Note: Descriptions are shown in the official language in which they were submitted.
CA 02798545 2012-12-05
SMART GRID DEPLOYMENT SIMULATOR
BACKGROUND
[0001] Various industries have networks associated with them. One
such
industry is the utility industry that manages a power grid. The power grid may
include one or all of the following: electricity generation, electric power
transmission, and electricity distribution. Electricity may be generated using
generating stations, such as a coal fire power plant, a nuclear power plant,
etc.
For efficiency purposes, the generated electrical power is stepped up to a
very
high voltage (such as 345K Volts) and transmitted over transmission lines. The
transmission lines may transmit the power long distances, such as across state
lines or across international boundaries, until it reaches its wholesale
customer,
which may be a company that owns the local distribution network. The
transmission lines may terminate at a transmission substation, which may step
down the very high voltage to an intermediate voltage (such as 138K Volts).
From
a transmission substation, smaller transmission lines (such as sub-
transmission
lines) transmit the intermediate voltage to distribution substations. At the
distribution substations, the intermediate voltage may be again stepped down
to a
"medium voltage" (such as from 4K Volts to 23K Volts). One or more feeder
circuits may emanate from the distribution substations. For example, four to
tens
of feeder circuits may emanate from the distribution substation. The feeder
circuit
is a 3-phase circuit comprising 4 wires (three wires for each of the 3 phases
and
one wire for neutral). Feeder circuits may be routed either above ground (on
poles) or underground. The voltage on the feeder circuits may be tapped off
periodically using distribution transformers, which step down the voltage from
"medium voltage" to the consumer voltage (such as 120V). The consumer voltage
may then be used by the consumer.
1
CA 02798545 2012-12-05
[0002] Power companies may manage the power grid, including managing
usage, faults, maintenance, and upgrades related to the power grid. However,
the
management of the power grid is often inefficient and costly. For example, a
power company that manages the local distribution network may manage faults
that may occur in the feeder circuits or on circuits, called lateral circuits,
which
branch from the feeder circuits. The management of the local distribution
network
often relies on telephone calls from consumers when an outage occurs or relies
on
field workers analyzing the local distribution network.
[0003] In order to mitigate inefficiencies with management of the
power
grid, power companies have attempted to upgrade the power grid using digital
technology, sometimes called a "smart grid." A smart grid is an intelligent
network
that may use one or more of sensing, embedded processing, digital
communications, and software to manage network-derived information. For
example, more intelligent meters (sometimes called "smart meters") are a type
of
advanced meter that identifies consumption in more detail than a conventional
meter. The smart meter may then communicate that information via a network
back to the local utility for monitoring and billing purposes (telemetering).
Through
smart meters and other infrastructure, the smart grid may include capabilities
to
support major business functions including power delivery, asset management,
and customer experience enablement.
[0004] Although upgrading to a smart grid may greatly improve
efficiency
and reduce costs in the long run, the challenge and costs of upgrading to a
smart
grid are not trivial. For example, power companies may be faced with questions
such as how will existing and emerging technologies impact the wired and
wireless
network infrastructure needed for smart grid; how much bandwidth, including
wireless spectrum, will be required to support the business in the future;
what are
the capital and operational costs to build, upgrade and maintain the network
based
on business and technology forecasts. Without accurate answers to these
2
CA 02798545 2012-12-05
questions, the power company may not be able to accurately estimate upgrade
costs, which can lead to extensive budget overruns. Also, the capacity of the
infrastructure of the smart grid may be insufficient if traffic is
underestimated or the
infrastructure may be substantially over-provisioned.
3
CA 02798545 2014-11-07
=
= SUMMARY
[0005] According to an embodiment, a decision management system
simulates a
smart grid communications network service deployment using business and
technology
changeable parameters, models describing traffic profiles for smart grid
domain devices
and smart grid applications, smart grid infrastructure and a cost model.
Candidate
solutions for deploying the smart grid service are determined for different
sets of
changeable parameters through the simulations. These solutions are analyzed to
identify a solution for deploying the smart grid service.
[0006] According to an embodiment, a method for simulating and
analyzing one
or more scenarios for a smart grid communications network service to be
deployed
includes receiving business and technology changeable parameters for the smart
grid
communications network service to be deployed. The method may further include
simulating, e.g., by a processor, the smart grid communications network
service using
the changeable parameters, device models describing traffic profiles for smart
grid
domain devices and smart grid applications, and a cost model to determine at
least one
candidate solution for deploying the service. The at least one candidate
solution may
include smart grid domain devices and a smart grid application for deploying
the smart
grid communications network service, estimated bandwidth needed for the smart
grid
communications network service and estimated costs for the smart grid
communications
network service. The method may include generating an analysis of the at least
one
candidate solution to evaluate an impact of the changeable parameters.
[0006a] In one aspect, there is provided a non-transitory computer
readable
storage medium including instructions executed by a computer system to perform
simulating and analyzing of one or more scenarios for a smart grid
communications
network service to be deployed, the instructions comprising: receiving
business and
technology changeable parameters for the smart grid communications network
service
to be deployed, wherein the business changeable parameters are financial data
for the
smart grid communications network service to be deployed; determining a
hierarchal
model of a smart grid including domains and utility sections for the smart
grid in different
levels of the hierarchal model; identifying, from the hierarchal model of the
smart grid, at
4
CA 02798545 2014-11-07
=
' least one domain and at least one utility section for the smart grid
communications
network service; selecting device models from a plurality of device models
based on the
identified at least one domain and at least one utility section, wherein the
selected
device models represent different smart grid domain devices in the identified
at least
one domain and at least one utility section and include traffic profiles for
the different
smart grid domain devices and smart grid applications executed by the devices;
simulating the smart grid communications network service using the changeable
parameters, the device models, and a cost model to determine at least one
candidate
solution for deploying the service, wherein the at least one candidate
solution includes
smart grid domain devices and a smart grid application for deploying the smart
grid
communications network service, estimated bandwidth needed for the smart grid
communications network service and estimated monetary costs for the smart grid
communications network service, wherein the simulating includes determining a
relationship between the identified at least one domain and another domain in
the
hierarchal model of the smart grid; and determining the estimated bandwidth
needed for
the smart grid communications network service based on the determined
relationship;
and generating an analysis of the at least one candidate solution to evaluate
an impact
of the changeable parameters.
[0006b] In another aspect, there is provided a decision management
system
comprising: data storage storing device models describing traffic profiles for
smart grid
domain devices and smart grid applications and a cost model; and a processor
executing a simulation engine to simulate deploying of a smart grid
communications
network service using business and technology changeable parameters, the
device
models and the cost model to determine at least one candidate solution,
wherein the
business changeable parameters are financial data for the smart grid
communications
network service to be deployed, and to simulate deploying of the smart grid
communications network service, the simulation engine is to determine a a
hierarchal
model of a smart grid including domains and utility sections for the smart
grid; identify,
from the hierarchal model of the smart grid, at least one domain and at least
one utility
section for the smart grid communications network service; select the device
models
from a plurality of device models based on the identified at least one domain
and at
4a
CA 02798545 2014-11-07
=
least one utility section; and use the traffic profiles from the selected
device models for
the simulation, wherein the simulation engine generates an analysis of the at
least one
candidate solution.
[0006c] In another aspect, there is provided a method for simulating and
analyzing
one or more scenarios for a smart grid communications network service to be
deployed,
the method comprising: receiving business and technology changeable parameters
for
the smart grid communications network service to be deployed, wherein the
business
changeable parameters are financial data for the smart grid communications
network
service to be deployed; determining a hierarchal model of a smart grid
including
domains and utility sections for the smart grid in different levels of the
hierarchal model;
identifying, from the hierarchal model of the smart grid, at least one domain
and at least
one utility section for the smart grid communications network service;
selecting device
models from a plurality of device models based on the identified at least one
domain
and at least one utility section, wherein the selected device models represent
different
smart grid domain devices in the identified at least one domain and at least
one utility
section and include traffic profiles for the different smart grid domain
devices and smart
grid applications executed by the devices; simulating, by a processor, the
smart grid
communications network service using the changeable parameters, the device
models,
and a cost model to determine at least one candidate solution for deploying
the service,
wherein the at least one candidate solution includes smart grid domain devices
and a
smart grid application for deploying the smart grid communications network
service,
estimated bandwidth needed for the smart grid communications network service
and
estimated costs for the smart grid communications network service, wherein the
simulating includes determining a relationship between the identified at least
one
domain and another domain in the hierarchal model of the smart grid; and
determining
the estimated bandwidth needed for the smart grid communications network
service
based on the determined relationship; and generating an analysis of the at
least one
candidate solution to evaluate an impact of the changeable parameters.
[0007] The method may be embodied as machine readable instructions stored
on a computer readable storage medium that are operable to be executed by a
processor to perform the method.
4b
CA 02798545 2012-12-05
BRIEF DESCRIPTION OF DRAWINGS
[0008] The embodiments of the invention will be described in detail
in the
following description with reference to the following figures.
[0009] Figure 1A illustrates a network environment for a decision
management system, according to an embodiment;
[0010] Figure 1B illustrates the decision management system,
according to
an embodiment;
[0011] Figures 2 illustrates domains in a communications network
service,
according to an embodiment;
[0012] Figure 3 illustrates relationships between values in models,
according to an embodiment;
[0013] Figure 4 illustrates an analysis report of a candidate
solution,
according to an embodiment;
[0014] Figure 5 illustrates a hierarchal model of a smart gird
including
domains and utility sections, according to an embodiment; and
[0015] Figure 6 illustrates examples of communications network
technologies that may be used in domains and utility sections of a smart grid,
according to an embodiment;
[0016] Figure 7 illustrates examples of business and technology
inputs and
candidate solution outputs for the decision management system, according to an
embodiment;
[0017] Figure 8 illustrates an example of hierarchical connectivity
between
domains for a smart grid service for both the electrical network and the
communications network, according to an embodiment;
5
CA 02798545 2012-12-05
[0018] Figures 9A-B show examples of estimations for required
bandwidth
and technology mix for a smart grid service in graphic form, according to an
embodiment;
[0019] Figures 10A-D show examples of estimations for required
bandwidth
and technology mix for smart grid services in text form, according to an
embodiment;
[0020] Figures 11A-B shows examples of traffic profiles in device
models
for devices and smart grid applications, according to an embodiment;
[0021] Figures 12A-B show examples of device models and their
communication characteristics (e.g., current and future) that may be used for
a
smart grid service for a transmission substation utility section of a smart
grid,
according to an embodiment;
[0022] Figures 13A-B show examples of device models and their
communication characteristics (e.g., current and future) that may be used for
a
smart grid service for a gas transmission substation, according to an
embodiment;
[0023] Figures 14A-B show examples of device models and their
communication characteristics (e.g., current and future) that may be used for
a
smart grid service for the distribution substations utility section, according
to an
embodiment;
[0024] Figures 15A-B show examples of device models and their
communication characteristics (e.g., current and future) that may be used for
electrical distribution circuits, according to an embodiment;
[0025] Figures 16A-B show examples of device models and their
communication characteristics (e.g., current and future) that may be used for
a
smart grid service for the distributed generator utility section, according to
an
embodiment;
6
CA 02798545 2012-12-05
[0026] Figure 17 shows examples of device models and their
communication characteristics (e.g., current and future) that may be used for
PHEV (plug-in hybrid electric vehicles), according to an embodiment;
[0027] Figures 18A-B show examples of device models and their
communication characteristics (e.g., current and future) that may be used for
a
smart grid service for automated meter reading and the home/customer utility
section, according to an embodiment;
[0028] Figure 19 shows examples of device models and their
communication characteristics (e.g., current and future) that may be used for
a
smart grid service for the mobile IT utility section; according to an
embodiment;
[0029] Figures 20-22 show examples of screenshots that may be
generated
through a user interface of the decision management system, according to an
embodiment;
[0030] Figure 23 illustrates a method 2300 for modeling and analyzing
scenarios for a communications network service to be deployed; according to an
embodiment;
[0031] Figure 24 illustrates a method 2400 for simulating the
communications network service; according to an embodiment; and
[0032] Figure 25 illustrates a computer system that may be used as a
platform for the decision management system and executing various functions,
according to an embodiment.
7
CA 02798545 2012-12-05
DETAILED DESCRIPTION OF EMBODIMENTS
[0033] For simplicity and illustrative purposes, the principles of
the
embodiments are described by referring mainly to examples thereof. In the
following description, numerous specific details are set forth in order to
provide a
thorough understanding of the embodiments. It will be apparent, however, to
one
of ordinary skill in the art, that the embodiments may be practiced without
limitation
to these specific details. In some instances, well known methods and
structures
have not been described in detail so as not to unnecessarily obscure the
embodiments.
[0034] According to an embodiment, a decision management system uses
quantitative methods and changeable parameters to simulate and analyze
different communication network service deployments for a smart grid. The
simulations and analysis may then be used to identify optimal service
deployment
scenarios to maximize business as well as technological objectives. Also, the
decision management system may be used by companies or other entities to
strategize about their existing network investments while simultaneously
deploying
new technologies and services.
[0035] A service includes the supplying or providing of information
over a
network, and is also referred to as a communications network service.
According
to an embodiment, these services include smart grid services. The smart grid
services may include person-to-person services, such as voice communications,
video, and mobile and stationary person-to-person services. Another example of
smart grid services are enterprise services, such as messaging, unified
communications and collaboration, telepresence, ubiquitous access to
applications, remote access and security, and mobility. Another example of
smart
grid services are machine-to-machine services, such as sensors and telemetry
(e.g., smart metering), fleet tracking and monitoring, and asset tracking and
monitoring. Another example of smart grid services are customer-facing
services,
8
CA 02798545 2012-12-05
,
such as web and mobile access to account management, portal-based services,
and collaborative interaction. The decision management system may be used to
simulate and analyze different service deployments for these services and
other
types of services related to smart grid.
[0036] The decision management system includes a scenario-based
simulator to simulate different scenarios encompassing different changeable
parameters or variables for services to be deployed. The simulator uses a
multi-
linear simulation engine to run simulations for different scenarios. The
output of
the simulator includes an analysis of each scenario and an analysis of
business
and technology sub-solutions for each simulation. A solution includes an
analysis
of different factors for deploying a service given changeable parameters,
constraints, and existing service parameters, if any. Sub-solutions provide an
analysis of different categories of the factors. For example, a business sub-
solution includes an analysis of business factors. A technology sub-solution
includes an analysis of technology factors. The simulations are operable to
take
into consideration existing implementations of infrastructure, operations and
services, and can be used to evaluate the impact on the existing services.
[0037] The decision management system can provide a complete,
holistic,
end-to-end analytic and support solution that helps companies engage in
sophisticated modeling and "what-if" planning based on different business,
technology and cost variables. The decision management system is operable to
consider business cost variables, operational cost variables, and
technological
variables, and provides a comprehensive analysis across different domains as
well
as an indication of how the variables in the different domains impact each
other. A
domain is a logical sectioning of a service. In one example, domains of a 4G
(4th
Generation Broadband Wireless) service include a radio domain, a backhaul
domain, a core network domain, and an operations domain. Domains of a smart
grid service include utility sections, devices, local area network (LAN)(wired
and/or
9
CA 02798545 2012-12-05
wireless), a backhaul domain, a wide area network (WAN) domain, head-end
domain and an operations domain. Changes in one domain may impact changes
in another domain, which is captured in the modeling described herein. This
type
of decision management system is invaluable for communications providers to
get
a complete picture and understanding of costs and potential profits for
service
deployments, as well as providing practical guidelines and what-if analysis
for
evaluating various network solutions. Furthermore, the decision management
system can provide analysis of different scenarios in real time. Thus, the
impact of
changing different variables in different domains to achieve a business
objective
can be quickly evaluated.
[0038] Some examples of the analytics and performed by the decision
management system includes estimating network traffic projections. The
estimations may include network traffic modeling based on critical versus non-
critical traffic, busty versus continuous traffic, and cell site-by-site
characteristics.
The estimations may include radio frequency comparison, including WiMAX
(Worldwide Interoperability for Microwave Access) versus LTE (Long Term
Evolution). The system may also provide network deployment planning, including
generating a network deployment view over a time period, such as a ten-year
period. The planning may include estimating capital and operational
expenditures,
including design, build, and run costs, over the time period. The analytics
may be
used to determine an acceptable use of private versus public leased
communications.
[0039] Figure 1A illustrates a network environment for a decision
management system 100. The decision management system 100 may be
implemented on one or more servers, each including computer hardware such as
processors and memory. The decision management system 100 may be
connected to a data storage system 120 and a web server 130. Users 155 may
connect to the decision management system 100 via network 150 and web server
CA 02798545 2012-12-05
130. The web server 130 manages user requests and sends responses from the
decision management system 100 to the users 155. The data storage system 120
may include a database or other type of data storage system. The data storage
system 120 stores any data used by the data management system 100 to perform
its functions. The dashed line represents network security, such as a
firewall. The
decision management system 100, data storage system 120 and web server 130
may sit behind a firewall to prevent unauthorized access to the systems.
[0040] Figure 1B illustrates the decision management system 100,
according to an embodiment. The decision management system 100 includes a
simulation engine 101, and models 110. The simulation engine 101 receives
inputs, including one or more of changeable parameters 102, constraints 103,
and
existing service parameters 104, that may be entered by a user via a graphical
user interface. From these inputs and the models 110, the simulation engine
101
simulates the deployment of a communications network service. The simulation
engine 101 generates an analysis of the simulation, which is shown as
candidate
solution 120. A candidate solution is a solution that may be selected to be
used to
deploy the service. Multiple candidate solutions can be determined and one may
be selected for deploying the service. For example, the changeable parameters
102 may be varied, for example by a user, to generate different candidate
solutions. Each candidate solution can be analyzed to determine the solution
that
best satisfies one or more objectives. One or more reports may be generated
for
each candidate solution that presents an analysis of the candidate solution. A
user may compare the analyses of each candidate solution. For example, a
solution may be selected that minimizes costs but provides the best customer
experience based on quality of service (QoS) or Radio Frequency (RF) coverage
constraints related to technology.
[0041] The constraints 103 may include requirements that must be met
by
the deployed service. One example of a constraint is a QoS constraint. Another
11
CA 02798545 2012-12-05
constraint may be a budget constraint. The existing service parameters 104
describe existing network infrastructure and services, if any. The deployment
of a
new service may impact or be impacted by existing network infrastructure and
services. For example, a new service may be cheaper to implement when largely
supported by the existing network infrastructure. In another example, existing
operations, such as customer help desk or technicians, may be leveraged to
support new services. The decision management system 100 may include a user
interface 105, which may be a graphical user interface. Through the user
interface
105, the users 155 shown in figure 1A may provide information, such as
changeable parameters 105, constraints 103, existing service parameters 104 to
the decision management system 100. Also, simulation results, including the
candidate solution 120, may be presented to the users 155 via the user
interface
105.
[0042] As described in more detail below, the models 110 take into
consideration business and technology factors across multiple domains that can
be used to simulate deploying of a smart grid service. These factors are
implemented in the models, and these factors when implemented in the models
are referred to as model parameters. Relationships, described below, between
the model parameters are used during the simulations to generate the candidate
solutions for deploying the service. Deploying of a service may include
building
network infrastructure, providing the service, and maintaining the service
after it is
operational.
[0043] The models 110 include different business and technology
model
parameters that may be derived from a historical analysis of various
communication network service deployments, and also may be derived from a
determination of the type of information that is needed to analyze deployment
of a
service. For example, if a particular type of equipment is needed for a
service
deployment, then cost of that equipment may be used as a model parameter. The
12
CA 02798545 2012-12-05
=
model parameters may include but are not limited to traditional types of costs
and
traditional technology variables that affect the service.
[0044] Models may be provided based on domain. A model may include
model parameters that are associated with one domain or mostly associated with
one domain. However, models do not need to be based on domain.
[0045] The simulation generates values for the model parameters. A
value
for a model parameter is an instance of the model parameter. For example, if a
model parameter is capital cost for equipment, then a value for that factor is
a
monetary amount, such as 28.3 million dollars. The values determined for the
factors are estimations based on relationships between the model parameters as
well as inputs, such as the changeable parameters and/or other inputs for the
simulation. As used herein, model parameters that are related are referred to
as
dependent model parameters. If a model parameter is dependent on another
model parameter in the same or a different model, those model parameters may
also be referred to as corresponding parameters that are dependent.
[0046] The relationships between dependent model parameters are
stored
in each of the models, and these relationships may be stored as relationship
curves or some other type of mapping. A curve can describe the relationships
between dependent model parameters. For example, a curve may capture that if
a value of a dependent model parameter exceeds a threshold, then the value for
the corresponding dependent model parameter may level off and maintain a
certain value even if the other dependent model parameter continues to get
larger.
Examples of relationships are described in further detail below. Note that the
values in the curves may be derived from a historical analysis of the model
parameters. Also, note that the relationships in the models may be between one
or more inputs to the simulation engine 101 and one or more model parameters.
Additionally, relationships may exist between more than two model parameters,
13
CA 02798545 2012-12-05
and relationships may be dependent on other relationships. Also, relationships
may be between different domains.
[0047] Examples of the models may include a base case model, a
network
model and a bandwidth model. These models may be used for simulating 4G
services, smart grid services or other types of network communication
services.
The base case model is for analyzing both capital and operational costs.
Examples of values for capital cost include labor rates and capital costs for
building and operating the network to provide the communications network
service. The operation costs estimate the ongoing costs of running the
service.
These costs are based on the operational aspect of the service, such as labor
rates for end-user customer support and network infrastructure maintenance
personnel. Material costs may include cost of replacement equipment.
Operational costs include the costs of IT support systems such as network
management systems and customer billing systems. These values may vary
depending on the type of technology selected for deploying the service.
[0048] The network cost model is used to estimate the expenditures
associated with building the network for providing the service. The network
cost
model may include values that impact capital and operational costs in the base
case model. Examples of costs in the network cost model for a wireless service
include radio costs, such as cell site costs, backhaul costs, which are
related to
infrastructure costs from the core network to the edge (e.g., cell sites), and
core
network costs, such as switches and building space.
[0049] The bandwidth model helps estimate network bandwidth
requirements for the service or combinations of services, such as voice, data
and
video services. The bandwidth model is especially useful for these type of
services, because each of these types of services offers a different profile
from a
capacity, a coverage and a performance standpoint. Operators can factor
findings
from this analysis into their network cost model to provide a more detailed
picture
14
CA 02798545 2012-12-05
of build-out costs. Based on the expected bandwidth demand, the bandwidth
model helps set the dimensions of the transport network and predicts yearly
bandwidth costs. For example, the bandwidth model is used to determine
bandwidth needed based on coverage area, number of customers, type of service,
and other values.
[0050] Other examples of models, which may be used for simulating
smart
grid services, include device models, traffic models for the devices, domain
models and aggregation models. These models may include characteristics of the
devices that can be used in smart grid services. The characteristics may
include
the device name, type, power usage, etc. The domain models may identify
changes in characteristics of a device depending on the domain, geography and
service for which the device is deployed. The traffic models may specify for
each
device the number of bytes it transmits, how often it transmits, which
protocol it
uses, and the latency. The aggregation models may specify how to aggregate
bandwidth for the devices to determine the amount of bandwidth needed in each
domain, service and geographic location. Cost models may be used to estimate
costs. The base case model, a network model and a bandwidth model may also
be used for modeling smart grid services, which may include wireless smart
grid
services.
[0051] The models described above identify relationships between domains
as well as relationships between different model parameters in different
models.
These relationships are stored in the models. The simulation engine 101 uses
the
relationships to generate the candidate solution 120. Furthermore, as a result
of
varying one or more of the changeable parameters 102, these relationships may
identify changes to different costs associated with different models, and
ultimately
are used to generate different candidate solutions.
[0052] Figure 2 shows different domains for a communications network
service deployment. In this example, the service being deployed is a service
that
CA 02798545 2012-12-05
needs a wireless cellular network as well as a backhaul and core network
(e.g.,
WAN) with connections to the Internet. The service may include a 4G service
providing multimedia applications to the user via a 4G network. The domains
include a radio domain 210, a backhaul domain 220, a core network domain 230,
and an operations domain 240. The radio domain 210 may include end user
equipment and machine-to-machine devices 211 with a cellular interface, such
as
cell phones, laptops, etc. The radio domain 210 may also include cell sites
and
radio transceivers 212 and radio network controllers 213. The backhaul domain
220 includes backhaul equipment 221 and transport aggregators 222. This may
include controllers and cabling, such as optical fiber carrying data between
the
cellular network and the core network. The core network domain 230 includes
switches 231 and core and edge Internet Protocol (IP) routers 232. The
switches
and routers may include nodes commonly used for 2G, 3G and 4G wireless
systems for transmitting voice, IP packets, and narrowband and broadband
traffic.
The operations domain 240 may include multiple network and IT support systems,
such as a fault management system 241, a performance management system
242, an equipment provisioning system 243, a billing system 244, and a
customer
care system 245, as well as equipment and personnel needed for each system
and aspect of operations.
[0053] Figure 3 shows examples of model parameters 310, 320, and 330
for the base case model 113, network cost model 111 and the bandwidth model
112, respectively, from figure 1. Figure 3 also shows relationships between
the
model parameters 310-330 in the different models 111-113 and also identifies
the
domains 210-240 associated with the model parameters. The radio domain 210,
the backhaul domain 220, the core network domain 230, and the operations
domain 240 are shown in figure 3 as RD 210, BHD 220, CND 230, and OD 240,
respectively, for each of the model parameters 310-330. The changeable
parameters 102 shown in figure 1B may identify an end user coverage area to
16
CA 02798545 2012-12-05
receive the 4G service, an estimated number of users in various areas in the
coverage area, and may also identify a downlink bandwidth of 124 kbps, as an
example, to the end user. Based on these parameters, the simulation engine 101
uses the bandwidth model 112 to identify bandwidth requirements for the
domains.
The downlink bandwidth was provided in this example as a changeable parameter,
and is shown as a model parameter for the bandwidth model 112. In another
example, the downlink bandwidth as well as an uplink bandwidth may be
estimated by the simulation engine 101 based on the type of service being
deployed and other factors. Another model parameter for the radio domain is
the
total spectrum required. If less spectrum is available, then more cell sites
may be
needed to account for demand.
[0054] Other model parameters determined using the bandwidth model
112
may include bandwidth needed per channel for the backhaul domain 220 and the
core network domain 230. These model parameters may be determined based on
the bandwidth requirements for the type of service being deployed, the number
of
end users, and other factors.
[0055] The model parameters 320 for the network cost model 111 may
include number of radio transceivers and cell sites, radio network cost, and
deployment cost, which can vary depending on whether the site is co-located
with
another site or a new build, backhaul cost, core network cost and Operational
systems costs. Relationships between different model parameters in the models
are shown as lines connecting the values. For example, relationship 341 shows
that the number of transceivers and cell sites may vary according to the
bandwidth
spectrum needed for the service. If less spectrum is available, then more cell
sites
may be needed to account for demand. Spectrum and number of transceivers and
cell sites are referred to as dependent parameters because there is a
relationship
between the parameters. Also, each dependent parameter has at least one
corresponding dependent parameter in a relationship. For example, the number
of
17
CA 02798545 2012-12-05
transceivers and cell sites is a dependent parameter and the spectrum is the
corresponding dependent parameter or vice versa.
[0056] Relationship 344 indicates a relationship between cell site
bandwidth
(e.g., uplink and downlink) and backhaul and core network costs. For example,
backhaul costs are driven by the cost per megabit (Mb) needed at the site and
for
the backhaul transport. Core network costs are also driven by the bandwidth
needed at the cell site.
[0057] Relationships 341 and 344 represent inter-model
relationships.
Intra-model relationships among model parameters also exist. For example, the
backhaul and network costs shown under network cost model 111 may increase
as the cell site density increase (shown as relationship 345).
[0058] Relationships may be inverse or direct. For example,
relationship
342 shown for network cost model 111 is between the number of transceivers and
cell sites and the antenna height. This relationship is inverse, because a
decrease
in antenna height per cell site results in less coverage, and as a result more
cell
sites are needed. Other relationships are direct. For example, relationship
343
represents that using antennas with greater height increases radio network
costs,
which may include cost per cell site. It should be noted that relationships,
whether
inverse or direct, may not be linear. For example, after a certain cell site
density is
reached, an increase in coverage requirement or an increase in bandwidth
requirement may be accommodated by the current cell site density.
[0059] The model parameters 310 are for the base case model 113.
Examples of the model parameters 310 may include costs of maintaining a radio
network including cell sites and transceivers, the backhaul, and core network
and
a cost of maintaining Operational systems. Relationship 346 represents a
relationship between number of transceivers and cell sites and cost of
maintaining
the radio network. For example, as the number of cell sites increases,
18
CA 02798545 2012-12-05
maintenance costs may also increase. The costs of maintaining the backhaul and
core network may have similar relationships based on the size of the backhaul
and
core network. Operational systems costs may be related to the number of
subscribers.
[0060] As shown by the example of relationships 341-346, relationships
may be between different model parameters in different models as well as
between model parameters in the same model. Relationships may be inverse or
direct. Also, the relationships may be between domains. For example,
relationship 344 represents a relationship between the radio domain 210 and
the
backhaul and core network domains 220 and 230. Furthermore, a relationship
may be between a single model parameter and multiple model parameters or may
be between a first set of multiple model parameters and a second set of
multiple
model parameters. In addition, the relationship may be a multi-hop
relationship.
For example, an increase in spectrum requirements may cause an increase in the
number of cell sites (e.g., relationship 341), which causes an increase in the
cost
of maintaining the cell sites (e.g., relationship 346).
[0061] The relationships are stored in the models 110-112. Thus, as
model
parameters in the models or the changeable parameters are varied, the
resulting
changes to other model parameters are captured by the simulation performed by
the simulation engine 101 shown in figure 1. Furthermore, the resulting
changes
are identified in different candidate solutions generated by the simulation
engine
101.
[0062] As described above, the simulation engine 101 uses curves or
other
types of mappings (e.g., tables) in the models to estimate values for
dependent
model parameters. The values in the curves may be substantially fixed or may
be
changed to improve the accuracy of the simulation. For example, if a model
parameter is based on a standard or is vendor-specific, the values for the
curves
involving that model parameter may not be changed unless the standard changes
119
CA 02798545 2012-12-05
or the vendor changes. In other instances, the model parameters are not as
static.
In these instances, data may be retrieved from external sources to determine
values for the curve. This may include real-time gathering, such as accessing
information on the Internet or accessing public or private databases, to
retrieve
information that may be pertinent to the dependent model parameters. For
example, if a model parameter is an equipment cost, then costs for different
equipment manufacturers may be retrieved and averaged to determine values for
curves using that model parameter. This type of value updating may improve the
accuracy of the simulation because the curves are updated with the most recent
information.
[0063] The candidate solutions provide an analysis of different
factors given
the changeable parameters 102, the constraints 103, and existing service
parameters 104, if any. The analysis may be presented in reports generated by
the simulation engine 101. The reports express values for the model parameters
from the models 110-112 and other values, which may be derived from the values
in the models 110-112.
[0064] Figure 4 shows a simplistic example of information in a report
400
providing a breakdown of costs for the domains 210-240 of figure 2. The
changeable parameters in this example are a percentage of area coverage 401
and a percentage of population coverage 402. These changeable parameters
may be varied by the user. Although not shown, constraints may be specified.
[0065] The report 400 shows the domain 410, the description 420, the
capital expenditure (CAPEX) 430, and the operations expenditure (OPEX) 440.
The domain 410 includes the radio domain, the backhaul domain, the core
network domain, and the operations domain. The description 420 provides a
description of the costs for each domain. The CAPEX 430 shows the capital
expenditure costs for each domain. The OPEX 440 shows the operational costs
for each domain estimated for a 5 year run rate. Examples of other information
CA 02798545 2012-12-05
that may be provided in the report include but are not limited to an
estimation of
consolidated financials and valuation of products and services, subscriber
penetration projections, and revenue estimations.
[0066] A user may vary the changeable parameters to generate
different
candidate solutions. For example, a service provider is weighing different
market
penetration scenarios as well as different bandwidth requirements to help
determine an optimal wireless broadband networking strategy. The decision
management system 100 generates candidate solutions in real-time projecting
business results for a 4 percent market penetration versus a 2 percent
penetration.
The service provider also assesses the cost impacts of different network
speeds,
such as a 124kbps downlink versus a 64kbps downlink. The candidate solutions
help determine the impact of different strategic and technological options
based on
a five or ten-year cost structure and on a net present value. The decision
management system 100 provides decision-making confidence across multiple
business and technology dimensions.
[0067] For a 4G service, the decision management system 100 may also
be
used to sort through the pros and cons of a variety of different access
technologies, which enables the analysis of strengths and weaknesses in a
company's existing technology infrastructure. The system 100 can be used as a
planning tool to create the most cost-effective and integrated approach to a
company's business support systems and operations support systems.
[0068] Filters may also be used to generate reports with the desired
model
parameters. A filter filters out the model parameters that are not needed for
the
report. For example, a chief technology officer may need to focus on model
parameters in a technology sub-solution. A filter can be used that generates a
report including only model parameters related to the technology sub-solution
for
the service deployment. Reports can be customized with the desired model
parameters as needed.
21
CA 02798545 2012-12-05
[0069] Figure 5 illustrates network domains for smart grid services.
The
domains, for example, include utility sections 501, devices 502, LAN 503,
backhaul 504, WAN 505, head-end/EMS 506 and operations 507. Figure 5 also
shows networked systems and devices for each utility section. For example, the
transmission substations utility section includes process busses, intelligent
electronic devices (IEDs) gathering and aggregating data for substations, and
LANs and gateways. The data gathered from IEDs and other devices and sensors
may be aggregated and transmitted to upstream domains by the LANs and
gateways through the smart grid communication network services. The data
transmission between domains is not limited to the upstream direction and may
also include downstream data transmission. For example, data may be
transmitted from upper-level domains, such as the operations domain 507 to the
devices domain 502. The data exchanged between the transmission substations
utility section and the other utility sections via the smart grid
communication
services and other domains is represented by the dashed lines.
[0070] The distributed substations utility section may include
devices and
LANs similar to the transmission substations utility section. The
distribution/feeder
lines utility section and the distributor generator utility section may also
include
IEDs gathering and sending data regarding the power distribution. The
home/customer utility section, the mobile IT and the campus and data center
utility
section may also communicate through smart grid communication services to
other domains.
[0071] Figure 6 shows examples of the different network technologies
that
may be used for the communications services in the domains and utility
sections
shown in figure 5. For example, as shown in figure 6, ZIGBEE, HOMEPLUG, and
WiFi are examples of technologies that may be used to gather data from
customer
smart grid devices. The technologies listed under grid/mobile IT/campus are
examples of network technologies that may be used for the distribution
substations
22
CA 02798545 2012-12-05
utility section, the distribution/feeder lines utility section and mobile IT
utility section
shown in figure 5. The technologies listed under WAN in figure 6 may be used
in
the WAN domain 506 shown in figure 5 or may be used to communicate with the
WAN domain 506. Figure 6 also shows examples of smart grid applications that
may be used in the operations domain 507 shown in figure 5.
[0072] As described above, the system 100 shown in figures 1A-B is
operable to generate candidate solutions for smart grid services. The
candidate
solutions may include spectrum required, technology mix, public versus private
infrastructure mix, and costs including CAPEX and OPEX. To determine the
candidate solutions, information about the domains 501-507 and the utility
sections shown in figure 5 is determined. Figure 7 shows information that may
be
input into the system 100, which may be included in the changeable parameters
102, constraints 103 and/or the existing service parameters 104 shown in
figure 1,
to determine the candidate solution 120. Figure 7 shows scenario inputs 701
that
may be provided as the input information for the system 100. The scenario
inputs
701 includes business assumptions 702, technology assumptions 703, cost
assumptions 704 and spectrum assumptions 705.
[0073] The business assumptions 702 may include coverage
requirements,
capacity requirements, timeframes, service quality and security requirements.
Some of this information may be determined from the models 110 (e.g.,
bandwidth
capacity requirements) and may be based on service requirements. The
technology assumptions 703 may include wireless technologies, wired
technologies, architecture, public versus private networks, technical
specifications,
protocols and traffic management. The cost assumptions 704 may include
company owned costs (e.g., unit, upgrade and maintenance costs, and scaling
factors) and public carrier costs. The spectrum assumptions 705 may include
frequency bands, licensed versus unlicensed bands, channel size, guard bands
and regulatory constraints. One or more of the scenario inputs 701 may be
23
CA 02798545 2012-12-05
provided by a user or derived from information provided by the user or
information
provided by other sources.
[0074] Figure 7 also shows scenario outputs 710, which may be
included in
the candidate solution 120 shown in figure 1B to describe the details of the
proposed service that is operable to be deployed to meet the user's
requirements.
The scenario outputs 710 shown in figure 7 include spectrum required 711,
wireless infrastructure 712, wired infrastructure 713 and costs 714. The
spectrum
required 711 may include an estimate of spectrum required per year, per
frequency band, per and/or per licensed versus unlicensed bands. The wireless
infrastructure 712 includes cost and sizing estimates for the wireless
infrastructure.
The cost and sizing estimates may be organized by utility section, link type,
frequency, technology and/or public versus company owned. The wired
infrastructure 713 may include similar cost and sizing estimates. The costs
714
may include CAPEX and OPEX costs, which may be organizer per annum and
may be aggregated.
[0075] The models used to determine the scenario outputs 710 for
smart
grid services may include device models including traffic profiles for devices
and
applications in utility sections and domains and cost models. These models may
be used to estimate present and future traffic and costs in each domain and
each
utility section shown in figure 5. Furthermore, the estimations may be
dependent
on data connectivity between the domains and utility sections.
[0076] Figure 8 shows data connectivity for each utility section and
for the
domains in smart grid, and shows the commodity flows. The commodity flows
include gas and electric flows through the smart grid systems, such as flows
from
generators and through transmission and distribution substations and feeder
lines
to the customer. The customer consumes the commodities through appliances
and other systems and devices. One example is shown as electric vehicles
(e.g.,
24
CA 02798545 2012-12-05
plug-in hybrid electric vehicles (PHEV)) that may eventually become pervasive
and
consume electricity at the customer premises when being recharged.
[0077] The data connectivity for the utility sections and the smart
grid
domains of figure 5 are also shown. Figure 8 shows that smart grid services
may
encompass network data connectivity between transmission substations,
distribution substations, distribution and feeder lines, customers, mobile
workforce
and the core network. Figure 8 includes numbers 1-8 representing the data
connectivity for each utility section. For example, 2 represents the data
connectivity for the transmission substations, and 4 represents the data
connectivity for the electrical distribution substations.
[0078] The models take into account the data connectivity shown in
figure 8
for estimating the bandwidth required, the wireless infrastructure, the wired
infrastructure and costs for service deployment, which may be included in a
candidate solution. For example, the system 100 shown in figures 1A-B uses the
models 110, including smart grid models, to make the estimations. Figure 9A
shows an example of the estimations for the data connectivity for the
transmission
substations utility section, shown as 2 in figure 8. Figure 9A shows
estimations for
the required bandwidth that would be needed today and in the future for the
smart
grid communications network service for the transmission substations utility
section. The bandwidth requirements are shown for different package sizes,
shown as small, medium and large. A package may include one or more devices.
Package size refers to network characteristics of the device. For example, a
larger package size may provide more bandwidth or may be able to monitor more
devices, and so on. The bandwidth requirements are also shown for current and
potential future protocols. Figure 9A also shows estimations for the
transmission
substation technology mix that would be needed for today and in the future to
implement the service. The technology mix includes the mix of public and
private
wired and wireless network that may be used for the service.
CA 02798545 2012-12-05
[0079] Figure 9B shows an example of the estimations for the data
connectivity for the electrical distribution substations utility section,
shown as 4 in
figure 8. Similar to figure 9A, figure 9B shows estimations for the required
bandwidth that would be needed today and in the future for the smart grid
service
for this data connectivity. Figure 9B also shows estimations for the
transmission
substation technology mix that would be needed for today and in the future to
implement the service for this data connectivity and service. Although not
shown,
the system 100 is operable to provide estimation for the data connectivity for
each
utility section and domain shown in figure 8.
[0080] As described above, figures 9A-B show examples of estimations of
for required bandwidth and technology mix. Figures 9A-B show the estimations
in
graphic form. Figures 10A-D show the estimations in a text form. Figures 10A-D
shows the estimations for all the utility sections and domains shown in figure
8.
[0081] Figures 11A-B show traffic profiles for device models for
smart grid
devices and applications that may be used to determine the estimations shown
in
figures 9A-B and 10A-D and the candidate solution 120 of figure 1. The smart
grid
device models may be included in the models 110 shown in figure 1. The traffic
profiles may differ depending on the use case, which is also shown. For
example,
the different use cases might be for using the devices in different utility
sections
and/or domains, which impacts the traffic profiles as shown in figure 11A-B.
The
traffic profiles are comprised of network characteristics, which may include
sample
size (bytes), sample rate per unit, sample unit (e.g., seconds, minutes,
hours), raw
rate (kbps), raw current overhead rate (kbps), raw future overhead rate
(kbps),
and latency (ms). for each monitoring application. Also, protocol overhead may
be
entered and used to determine the traffic profiles for various devices.
[0082] Figures 12-18 also show data for device models for different
utility
section and domains of the smart grid. Figures 12A-B show device models for
the
transmission substations utility section of a smart grid for current and
future
26
CA 02798545 2012-12-05
deployments. In figures 12A-B and other figures, different package sizes that
may
be implemented for this utility section are shown. Each package size may
include
different devices and/or applications. For example, in figure 12A, video
surveillance is shown as included for the large package size but not for the
small
and medium package sizes. Also, the packages may differ from current and
future
deployments. For example, for transmission substation 1, video surveillance
may
additionally be included in future deployment of a smart grid service, which
is
shown in figure 12B. This impacts costs and bandwidth estimations over time,
which are presented as part of a candidate solution.
[0083] Figures 13A-B show examples of device models (e.g., current and
future) that may be used for a smart grid service for a gas transmission
substation.
Figures 14A-B show examples of device models (e.g., current and future) that
may
be used for a smart grid service for the distribution substations utility
section.
Figures 15A-B show examples of device models (e.g., current and future) that
may
be used for electrical distribution circuits. Figures 16A-B show examples of
device
models (e.g., current and future) that may be used for a smart grid service
for the
distributed generator utility section. Figure 17 shows examples of device
models
(e.g., current and future) that may be used for PHEV. Figures 18A-B show
examples of device models (e.g., current and future) that may be used for a
smart
grid service for automated meter reading and the home/customer utility
section.
Figure 19 shows examples of device models (e.g., current and future) that may
be
used for a smart grid service for the mobile IT utility section.
[0084] The system 100 shown in figures 1A-B includes the user
interface
105. The user interface 105 may include a graphical user interface (GUI) where
a
user can enter and modify parameters and view simulation results generated by
the simulation engine 101. Figures 20A-B illustrate an example of a snapshot
of a
dashboard that maybe presented via the user interface 105. The dashboard
allows users to change radio network and spectrum parameters in order to
27
CA 02798545 2012-12-05
simulate how much spectrum will be required and how much capacity is in the
network for up to ten years into the future. The simulation results presented
in the
dashboard indicate where there is excess capacity or insufficient capacity or
spectrum in the network. For example, a user may modify parameters in the
window 2001, such as number of cell sites, whether a cell site is split,
whether all
traffic is loaded on broadband radio frequencies, and other network and
spectrum
parameters, which may not be shown.
[0085] Windows 2002 and 2003 show examples of simulation results for
the
ten-year period. Window 2002 shows the ten-year throughput demand for critical
and non-critical layers in the domains and a total ten-year throughput demand.
Window 2003 includes an analysis of traffic demand versus network capacity for
the ten-year time period. For example, window 2003 indicates whether the
demand is met by the capacity for each channel. The amount of spare capacity
is
also shown if capacity exceeds demand. If the capacity is insufficient to meet
the
demand, the user may adjust the parameters, such as number of channels or cell
sites, to increase the capacity. If there is too much spare capacity, the user
may
adjust the parameters to reduce capacity and save costs.
[0086] Figure 21 shows a screenshot of an executive dashboard
summary
of the estimated network outputs generated by the simulation. The dashboard
summary shows the percentage of public versus private communications network
infrastructure required, wired versus wireless network infrastructure
required,
percentage of broadband wireless infrastructure required, and total bandwidth
and
traffic consumption (tonnage) over ten years. The network output is shown for
different domains, such as a generation layer, transmission layer,
distribution
layer, etc.
[0087] Figure 22 shows a screenshot of an executive dashboard
summary
of the estimated cost outputs generated by the simulation. The summary of
costs
include costs over ten years to deploy, upgrade and maintain the smart grid
28
CA 02798545 2012-12-05
communications network. Cost categories may include: CAPEX, OPEX, Labor,
Headcount, Tax, Spares, Overhead, Expense, Capital, Equipment, etc. Figure 22
is a graphical representation of some of the costs. A textual representation
may
also be provided, although not shown.
[0088] Figure 23 illustrates a method 2300 for modeling and analyzing
scenarios for a communications network service to be deployed. The method
2300 is described with respect to one or more of figures 1-4 by way of
example.
The method 2300 may be practiced in other systems. Also, the method 2300 may
be used to model and analyze a smart grid communications network service, a 4G
communications network service, or other types of communications network
services.
[0089] At step 2301, changeable parameters are received. For
example,
the changeable parameters 102 shown in figure 1B may include business and
technology changeable parameters. Business parameters are related to financial
data for the service to be provided, such as costs, revenue, profit, budget,
etc.
Technology parameters relate to technology data for the service. Examples
include coverage area, downlink bandwidth, QoS, etc. Other changeable
parameters may include one or domains and utility sections for a smart grid
service, package size, devices, and applications. The changeable parameters
may be provided from a user via a user interface.
[0090] At step 2302, constraints are received. These may include
parameters that must be met by the service to be deployed. For example, a
certain level of QoS for a service may be required to provide the service, and
a
budget constraint identifies maximum expenditure.
[0091] At step 2303, existing service parameters are received. These
parameters describe an existing service. For example, a service provider
planning
on deploying a 4G service or a smart grid service, may already provide 3G
29
CA 02798545 2012-12-05
services or some portion of a smart grid service. Business and technology
parameters describing the existing service are provided.
[0092] At step 2304, the service to be deployed is simulated using
one or
more of the changeable parameters, constraints, and existing service
parameters.
The system 100 is used to simulate the service. The simulation is performed
using the models 110, which may include cost models, device models, network,
IT
systems and bandwidth models. For example, the models 110 store relationships
between model parameters. The inputs, such as the changeable parameters,
constraints, and existing service parameters, are determined. Values for a
first set
of model parameters dependent on these inputs are determined based on the
stored relationships. Also, values for a second set of model parameters
dependent on the first set of model parameters are determined based on stored
relationship, and so on until values are determined for all or most of the
model
parameters.
[0093] For example, the relationship 341 shown in figure 3 represents that
the number of cell sites may vary according to the bandwidth spectrum needed
for
the service. The number of cell sites is described as a dependent model
parameter dependent on the corresponding spectrum model parameter. The
relationship may indicate that if less spectrum is available, then more cell
sites
may be needed to account for demand. A bandwidth model stores an indicator
that the spectrum model parameter is related to the number of cell sites model
parameter in the network cost model. The bandwidth model may also store a
relationship curve that is used to identify the estimated number of cell sites
for a
spectrum model parameter value. Thus, given a value for the spectrum model
parameter, a value for the number of cell sites model parameter is determined
from the curve. Then, the models may store a relationship between number of
cell
sites and capital costs, and a value for capital costs may be determined at
least
partially based on the number of cell sites. Curves may be based on historic
CA 02798545 2012-12-05
values for existing services. The simulation engine 101 uses the curves to
estimate the values for model parameters. These type of mappings for the
relationships may be stored for sets of related values, and are used to make
educated estimations for the candidate solution.
[0094] At step 2305, a candidate solution is determined from the
simulation,
and at step 2306 an analysis is generated describing the candidate solution.
The
candidate solution may include business and technology sub-solutions related
to
financial data and technology data describing the service to be deployed. The
generated analysis may include reports describing the sub-solutions. Figure 4
shows an example of a report. The reports include the values for the model
parameters determined from the simulations. The reports may be used to
compare different candidate solutions. For example, the method 2300 is
repeated
for different sets of changeable parameters. Each candidate solution is
analyzed
to determine which best satisfies business and technology objectives.
[0095] Figure 24 illustrates a method 2400 for simulating the
communications network service. The steps of the method 2400 may be
performed for the step 2004 of the method 2000. The method 2400 is described
with respect to a smart grid service by way of example. The method can apply
to
other types of communications network services. The method 2400 is described
with respect to the simulation engine in figure 1B but may be practiced in
other
systems.
[0096] At step 2401, the simulation engine 101 determines a
hierarchal
model of a smart grid including domains and utility sections for the smart
grid. The
hierarchal model may be stored in data storage used by the simulation engine
101. The hierarchal model may include relationships between different domains
and utility sections in the hierarchy. An example of the hierarchy is shown in
figure
5.
_31
CA 02798545 2012-12-05
[0097] At step 2402, the simulation engine 101 identifies, from the
hierarchal model of the smart grid, at least one domain and at least one
utility
section for the smart grid communications network service to be simulated. For
example, the smart grid service may be a grid operations application that
requires
communication through one or more of the domains 501-507 and the utility
sections shown in figure 5. These domains and utility sections performing the
communications are selected.
[0098] At step 2403, the simulation engine 101 selects device models
from
a plurality of device models based on the identified domains and utility
sections.
Examples of the device models are described above and shown in figures 12-19.
The device models may also be selected based on the changeable parameters
that describe the criteria for the service. Device models for particular
applications
and package sizes that are specific to the service are selected.
[0099] At 2404, the simulation engine 101 simulates the smart grid
communications network service using the traffic profiles from the selected
device
models. For example, the simulation determines estimations of bandwidth needed
and costs for the service based on traffic profiles in the selected device
models
and cost models. Estimating the bandwidth may include determining a
relationship between the identified at least one domain and another domain in
the
hierarchal model of the smart grid and determining the estimated bandwidth
needed for the service based on the determined relationship. For example, the
simulation engine 101 may include a bandwidth estimation tool that aggregates
bandwidth for the applications and devices in a lower domain to determine the
bandwidth needed in an upper domain to transmit the data for the devices and
applications. An example is shown in figure 5 by the dashed lines in the
distribution/feeder line utility section and the distributed generator utility
section.
The dashed line represent the aggregation from the lower device and LAN
domains 502 and 503 to the backhaul domain 503. The aggregation is used to
32
CA 02798545 2012-12-05
determine the bandwidth needed in the backhaul and WAN domains 504 and 505.
The estimated bandwidth and costs are provided in a candidate solution.
[0100] The decision management system 100 performs technical steps in
the process of simulating different network configurations via scenarios
having
different bandwidths. Moreover, bandwidth requirements and other requirements
are determined based on a bandwidth model and other models. The model
parameters are related to technical data which can be physically measured,
i.e.
the bandwidth of a data connection. The bandwidth model, for example, is a
model including model parameters related to the bandwidth of a data
connection.
Furthermore, the bandwidth requirements for each scenario are determined based
on the technical model parameters. Thus, the embodiments utilize technical
features and do not simply achieve a result related to technical features.
[0101] The decision management system 100 is operable to simulate
various configurations related to technical parameters prior to building out
of a
smart grid network. Moreover, due to the simulation, it is possible that
extensive
resource consumption through testing of real life communications network
services
may be avoided. Therefore, the user is relieved from the mental task of
carrying
out a number of operations individually. Furthermore, the user may use the
optimal configuration provided by the decision management system 100 for a
task
the user has to perform, i.e. deployment of a communications network.
[0102] Further, the decision management system 100 provides an
improved
man-machine interaction, wherein a user is in a position to process data in a
simple and more efficient manner. As an example, the simulation may allow that
data may be processed and provided to the user in a faster manner, than with
the
efforts of a human alone. The simulation performs several simulations
determining the impact of different configurations at the direction of a user
wanting
to determine an optimal configuration. These simulations are based on the
relationships between parameters of the different models. The user may
33
CA 02798545 2012-12-05
additionally run more simulations once the first simulations are complete.
Also,
simulation results may be presented in a manner such as shown in figures 20-22
that allows a user to quickly and efficiently identify an optimal
configuration.
[0103] Figure 25 shows a computer system 2500 that may be used with
the
embodiments described herein. The computer system 2500 represents a generic
platform that includes components that may be in a server or other computer
system. The computer system 2500 may be used as a platform for the decision
management system 100 and for executing one or more of the methods, functions,
and steps described herein. These steps may be embodied as software stored on
one or more non-transitory computer readable storage mediums.
[0104] The computer system 2500 includes a processor 2502 that may
implement or execute software instructions performing some or all of the
methods,
functions and other steps described herein. Commands and data from the
processor 2502 are communicated over a communication bus 2504. The
computer system 2500 also includes a main memory 2506, such as a random
access memory (RAM), where the software and data for processor 2502 may
reside during runtime, and a secondary data storage 2508, which may be non-
volatile and stores software and data. The memory and data storage are
examples of computer readable storage mediums.
[0105] The computer system 2500 may include one or more I/O devices
2510, such as a keyboard, a mouse, a display, etc. The computer system 2500
may include a network interface 2512 for connecting to a network. It will be
apparent to one of ordinary skill in the art that other known electronic
components
may be added or substituted in the computer system 2500.
[0106] The models 110 in the decision management system 100 of figures
1A-B may be stored in a database provided in the secondary data storage 2508.
The simulation engine 101 may be executed by the processor 2502 to generate
34
CA 02798545 2012-12-05
the candidate solution 120. Also, a user interface for the system 100 may be
generated by the processor 2502 and presented using the I/O device 2510. The
user interface can output reports for the candidate solutions and receive user
input, which may include parameters 102 and constraints 103.
[0107] One or more of the steps of the methods described herein and other
steps described herein and one or more of the components of the systems
described herein may be implemented as computer code stored on a computer
readable storage medium, such as the memory and/or secondary storage, and
executed on a computer system, for example, by a processor, application-
specific
integrated circuit (ASIC), or other controller. The code may exist as software
program(s) comprised of program instructions in source code, object code,
executable code or other formats. Examples of computer readable storage
medium include conventional computer system RAM (random access memory),
ROM (read only memory), EPROM (erasable, programmable ROM), EEPROM
(electrically erasable, programmable ROM), hard drives, and flash memory.
[0108] While the embodiments have been described with reference to
examples, those skilled in the art will be able to make various modifications
to the
described embodiments without departing from the scope of the claimed
embodiments.
35
