U.S. patent application number 14/299552 was filed with the patent office on 2014-09-25 for method and system for analysis of infrastructure.
This patent application is currently assigned to General Electric Company. The applicant listed for this patent is Richard Patrick Joyce, JR., Richard Scott Snow, Neal Lewis Zaher. Invention is credited to Richard Patrick Joyce, JR., Richard Scott Snow, Neal Lewis Zaher.
Application Number | 20140288993 14/299552 |
Document ID | / |
Family ID | 48744546 |
Filed Date | 2014-09-25 |
United States Patent
Application |
20140288993 |
Kind Code |
A1 |
Zaher; Neal Lewis ; et
al. |
September 25, 2014 |
METHOD AND SYSTEM FOR ANALYSIS OF INFRASTRUCTURE
Abstract
A method and a system for analyzing infrastructure are provided.
In one embodiment, a system for analyzing infrastructure is
provided. The system includes a grid intelligence quotient as a
service (GIQAAS) system. The GIQAAS system includes an
infrastructure data gathering system having a first input form
configured to collect an economic data related to an
infrastructure. The GIQAAS system further includes an economic
modeling system configured to import the economic data and to
derive an economic analysis based on the economic data. The GIQAAS
system is configured to provide the infrastructure data gathering
system to a first user for collection of the economic data, and to
provide the economic modeling system to a second user for
derivation of the economic analysis.
Inventors: |
Zaher; Neal Lewis; (Atlanta,
GA) ; Joyce, JR.; Richard Patrick; (Suwanee, GA)
; Snow; Richard Scott; (Ponte Vedra Beach, FL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Zaher; Neal Lewis
Joyce, JR.; Richard Patrick
Snow; Richard Scott |
Atlanta
Suwanee
Ponte Vedra Beach |
GA
GA
FL |
US
US
US |
|
|
Assignee: |
General Electric Company
Schenectady
NY
|
Family ID: |
48744546 |
Appl. No.: |
14/299552 |
Filed: |
June 9, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
13344554 |
Jan 5, 2012 |
8751291 |
|
|
14299552 |
|
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|
Current U.S.
Class: |
705/7.25 |
Current CPC
Class: |
G06Q 10/0875 20130101;
G06Q 10/06375 20130101; G06Q 10/06315 20130101; G06Q 40/10
20130101; G06Q 10/0637 20130101; G06Q 50/06 20130101; G06Q 10/0639
20130101 |
Class at
Publication: |
705/7.25 |
International
Class: |
G06Q 50/06 20060101
G06Q050/06; G06Q 10/06 20060101 G06Q010/06 |
Claims
1. A system for analyzing infrastructure, comprising: a Software as
a Service (SaaS) system comprising: a smart grid asset gathering
system having a first input form configured to collect an economic
data related to the smart grid asset; and an economic modeling
system configured to import the economic data and derive an
economic analysis based on the economic data, wherein the SaaS
system is configured to provide the infrastructure data gathering
system to a first user for collection of the economic data, and to
provide the economic modeling system to a second user for
derivation of the economic analysis.
2. The system of claim 1, wherein the infrastructure comprises a
smart grid system infrastructure, and the first user comprises a
purchaser of smart grid system infrastructure, a lessee of smart
grid infrastructure, or a combination thereof, and the second user
comprises a seller of smart grid system infrastructure, a lessor of
smart grid infrastructure, or a combination thereof
3. The system of claim 2, wherein the smart grid system
infrastructure comprises at least one of an advanced metering
infrastructure (AMI), an outage management system (OMS), a
distribution management system (DMS), a geographic information
system (GIS), a customer information system (CIS), an interactive
voice response (IVR) system, a power plant component, a gas
processing plant component, a water processing plant component, an
electrical distribution grid component, a water distribution grid
component, a gas distribution grid component, or a utility control
center component, or a combination thereof.
4. The system of claim 3, wherein the infrastructure data gathering
system comprises a second input form configured to collect second
inputs related to a first suite of offerings, and a third input
form configured to collect third inputs related to a second suite
of offerings, and wherein the first input form is configured to
collect first inputs related to at least one of residential
customers, or commercial customers, or a combination thereof.
5. The system of claim 4, wherein the first inputs comprise at
least one of a cost per truck roll, a reconnect charge, a carry
cost of post-pay, a bad debt, an inflation, a discount rate, an
electric endpoint input, or a gas endpoint input, a water endpoint
input, or a combination thereof.
6. The system of claim 4, wherein the second and the third inputs
comprise cost and benefit inputs related to at least one of the
advanced metering infrastructure (AMI), the outage management
system (OMS), the distribution management system (DMS), the
geographic information system (GIS), the customer information
system (CIS), the interactive voice response (IVR) system, the
power plant component, the gas plant component, the water plant
component, the electrical distribution grid component, or the water
distribution grid component, the gas distribution grid component,
or a combination thereof.
7. The system of claim 4, wherein the second suite of offerings is
provided by a single manufacturer.
8. The system of claim 1, wherein the economic modeling system
comprises an economic model configured to process the economic data
to derive the economic analysis, and wherein the economic model
comprises at least one of an inflation forecast model, an operating
cost model, a monthly revenue model, or a customer defaults model,
a finance charges model, or a combination thereof.
9. The system of claim 8, wherein the economic analysis comprises a
comparison between a first cost and a first benefit related to a
first suite of infrastructure offerings, and a second cost and a
second benefit related to a second suite of infrastructure
offerings.
10. The system of claim 1, comprising a first spreadsheet having
the infrastructure data gathering system and a second spreadsheet
having the economic modeling system, or a first website having the
infrastructure data gathering system and a second website having
the economic modeling system, or a first software system having the
infrastructure data gathering system and a second software system
having the economic modeling system, or a combination thereof.
11. A non-transitory machine readable media, comprising:
instructions configured to collect an economic data from a first
user related to a smart grid infrastructure; instructions
configured to import the economic data; instructions configured to
derive an economic analysis based on the economic data; and
instructions configured to present the economic analysis to a
second user, wherein the economic analysis comprises an economic
comparison between a first suite of smart grid infrastructure
offerings and a second suite of smart grid infrastructure
offerings.
12. The non-transitory machine readable media of claim 11, wherein
the instructions configured to collect the economic data from the
first user comprise instructions configured to collect first inputs
related to at least one of residential customers, or commercial
customers, or a combination thereof, second inputs related to the
first suite of smart grid infrastructure offerings, and third
inputs related to the second suite of smart grid infrastructure
offerings, and the first user comprises a smart grid infrastructure
purchaser, a smart grid infrastructure lessee, or a combination
thereof.
13. The non-transitory machine readable media of claim 12, wherein
the instructions configured to collect the first inputs related to
at least one of residential customers, or commercial customers, or
a combination thereof, comprise instructions configured to collect
at least one of a cost per truck roll, a reconnect charge, a carry
cost of post-pay, a bad debt, an inflation, a discount rate, an
electric endpoint input, a gas endpoint input, or a water endpoint
input, or a combination thereof.
14. The non-transitory machine readable media of claim 12, wherein
the instructions configured to collect the second inputs and the
third inputs comprise instructions configured to collect at least
one of an advanced metering infrastructure (AMI) input, an outage
management system (OMS), a distribution management system (DMS), a
geographic information system (GIS), a customer information system
(CIS), an interactive voice response (IVR) system, a power plant
component, a gas plant component, a water plant component, an
electrical distribution grid component, a water distribution grid
component, or a gas distribution grid component, or a combination
thereof.
15. The non-transitory machine readable media of claim 11, wherein
the instructions configured to derive the economic analysis based
on the economic data comprise instructions configured to use an
economic model to process the economic data to derive the economic
analysis, wherein the economic model comprises at least one of an
inflation forecast model, an operating cost model, a monthly
revenue model, a customer defaults model, or a finance charges
model, or a combination thereof.
16. The non-transitory machine readable media of claim 11, wherein
the instructions configured to present the economic analysis to the
second user comprise instructions configured to display a first
column of economic data related to the first suite of smart grid
infrastructure offerings and a second column of economic data
related to the second suite of smart grid infrastructure offerings,
and a graph configured to visually graph the first column and the
second column of economic data, and the second user comprises a
smart grid infrastructure seller, a smart grid infrastructure
lessor, or a combination thereof.
17. A method comprising: calculating a monthly cost by using an
inflation forecast model configured to derive an inflation and an
infrastructure operating cost model configured to derive a smart
grid infrastructure operating cost; calculating an as-is net
monthly revenue by using the monthly cost, a monthly revenue model
configured to derive an operating monthly revenue, a customer
defaults model configured to derive a customer default cost, and a
finance charges model configured to derive a financing cost;
calculating a first net monthly revenue by using first inputs
related to a first suite of smart grid infrastructure offerings;
calculating a second net monthly revenue by using second inputs
related to a second suite of smart grid infrastructure offerings;
and presenting the as-is, first, and second monthly revenues for
comparison.
18. The method of claim 17, wherein the first inputs are used by
the inflation forecast model, the operating costs model, the
monthly revenues model, the customer defaults model, the finance
charges model, or a combination thereof, to calculate the first net
monthly revenue.
19. The method of claim 17, wherein the second inputs are used by
the inflation forecast model, the operating costs model, the
monthly revenues model, the customer defaults model, the finance
charges model, or a combination thereof, to calculate the second
net monthly revenue.
20. The method of claim 17, wherein the first and the second inputs
comprise cost and benefit inputs related to at least one of an
advanced metering infrastructure (AMI), an outage management system
(OMS), a distribution management system (DMS), a geographic
information system (GIS), a customer information system (CIS), an
interactive voice response (IVR) system, a power plant component, a
gas plant component, a water plant component, an electrical
distribution grid component, a water distribution grid component,
or a gas distribution grid component, or a combination thereof.
Description
[0001] This application is a Continuation Application of U.S.
non-provisional application Ser. No. 13/344,554 entitled "METHOD
AND SYSTEM FOR ANALYSIS OF INFRASTRUCTURE", filed Jan. 5, 2012,
which is herein incorporated by reference.
BACKGROUND
[0002] The invention relates generally to infrastructure, and more
particularly to a method and system for analyzing
infrastructure.
[0003] Infrastructure, such as a smart grid, includes a variety of
systems and components. In the smart grid example, systems may
include power generation systems, power transmission systems,
advanced metering systems, digital communications systems, control
systems, and their related components. Other items associated with
the infrastructure may include contracts, service level agreements,
and like, detailing capitalization, cost, and revenues for the
infrastructure. Designing and analyzing such infrastructure can be
complex and costly.
BRIEF DESCRIPTION
[0004] Certain embodiments commensurate in scope with the
originally claimed invention are summarized below. These
embodiments are not intended to limit the scope of the claimed
invention, but rather these embodiments are intended only to
provide a brief summary of possible forms of the invention. Indeed,
the invention may encompass a variety of forms that may be similar
to or different from the embodiments set forth below.
[0005] In one embodiment, a system for analyzing infrastructure is
provided. The system includes a grid intelligence quotient as a
service (GIQAAS) system. The GIQAAS system includes an
infrastructure data gathering system having a first input form
configured to collect an economic data related to an
infrastructure. The GIQAAS system further includes an economic
modeling system configured to import the economic data and to
derive an economic analysis based on the economic data. The GIQAAS
system is configured to provide the infrastructure data gathering
system to a first user for collection of the economic data, and to
provide the economic modeling system to a second user for
derivation of the economic analysis.
[0006] In a second embodiment, non-transitory machine readable
media is provided. The non-transitory machine readable media
includes instructions configured to collect an economic data
related to a smart grid infrastructure and instructions configured
to import the economic data. The non-transitory machine readable
media further includes instructions configured to derive an
economic analysis based on the economic data. The non-transitory
machine readable media also includes instructions configured to
present the economic analysis to a user. The economic analysis
includes an economic comparison between a first suite of smart grid
infrastructure offerings and a second suite of smart grid
infrastructure offerings.
[0007] In a third embodiment, a method is provided. The method
includes calculating a monthly cost by using an inflation forecast
model configured to derive an inflation and an infrastructure
operating cost model configured to derive a smart grid
infrastructure operating cost. The method also includes calculating
a as-is net monthly revenue by using the monthly cost, a monthly
revenue model configured to derive an operating monthly revenue, a
customer defaults model configured to derive a customer default
cost, and a finance charges model configured to derive a financing
cost. The method further includes calculating a first net monthly
revenue by using first inputs related to a first suite of smart
grid infrastructure offerings. The method additionally includes
calculating a second net monthly revenue by using second inputs
related to a second suite of smart grid infrastructure offerings,
and presenting the as-is, first, and second monthly revenues for
comparison.
DRAWINGS
[0008] These and other features, aspects, and advantages of the
present invention will become better understood when the following
detailed description is read with reference to the accompanying
drawings in which like characters represent like parts throughout
the drawings, wherein:
[0009] FIG. 1 is a block diagram of an embodiment of an
infrastructure (e.g., a smart grid infrastructure) and a grid
intelligence quotient as a service (GIQAAS) system;
[0010] FIG. 2 is a screen view of an embodiment of an
infrastructure data gathering system of the GIQAAS system of FIG.
1;
[0011] FIG. 3 is a screen view of an embodiment of an input form
for a first suite of infrastructure offerings included in the
infrastructure data gathering system of FIG. 2;
[0012] FIG. 4 is a screen view of an embodiment of an input form
for a second suite of infrastructure offerings included in the
infrastructure data gathering system of FIG. 2;
[0013] FIG. 5 is a screen view of an embodiment of a worksheet form
included in the infrastructure data gathering system of FIG. 2;
[0014] FIG. 6 is another screen view of an embodiment of the
worksheet form included in the infrastructure data gathering system
of FIG. 2;
[0015] FIG. 7 is a screen view of an embodiment of an economic
modeling system of the GIQAAS system of FIG. 1;
[0016] FIG. 8 is another screen view of an embodiment of the
economic modeling system of the FIG. 7;
[0017] FIG. 9 is illustrative of an embodiment of a dialog box
included in the economic modeling system of FIG. 7;
[0018] FIG. 10 is a screen view of an embodiment of a results form
included in the economic modeling system of FIG. 7;
[0019] FIG. 11 is illustrative of an embodiment of a line graph
included in the economic modeling system of FIG. 7;
[0020] FIG. 12 is illustrative of an embodiment of a bar graph
included in the economic modeling system of FIG. 7;
[0021] FIG. 13 is block diagram of an embodiment of an economic
model included in the economic modeling system of FIG. 7; and
[0022] FIG. 14 is flow chart of an embodiment of a process enabling
the economic analysis of infrastructure using the GIQAAS system of
FIG. 1.
DETAILED DESCRIPTION
[0023] One or more specific embodiments of the invention will be
described below. In an effort to provide a concise description of
these embodiments, all features of an actual implementation may not
be described in the specification. It should be appreciated that in
the development of any such actual implementation, as in any
engineering or design project, numerous implementation-specific
decisions must be made to achieve the developers' specific goals,
such as compliance with system-related and business-related
constraints, which may vary from one implementation to another.
Moreover, it should be appreciated that such a development effort
might be complex and time consuming, but would nevertheless be a
routine undertaking of design, fabrication, and manufacture for
those of ordinary skill having the benefit of this disclosure.
[0024] When introducing elements of various embodiments of the
invention, the articles "a," "an," "the," and "said" are intended
to mean that there are one or more of the elements. The terms
"comprising," "including," and "having" are intended to be
inclusive and mean that there may be additional elements other than
the listed elements.
[0025] Certain infrastructure, such as an electric smart grid, may
include a variety of interconnected systems and components. For
example, the smart grid may include power generation systems, power
transmission and distribution systems, metering systems, digital
communications systems, control systems, and their related
components. Other items associated with the smart grid may include
contracts, service level agreements, and like, detailing costs
(e.g., capitalization costs, operating costs), and revenues for the
smart grid. The embodiments described herein enable an
infrastructure designer to compare and contrast various
combinations of components from one or more manufacturers to
determine a component package that minimizes cost, maximizes a
return on investment (ROI), and performs desired functions. In
certain embodiments, a Grid Intelligence Quotient as a Service
(GIQAAS) system is provided that enables a more efficient selection
and analysis of various smart grid components. The GIQAAS may
include non-transitory machine readable media storing code or
computer instructions that may be used by a computing device to
implement the techniques disclosed herein. The GIQAAS may include
an infrastructure data gathering system that enables the collection
of data related to a variety of smart grid technology offerings,
including cost and benefit data. The offerings may include
hardware, software, and/or service offerings from one or more
manufacturers or service providers. In one embodiment, a first
suite of infrastructure offerings from the one or more
manufacturers or service providers may be compared to a second
suite of infrastructure offerings from a single manufacturer or
service provider, such as one or more smart grid packages available
from General Electric Co., of Schenectady, N.Y. The comparison may
include a detailed analysis of purchasing or leasing the first
suite of offerings as compared to purchasing or leasing the second
suite of offerings. For example, a value analysis may be performed,
comparing costs, benefits, and a return on investment (ROI) of the
first and the second suite of infrastructure offerings.
[0026] In certain embodiments the GIQAAS may use a model, such as
an economic model, to analyze the one or more suite of offerings.
In one embodiment, the model may be separate from the
infrastructure data gathering system included in the GIQAAS system.
For example, the economic modeling system may include the economic
model. The infrastructure data gathering system may be provided to
a firs user or customer (e.g., a purchaser or lessee of smart grid
infrastructure, such as a water, gas, or electric utility), and may
enable the collection of infrastructure data, including a type of
infrastructure (e.g., technology category or class), usage for the
infrastructure, capital expenditure data, financing data (e.g.,
interest rates, payment options, revenue collections, inflation),
and the like, as described in more detail below. The economic
modeling system may then be used to analyze the data gathered. For
example, the economic modeling system may be provided to a second
user, such as a seller or lessor of smart grid infrastructure. The
separation between the modeling activities and the data collection
activities minimizes or eliminates any erroneous manipulation of
the model by the data gathering team. Indeed, the infrastructure
data gathering system may be emailed to an end user having little
or no knowledge of modeling techniques, or may be provided through
the internet via web pages or websites. The model may then use the
data collected by the infrastructure data gathering system to
derive the detailed analysis and comparison between the first and
the second suite of offerings. By providing for an analysis between
various hardware, software, and/or service configurations, an
infrastructure designer may design infrastructure that meets
desired properties while improving the costs and revenues
associated with the infrastructure. In this way, a seller of
infrastructure may provide a buyer of infrastructure for a
comprehensive economic analysis, including cost comparisons between
the seller's offerings and/or competitor's offerings.
[0027] With the foregoing in mind, it may be useful to describe an
embodiment of an infrastructure, such as an example smart grid
system 10 illustrated in FIG. 1. It is to be noted that the systems
and methods described herein may apply to other infrastructure,
including but not limited to cloud computing infrastructure,
airport infrastructure, transit infrastructure, and
telecommunications infrastructure. As depicted, the smart grid
system 10 may include one or more utilities 12. The utility 12 may
provide for oversight operations of the smart grid system 10. For
example, utility control centers 14 may monitor and direct power
produced by one or more power generation stations 16 and
alternative power generation stations 18. The power generation
stations 16 may include conventional power generation stations,
such as power generation stations using gas, coal, biomass, and
other carbonaceous products for fuel. The alternative power
generation stations 18 may include power generation stations using
solar power, wind power, hydroelectric power, geothermal power, and
other alternative sources of power (e.g., renewable energy) to
produce electricity. Other infrastructure components may include a
water processing plant 20 and gas processing plant 22. For example,
water processing plants 20 may provide for potable water, and gas
processing plants 22 may provide for natural gas.
[0028] The power generated by the power generation stations 16 and
18 may be distributed through a power transmission grid 24.
Likewise, the water and gas provided by the plants 20 and 22 may be
delivered through a water distribution grid 26 and a gas
distribution grid 28. The grids 24, 26, and 28 may cover a broad
geographic region or regions, such as one or more municipalities,
states, or countries. In the depicted embodiment, an advanced
metering infrastructure (AMI) 30 may be used to measure, collect,
and analyze electricity, water, and/or gas usage. The AMI 30 may be
communicatively coupled to one or more of the components of the
smart grid 10, including the grids 24, 26, 28. Additionally, the
AMI 30 may enable two-way communication between commercial sites
32, residences 34 and the utility control center 14, providing for
a link between consumer behavior and utility consumption (e.g.,
electric, water, and/or gas consumption). For example, AMI meters
30 may track and account for pre-paid electricity, water and/or gas
in a similar fashion to pre-paid cell phone usage. Likewise, the
utility's consumers 32 and 34 may benefit from lower utility
charges by optimizing their utility use, for example, to take
advantage of lower rates during low demand hours. Washer/dryers,
electric car chargers, and other flexible power consumption
appliances may be programmed to operate during low demand hours,
resulting in lower utility bills and a more balanced utilization of
energy.
[0029] An outage management system (OMS) 36 may predict and respond
to outage events, for example, by using an outage prediction
engine. The OMS 30 may interact with a distribution management
system (DMS) 38 suitable for re-routing power, water, and/or gas
from plants 16, 18, 20, and 22 experiencing lesser demand and away
from plants 16, 18, 20, and 22 experiencing greater demand. A
geographic information system (GIS) 40 may also be used to provide
for physical location information of the plants 16, 18, 20, and 22,
grid 24 components (e.g., electrical cabling, transformers,
distribution towers), grid 26 components (e.g., water pipes,
valves, pressure reducers), grid 28 components (e.g., storage
tanks, gas pipes, valves), the AMI 30, the commercial sites 32 and
the residential sites 34. The physical location information may be
used, for example, by the OMS 36 and the DMS 38 in predicting and
resolving outage issues. Additionally, the GIS 40 may be used to
visualize the various locations on a map or other suitable visual
medium (e.g., globe, chart). Likewise, a customer information
system (CIS) 42 may be used to provide customer information (e.g.,
sites 32 and 34), including billing information, electric usage
information, water usage information, gas usage information,
billing rates, and the like. Additionally, an interactive voice
response (IVR) system 44 may provide automated voice recognition
and menu navigation suitable for processing customer requests over
telephonic lines.
[0030] The smart grid system 10 enables the utility 12 to improve
capacity, improve energy distribution and usage, and to dynamically
manage loads more effectively. However, the smart grid system 10
may include components, such as but not limited to components of
the control center 14, components of the plants 16, 18, 20, and 22,
components of the grid 24, components of the grid 26, components of
the grid 28, the AMI 30, the OMS 36, the DMS 38, the GIS 40, the
CIS 42, and/or the IVR 44 that may be provided by multiple
manufacturers. It would be beneficial for the utility 12 to compare
and contrast the aforementioned components of the smart grid system
10 to derive a value analysis useful in determining costs,
benefits, and/or ROI based on income received from the customer
base (e.g., commercial sites 32 and residences 34) and costs
associated with capitalization or leasing of the aforementioned
smart grid system 10 components. By deriving an economic analysis,
a more optimal suite of components and services may be used in the
smart grid 10. Advantageously, the embodiments describe herein,
such as a GIQAAS system 46 having an infrastructure data gathering
tool 48 and an economic modeling tool 50, may be used to provide
for an economic analysis of the smart grid system 10 component, as
described in more detail below.
[0031] FIG. 2 is a screen view 52 of an embodiment of an input form
or tab 54 of the infrastructure data gathering tool 48 included in
the GIQAAS system 46. The data gathering tool 48 may be provided,
for example, to the utility 12 for use in collecting information
related to the technologies (e.g., components of the control center
14, components of the plants 16, 18, 20, and 22, components of the
grid 24, components of the grid 26, components of the grid 28, the
AMI 30, the OMS 36, the DMS 38, the GIS 40, the CIS 42, and/or the
IVR 44) that may be used during implementation or rollout of the
smart grid system 10, as well as the costs and benefits of using
such technologies. In the depicted embodiment, the data gathering
tool 48 is implemented by using a computer-based spreadsheet. In
other embodiments, the data gathering tool 48 may be implemented as
a web-based system or in any form executable by a computing device
(e.g., laptop, workstation, table, cell phone), such as a software
system or program provided in a computer-readable medium (e.g.,
DVD, flashdrive).
[0032] The tab 54 enables the gathering of smart grid 10 customer
value analysis data. For example, the tab 54 may be used to collect
data generally related to the value or profit garnered from
customers 32 and 34 of the utility 12, as depicted in the screen
view 52. The collected data may include rows 56, which include a
customer name 58, a contract start date 60, a contract term 62, a
cost per truck roll 64 (e.g., cost to deploy a service vehicle), a
reconnect charge (e.g., cost to reconnect a customer that has been
previously disconnected) 66, a carry cost of post-pay 68 (e.g.,
percent of total cost to carry for customers that post pay bills),
a bad debt 70 (e.g., percent of customers that do not pay the
monthly bill), an inflation 72 (e.g., average national or local
inflation rate), a discount rate 74 (e.g., average interest rate),
a residential electric 76 flag (e.g., a true of false value based
on if the analysis to be performed will include electric meters), a
residential water flag 78 (e.g., a true of false value based on if
the analysis to be performed will include water meters), and a
residential gas flag 80 (e.g., a true of false value based on if
the analysis to be performed will include gas meters).
[0033] Rows 82 generally collect data related to the value or
profit garnered from electric customers 32, 34. Accordingly, the
rows 82 include an endpoints (electric) 84, where an endpoint
represents a residential site 32 or a customer site 34. The rows 82
also include an average customer monthly bill amount (electric) 86,
a cost per bill (electric) 88 (e.g., a cost of generating each
customer bill), a monthly disconnect rate (electric) 90 (e.g.,
percent of customers that disconnect each month), a high risk
percent (electric) 92 (e.g., customers having a higher risk of
defaulting on payment), and a percent loss (electric) 94 (e.g.,
percent of electricity that is lost before arriving at the
endpoint).
[0034] Likewise, rows 96 generally collect data related to the
value or profit garnered from customers purchasing water.
Accordingly, the rows include an endpoints (water) 98, an average
customer monthly bill (water) 100, a cost per bill (water) 102, a
monthly disconnect rate (water) 104, a high risk percent (water)
106, and a percent loss (water) 108. For customers purchasing gas
(e.g., natural gas), rows 110 may be used. Thus, the rows 110
include an endpoints (gas) 112, an average customer monthly bill
(gas) 114, a cost per bill (gas) 116, a monthly disconnect rate
(gas) 118, a high risk percent (gas) 120, and a percent loss (gas)
122. As depicted, each row includes a column 124 labeled
"Description" that described the items in the rows 56, 82, 96, and
110. Likewise, a column 126 labeled "Customer" is used to collect
data related to the rows 56, 82, 96, and 110. A column 128 labeled
"Comments" is also provided as a help or guidance column that
includes a description for each row illustrated. By capturing
detailed customer data in the tab 54, the GIQAAS system 46 enables
an improved analysis of revenue and cost streams, including
customer 32, 34 revenue and cost streams. The data gathering tool
48 may then collect capital expenditure information using, for
example, a tab 130, as described below in more detail with respect
to FIG. 3.
[0035] FIG. 3 is a screen view 132 of an embodiment of the tab or
input form 130 labeled "CapEx" of the infrastructure data gathering
tool 48. In the depicted embodiment, the tab 130 may be used to
collect data related to a first suite of hardware, software, and/or
service offerings from the one or more manufacturers or service
providers. Indeed, smart grid 10 components and services from
multiple manufacturers may be aggregated as a first suite of
infrastructure offerings 134, depicted as rows in the screen view
132. In the illustrated example, there are approximately 35
individual offerings or rows 134 grouped into the first suite of
offerings 134. It is to be understood that, in other examples,
fewer or greater number offerings (e.g., rows) may be included. A
user, such as a utility 12, may enter capitalization expenditure
data for the suite of offerings 134. The capitalization expenditure
data may include cost and benefit (e.g., revenue) data as depicted
in the screen view 132. The collected data may then be analyzed,
for example, to compare the suite of offerings 134 to another suite
of offerings, such as a second suite of infrastructure offerings
described in more detail with respect to FIG. 4. By comparing two
or more suite of infrastructure offerings, the GIQAAS 46 may enable
a more efficient smart grid system 10 having a higher ROI and lower
costs.
[0036] A column 136 labeled "Benefit" may be used to uniquely
identify each offering (e.g., row) of the first suite 134 by
providing, for example, a unique identifier (ID) for each row. A
column 138 labeled "Included" may be used to denote whether an
individual offering (e.g., row) is used as part of the analysis.
That is, the user may toggle individual offerings on by placing a
"True" as the cell value or off by placing a "False" as the cell
value. A column 140 labeled "Description" may be used to provide
the description of each individual offering. A column 142 labeled
"Benefit" may be used to enter any economic benefit or revenue
associated with the offering. A column 144 labeled "Rollout" may be
used to input a desired month (e.g., 1 through 12) when the
offering would be provided or "rolled out" to the customers 32
and/or 34.
[0037] A column 146 labeled "Cost" may be used to input an economic
cost associated with the offering, if any. A column 148 labeled
"Recurring" may contain the value "True" if the cost is a recurring
or ongoing cost, and the value "False" if the cost is a one-time
cost. A column 150 labeled "Category" may be used to describe a
technology category for the offering. As illustrated, AMI 30, OMS
36, GIS 40, CIS 42, and IVR 44 technology categories are shown in
column 150. Other technology categories may include components of
the control center 14, components of the plants 16, 18, 20, and 22,
components of the grid 24, components of the grid 26, and/or
components of the grid 28 shown in FIG. 1. "Prepay" technology
categories are also provided as input, which relate to the
pre-payment of utility bills, as described above. Likewise,
"System" technology categories are additionally provided in column
150, related to the smart grid 10 system as a whole. Columns 152,
154, and 156 labeled "E", "W", and "G", respectively, may be used
to denote if the offering is related to electricity (E), water (W),
and/or Gas (G).
[0038] By providing the user with the screen view 132 that enables
the data input of offerings from any one or more manufacturers or
service providers, the first suite of offerings 134 may be compared
to a second suite of offerings, for example, a second suite of
offerings provided by a manufacturer, such as General Electric Co.,
of Schenectady, N.Y. It is to be understood that, while in the
contemplated embodiment the second suite of offerings is provided
by a single manufacturer, in other embodiments the second suite of
offerings may be provided by one or more manufacturers or service
providers. In one embodiment, the second suite of offerings may be
provided in an input form or tab, such as the tab as depicted in
FIG. 4.
[0039] FIG. 4 is a screen view 158 of an embodiment of a tab or
input form 160 labeled "SGaaS" of the infrastructure data gathering
tool 48. In the depicted embodiment, the tab 160 may be used to
collect data related to a second suite of offerings 162 (e.g.,
Smart Grid as a Service [SGaaS] offerings), including hardware,
software, and/or service offerings from a single manufacturer.
Although the presently contemplated embodiment of the tab 160
includes the second suite of offerings 162 from the single
manufacturer or service provider, other embodiments may include
additional tabs having other suites of offerings, including suites
of offerings from a combination of manufacturers or service
providers.
[0040] As in FIG. 3, the suite of offerings 162 includes a
multitude of rows (e.g., offerings) capturing information related
to various technology categories (e.g., components of the control
center 14, components of the plants 16, 18, 20, and 22, components
of the grid 24, components of the grid 26, components of the grid
28, the AMI 30, the OMS 36, the DMS 38, the GIS 40, the CIS 42,
and/or the IVR 44). For a more efficient comparison between the
first suite of offerings 134 shown in FIG. 3 and the second suite
of offerings 162 shown in FIG. 4, the screen view 158 includes
columns 164, 166, 168, 170, 172, 174, 176, 178, 180, 182, and 184,
which contain the same type of information (e.g., ID, included as a
package, description, benefit, rollout month, cost, recurring cost,
category, E, W, and G) as described above with respect to FIG. 3.
Accordingly, the user may switch between tabs 130 and 160 to
compare and contrast between the first suite of offerings 134 and
the second suite of offerings 162. Indeed, the tabs 130 and 160
contain the same category or type of information in a similar
arrangement, so that an offering-by-offering comparison between two
suites of offerings 134 and 162 may be more easily performed and
visualized. Additionally, a worksheet tab 186 may be provided, as
described in more detail below with respect to FIG. 5, that may
enable a more efficient analysis of smart grid system 10
components, including comparison between the two or more suites of
offerings 134 and 162.
[0041] FIG. 5 is a screen view 188 of an embodiment of the
worksheet form or tab 186 labeled "Worksheet" enabling the economic
analysis of the smart grid system 10 and/or comparison between the
first suite of offerings 134 shown in FIG. 3 and the second suite
of offerings 162 shown in FIG. 4. The screen view 188 includes
linking features that "link" or otherwise enable the retrieval and
use of data from the tabs 54, 130, and 160. For example, the
linking features may include the linking of cells from any of the
data found in the tabs 54, 130, and 160. The screen view 188 may
thus process data from the tabs 54, 130, and 160 and present the
data in a format suitable for more efficiently visualizing costs
and benefits of the smart grid system 10. For example, the screen
view 188 includes a market pricing for certain applications column
190 based on retrieving the number of electric, water, and gas
endpoints 84, 98, and 112 from tab 54 shown in FIG. 2. An
integration factor may be entered by the user into column 192. The
integration factor may be related to the integration of
installation of smart grid system 10 components (e.g., components
of the control center 14, components of the plants 16, 18, 20, and
22, components of the grid 24, components of the grid 26,
components of the grid 28, the AMI 30, the OMS 36, the DMS 38, the
GIS 40, the CIS 42, and/or the IVR 44). The market pricing column
190 may be multiplied by the integration factor column 192 to
arrive at a cost with integration column 194.
[0042] A cost per meter column 196 may be used derive the meter
costs with integration for each endpoint or meter. For example, the
cost with integration column 192 may be divided by a total number
of endpoints 198 to compute the cost per meter column 196. A annual
support rate or percentage 200 column may then capture a support
percentage provided to customers of each type of technology
category (e.g., components of the control center 14, components of
the plants 16, 18, 20, and 22, components of the grid 24,
components of the grid 26, components of the grid 28, the AMI 30,
the OMS 36, the DMS 38, the GIS 40, the CIS 42, and/or the IVR 44).
Based on the annual support rate percentage column 200, an annual
support per meter per month column 202 may be calculated. More
specifically the annual support per meter per month column 202 may
be calculated by multiplying the cost with integration column 194
with the annual support rate percentage 200 column, and then
dividing by 12 months, and again dividing by the total number of
endpoints 198. Additionally, the cost per meter column 196 may be
added to the annual support per meter per month column 202 to
derive a total per meter investment column 204. The total per meter
investment column 204 may then be multiplied by the number of
endpoints 198 to arrive at a total investment column 206. By
providing for the collection of data and the derivation of columns
194, 196, 202, 204, and 206, the screen view 188 may enable a more
efficient analysis and display of cost and revenue data for the
smart grid system 10.
[0043] Additionally, a total technology cost 208 may be computed
based on the sum of the market pricing for certain applications
column 190. Likewise, a total cost with integration 210 may be
derived based on the sum of the cost with integration column 210. A
cost of refreshing current technology 212 with the smart grid
system 10 components may also be derived. For example, a percent
refreshment rate 214 may be applied to the total technology cost
208 to compute the cost of refreshing current technology 212. The
cost of refreshing current technology 212 may then be used to
compute a technology refreshment cost 216 spread over a certain
time period (e.g., 1 year, 2 years, 3 years, 5 years, 10 years). A
technology total cost of ownership (TCO) 218 may also be computed
as a sum of the technology refreshment cost 216 and the total cost
with integration 194. The TCO cost 218 may then be divided by the
total number of endpoints 198 to arrive at a TCO per meter cost
220. Likewise, a technology refreshment cost per meter occurrence
222 may be derived by dividing the technology refreshment cost 212
with the total number of endpoints 198.
[0044] An annual cost of technology support labor 224 may be
provided, useful in collecting the labor cost associated with
customer support related to the first suite of offerings 134. In
one embodiment, in the second suite of offerings 162, this may be a
benefit, since the labor costs may be included as one of the rows
of the second suite of offerings 162. A personnel count 226 may be
multiplied to the labor cost 224 to derive a total labor cost 228.
A cost of labor avoided per month per meter 230, for example,
avoided by using the second suite of offerings 162, may then be
derived by dividing the total labor cost 228 by 12 months, and then
dividing again by the total number of endpoints 198. A TCO per
meter per month 232 may then by calculated by adding the per meter
cost 220 to the cost of labor avoided per month per meter 230.
Multiplying the TCO per meter per month 232 by the total number of
endpoints 198 may then compute a TCO per month 234. By deriving the
revenues and costs described with respect to FIG. 5, a
comprehensive economic analysis may be provided.
[0045] FIG. 6 is a screen view 236 of an embodiment the worksheet
form or tab 186 enabling a derivation of various benefits
associated with the first and the second suite of offerings 134 and
162 shown in FIGS. 3 and 4, respectively. In the depicted example,
the average electric bill 86 shown in FIG. 2 may be displayed by
the screen view 236, along with the monthly disconnect rate (MDR)
90. A total number of meters disconnected per month 238 may then be
derived, for example, by multiplying the monthly disconnect rate 90
with the total number of electric endpoints 84 shown in FIG. 2. A
prepay MDR reduction percent 240 may then be collected from the
user. Accordingly, a MDR reduction 242 based on the MDR reduction
percent 90 may be derived, for example, by multiplying the total
number of meters disconnected per month 238 with the prepay MDR
reduction percent 240. A revenue improvement or benefit per meter
per month 244 may then be calculated by subtracting the MDR
reduction 242 from the total number of meters disconnected per
month 238, multiplying by the average electric bill 86, and
dividing by the total number of electric endpoints 84. Likewise, a
total monthly revenue for electric service 246 may be computed, by
multiplying the average electric bill 86 with the total number of
electric endpoints 84. Similar benefits may be computed for gas
endpoints and water endpoints.
[0046] Other benefits, such as avoidance of truck rolls per meter
per month 248 may be calculated. Truck rolls may be avoided, for
example, because prepayment of utility services may lower the MDR.
The truck roll (TR) cost 250 may be multiplied by an avoided TR
monthly occurrence 252 to arrive at a monthly TR costs avoidance
254. The avoidance of truck rolls per meter per month 248 may then
be calculated by dividing the monthly TR costs avoidance 254 by the
total number of electric endpoints 84. Other benefits may also be
calculated, such as a percent prevention of move in/move outs per
month benefit 256. The percent prevention of move in/move outs per
month 256 may be calculated by multiplying a cost per occurrence
258 with a number of move in/move outs prevented 260, and then
dividing by the total number of electric endpoints 84. Benefits may
also include theft prevention benefits. For example, prepayment of
electricity would reduce or eliminate a theft caught by prepay loss
262 incurred by the theft of electricity. A loss prevention benefit
264 assuming 1/3 of the loss 262 is prevented by prepaying may then
be calculated by multiplying the theft caught by prepay loss 262
with the total monthly electric service revenue 246, and then
dividing by 3. The divisor (e.g., 3) may be chosen based on a theft
rate prevented by prepayment, and may be other rates (e.g., 1, 2,
4, 5). Higher prevention rates may lead to lower divisors, and vice
versa. A per meter theft prevention benefit 266 may then be derived
by dividing the loss prevention benefit 264 with the total number
of electric endpoints 84.
[0047] Economic benefits from the use of technologies such as the
components of the plants 16, 18, 20 and 22, grid 24 components,
grid 26 components, grid 28 components, AMI 30, the OMS 36, the DMS
38, the GIS 40, the CIS 42, and/or the IVR 44 may also be estimated
by the user. For example, the user may enter an estimated
technology benefit percentage 268 for a technology category (e.g.,
components of the control center 14, components of the plants 16,
18, 20, and 22, components of the grid 24, components of the grid
26, components of the grid 28, the AMI 30, the OMS 36, the DMS 38,
the GIS 40, the CIS 42, and/or the IVR 44). This estimate 268 may
be used for deriving other calculations, including a total AMI
meter reading benefit 270, which may be multiplied by the total
number of electric endpoints 84 to arrive at a total AMI benefit
per month 272. Other benefits include an employee health and
service accident avoidance benefit 274 and a theft caught by meter
tampering benefit 276. A total AMI meter reading benefit 278 may
also be provided, suitable for deriving the total benefit of using
AMI meter readings.
[0048] Benefits may also be grouped. For example, a total prepay
benefit per meter per month 280 may be additively grouped by adding
the benefits 248, 266, and 244, among others. By providing for both
costs as well as benefits associated with implementing the smart
grid system 10, the screen view 236 enables an in-depth economic
analysis of the impact of implementation of the smart grid system
10. Additionally, the economic modeling tool 50 may be provided, to
further enable an economic analysis of the smart grid system 10
components, as described in more detail with respect to FIG. 7
below.
[0049] FIG. 7 is a screen view 282 depicting an embodiment of the
economic modeling tool 50 of the GIQAAS system 46. In the depicted
embodiment, the economic modeling tool 50 is implemented by using a
computer-executable spreadsheet. In other embodiments, the economic
modeling tool 50 may be implemented as a web-based system or in any
other form executable by a computing device (e.g., laptop,
workstation, table, cell phone). The economic modeling tool 50 may
include a legal tab 284, an input tab 286, a results tab 288, a
model tab 290, and revision history tab 292. In the depicted legal
tab 284, a button 294 may be provided to contractually bind the
user to usage terms 296 of using the economic modeling tool 50. By
actuating the button 294, the user contractually agrees to the
usage terms 296. Otherwise, the user may actuate a button 298 and
exit the economic modeling tool 50. In one embodiment, actuating
the button 294 may then activate the input tab 286, as depicted in
FIG. 8. The model tab 290 may include a computer executable
economic model, as described in more detail below with respect to
FIG. 13. The revision history tab 292 may include a log of
programming revisions or version histories of the economic modeling
tool 50.
[0050] FIG. 8 is a screen view 300 on an embodiment of the input
tab 286 of the economic modeling tool 50. In the depicted
embodiment, an edit inputs button 302, an import data button 304,
and a view results button 306 are provided. In one embodiment,
activating the import data button 304 enables the loading of data
gathered by using the data gathering tool 48 described above with
respect to FIGS. 2-6. In one example, all of the data gathered by
the data gathering tool 48 may be imported into the economic
modeling tool 50. In another example, a subset of the data from the
data gathering tool 48, such as data from tabs 54, 130, 160, and/or
186 shown in FIGS. 2-6, may be imported into the economic modeling
tool 50. The data may then be displayed onto a column 308 of the
screen view 300. By separating the data gathering activities from
the modeling activities, the GIQAAS system 46 reduces or eliminates
any erroneous manipulation of the economic model shown in FIG. 13
by the data gathering team. Actuating the edit inputs button 302
may then activate a data editing dialog box as described in more
detail below with respect to FIG. 9.
[0051] FIG. 9 depicts an embodiment of a dialog box 310 that may be
activated by actuating the button 302 shown in FIG. 8. The dialog
box 310 may enable the entry and/or editing of data, including all
the data imported into the economic modeling tool 50. For example,
a contract assumptions tab 312 may be used to select and edit data
imported from the tab 54 and screen view 52 shown in FIG. 2.
Likewise, a cap expenditures (capex) tab 314 may be used to select
and edit data imported from the tab 130 and screen view 132 (e.g.,
data related to the first suite of offerings 134) shown in FIG. 3.
Additionally, a smart grid as a service (SGaas) tab 316 may be used
to select and edit data imported from the tab 160 and screen view
158 (e.g., data related to the second suite of offerings 162) shown
in FIG. 4. By enabling the entry and/or editing of data, including
imported data, the dialog box 310 may enable a more efficient data
editing. For example, rather than editing the data in the data
gathering tool 48, and then re-importing the edited data, the user
may activate the dialog box 310 and edit the data directly in a
data editing section 318. After the data has been edited, a save
& exit button 320 may be used to save the data and return to
the screen view 300 shown in FIG. 8. If data editing is to be
cancelled, a discard & exit button 322 may be used to return to
the screen view 300 without saving the edited data. A restore
defaults button 324 is also provided, which may automatically enter
default values for all the data in the data editing section
318.
[0052] FIG. 10 is a screen view 326 of an embodiment of the results
form or tab 288 of the economic modeling tool 50. As mentioned
above, the results tab 288 may be activated by actuating the view
results button 306 of the screen view 300 shown in FIG. 8. In the
depicted embodiment, the results derived by using an economic
model, such as the economic model shown in FIG. 13, may be derived
and presented. For example, a net utility revenue table 328 may be
derived and presented, suitable for comparing net revenue streams
(e.g., profit minus costs). In the illustrated screen view 326, a
first net revenue column 330 is depicted side-by-side with a second
net revenue column 332 and a third net revenue column 334. A
timeline column 335 is also provided, depicting yearly data. In one
embodiment, the first net revenue column 330 is representative of
revenue for the utility 12 with no new capital costs. That is, the
first net revenue column 330 does not include costs associated with
acquiring infrastructure, such as smart grid system 10 components.
The second net revenue column 332 includes revenue minus costs
based on acquiring the first suite of offerings 134 described above
with respect to FIG. 3. Likewise, the third net revenue column 334
includes revenue minus costs based on acquiring the second suite of
offerings 162 described above with respect to FIG. 4. As mentioned
above, each of the net revenue columns 330, 332, and 334 may be
derived by using the economic model described in more detail below
with respect to FIG. 13. By presenting the columns 330, 332, and
334 side-by-side, a more efficient comparison of the economic
impact of the suites 134 and 162 may be provided.
[0053] The screen view 326 also includes a first cost column 336
and a second cost column 338. In one embodiment, the first cost
column 326 corresponds to capitalization costs incurred by
purchasing or leasing the first suite of offerings 134. Likewise,
the second cost column 338 corresponds to capitalization costs
incurred by purchasing or leasing the second suite of offerings
162. In the depicted embodiment, the data in columns 330, 332, 334,
336, and 338 is shown as yearly data. However, in other
embodiments, daily, weekly, monthly, and/or quarterly data may be
shown. A total for each column 330, 332, 334, 336, and 338 may be
provided by row 340. By deriving and displaying columns 330, 332,
334, 336, and 338, and their total values 340, the economic
modeling tool 50 of the GIQAAS system 46 may more efficiently
enable a comparison of revenue, including yearly net revenue, of
purchasing the first or the second suite of offerings 134, 162.
Indeed, the user may easily visualize any differences between
procuring the first and the second suite of offerings 134 and 162.
Additionally, the screen view 326 may present a summary of certain
data in a screen section 342, for example, data imported by using
the data gathering tool 48. The summary of the data shown in screen
section 342 may provide for an overview of certain values used
throughout the economic calculations.
[0054] The depicted screen view 326 also provides a cumulative
(e.g., gross) set of revenue calculations. For example, a
cumulative utility revenue table 344 may be derived and presented,
suitable for comparing cumulative revenue streams. In the
illustrated embodiment, a first cumulative revenue column 346 is
depicted side-by-side with a second cumulative revenue column 348
and a third cumulative revenue column 350. Each of the cumulative
columns 346, 348, and 350 include rows that accumulate cost based
on previous rows. That is, subsequent rows of the columns 346, 348,
and 350 are additively summed from the previous rows, as depicted.
A timeline column 352 is also provided, depicting yearly data. In
one embodiment, the first cumulative revenue column 346 is
representative of the cumulative revenue of the utility 12, with no
new capital costs. That is, the first cumulative revenue column 346
does not include costs associated with acquiring infrastructure,
such as smart grid system 10 components. The second cumulative
revenue column 348 includes cumulative revenue minus costs based on
acquiring the first suite of offerings 134 described above with
respect to FIG. 3. Likewise, the third cumulative revenue column
350 includes cumulative revenue minus costs based on acquiring the
second suite of offerings 162 described above with respect to FIG.
4. Each of the cumulative revenue columns 346, 348, and 350, may be
derived by using the economic model of FIG. 13.
[0055] The screen view 326 also includes a first cumulative cost
column 354 and a second cumulative cost column 356. In one
embodiment, the first cumulative cost column 354 corresponds to
cumulative capitalization costs incurred by purchasing the first
suite of offerings 134. Likewise, the second cumulative cost column
356 corresponds to cumulative capitalization costs incurred by
purchasing the second suite of offerings 162. A total for each
column 346, 348, 350, 354, and 356 may be provided by row 358. In
the depicted embodiment, the data in columns 346, 348, 350, 352,
354, and 356 is shown as yearly data. However, in other
embodiments, daily, weekly, monthly, and/or quarterly data may be
shown. By deriving and displaying columns 346, 348, 350, 352, 354,
and 356, the economic modeling tool 50 of the GIQAAS system 46 may
more efficiently enable a comparison of revenue, including yearly
cumulative revenue, of purchasing the first and the second suite of
offerings 134, 162.
[0056] Additionally, the screen view 326 may present a total of net
revenue without capitalization costs 360, a total net revenue with
capitalization costs 362 based on purchasing the first suite of
offerings 134, and a total net revenue with capitalization costs
364 based on purchasing the second suite of offerings 162.
Likewise, a first net cumulative differential (NCD) 366 may be
derived by subtracting the total net revenue with capitalization
costs 362 based on purchasing the first suite of offerings 134 from
the total of net revenue without capitalization costs 360. A second
NCD 368 may also be derived by subtracting the total net revenue
with capitalization costs 364 based on purchasing the second suite
of offerings 162 from the total of net revenue without
capitalization costs 360. Similarly, a third NCD 370 may be
computed by subtracting the total net revenue with capitalization
costs 364 based on purchasing the second suite of offerings 162
from the total net revenue with capitalization costs 362 based on
purchasing the first suite of offerings 134. In this way, the
first, second and third NCDs 366, 368, 370 may be compared.
Graphical comparisons of revenues and costs may also be provided,
as described in more detail below with respect to FIGS. 11 and
12.
[0057] FIG. 11 is a screen view 372 of an embodiment of a graph 374
provided by the economic modeling tool 50 of the GIQAAS system 46.
The graph 374 provides for a line graph presentation of the columns
346, 348, and 350 shown in FIG. 10. That is, line 376 may be
representative of the data in column 346, line 378 may be
representative of data in column 348, and line 380 may be
representative of data in column 350. By representing the columns
346, 348, and 350 as lines 376, 378, and 380, respectively, the
graph 374 may provide for an intuitive visual presentation of the
economic differences between the suites 134 and 162. Other types of
graphs or visuals may be presented, such as a graph illustrated in
FIG. 12.
[0058] FIG. 12 is illustrative of a screen view 382 of an
embodiment of a graph 384 provided by the economic modeling tool
50. In the illustrated embodiment, the graph 384 provides for a bar
chart presentation of the data in columns 346, 348, and 350 shown
in FIG. 10. For example, bars 386 may be representative of the data
in column 346, bars 388 may be representative of data in column
348, and bars 390 may be representative of data in column 350. By
representing the columns 346, 348, and 350 as bars 386, 388, and
390, respectively, the graph 384 may provide for an intuitive
visual presentation of the economic differences between the suites
134 and 162. Indeed, all or some of the data presented in the
screen view 326 of FIG. 10 may be presented in any number of graphs
and charts, including line graphs, bar graphs, and pie charts. It
is to be noted that, in one embodiment, that all of the screens and
charts depicted in FIGS. 2-12 are included in the GIQAAS system 46.
Indeed, the GIQAAS system 46 may include all of the elements and
associated descriptions related to FIGS. 2-12.
[0059] As mentioned above, an economic model may be used to derive
the calculations depicted in FIGS. 10, 11, and 12. FIG. 13 shows an
embodiment of an economic model 400 useful in providing for the
aforementioned economic calculations, including costs, revenues,
and ROI. The economic model 400 may include instructions or
computer code executable by a computing devices such as a desktop,
a laptop, a tablet, and a cell phone. In the depicted embodiment,
an inflation forecast model 402 may be used to forecast projected
inflation. In one example, a Federal Reserve inflation model, such
as a rational expectations model of inflation available from the
United States Federal Reserve, Washington D.C., may be used for the
model 402. The inflation forecast model 402 may be used jointly
with an operating costs model 404 to calculate projected monthly
costs (block 406). The operating cost model 404 may be derived by
using activity based costing (ABC), constructive cost models
(COCOMO), operational cost calculators, cost estimation models,
cost driver models, engineering cost models, and/or microeconomic
cost models. A monthly revenue model 408 may be combined with a
customer defaults model 410 and a finance charges model 412 to
calculate a net "as-is" monthly revenue (block 414) based on
derived monthly costs 416.
[0060] For example, the monthly revenues model 408 may forecast
monthly revenues based on historical analysis (e.g., statistical
analysis) of revenue streams, monetization of services models,
and/or bottom-up revenue forecasting. The customer defaults model
410 may use statistical techniques such as linear regression,
non-linear regression, data mining, actuarial forecasting, curve
forecasting (e.g., Bell curve, Gaussian distribution curves,
non-Gaussian distribution curves) to derive a number of customers
defaults. The finance charges model 412 may use banking finance
rates, utility 12 finance rates, flat interest rates, and
progressive interest rates to derive charges related to financing
services and operations. The models 408, 410, and 412 may use the
monthly costs 416 to calculate the net "as-is" monthly revenue
(block 414). In the depicted embodiment, the "as-is" revenue does
not include costs associated with the first or the second suites of
offerings 134 and 162.
[0061] User inputs 418 may be captured, for example, by the data
gathering tool 48, and used to calculate a first suite of offerings
revenue (block 420). In one embodiment, the models 402, 404, 408,
410, and 412 may be used, as mentioned above, to process the user
inputs 418 and calculate the first suite of offerings revenue
(block 420) based on the first suite of offerings 134 shown in FIG.
3. Likewise, the models 402, 404, 408, 410, and 412 may be used
with the user inputs 418 to calculate a second suite of offerings
revenue (block 422) based on the second suite of offerings 162
shown in FIG. 4. The blocks 414, 420, and 422 may derive revenue
cases 424, e.g., revenues related to the first suite of offerings
134, the second suite of offerings 162, and "as-is" revenues. The
revenue cases 424 may then be presented for comparison and further
analysis, as described above with respect to FIGS. 8-12.
[0062] FIG. 14 is illustrative of an embodiment of a process 426
that may use the data gathering tool 48, the economic modeling tool
50, and the economic model 400 to compare the first suite of
offerings 134 with the second suite of offerings 162. The process
426 may include instructions or computer code executable by a
computing device such as a desktop, a laptop, a tablet, and a cell
phone. In the depicted embodiment, the user may use the data
gathering tool 48 to collect costs and benefits data (block 428),
as described above. The collected data may then be imported (block
430), for example, into the economic modeling tool 50 by using the
import data button 304 shown in FIG. 8. The imported data may then
be modified by the user to reflect desired case studies (block
432). That is, the user may edit the data as described above with
respect to the dialog box 310 described in FIG. 9 to adequately
provide for desired inputs (e.g., costs and benefits) related to
the first and the second suite of offerings 134, 162. The economic
model 400 may then be executed to produce economic results (block
434), as described above with respect to FIG. 13. The user may then
review and analyze the model results (block 436). For example, the
user may compare and contrast the benefits, costs and ROI
associated with the first and second suite of offerings 134 and 162
by using columns and graphs, as described above with respect to
FIGS. 10, 11, and 12. If desired, alternate case studies or
analysis may be performed (block 438). Data may be gathered again
(block 428) and the blocks of the process 400 may be followed to
provide for new results. By comparing and contrasting various
inputs, the first and the second suite of offerings 134 and 162 may
be more thoroughly analyzed to derive a more efficient,
cost-effective, profitable smart grid system 10.
[0063] Technical effects of the invention include a grid
intelligence quotient as a service (GIQAAS) system enabling the
collection and analysis of inputs related to infrastructure, such
as smart grid infrastructure, to more efficiently design the smart
grid infrastructure. An infrastructure data gathering is provided,
suitable for use by a user, enabling the collection of
infrastructure data. The user may enter, for example, cost and
benefits data related to various components and technologies
included in a first and a second suite of infrastructure offerings.
An economic modeling system having an economic model may then be
provided to analyze the first and the second suite of
infrastructure offerings. The separation between the economic
modeling and the data collection minimizes or eliminates any
erroneous manipulation of the economic model by the data gathering
team. Indeed, the infrastructure data gathering system may be
emailed to an end user having little or no knowledge of economic
modeling techniques. By comparing and contrasting the first suite
of offerings with the second suite of offerings, the user may
efficiently compare costs and benefits related to the suites of
offerings.
[0064] This written description uses examples to disclose the
invention, including the best mode, and also to enable any person
skilled in the art to practice the invention, including making and
using any devices or systems and performing any incorporated
methods. The patentable scope of the invention is defined by the
claims, and may include other examples that occur to those skilled
in the art. Such other examples are intended to be within the scope
of the claims if they have structural elements that do not differ
from the literal language of the claims, or if they include
equivalent structural elements with insubstantial differences from
the literal language of the claims.
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