2 The Smart Grid ......................................................................................... 3

1.3 Automated Metering Infrastructure ............................................................ 5

1.4 Sensus AMI Meters ................................................................................... 6

1.5 Research Objective ................................................................................... 6

1.6 Study Area ................................................................................................ 7

CHAPTER 2: LITERATURE REVIEW ............................................................. 10

2.1 Broadband Communication with AMI Meters .......................................... 10

2.2 Location-allocation Problems .................................................................. 11

2.3 Location-allocation Models ...................................................................... 13

2.4 Location-allocation and GIS .................................................................... 16

CHAPTER 3: FRAMEWORK AND METHODS ............................................... 18

3.1 Data Description ..................................................................................... 18

3.1.1 Bing Maps Hybrid ............................................................................. 18

3.1.2 Georgia County Outlines .................................................................. 19

3.1.3 Elevation DEM .................................................................................. 19

3.1.4 AMI Meters ....................................................................................... 19

3.1.5 Radio Frequency Communication Towers ........................................ 19

3.2 Research Methods .................................................................................. 20

3.2.1 Identify Meters with Demand ............................................................ 20

3.2.2 New Communication Tower Site Selection ....................................... 20

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3.2.3 Slope Analysis .................................................................................. 24

3.2.4 Create Tower Locations ................................................................... 24

3.2.5 Run Viewshed Analysis .................................................................... 25

3.2.6 Increase Tower Height ..................................................................... 25

3.2.7 Create Towers and Run Viewshed ................................................... 25

3.2.8 Create More Towers and Run New Viewshed .................................. 25

3.2.9 Buffer Each Tower to Capture Meters .............................................. 26

3.2.10 Combine Viewshed with Buffer to Optimize Towers ....................... 26

CHAPTER 4: ANALYSIS RESULTS ............................................................... 27

4.1 AMI Meter Demand ................................................................................. 27

4.2 Site Selection Methods ........................................................................... 28

4.3 AMI Meter Coverage Gaps ..................................................................... 35

4.4 Buffer and Viewshed Combined Analysis ............................................... 39

4.5 Final Proposed Tower Selection and Optimization ................................. 49

CHAPTER 5: CONCLUSION............................................................................ 54

5.1 Study Limitations ..................................................................................... 59

5.2 Future Improvement ................................................................................ 61

REFERENCES ................................................................................................. 63

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LIST OF FIGURES

Figure 1. Electric power grid ................................................................................. 2

Figure 2. Study area overview .............................................................................. 8

Figure 3. Rabun County, GA overview ................................................................. 9

Figure 4. DEM, AMI meters, and towers ............................................................... 9

Figure 5. Voronoi diagrams ................................................................................ 15

Figure 6. Meters located inside original viewshed .............................................. 21

Figure 7. Flowchart ............................................................................................. 22

Figure 8. AMI meter “Buddy Mode” ................................................................... 28

Figure 9. AMI meter buffer .................................................................................. 29

Figure 10. Communication tower buffer .............................................................. 30

Figure 11. Elevation profile for DEM raster ......................................................... 30

Figure 12. Analysis results based on Equation 1 ................................................ 32

Figure 13. Top five recommendation areas ........................................................ 32

Figure 14. Slope less than 15 degrees ............................................................... 34

Figure 15. Eleven proposed towers from initial analysis ..................................... 34

Figure 16. Remaining meters with 30 m towers .................................................. 36

Figure 17. Viewshed comparison – 30 m vs. 50 m ............................................. 36

Figure 18. Final nine AMI meters not covered .................................................... 37

Figure 19. Final viewshed with twenty-six proposed towers ............................... 38

Figure 20. Round one proposed towers with covered meters ............................ 41

Figure 21. Round two proposed towers with covered meters ............................. 43

Figure 22. Round three proposed towers with covered meters .......................... 44

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Figure 23. Twenty towers covering 100 meters .................................................. 47

Figure 24. Final five proposed towers with meter coverage ............................... 53

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LIST OF TABLES

Table 1. Proposed tower coverage with overlap ................................................. 40

Table 2. Covered meters from round one of proposed towers............................ 41

Table 3. Covered meters from round two of proposed towers ............................ 43

Table 4 Covered meters from round three of proposed towers .......................... 44

Table 5. Individual meter coverage by proposed towers .................................... 45

Table 6. Twenty proposed towers before optimization ....................................... 49

Table 7. Eight proposed towers prior to meter coverage optimization ................ 50

Table 8. Multiple proposed tower combinations for meter coverage ................... 51

1

CHAPTER 1: INTRODUCTION

The power grid used to supply electricity in the United States and in many

other parts of the world is based on technology developed in 1888 by Nikola

Tesla. Many utilities across the United States are working on updating and

improving this important piece of industrial infrastructure by building what is

known as the “Smart Grid”, and the deployment of intelligent metering systems is

one piece of that puzzle. This research looked at some key definitions and

features of the Smart Grid including “smart” meters and the portion of

electromagnetic spectrum they use for wireless communication. Providing radio

tower facilities in the field to read these meters is an important capital investment

for electrical utilities, and locating land areas to build them is a complicated

process. With a wide distribution of meters across the countryside over varying

types of terrain, finding a place to build towers becomes a spatial problem that a

Geographic Information System is well suited to handle. This research

presented a suitability model produced to solve what is referred to as the “Tower

Location Problem” and then tested the results using location-allocation concepts.

1.1 The Power Grid

Some key definitions of terms used to describe electricity and the

components of an electrical power grid are:

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Key Electricity Definitions (Hoecker et al. 2009)

Voltage – electrical pressure in volts

Current – movement of electrons in amperes

Power – rate at which electricity does work, measured in watts (usually

Kw, Mw)

Energy – amount of work done by electricity, measured in watt-hours

Electric Supply Chain (Hoecker et al. 2009)

The typical electric supply chain consists of three main pieces (Figure 1):

Power generation (coal plants, nuclear, wind turbines, hydro, and solar

power)

Power distribution (transmission and distribution networks)

Power load (primary, secondary, and subtransmission customers)

Figure 1. Electric power grid (Adapted from Hoecker et al. 2009)

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Electricity is generated at a power station (coal, natural gas, nuclear

plants, wind turbines, hydro, and solar power), stepped up to a high voltage for

long distance transport over transmission lines, and then stepped down again at

a substation. The substation is the link between the two main sections of the

power grid, the transmission network which carries the power over great

distances at high voltages, and the distribution network where the majority of

electricity is delivered to consumers in a usable lower voltage (Hoecker et al.

2009).

1.2 The Smart Grid

The electric power grid is an aging infrastructure that is in need of

modernization, and the term “Smart Grid” has been in common use since 2005

(Massoud and Wollenberg 2005) to describe the many efforts aimed at improving

the efficiency and reliability of this infrastructure that is the backbone of modern

society.

There are eight main concepts that form the foundation of most

implementations of a Smart Grid (Miller 2009).

1. It can heal itself

2. Consumers will actively participate in grid operations

3. It will resist attack

4. Provide higher quality power that will save money wasted from outages

5. Accommodate all generation and storage options

6. Enable electricity markets to flourish

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7. It will run more efficiently

8. Two way communications and tracking of where electricity is in the

system

One of the major challenges facing the electrical utility industry and the

power grid itself is tracking where the electricity is flowing and how much is being

used. Some of the hallmarks of a Smart Grid are sophisticated energy

management and two-way communications (Erol-Kantarci and Mouftah 2010)

and (Wissner 2011). Usage monitoring has historically been handled manually

and via slow communication methods. The mechanical electricity meter has

been for many years a link between the producers of electricity and the

consumers (Wissner 2011). The most contact consumers have with their

electricity provider is the bill sent out in the mail each month. Many industrial and

commercial consumers have monitoring controls and special meters a utility can

oversee in place to modulate the flow of electricity (Erol-Kantarci and Mouftah

2010). Until recently there has not been a reliable way to measure the flow of

electricity into residential structures, and power companies need to monitor flow

and handle fluctuating demand for this category of consumer. The technology

needed to monitor this large and widely dispersed set of residential customers

and one that is being rapidly deployed across many markets throughout the

United States is the “Smart” or Automated Metering Infrastructure (AMI) meter.

Georgia Power Company (GPC) began a program of installing smart meters for

its customers in 2009 and hopes to finish residential deployment in 2012.

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1.3 Automated Metering Infrastructure

Electrical meters are the “load” and considered the endpoints in the supply

chain of the power grid. Meters are attached to any home or business that gets

electricity from a utility company, and they record the amount of energy used by

a consumer in terms of kilowatt-hours. Older mechanical meters only record the

electricity being used. Meter reading personnel have been a staple of electrical

utilities for years, travelling to the service area to record usage data from

individual buildings. The advent of the AMI meter is changing this. Using both

wired and wireless communication technologies, the utility can “talk” to the meter

at someone’s home and actually modulate the amount of electricity flowing from

the grid into the house based on supply and demand (Wissner 2011). This

technological advance also has the benefit of eliminating the need for someone

to visit a house and collect the electrical usage information (Gungor and Lambert

2006). For an electric company, this means having fewer vehicles on the road

and leads to lower costs and less environmental impact. Improving reliability and

lowering the environmental impact of electrical delivery are two additional goals

of a Smart Grid. AMI meters can communicate outage information and power

delivery quality to an operating headquarters before a customer even realizes

something is wrong or calls to report an outage. By enhancing the meters with

two-way communication electronics, they become “smart”. Getting this usage

information the meters are collecting from the consumer back to the utility

operating headquarters poses a big logistical challenge.

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1.4 Sensus AMI Meters

SENSUS is the company that GPC has contracted to provide the AMI

communication network. This network consists of meters in the field that send

signals back to a communication tower; then the data is collected and forwarded

to computer servers and databases for analysis according to Mitch Cason

(2010), one of the engineers in the GPC AMI department. An important feature

of the AMI meters in the field is the ability to operate in “Buddy Mode”. This is a

communication strategy that the meters can use to obtain a signal from an out-of-

reach meter. Ideally, each meter should be able to broadcast a signal back to a

communication tower. The Buddy Mode is an alternate method of sending that

signal in which one AMI meter can transmit its signal to a neighboring meter that

does have a communication link to a tower. Some meters in this study have not

yet transmitted a signal back to a tower even with Buddy Mode enabled. While

the main objective of this research was to find multiple towers to cover all the

meters, it is assumed that at a point the Buddy Mode operation of these meters

would help some of the non-reporting meters send a signal.

1.5 Research Objective

AMI meters in the field send radio frequency signals back to dedicated

antennas mounted on a communication tower. Many of these towers’ initial

purpose was to provide cell phone coverage, and the electrical utilities typically

lease space for their antennas on them. Different portions of the radio frequency

spectrum are available for this type of communication, but there are transmission

7

limitations depending on the frequency. Varying terrain such as hills, mountains,

tree cover, man-made objects and distance from the tower to the meter all

present themselves as major obstacles to getting a signal from the meter back to

the tower. The research objective was to use suitability analysis and location-

allocation concepts to find new locations for communication towers in Rabun

County, GA that could overcome the terrain limitations of the area and provide

service to AMI meters not already covered. A reasonable attempt was made to

limit the number of proposed communication towers in order to keep budget

constraints in mind and to also assume that at some point the “Buddy Mode”

operation of the meters would fill in the communication gaps when new towers

are in place.

1.6 Study Area

Rabun County is the most north-eastern county in the state of Georgia

(Figure 2). The majority of Rabun County is mountainous terrain with peaks and

valleys. The highest point in the county is Rabun Bald at 4696 feet (1431 m),

and there are over 60 peaks that range between 3000 and 4000 feet. Figure 3

is a Bing Maps overview of the county including the town of Clayton, GA. The

seat of Rabun County is Clayton, GA (Georgia.gov 2011). Most of the AMI

meters that need coverage fall along Warwoman Road, an east-west county road

that flows east from Clayton, GA and follows a major valley feature in the area.

This study area is served by two communication towers and contains 100 AMI

meters. Figure 4 shows the boundary of the research area as defined by

8

available LiDAR data used to create a DEM raster along with the AMI meters

under study.

Figure 2. Study area overview

9

Figure 3. Rabun County, GA overview (Source: Microsoft Bing Map)

Figure 4. DEM, AMI meters, and towers (Source: Microsoft Bing Map)

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CHAPTER 2: LITERATURE REVIEW

2.1 Broadband Communication with AMI Meters

Radio waves have some of the longer wavelengths in the electromagnetic

spectrum ranging from miles long to down to a few centimeters in length. AMI

meters operate in this broad portion of the spectrum along with cell phones using

wireless signals similar to Wi-Fi and broadband. Wireless systems have been

deployed using Ultra-High Frequency (UHF) waves ranging anywhere from 400

MHz up to 30,000 MHz (30GHz), and the waves have different physical

properties (Sirbu et al. 2006). Lower frequency waves typically below 300 MHz

(Very-High Frequency or VHF) are better at penetrating foliage and buildings,

and they operate well in non line-of-sight situations as well as exhibiting long

ranges. UHF radio waves used with wireless systems have more difficulty

penetrating obstacles such as buildings and tree cover. These shortcomings are

typically overcome by increasing the power of the signal. There is an inverse

relationship between frequency and service range. Given roughly equal power

levels, a 20 GHz signal has a 2 km range whereas an 800 MHz one has a 45 km

range (Sawada et al. 2006). By increasing the transmission power (up to two

watts from one for instance), high frequency waves benefit by having a much

better penetration ability and a longer range; in some cases up to 32 km (20

miles) (SENSUS 2010a). Even the antenna used to receive high frequency

signals can be built smaller to match the wavelength of the high frequencies

(Sirbu et al. 2006).

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Some disadvantages found with the 800 MHz to 2.4 GHz range of

frequencies in general are the lack of bandwidth and a poor signal-to-noise ratio

caused by this being a “crowded” and mostly unregulated portion of the spectrum

(Sirbu et al. 2006). Many devices such as household telephone handsets use

these frequencies. The radio signals that SENSUS leases from the Federal

Communications Commission (FCC) on behalf of GPC are in the 890-960 MHz

portion of the spectrum (SENSUS 2010c). Using the SENSUS FlexNet System

(SENSUS 2010b), GPC can take advantage of the exclusivity, high frequency,

high power, and low signal-to-noise qualities of these licensed signals. Even with

the high power and regulated use of this portion of the spectrum, these radio

waves still have difficulty penetrating heavy foliage, buildings, and hilly or

mountainous terrain. The need arises for a sufficient number of communication

towers to overcome coverage difficulties and to receive the signals from AMI

meters in the field. The effective range for communication towers operating in

the GPC service footprint ranges from as low as 1.6 km (1 mile) in urban regions

to about 16 km (10 miles) in rural areas according to Cason (2010). For a

suburban region the effective range is about 8 km (5 miles).

2.2 Location-allocation Problems

A literature review on the subject of the “Tower Location Problem” turned

up a good deal of research on this type of location-allocation problem. Location

problems can be classified into two broad categories. One describes an existing

pattern of activity, and the other prescribes the configuration of facilities to

12

accomplish a service or activity in an efficient manner (Church and Sorensen

1994). Wei and Murray (2006) described the p-centre covering problem where

the most demand is covered within an acceptable maximum range, and the goal

is to find p facilities where the maximum distance from any demand site to its

closest facility is minimized. The other is the p-median problem, which is trying

to minimize average distances given a known amount of facilities (Smith et al.

2011). The primary goal of solving a p-median problem is to minimize the

average distance to demand points given a specified number of facilities and is

demand weighted, where a p-centre problem is not demand weighted (Jiang

2010). These problems belong to a larger class known as minisum location-

allocation problems, which seek to find medians among existing points and have

been formally around since the 17th century (Church 2002). The simplest

example of this type of problem is to take a triangle with three points on a plane,

with the goal of finding the place for a fourth center point so that the sum of the

distances to the other points is minimized.

Akella et al. (2010) studied how to provide additional cell phone towers for

a rural county in New York State. Sawada et al. (2006) looked into the issue of

providing broadband wireless services to rural parts of Canada. Bah and Tsiko

(2011) investigated ways of finding the best location for a new landfill via a multi-

criteria decision analysis. Finding the optimal location for a facility with a demand

placed on it is a central idea in location-allocation problem solving. Wei and

Murray (2006) examined the case of a small town in need of emergency sirens

and attempted to find the least number of warning sirens that would cover a

13

specific geographic region while providing coverage for the whole area. Their

methodology is applicable to this research because GPC would want to cover as

many meters as possible with the fewest towers. The central issue of this

research was the “Tower Location Problem.” The objective was to get a signal

from multiple meters in a large geographic area back to a tower while minimizing

the number of towers necessary in the field which falls into the second category

of efficiency location problems.

2.3 Location-allocation Models

For solving spatial problems, location-allocation models have been used

to solve retail site selection, transportation planning, power distribution systems

design, and emergency response system management. Liao and Guo (2008)

researched methods aimed at optimizing telecommunications network design

which closely mirrors the purpose of this current research. Mathematicians and

other researchers have made many attempts to define and test models that solve

location-allocation problems (Reese 2005), and solutions devised to solve

location problems range from computational geometry and genetic algorithms to

mathematical software programs and GIS.

Location models can be developed to solve two main types of location-

allocation problem which are generally classified based on how many facilities

are needed in a space. Many of the techniques for solving these problems

involve the use of metaheuristics and approximation algorithms which all propose

mathematical equations to solve the problem (Reese 2005). The location set

14

covering problem (LSCP) and alternately named set covering problem (SCP) are

research methods that seek to find the minimum number of facilities needed to

cover all the demand nodes over a specified distance (Straitiff and Cromley

2010). Despite the numerous other names given to location-allocation theories

such as the stochastic capacity and facility location problem (SCFLP) used by

Baron et al. (2007), each of these methods and solutions deconstructs to finding

a solution to a spatial problem.

The term heuristic algorithm is used frequently in much of the literature in

studies on location-allocation models including Wei and Murray (2006), Avella et

al. (2003), Baron et al. (2007) and Church (2002). This type of algorithm

generally refers to a systematic procedure that makes a tradeoff in the quality of

the solution versus the amount of time it takes to process the analysis (Smith et

al. 2011). These algorithms provide procedures that when given an upper and

lower bound to the value of an objective function; the two bounds converge to an

optimal solution in the middle.

Taking into account the techniques for solving these problems which

involve the use of metaheuristics and approximation algorithms, they all propose

mathematical equations to solve the problem (Reese 2005). Some of the studies

such as the one conducted by Baron et al. (2007) look at the problem of locating

X facilities in a unit square to minimize the maximum demand on a facility with

uniform demand in the space. This research uses a Voronoi region, which is a

decomposition of metric space made by defining specific distances to points in

that space. Figure 5 is a simple diagram illustrating how the numbered point

15

inside each polygon represents the place in that space which is equal distance

from all the boundaries that define the polygon.

Voronoi diagrams are also referred to as Thiessen regions or proximity

polygons (Smith et al. 2011). Solving a p-centre covering problem can be

accomplished using Voronoi diagram concepts. It is easy to visualize the

communication tower as a center point within a polygon containing the meter

points that are nearest the tower.

Figure 5. Voronoi diagrams (Adapted from Baron et al. 2007)

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2.4 Location-allocation and GIS

Given that methods for solving location-allocation problems like the p-

median and p-centre have been around for centuries, it is only in the last few

decades that GIS has been utilized to carry out site analysis and solve the

location-allocation problem. Using GIS as a tool to solve the siting problems

didn’t really start until the 1970s (Church 2002). GIS allows for rapid combination

and analysis of data sets combined with maps to make siting studies run much

faster, and it greatly expands the number of points or data nodes that can be

considered simultaneously in the location-allocation problems.

Many studies and researchers have used computational geometry and

diagrammatic approaches such as the Voronoi diagram to study location-

allocation issues without the help of specialized software or computers. Modern

GIS software has given the casual researcher the ability to study location-

allocation problems by providing a tool that serves as a place to input the raw

data and also display the model results (Church 2002). Two of the more

common data formats used in GIS are the raster based digital elevation model

(DEM) and the vector based triangular irregular network (TIN). Both of these

data formats are very good at representing a continuous surface and are

essential to solving surface modeling problems. They both produce results that

are easy to illustrate graphically in a map or on a computer screen. Using these

surface models, a viewshed region can be defined, which is the area of land

visible from a fixed point in space and can be considered the line-of-sight from

that point to all other points (Church 2002).

17

Domenikiotis et al. (2010) used a multiple analytical method in GIS to

solve the problem of where to place weather monitoring stations. This siting

procedure started with the use of a DEM for a surface analysis which included

slope analysis, use of buffer zones for objects like road networks and protected

areas, and land cover considerations. These thematic layers were used in an

overlay fashion to find areas of interest, and then the elevations were classified

based on suitability. Lastly, a viewshed analysis was used on selected points to

get the most coverage, and sites were identified that showed the best potential

for a tower location. SENSUS, the company that GPC originally contracted to

deploy the AMI meter infrastructure, used a similar analytical approach to

develop a propagation model using four thematic layers: meter locations,

prospective collector locations, a DEM, and land use evaluations (SENSUS

2010c). This research relied heavily on the concepts and methods used by

Domenikiotis et al. (2010) and SENSUS (2010b) for suitability analysis.

18

CHAPTER 3: FRAMEWORK AND METHODS

The main goal of this research was to find a way of ensuring that all the

GPC AMI meters located in the field are able to send a radio frequency signal

back to a reporting station inside a communication tower. This became a

location-allocation set covering problem where there is a spatially dispersed

demand that needed to be met by locating new facilities to satisfy the demand.

3.1 Data Description

All data used in this research study was provided by Georgia Power

Company. The coordinate system of the meters, towers, and land base data was

in NAD_1983 Georgia Statewide Lambert. All data sets were re-projected into

the WGS_1984_Web_Mercator_Auxiliary_Sphere if not already in that projection

upon receipt in order to line up best with Bing mapping layers. The linear unit is

in meters, and the angular unit is degrees. The boundary of the research area

was defined by the availability of LiDAR data for Rabun County, GA.

3.1.1 Bing Maps Hybrid

Bing Maps data was used solely for reference and as a visual aid for

identifying roads and town features on the maps presented in this research.

Consideration for new tower sites took into account the proximity of nearby roads

along with residential and commercial structures made visible by the Bing Maps

imagery.

19

3.1.2 Georgia County Outlines

Georgia county boundaries were obtained from Georgia Power Company

and cover the entire state. Rabun County, GA is the focus of this research.

3.1.3 Elevation DEM

LiDAR readings were provided by the GPC Land Department. This

information was delivered in a 255.45 MB image in IMAGINE format that

encompassed a 479.15 sq. Km (185 square mile) region ranging from 356 m

(1168 feet) to 1432 m (4698 feet). This data defined the boundary of the analysis

area.

3.1.4 AMI Meters

The location data for the AMI meters used in this study was obtained from

the GPC AMI metering department and used with permission. The initial

population of AMI meters consisted of 126 points throughout Rabun County, and

due to LiDAR elevation data only 100 of these meters fell within the boundary of

the DEM raster.

3.1.5 Radio Frequency Communication Towers

The existing communication tower locations and data were obtained from

the AMI metering department and used with permission. Two towers within the

research boundary were extracted from the statewide population of

communication towers.

20

3.2 Research Methods

3.2.1 Identify Meters with Demand

This research focused on finding coverage for AMI meters in the field so

the demand features are the meters themselves and the communication towers

serve the meter demand. There is a theoretical service area distance of 8 km (5

miles) that each tower is capable of meeting based on factors such as the terrain

and density of meters in the field according to Cason (2010). Since the portion of

the radio frequency spectrum used by the meters to communicate with the

towers is affected by line-of-sight considerations, a viewshed analysis (Figure 6)

was conducted to find meters outside the “view” of the two towers already

servicing the area. Out of 100 meters located inside the study area defined by

LiDAR data, only six fell within the viewshed area defined by the two existing

communication towers. Even though these meters are within the viewshed of an

existing tower, a decision was made to include these meters in the analysis steps

because they still need coverage that is not being provided by the existing towers

even though they theoretically should be covered based on being in the

viewshed of the existing towers.

3.2.2 New Communication Tower Site Selection

Figure 7 illustrates the overall research framework of the site selection

methodology. The entire analysis area is defined by the available LiDAR data for

Rabun County. Since the theoretical range of the existing communication towers

was set to 8 km (5 miles), buffer areas were created around the communications

21

towers and all 100 AMI meters. The buffers for both meters and towers were

classified into six classes with breakpoints set starting at 1600 m (1 mile) and

continuing up to and past 8 km (5 miles). The DEM was also classified into six

classes using natural breaks in a range starting at 356 m up to 1432 m. The

highest ranking in the meter buffer was given to the portion of the buffer closest

to each meter. The highest ranking for the tower buffer was the portion of the

buffer furthest from the tower location. The highest ranking for the DEM

elevation raster was given to the highest altitude. Map algebra was performed

on all three rasters by using a weighted equation (Equation 1) that gave the

highest priority to the elevation by making it account for half of the total.

(([RCL_DIST_MTR_1] * .3) +([RCL_ELEV_1] * .5) +([RCL_DIST_TWR] * .2))

(Equation 1)

Figure 6. Meters located inside original viewshed

22

Figure 7. Flowchart

23

Figure 7. Flowchart (continued)

24

The weighted equation (Equation 1) gave the elevation factor the

strongest influence (.5), followed by the meter distance (.3) and the tower

distance (.2). Based on discussions with Mitch Cason (2010) it was indicated

that elevation should count towards at least half of the weight, followed by how

close a meter was to a tower which is how the weights were assigned. The

resulting raster contains eighteen classes ranked from 1 to 18. This raster file

was reclassed and only the top five regions with the highest ranking were given

values. All other cell values were assigned as NODATA to remove them from

consideration. The top five were chosen in an attempt to narrow down the

possible locations for new towers.

3.2.3 Slope Analysis

The slope analysis was performed within the top five regions derived from

the New Communication Tower Site Selection step in 3.2.2. Only regions that

were 15 degrees or less of slope were extracted from the raster. This narrowed

down the possible locations for proposed communication towers.

3.2.4 Create Tower Locations

Eleven communication tower locations were placed within areas identified

from the slope analysis and the height of each tower was set to 30 meters. An

attempt was made to identify locations derived from the analysis that were near

clusters of AMI meters. Chapter 4 explains in greater detail the considerations

used to select these locations.

25

3.2.5 Run Viewshed Analysis

With eleven new towers in place the viewshed analysis was used to

determine which AMI meters out of the 100 needing coverage had line-of-sight to

a tower. Twenty-three AMI meters remained that needed coverage.

3.2.6 Increase Tower Height

A decision was made to increase the tower height to 50 m to capture more

meters that may be hidden in trench or valley features. Increasing the height of

the towers only captured four more AMI meters and left nineteen outside the

viewshed area.

3.2.7 Create Towers and Run Viewshed

Using a method similar to step 3.2.4, six additional towers (at 50 m height)

were placed in locations defined by the suitability analysis raster and in close

proximity to the remaining twenty meters. After running the viewshed tool on all

seventeen new towers, nine AMI meters remain uncovered.

3.2.8 Create More Towers and Run New Viewshed

At this point nine new communication towers were placed near the

remaining ten AMI meters so that each could potentially fall within the viewshed

of a proposed tower. A final viewshed analysis was run with 26 proposed

26

communication towers, and after this step all of the original 100 AMI meters fell

within the viewshed.

3.2.9 Buffer Each Tower to Capture Meters

An 8 km (5 mile) buffer zone was created around each proposed

communication tower in order to see how many meters fell within the buffer.

Most meters belonged in the buffer zone of multiple towers and there was

considerable overlap for some.

3.2.10 Combine Viewshed with Buffer to Optimize Towers

An individual viewshed analysis was performed on each of the twenty-six

proposed towers and combined with the 8 km (5 mile) buffer to see how many

meters fell within the viewshed of each proposed tower as well as within the

buffer zone. If a meter fell within both, it was considered to be covered by that

proposed tower. This step was necessary to optimize the proposed tower sites.

A detailed chart of how each meter was covered by each proposed tower was

generated in order to determine how much overlap coverage each proposed

tower provided. Towers that did not provide unique coverage for a meter were

removed from the proposal list, and the remaining proposed towers were

optimized resulting in four options plus an existing tower with no antenna that

covered the most meters with the least amount of overlap. Chapter 4 contains a

detailed explanation of these results.

27

CHAPTER 4: ANALYSIS RESULTS

4.1 AMI Meter Demand

The original dataset of AMI meters that did not have communication tower

coverage in Rabun County, GA included 126 meters. The available DEM raster

for the region under study encompassed 100 of the original meters and was the

basis for the viewshed analysis. There were only two existing communication

towers within the DEM footprint. A third communication tower exists to the south

and was out of bounds for analysis. Most of the AMI meters that have coverage

problems in this research tend to lie along a valley in the center of the study area.

While line-of-sight communication between the towers and AMI meters is not

completely necessary for the actual radio waves to transmit, it was easiest to

assume as the optimal configuration for the best coverage.

As previously mentioned the AMI meters are able to operate in what is

referred to as “Buddy Mode” (Figure 8). This is a two-way communication

method where one AMI meter can transmit its signal to a neighboring meter that

does have a communication link to a tower. While Buddy Mode can serve as a

backup method, it is not preferred. This was taken into consideration as the

impetus for locating a communication tower that had line-of-sight with each meter

that had a signal transmission demand.

28

4.2 Site Selection Methods

The proposed areas where towers could be built to meet the demand of

the uncovered AMI meters where chosen based on the optimization of four

criteria:

-high elevation

-far away from existing towers as possible

-closest to the AMI meters with demand

-slope of < 15 degrees

Figure 8. AMI meter “Buddy Mode”

29

The initial analysis was carried out using an 8 km (5 mile) buffer around

the existing communication towers, an 8 km (5 mile) buffer around the AMI

meters with demand, and the DEM raster with elevation values. In a densely

populated area such as Atlanta, GA, the effective range of communication towers

is reduced to about one to two miles based on signal clutter and interference. In

a flat, rural area the range for radio signals can exceed 16 km. Since the area

under study was a combination of rural and suburban types an average of 8 km

was selected. Figure 9 illustrates the boundary buffer zones created for the AMI

meters. Each color change spreading outward from an individual meter

represents 1.6 km of additional distance. Figure 10 shows the same buffer

treatment for the communication towers and it should be noted the two current

towers exhibit no overlap in coverage at an 8 km radius. Figure 11 represents

the elevation profile of the study area with lighter shades representing higher

elevations and mountain peaks.

Figure 9. AMI meter buffer

30

Figure 10. Communication tower buffer

Figure 11. Elevation profile for DEM raster

31

The selection of spots to build a tower was based on high elevation, close

proximity to the AMI meters with demand, and far away from the existing

communication towers. This would naturally favor mountain peaks close to

groups of AMI meters and far from existing towers. In the suitability analysis

model, the most weight was given to high elevation followed by proximity to the

meters and then distance from the existing towers (Equation 1). A high point

near many meters became in most cases the preferred location. Each raster

was classed into six categories in order to rank the values of elevation, closeness

to AMI meters, and distance from communication towers.

The resulting raster ranked each spot in the study area. The rankings ran

from 4.5 up to 18 as shown in the legend of Figure 12. A ranking in the range of

13-14 was set as the lowest acceptable value for optimal locations. Only

locations in the top five rankings would be considered for further analysis in order

to narrow down the potential spots for new communication towers.

Figure 13 illustrates the top five ranked areas from the intial analysis.

After examination of these results, it was determined that there were too many

potential locations identified for towers. A decision was made to narrow down

the top five recommended areas with a slope analysis. Using the raster

containing only the top five areas as a calculation mask, the slope was computed

for the area. After determining the slope, only spots in the elevation that were 15

degrees or less were chosen. Any slope greater than 15 degrees would present

construction challenges. Figure 14 displays the results of the slope analysis.

32

Figure 12. Analysis results based on Equation 1

Figure 13. Top five recommendation areas

33

Now that the potential sites were minimized based on the four criteria

(elevation, distance from towers, distance to meters, slope), examination of the

areas began. The first consideration was to locate a potential communication

tower within close proximity to as many AMI meters as possible. Secondary

consideration was given to continuous areas of land roughly equivalent to 50

square meters. Two additional considerations taken into account were proximity

to a nearby road and no encroachment upon existing residential or commercial

structures. This part of the analysis was made possible by using the Bing Maps

layer as a background. During this process of manually searching the sites

(including the considerations just outlined) produced by the suitability analysis, it

was discovered that one of the locations already had a communication tower in

place. This tower was not part of the data obtained from the Georgia Power AMI

metering group because the company does not have an antenna space leased

on the tower. It was found via an internet search of www.cellreception.com

(CellReception.com 2011). This tower was included as one that did not require

construction and named PROP1. Figure 15 is an illustration of where the first

eleven proposed communication towers were placed.

http://www.cellreception.com/

34

Figure 14. Slope less than 15 degrees

Figure 15. Eleven proposed towers from initial analysis

35

4.3 AMI Meter Coverage Gaps

After the initial eleven communication tower sites were selected a

repetitive process began to find which meters in the service area defined by the

DEM raster were still without coverage. A viewshed was performed with the new

tower locations in place to find out which meters did not have coverage. The

viewshed analysis has settings for the tower attributes where the height of the

tower observation point (OffsetA), the height of the objects being viewed

(OffsetB) and the elevation of the observation point (Spot) are manually entered.

The OffsetA value for the communication towers was set uniformly to 30 m, and

the assumed height of each AMI meter was set to one meter from the ground.

The elevation Spot for each tower varied by location and was derived from the

DEM raster. Figure 16 shows the result of the first viewshed analysis for the

proposed towers. After performing a select by location on the AMI meters,

twenty-three of them remained outside the viewshed area.

Next a decision was made to increase the height of each tower uniformly

to 50 m and see if that change was enough to cover the twenty-three remaining

meters. The same viewshed analysis was run using the new tower heights and

setting the OffsetA value to 50 m. This change did not result in a significant

increase of covered AMI meters. After the tower height increase, nineteen

meters remained as illustrated in Figure 17.

36

Figure 16. Remaining meters with 30 m towers

Figure 17. Viewshed comparison – 30 m vs. 50 m

37

A new round of sites was selected to add new communication towers. In

addition to the original eleven proposed, six new tower locations were added to

the map in the same manner as the first eleven. Once again considerations were

given to the amount of space available for tower construction, proximity to nearby

road networks and away from existing man-made structures. The tower heights

were uniformly set to 50 m in the attributes table. The new viewshed area

created from this round of analysis was used to subtract additional meters from

the remaining nineteen. Nine AMI meters remained without a line-of-sight view

to a communication tower. Figure 18 shows the results of first adding eleven

proposed towers, increasing the height of the towers, then adding six additional

towers at the increased height of 50 m.

Figure 18. Final nine AMI meters not covered

38

Now that only nine AMI meters remain without coverage, a new round of

communication towers was added to ensure that every meter within the bounds

of the DEM raster had line-of-sight coverage. This time nine new proposed

towers were added to locations within the original proposal regions that should

provide coverage to the remaining nine meters. A final viewshed analysis was

performed with all twenty-six communication tower sites in place and finally no

meters were left without coverage.

Figure 19 shows the results of the viewshed after the final round of

proposed towers was added. In terms of analysis the problem of covering all the

demand that the meters represented was a location set covering problem, where

the minimum amount of facilities were proposed to cover the demand. This

result led to the final step for this research

Figure 19. Final viewshed with twenty-six proposed towers

39

4.4 Buffer and Viewshed Combined Analysis

Given the low likelihood that Georgia Power was going to build or acquire

space for twenty-six radio communication towers, the next step of analysis was

intended to take the results of many rounds of viewshed analysis and choose the

optimal locations to build towers or lease space on existing towers. The

combined buffer and viewshed analysis was helpful with this step of the decision

making process because it provided a way to define the meters covered by each

of the proposed towers. It was assumed at the outset of this research that not

every meter would end up with tower coverage, and this methodology was

intended to cover the most meters with fewest proposed towers. To accomplish

this an 8 km (5 mile) buffer region was created for each of the twenty-six towers

along with a viewshed region. Each tower buffer was used to select AMI meters

that were within the 8 km (5 mile) range, and then an additional selection was

made from the selected meters that were also in the viewshed. This is how

meter coverage was determined for each proposed tower. Table 1 shows each

proposed tower with the number of meters that fell within the buffer, the number

of meters in each viewshed, and then the “covered” meters that were in both the

buffer and viewshed. Table 1 also has statistics on the number of unique meters

along with overlap meters covered by each proposed tower. A meter was

considered to have overlap coverage if a minimum of two proposed towers gave

it coverage. Some meters were covered by four or five proposed towers. A

meter is covered by a proposed tower only if it falls within the viewshed and the

buffer of that tower at the same time.

40

Table 1. Proposed tower coverage with overlap

The proposed towers were added in three separate rounds. Table 2

shows the first eleven proposed towers added from round one and the meters

that they covered. Figure 20 shows the same eleven proposed towers in the

study area along with the covered and uncovered meters.

Tower # Meters in

Viewshed

Meters in

Buffer

Unique meter

coverage

Overlap

meter

coverage

Covered

(viewshed+

buffer)

PROP01 3 33 0 3 3

PROP02 18 54 5 12 17

PROP03 15 47 2 12 14

PROP04 35 76 7 23 30

PROP05 31 48 4 21 25

PROP06 9 83 2 7 9

PROP07 16 52 0 12 12

PROP8 4 7 0 3 3

PROP9 2 7 0 2 2

PROP10 3 11 0 3 3

PROP11 3 4 0 3 3

PROP12 4 25 2 1 3

PROP13 8 40 1 4 5

PROP14 27 52 4 21 25

PROP15 3 45 0 3 3

PROP16 8 77 0 6 6

PROP17 2 8 0 1 1

PROP18 3 9 1 2 3

PROP19 2 2 1 1 2

PROP20 5 39 1 4 5

PROP21 16 51 1 15 16

PROP22 7 81 1 6 7

PROP23 11 74 1 10 11

PROP24 4 51 1 3 4

PROP25 3 50 2 1 3

PROP26 4 38 1 3 4

41

Table 2. Covered meters from round one of proposed towers

Figure 20. Round one proposed towers with covered meters

Tower# #Meters

Covered

Unique Meters

Covered

Overlap

Meters

Covered Meters

PROP1 3 0 3 25, 33, 51

PROP2 17 5 12

30, 31, 39, 41, 53, 55, 67, 71, 80, 83, 84, 90,

104, 109, 110, 111, 126

PROP3 14 2 12

35, 43, 47, 48, 57, 59, 62, 68, 74, 78, 79, 85,

91, 113

PROP4 30 7 23

33, 35, 38, 41, 43, 45, 47, 52, 59, 65, 66, 67,

69, 70, 72, 76, 78, 80, 85, 86, 88, 93, 94, 102,

106, 114, 115, 116, 117, 125

PROP5 25 4 21

33, 34, 35, 36, 37, 43, 47, 48, 49, 57, 58, 59,

60, 62, 68, 72, 76, 85, 87, 91, 96, 97, 98, 103,

114

PROP6 9 2 7 32, 35, 59, 66, 69, 72, 81, 110, 125

PROP7 12 0 12

46, 54, 55, 56, 63, 66, 82, 93, 100, 102, 111,

112

PROP8 3 0 3 25, 29, 123

PROP9 2 0 2 122, 123

PROP10 3 0 3 28, 119, 122

PROP11 3 0 3 25, 29, 89

42

Table 3 shows the results of adding more proposed towers from round

two. Figure 21 is a map showing the remaining uncovered meters after two

rounds of proposed tower additions. The covered meters from the first round of

proposed towers are included to show that only nine meters remain uncovered

after the second round.

Table 4 shows the nine proposed towers that were added to cover the

remaining nine meters as described in section 4.3. Figure 22 is a map showing

all 100 AMI meters covered after the three rounds of proposed tower additions.

It was determined from the outset of this research that based on how

proposed towers were added to the service area that there would be overlap

coverage for many of the AMI meters. Table 5 was created to show every meter

and the proposed tower(s) that gave it coverage. Meters were highlighted

different ways if a tower provided unique coverage versus overlap coverage.

Based on examining the results from this table, a proposed tower was removed

from the twenty-six total towers if the only coverage it provided to a meter was

handled by another tower that had unique meter coverage. In some cases a

proposed tower added in the first two rounds that only covered a few meters was

found to be redundant and not necessary for the final meter coverage after the

third round of additions. Figure 23 shows the final proposed towers after

removing PROP7, PROP8, PROP9, PROP15, PROP16, and PROP17.

43

Table 3. Covered meters from round two of proposed towers

Figure 21. . Round two proposed towers with covered meters

Tower# #Meters

Covered

Unique Meters

Covered

Overlap

Meters

Meters Covered

PROP12 3 2 1 119, 120, 121

PROP13 5 1 4 33, 47, 51, 68, 73

PROP14 25 4 21

38, 40, 42, 46, 53, 54, 56, 61, 64, 66, 67, 75,

83, 90, 93, 99, 100, 101, 102, 112, 115, 116,

117, 125, 126

PROP15 3 0 3 63, 82, 107

PROP16 6 0 6 33, 40, 47, 51, 99, 110

PROP17 1 0 1 123,

44

Table 4. Covered meters from round three of proposed towers

Figure 22. Round three proposed towers with covered meters

Tower# #Meters

Covered

Unique Meters

Covered

Overlap

Meters

Meters Covered

PROP18 3 1 2 28, 118, 123

PROP19 2 1 1 27, 89

PROP20 5 1 4 47, 51, 79, 103, 105

PROP21 16 1 15

35, 37, 47, 48, 49, 50, 57, 58, 59, 60, 72, 85,

87, 91, 96, 103

PROP22 7 1 6 41, 66, 69, 92, 102, 117, 125

PROP23 11 1 10 42, 47, 52, 66, 67, 71, 93, 106, 108, 109, 114

PROP24 4 1 3 44, 53, 55, 90

PROP25 3 1 2 53, 77, 107

PROP26 4 1 3 63, 82, 95, 107

45

Table 5. Individual meter coverage by proposed towers

METER_ID P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 P14 P15 P16 P17 P18 P19 P20 P21 P22 P23 P24 P25 P26

30 x x x x x x x x x x x x


32 x x x x x x x x x x x x x





37 x x x x x x x x x x x







45 x x x x x x x x x x x x x x





50 x x x x x x x x x x


52 x x x x x x x x











63 x x x x x x x










73 x x x x x x x x x




77 x x x x x x x x


79 x x x x x x x x


Tower Buffer and Viewshed Coverage

46

METER_ID P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 P14 P15 P16 P17 P18 P19 P20 P21 P22 P23 P24 P25 P26


82 x x x x x x x























107 x x x x x x x x







114 x x x x x x x x







27 x x

28 x x x x

29 x x x x x x


89 x x

95 x x x x x x x

118 x x x x x x




122 x x x x x

123 x x x x x x

Unique 0 5 2 7 4 2 0 0 0 0 0 2 1 4 0 0 0 1 1 1 1 1 1 1 1 1

Overlap 3 12 12 23 21 7 12 3 2 3 3 1 4 21 3 6 1 2 1 4 15 6 10 3 2 3

Total 3 17 14 30 25 9 12 3 2 3 3 3 5 25 3 6 1 3 2 5 16 7 11 4 3 4

47

In Table 5 the six proposed towers that were removed from consideration

are highlighted at the top in blue (PROP7, PROP8, PROP9, PROP15, PROP16,

and PROP17). Once After these towers were removed from the set, all 100 AMI

meters still have coverage from the remaining twenty towers. It should also be

noted that none of these six proposed towers provided any unique meter

coverage.

Figure 23. Twenty towers covering 100 meters

48

From the three proposed towers removed from round one the PROP7

tower covered twelve meters (#46, 54, 55, 56, 63, 66, 82, 93, 100, 102, 111,

112), the PROP8 tower covered three meters (#25, 29, 123) and PROP9 only

covered two meters (#122, 123). From round one, PROP2, PROP10 and

PROP11 each gained a unique meter to cover after removal of the uneeded

towers. From the round two removals of PROP15, PROP16, and PROP17, the

PROP14 tower gained seven unique meters (#40, 46, 54, 56, 99, 100, 112) the

PROP18 (from round three additions) gained one unique meter (#123), and the

PROP26 proposed tower gained two unique meters(#63, 82).

Table 6 summarizes the twenty remaining proposed towers and their

unique and overlap meter counts after removal of the uneeded towers from all

three rounds of proposed tower additions. The PROP14 proposed tower gained

seven unique meters covered from the removal of the six unneeded towers.

Section 4.5 outlines the process of choosing the top four proposed towers from

the twenty that remained after removing the towers that had no unique meter

coverage and were considered redundant to the overall meter coverage.

At this point, despite not covering any unique meters the PROP1

proposed tower remained a candidate for providing final meter coverage because

it was the only tower that did not require construction. Adding an antenna to an

existing tower is magnitudes less expensive for Georgia Power than building a

new tower.

49

Table 6. Twenty proposed towers before optimization

4.5 Final Proposed Tower Selection and Optimization

In order to start making a final selection of proposed towers, an

optimization scheme was used to choose towers based on how many meters any

one tower covered, and how many meters each tower provided unique coverage

for. The criteria used for choosing the best candidates for the final meter

coverage were based on how many meters a proposed tower covered regardless

of overlap, and how many unique meters a proposed tower covered. Based on a

minimum overall coverage of ten meters and a unique meter coverage minimum

of three, the proposed towers that met these criteria were PROP2, PROP3,

PROP4, PROP5, PROP14, PROP21, PROP23 and PROP26.

The goal of this step in the research was to narrow down the final proposed

tower selection to four optimal proposed towers that would cover the highest

percentage of AMI meters. When comparing the proposed tower PROP1 to the

eight towers chosen for final optimization, it covered two unique meters that were

not covered by any of the other eight towers (#25, 51). PROP1 was the only

special case in this research based on the fact that a tower already exists at this

location that Georgia Power was not utilizing. When the final statistics were

Tower P1 P2 P3 P4 P5 P6 P10 P11 P12 P13 P14 P18 P19 P20 P21 P22 P23 P24 P25 P26

#Unique meters 0 6 2 7 4 2 1 1 2 1 11 2 1 1 1 1 1 1 1 3

#Overlap meters 3 11 12 23 21 7 2 2 1 4 14 1 1 4 15 6 10 3 2 1

Total meter coverage 3 17 14 30 25 9 3 3 3 5 25 3 2 5 16 7 11 4 3 4

50

Table 7. Eight proposed towers prior to meter coverage optimization

computed for overall meter coverage, the two unique meters that PROP1

covered went into those calculations. Table 7 shows the remaining eight

proposed towers under consideration along with the coverage counts and the

meters each covered.

The goal of these final optimization steps was to choose the combination

of four proposed towers that would provide the highest percentage of covered

meters. The most logical approach was to start with the PROP4 proposed tower

since it covered the highest number of meters overall at thirty. The PROP4 tower

was then combined individually with each of the other seven proposed towers to

see which combination produced the highest count of covered meters.

Proposed

Tower PROP2 PROP3 PROP4 PROP5 PROP14 PROP21 PROP23 PROP26

Total

Coverage 17 14 30 25 25 16 11 4

Unique

count 6 2 7 4 11 1 1 3

Overlap

count 11 12 23 21 14 15 10 1

Meters

Covered

30, 31, 39,

41, 53, 55,

67, 71, 80,

83, 84, 90,

104, 109,

110, 111,

126

35, 43, 47,

48, 57, 59,

62, 68, 74,

78, 79, 85,

91, 113

33, 35, 38,

41, 43, 45,

47, 52, 59,

65, 66, 67,

69, 70, 72,

76, 78, 80,

85, 86, 88,

93, 94,

102, 106,

114, 115,

116, 117,

125

33, 34, 35,

36, 37, 43,

47, 48, 49,

57, 58, 59,

60, 62, 68,

72, 76, 85,

87, 91, 96,

97, 98, 103,

114

38, 40, 42,

46, 53, 54,

56, 61, 64,

66, 67, 75,

83, 90, 93,

99, 100, 101,

102, 112,

115, 116,

117, 125,

126

35, 37, 47,

48, 49, 50,

57, 58, 59,

60, 72, 85,

87, 91, 96,

103

42, 47, 52,

66, 67, 71,

93, 106,

108, 109,

114

63, 82, 95,

107

51

Table 8. Multiple proposed tower combinations for meter coverage

Table 8 is an illustration of the first round of this combinatorial process.

The PROP5 and PROP14 proposed towers both provided an additional sixteen

meters when combined with PROP4. PROP14 was chosen as optimal due to the

higher count of unique meter coverage (eleven versus four for the PROP5

proposed tower).

Proposed

Tower

#meters

covered Meters

Non-overlap

meter count

PROP4 30

33, 35, 38, 41, 43, 45, 47, 52, 59, 65, 66, 67, 69, 70, 72, 76, 78, 80,

85, 86, 88, 93, 94, 102, 106, 114, 115, 116, 117, 125

PROP14 25

38, 40, 42, 46, 53, 54, 56, 61, 64, 66, 67, 75, 83, 90, 93, 99, 100,

101, 102, 112, 115, 116, 117, 125, 126 16

total covered 46

PROP4 30

33, 35, 38, 41, 43, 45, 47, 52, 59, 65, 66, 67, 69, 70, 72, 76, 78, 80,

85, 86, 88, 93, 94, 102, 106, 114, 115, 116, 117, 125

PROP5 25

33, 34, 35, 36, 37, 43, 47, 48, 49, 57, 58, 59, 60, 62, 68, 72, 76, 85,

87, 91, 96, 97, 98, 103, 114 16

total covered 46

Prop4 30

33, 35, 38, 41, 43, 45, 47, 52, 59, 65, 66, 67, 69, 70, 72, 76, 78, 80,

85, 86, 88, 93, 94, 102, 106, 114, 115, 116, 117, 125

PROP2 17 30, 31, 39, 41, 53, 55, 67, 71, 80, 83, 84, 90, 104, 109, 110, 111, 126 14

total covered 44

PROP4 30

33, 35, 38, 41, 43, 45, 47, 52, 59, 65, 66, 67, 69, 70, 72, 76, 78, 80,

85, 86, 88, 93, 94, 102, 106, 114, 115, 116, 117, 125

PROP3 14 35, 43, 47, 48, 57, 59, 62, 68, 74, 78, 79, 85, 91, 113 8

total covered 38

PROP4 30

33, 35, 38, 41, 43, 45, 47, 52, 59, 65, 66, 67, 69, 70, 72, 76, 78, 80,

85, 86, 88, 93, 94, 102, 106, 114, 115, 116, 117, 125

PROP21 16 35, 37, 47, 48, 49, 50, 57, 58, 59, 60, 72, 85, 87, 91, 96, 103 11

total covered 41

PROP4

33, 35, 38, 41, 43, 45, 47, 52, 59, 65, 66, 67, 69, 70, 72, 76, 78, 80,

85, 86, 88, 93, 94, 102, 106, 114, 115, 116, 117, 125

PROP26 63, 82, 95, 107 4

total covered 34

PROP4

33, 35, 38, 41, 43, 45, 47, 52, 59, 65, 66, 67, 69, 70, 72, 76, 78, 80,

85, 86, 88, 93, 94, 102, 106, 114, 115, 116, 117, 125

PROP23 42, 47, 52, 66, 67, 71, 93, 106, 108, 109, 114 4

total covered 34

52

Now that the PROP14 and PROP4 proposed tower combination was

selected with 46 meters covered, the next six towers were combined to see

which combination of three towers would give the highest meter count. The

PROP4 + PROP14 + PROP5 combination produced 62 meters covered. The

addition of PROP2 resulted in 56 meters, PROP3 covered 54, PROP21 covered

57, PROP23 covered 49, and PROP26 covered 50. PROP5 would now be

added to PROP4 and PROP14 in the second round of comparisons because it

produced the highest count with 62 meters covered. With three towers combined

(PROP4, PROP5, and PROP14) the five remaining proposed towers were added

to see which gave the highest meter count. Including PROP2 gave an additional

ten meters to total 72 covered. PROP3 contributed three meters to raise the

count to 65. PROP21 contributed one, PROP23 three, and PROP26 provided

four additional meters. Based on these numbers it was determined that by

combining four proposed towers (PROP4, PROP14, PROP5, and PROP2) the

highest count of covered meters was 72. This satisfied the goal of choosing the

four most optimal towers that would cover the most AMI meters. Figure 24 is an

illustration of the PROP4, PROP14, PROP5, and PROP2 proposed towers.

PROP1 is included as well since this proposed tower does not require

construction. It contributed two covered meters (#25, 51) that were not covered

by any other remaining proposed towers during the final rounds of optimization.

The four proposed towers plus PROP1 account for 74 meters covered, a 74%

rate of coverage based on the original 100 meters that needed coverage.

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Figure 24. Final five proposed towers with meter coverage

54

CHAPTER 5: CONCLUSION

The goal of this research was to find a way to provide radio signal

coverage to AMI smart meters that Georgia Power had identified as experiencing

coverage problems. Placing new antenna on existing communication towers or

building new towers will help solve this problem. The portion of the

electromagnetic spectrum in the 890-960 MHz range that the meters and towers

communicate with is known to be affected by line-of-sight issues. The question

that needs to be asked is where to build or locate new communication towers

that can “see” the meters in the field. This question becomes a set covering

problem (Straitiff and Cromley 2010) because the goal is to provide signal

coverage to as many meters as possible while minimizing the number of new

communication towers. The methods presented in this research are just one of

many different solutions to this type of problem. When it comes to constructing

or purchasing new assets in the field, the return on investment factor is high with

a company like Georgia Power. The AMI meters were deployed to improve the

efficiency of the “Smart Grid” Georgia Power is building and to save money in the

long term. Building every tower recommended by this research is not a plausible

real-world solution, but finding these potential tower locations arms the company

with more information to make intelligent decisions.

In this research, it was assumed that when an AMI meter is sending a

signal back to the base station antenna on the communication tower that there

needs to be a clear line-of-sight between the two. While this is an optimal

55

situation, it is not completely necessary for the signal to propagate. Man-made

structures, foliage, and the natural topography of the landscape all contribute

certain amounts of interference with the signal, yet the majority of the meters in

the field are able to transmit without too many problems. The “Buddy Mode” AMI

meters are designed to take advantage of also helps in this regard by allowing

the signal from a meter to “hop” to another nearby meter that has a

communication link back to the tower.

Finding a location in the field for a new tower might seem too obvious - go

to the highest mountain in the area and build it! While this may be an intuitive

approach, the fact that the initial viewshed analysis of the existing two

communication towers in the area only captured six out of 100 AMI meters tells a

different story. The terrain in Rabun County GA is very mountainous and a line-

of-sight from any high point misses many features in valleys and the other sides

of equally tall places. Developing a method for finding the best location for a new

tower requires a methodical approach which this research explores.

Given many factors to contend with in this analysis, the main ones settled

upon were the elevation, the slope of land, the distance from meters, and the

distance from existing towers. Putting these factors into a suitability analysis, the

elevation is the most important. It makes sense that a tower located on higher

ground will cover more of any given area. The proximity to the AMI meters was

important, because there is no need for a tower on top of a mountain that is miles

from the nearest meter. The distance from existing towers was considered as

well because tower coverage overlap would be inefficient and each had a usable

56

range of 8 km (5 miles) given the terrain and population density of Rabun County

according to Cason (2010). The slope of land where a tower could be built

factored into the analysis as well. If an area is very steep too much construction

and land grading would be needed to build a structure as large as a radio

communication tower.

This suitability analysis found many good places to build new towers. At

this point in the research land use became an issue. The communication towers

should not be too close to existing residential or commercial structures, and it

would be helpful if the construction site was near an existing road or gravel path.

Land use analysis was part of the suitability analysis that the company SENSUS

used in deploying AMI meters and communication tower antenna when Georgia

Power began rolling out smart meters (SENSUS, 2010b). This research did not

directly rely on land use data for analysis, but it became a consideration and

Microsoft Bing mapping aerial overlays helped to determine where road networks

and man-made structures existed.

After placing eleven new towers and then scanning the area with a

viewshed analysis, twenty-three of the original 100 meters did not have line-of-

sight to a tower. This was expected due to the irregular terrain in the area.

Increasing the height of the towers from 30 m to 50 m high each only captured

four additional meters. Repetition of the add towers/run a viewshed cycle twice

more led to enough towers placed to cover all the AMI meters. However, in a

relatively small area of Georgia compared to the state as whole, there were now

26 proposed towers. According to data obtained at the beginning of this study,

57

there were less than 60 towers across the entire State of Georgia that Georgia

Power had an antenna on. The number of proposed towers was reduced by

analyzing them using a theoretical coverage area that combined the 8 km (5

mile) buffer with the viewshed of each tower. It was assumed that a proposed

tower would optimally be able to provide coverage for a meter if that meter was

both visible to the tower and was within the 8 km buffer zone. In reality, a

proposed tower could provide coverage to a meter outside its buffer zone but not

reliably. The fact that there were six meters within the viewshed of the existing

Rabun County towers, and three that should have been “covered” by the existing

towers goes to show that even under this ideal situation a meter can still

experience signal transmission issues. This was the main reason that the six

meters that should have had coverage from the existing towers were considered

during the analysis to need coverage from a new tower. Narrowing down the

results using location-allocation concepts, there were twenty proposed towers

produced from the original twenty-six based on providing unique (non-

overlapping) coverage to at least one meter. Most were located along

Warwoman Road heading east out of Clayton, GA along a mountain valley. The

majority of the meters experiencing issues were found along this route as well, so

this was not a surprising result from the analysis. A small number of meters to

the west of Clayton, GA remained uncovered, and they will eventually need to be

accounted for which goes beyond the scope of this research.

The 8 km (5 mile) buffer zone and the viewshed of each proposed tower

were used to define coverage for meters. Defining unique coverage limits and

58

overall meter coverage amounts was an attempt to truly optimize the meter

coverage while minimizing the number of proposed towers. Looking at proposed

towers that gave the highest overall coverage along with the highest unique

meter coverage identified four optimal towers. Comparing different combinations

of high coverage proposed towers gave the most efficient results for coverage

while minimizing the number of towers. Given that it required twenty-six

proposed towers to cover all 100 meters within a viewshed, it was a surprising

result to find that it only required four proposed towers to raise the viewshed and

buffer coverage to 72% of the meters. The addition of the PROP1 tower raised

the final meter count to 74% of the original 100.

This research was meant to showcase the application of GIS based

suitability analysis to solving real-world problems and also serves as a tool that

Georgia Power can employ to solve asset management issues. Instead of

blindly picking places on a map that look like they might be a good place for a

tower, this research methodology eliminates much of the guesswork. This study

saves the company time, resources, and money as it attempts to build out the

smart electrical grid for Georgia. The research was successful in identifying an

existing tower just south of the town of Clayton, GA where Georgia Power does

not have a leased antenna. This tower (PROP1) failed to provide unique

coverage to any single meter until the final four proposed towers were selected,

but it is very likely that the company would want to invest in an antenna at this

location because leasing space on a tower is magnitudes less costly than

building a new one.

59

Optimizing the towers resulting from the research using first the viewshed

analysis then the combination of a viewshed with a buffer zone was an attempt to

maximize the return on investment factor for Georgia Power. Just because one

of the proposed towers did not cover that many meters in its combined viewshed

and buffer zone does not mean the company cannot decide at some point later to

construct a tower at the location. This research was intended to provide an

educated guess for potential locations. Field studies of radio signal strength

would ultimately need to be performed to make any final decisions. The “Buddy

Mode” where each meter can broadcast a signal to another nearby meter instead

of directly to the communication tower can help fill in gaps left by the towers.

Despite this functionality in the meters, monitoring by the AMI metering group

shows that a small percentage of meters deployed across the state still

experienced a lack of communication even in parts of Georgia where the terrain

does not contribute heavily to the problem.

5.1 Study Limitations

One of the biggest obstacles that this research encountered was a lack of

additional LiDAR coverage for the remaining portions of Rabun County. A third

existing communication tower already in operation within the county was not

included in this study because it was too far south of the study area defined by

elevation data. Only 100 AMI meters out of 126 total fell within the extent of the

LiDAR coverage. Including these twenty-six additional meters would have made

the recommendation for additional tower sites more robust. Having additional

60

knowledge about the installation height of each meter would have improved the

viewshed analysis. One meter (three feet) was the assumed height assigned to

each AMI meter based on the location of a typical meter on a residential

structure. This study relied on the three inputs of elevation, distance to the

nearest tower, and distance from the AMI meters to calculate a raster that would

select land areas based on the optimization of these factors. Another factor that

could have improved and further reduced the potential tower sites was land use

data. Prior knowledge of land use would prevent the selection of unusable

locations and remove the educated guess work used to pick locations that were

analyzed in this study. Another piece of information that could have been added

to the initial suitability analysis was road network data. GPC has this data

available in the distribution network landbase information. Unfortunately the

further away from dense urban areas one goes, the worse the road centerlines

match up with other data sources such as Bing and Google maps. In some

instances the road data is 700 feet offset or worse from reality. Another source

of road network data could have been found from the Rabun County website or

other state governmental agencies. A decision was made not to pursue this data

because the proximity of a proposed tower site to the road network was the least

important factor to consider given other land use data such as buildings and

other structures. Many of the proposed tower site locations were also rural

enough that obtainable road data may not have helped with site identification.

Many times old logging roads or other unpaved paths are not listed in data

sources even though they may be visible from aerial photography. Additional

61

modifications of the weights assigned to each factor could have also changed the

land areas that were chosen for further analysis. Favoring an emphasis on either

the distance to the meters or the distance to a tower over the elevation factor

would have altered the land areas that made it into the top five rankings of

distance and elevation.

5.2 Future Improvement

The suitability model this research produced can always be modified and

improved by including additional factors to produce a more focused solution to

the “Tower Location Problem”. Additional elevation data for the meters could

have improved the viewshed analysis. Including land use data in the initial raster

calculations would have the effect of further reducing the potential locations for

new towers. Without knowing ownership or the land use of locations selected

from the suitability analysis, the placement of proposed towers came down to an

“educated” guess. The Bing Map layers were helpful in showing the location of

structures and roads which are important for tower construction considerations,

but there is an inherent lack of detail with this publicly available information.

Relying on the AMI meter “Buddy Mode” to fill in coverage gaps is another factor

that could reduce the number of towers needed for full coverage. A meter

located deep within a valley could potentially have contact with another meter in

the area that is within the viewshed of a communication tower. This would

eliminate the need to place a single tower in order to serve only one meter. More

information on the broadcast and range abilities of the meters would have been

62

necessary to include as data in the analysis. Details such as these add levels of

complexity to this type of analysis, but the trade-off is a more exacting solution to

the problem. Adding sophistication to this model by including more factors such

as land use data and the signal range of individual meters would pinpoint better

and fewer potential tower locations. The return on investment for each new

tower will be higher depending on how many meters it can reach. This research

can serve as a framework for continued analysis and research efforts aimed at

solving the “Tower Location Problem”.

63

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