Contents Introduction .................................................................................................................................................... 2

Smart Meter Capabilities ................................................................................................................................ 2

Integration of SAP IS-U and Smart Metering .................................................................................................. 3

Difference between Conventional Meter and Smart Meter ...................................................................... 4

Benefits of a Smart Meter .......................................................................................................................... 5

Meter Data Management ............................................................................................................................... 7

Who Owns the Data? .................................................................................................................................. 7

Handling Meter Data Management (MDM) ............................................................................................... 8

Meter Event Data: Beyond Interval Reads ................................................................................................. 9

Potential Business Areas for Events Data Insights .................................................................................... 10

Deriving Business Value ............................................................................................................................ 10

Improving Outage Management through Meter Events .......................................................................... 12

Smart Meter Event Processing: Business Context Diagram ..................................................................... 14

Smart Meter Event Processing Solution ................................................................................................... 14

Conclusion: From Data to Insights ............................................................................................................ 15

Future of a Smart Meter: - “Smart Home” ................................................................................................... 16

Introduction Many utilities today are evaluating the potential benefits of Smart Metering for all utility

stakeholders. Deployment of smart meters and smart devices is gathering momentum all over

the world. Around 60 million smart meters have already been installed in the last ten years and

several large scale deployments by utilities have either already started or are in the advanced

planning stages. While concerns on costs, privacy, safety and security continue to surface,

utilities have conclusively moved from ‘why smart meters’ debate to ‘when smart meters’

planning. Active support from policy makers and regulators, and harmonization of standards are

expected to accelerate the proliferation of smart meters in the current decade and beyond.

However, deployment of smart meters constitutes only the first lap in a long and complex

journey towards realization of “Smart Grid” - a system that ensures quality, reliability and

security of power while empowering consumers and utilities to make informed and dynamic

decisions.

At a micro level, smart meters can be considered as distributed sources that produce

data, analogous to the distributed generation sources that produce electricity. This

humongous amount of data, generated at rapid frequency, needs to be acquired, stored,

processed, analysed and used in near real time to achieve major Smart Grid objectives.

At the same time, smart meters can be dynamically configured to respond to consumer

choices and prevailing supply conditions. Availability of this data and configurability

options, by themselves, provide opportunities for utilities to IT-enable and reengineer

business processes like never before.

Smart Meter Capabilities A smart meter has the following capabilities

Real-time or near-time registration of energy use and possibly energy generated locally

Offering the possibility to read the meter both locally and remotely (on demand)

Remote limitation of the throughput through the meter (in the extreme cases stopping

power supply to the customer)

Interconnection to premise-based networks and devices (e.g., distributed generation)

Ability to read other, on-premise or nearby commodity meters (e.g., gas, water)

Integration of SAP IS-U and Smart Metering The smart metering system needs to communicate with ERP systems dealing with billing &

customer service like SAP IS-U (industry solution for utilities). A successful smart metering

system requires seamless integration of all the systems involved from the smart meter

infrastructure through to the SAP IS-U. The infrastructure basically includes a SAP Adapter that

integrates with MDUS (Meter data unification and synchronization) and SAP for utilities

Supporting key business processes. A successful smart metering system requires seamless

integration of all the systems involved from the smart meter infrastructure through to the SAP

IS-U. The infrastructure basically includes a SAP Adapter that integrates with MDUS (Meter data

unification and synchronization) and SAP for utilities supporting key business processes.

The above diagram shows the integration of SAP IS-U system with smart meters. This integration

typically depicts Advanced Metering Infrastructure (AMI) and its integration with SAP IS-U

system. However simple integration is also possible wherein files are sent to and fro to establish

communication between SAP and smart metering system. The below example shows interfaces

designed for utility company where meter reading request is sent

From SAP to smart metering system and meter readings taken by smart meters are uploaded

into SAP IS-U.

The above diagram shows the integration of SAP IS-U system with smart meters. This integration

typically depicts Advanced Metering Infrastructure (AMI) and its integration with SAP IS-U

system. However simple integration is also possible wherein files are sent to and fro to establish

communication between SAP and smart metering system. The below example shows interfaces

designed for utility company where meter reading request is sent

From SAP to smart metering system and meter readings taken by smart meters are uploaded

into SAP IS-U.

Example 1: Meter Reading request from SAP IS-U to Smart Meter Database

This interface sends meter reading order with scheduled meter reading date for which meter

reading is to be taken in a file and upload the same at SAP Application Server.

Example 2: Meter Reading Upload Interface from Smart Meters to SAP IS-U

Meter reading results are required to be uploaded into the SAP-ISU daily in order to bill the

customers. The meter readings are uploaded to a common database of smart meters. The

readings are sent from database based on the meter reading orders from SAP. The Meter

reading files are then uploaded into SAP-ISU through this interface.

Difference between Conventional Meter and Smart Meter

The basic difference between conventional meters and smart meters is that conventional

meters provide one-way of communication whereas smart meters provide two-way

communication. For instance, in order to carry out a meter reading using a conventional meter,

the meter reader needs to physically visit the customer premise and take reading. This reading

will be sent to the utility company for billing. But in case of smart meters, this can be done

automatically. The system operator will create a meter read request from the utility company

office. The smart meter sends the meter reading as per the request to the utility company. This

avoids manual intervention during meter reading and provides more accurate, real-time data to

the company.

Benefits of a Smart Meter

The benefits of a smart meter can impact both electricity provider and the consumer. In

addition, the use of a smart meter also has a direct impact on environment protection. The

following points outline the benefits from the consumers and electricity companies’ perspective

and its impact on the environment:

From the Consumers’ Perspective

Consumers have greater control over their energy consumption as they provide the

ability to measure their energy usage in real-time or based on hourly increments.

Consumers can select different pricing plans based on time-based rates and have the

option to lower their consumption based on peak periods set by the utilities. This can

give consumers significant savings on their monthly bills.

Users can further increase their energy savings by changing their energy consumption

habits based on their understanding as to what smart meters are telling them about their

usage in real-time. Consumers can shut down appliances during peak periods or pre-

program appliances and devices to operate only at predetermined timeframes.

More transparency can be reflected on consumer bill and thereby reducing the consumer

grievance instances

From the Electricity Companies’ Perspective

The automated meter reading capabilities of smart meters allow for automatic and

remote readings on consumers’ energy consumption thereby removing the need for

agents to go from house to house to read meters.

A smart meter can be controlled remotely from the central information system proving

utilities better management of the electric network.

Faults and outages can be detected in real-time allowing for quick response times for

intervention either by remote or by quick dispatch. Utilities can also monitor network

irregularities and performance of transformers to detect and minimize losses.

Most Smart Metering applications permit remote theft-detection tests geared to the

type of meter and the type of utility service. They can ensure that almost all bills are

based on actual meter reads rather than on estimates; this reduces calls to the contact

centre and improves customer satisfaction.

Smart Metering applications often permit utilities to check meter status (“ping the

meter”) prior to sending a repair crew in response to a customer call. These checks

prevent needless field crew dispatch to customer sites where problems are not the

utility’s responsibility.

Consumers are billed based on actual consumption and not just based on averages and

estimates. Fraud can also be easily detected through the smart network.

New Products and Services: Smart Metering systems can frequently accommodate

prepayment meters with multiple options for payment, such as recharging or via Internet

or telephone, and with emergency overrides. Some utilities are looking at the possibility

that a single prepayment meter for gas, water, and electricity may bring down the total

cost of prepayment and permit utilities to respond cost-effectively to an option many

consumers request as a tool to help them budget.

Impact on the Environment

Consumers who become more aware of their use through on-premises real-time displays will

explore ways to reduce consumption. With a smart meter, consumers can use energy efficiently

and thus lower their energy demands.

With more accurate demand estimation based on consumer real time consumption

pattern demand can be managed well, there will be no need to build additional power

plants that will generate greenhouse gases.

During peak demand hours, utilities are sometimes forced to use “peaker” plants which

produce higher carbon emissions. With the use of a smart meter, consumers can reduce

their demand during peak hours thus eliminating the need for “peaker” plants.

Time-of-use rates also help even out grid use and thus reduce the need for habitat- and

landscape-damaging grid expansions

A smart home is a home that is equipped with special structured wiring to enable

occupants to remotely control or program an array of automated home electronic

devices by entering a single command. For example, a homeowner on vacation can use a

Touchtone phone to arm a home security system, control temperature gauges, switch

appliances on or off, control lighting, program a home theatre or entertainment system,

and perform many other tasks.

Meter Data Management

Who Owns the Data?

In traditional, regulated utility models, utilities generally own meter data and can use it for

any purpose approved by regulators, so long as they guard individual rights of privacy. It is

common, however, for deregulating jurisdictions to grant meter data ownership to

customers. Customers grant data access to a chosen retailer or supplier as a condition of

receiving supply. Some see customer ownership of data as an impediment to full use of

Smart Metering data. In some jurisdictions, advocates argue that customers should have

the right to limit access to their data or should be compensated by parties using it.

Jurisdictions moving forward with Smart Metering under both regulated and deregulated

market conditions appear to resolve the issue by specifying conditions under which the

various entities within the utility industry may access and use customer data.

Handling Meter Data Management (MDM)

An alternative way to handle data volume and multiple data requests is to offload it into a

stand-alone meter data management (MDM) application.

MDM gathers and stores meter data. It can also perform the preliminary processing

required for different departments and programs. Most important, MDM gives all units

equal access to commonly held meter data resources. MDM provides an easy pathway

between data and the multiple applications and departments that need it. It can more

easily consolidate and integrate data from multiple meter types. It can reduce the cost of

building and maintaining application interfaces. And it provides a place to store and use

data, whose flow into the system cannot be regulated, such as the flood of almost

simultaneous messages from tens of thousands of meters sending a “last gasp” during a

major outage.

MDM’s independent service function may be further refined through the addition of a

meter data warehouse. In situations where both exist, the MDM typically manages real-

time, transactional processing while the warehouse handles data extraction, reporting,

and analytical processing.

Separating the MDM from the billing solution has clear advantages. It maintains bill

production efficiency while providing even-handed data access to all departments. It

permits a utility to add security to meter communications and data without complicating

customer access to bill payment and analysis websites. And it lets utilities change the

source of the meter data with no negative effect on other IT systems and architecture.

The IT Implications of Meter Data Management (MDM) is, for most utilities, a new type of

application. It shatters the typical utility IT model in which each department “owns” its

own set of applications. MDM treats every department as its “owner.” It thus forces

departments to work together. If MDM is to serve all equally efficiently, then the various

stakeholders must share information. They must agree to application configurations that

serve all needs optimally.

This process of information sharing is proving eye opening to departmental heads.

Suddenly, sharp minds have the knowledge and tools to propose better, more efficient

program administration.

In other words, MDM is becoming an avenue for rethinking utility business processes

independent of existing departmental boundaries. It is the first major utility silo-breaking

application.

Meter Event Data: Beyond Interval Reads

Smart meters are well known for their ability to provide meter read data at smaller intervals,

such as every 15, 30 or 60 minutes, as well as bi-directional communication and remote

operating capabilities. In addition to these features, smart meters also generate hundreds of

meter events. An event is information that originates from the meters’ endpoints and can

have several attributes, including source and proxy information, severity level and event

category. The source is normally the device that originates the event, while the proxy is the

device responsible for detecting and communicating the event. Severity levels include

emergency, information, error, warning and clear. The event category provides information

regarding the process to which the event is related. There are four basic event categories:

Meter or device status events, such as “power restore” and “last gasp.”

Power quality events, such as voltage sag, swell and high/low voltage alarms.

Meter or device tamper flags, such as reverse energy flow.

Meter hardware information, such as low battery alarms and battery critical alerts.

Potential Business Areas for Events Data Insights

Some of the potential business areas where information from meter events can be used to

derive useful business insights are:

Customer experience: Events like last gasp and power restore, which can identify field outages

and take proactive action even before the customer calls, as well as alerts and notifications to

customers regarding power outages.

Outage management: Events to detect outages at the right device level and create proactive

tickets, as well as “power restore” to identify nested outages after large-scale outage

restoration.

Power quality: Events like “voltage sag” and “voltage swell,” in correlation with other device

status information to proactively identify open neutrals and flickering lights.

Revenue assurance: Events like meter inversion and reverse energy flow, along with meter

reads to identify power theft and abnormal usage/demand patterns.

Smart meter network operations and monitoring: Events and meter ping commands to

identify damaged/defective meters, access relays and other devices, as well as hardware

events to provide information regarding device hardware such as battery information,

firmware version, etc.

Deriving Business Value

By now, many utilities are broadly aware of the possible areas where they would like to leverage

information from events. However, the real challenge lies in how to develop the processes and

systems to continuously convert data into actionable information and then further refine the

models based on the results.

This challenge arises because of the nature of event data, both status and exception. Event data

is a raw data stream and is also associated with high volumes because there are hundreds of

events generated for normal operations, as well as for changed conditions. These events also

need to be validated with other relevant information, as they basically manifest the conditions

of the network (meter or grid) and also some aspects of customer behavior.

To manage the above needs, we believe that utilities need to focus on two key dimensions:

Systems to manage large volumes of events data, both real-time and batch.

Logical and statistical techniques that will help identify the right events and correlate

with various conditions, both event- and business-related, and, finally, predict the outcomes.

Key logical and statistical techniques that could be used include:

Data filtering: This refers to the analysis of events and intelligent filtration of redundant

data based on predefined conditions from the event data stream. This technique uses Boolean

logic. Based on our experience, events like last gasp and power restore are relayed multiple

times from the smart meters due to reliability considerations. These kinds of events have the

same event occurrence intervals but different event insertion times. Hence, in such cases,

duplicate traps could be filtered from processing using timing conditions.

Association rules: Algorithms or business rules to enable the discovery of relationships

between events and other variables. Inputs received from other systems, such as work

management systems (WMS), customer information systems (CIS) and supervisory control and

data acquisition (SCADA) systems, may be associated with event information to determine

device-level issues before rolling out to the field crews. Also, events received from the smart

meters can be logically segregated based on the inputs received from such systems.

Point-of-detection algorithms: These algorithms can help develop patterns of their occurrence,

which can help in taking proactive actions. For instance, time-wise and day-wise patterns for

events can be developed. Further, filtration criteria can be applied to remove all patterns caused

by electric, communication or network issues, and then the remaining patterns can used to

explain occurrences of certain business outcomes, such as outages, power quality or device

tampering.

Data clustering: This is an unsupervised model that uses data similarity to group the data points.

Similar categories of events can be clustered together, with analysis performed to extract

business value from the clusters of events. For example, we can identify clusters among all event

types and then develop relationships between outcomes and clusters of events. Device status,

meter tamper and power quality events can be a cluster to determine issues such as open

neutrals or flickering lights.

Correlation: This measures the association between two variables, while assuming there is no

causal relationship between the two. We can develop a correlation among various events and

other outcomes to determine future behaviour. For example, correlation between event type

and consumption fluctuation can help with revenue assurance.

Factor analysis: This allows variables to be grouped into common sub-groups in order to reduce

the number of factors to be initially analysed. For example, by performing factor analysis, we

can identify dominating factors that contribute to events or a set of events or an outcome.

Regression: This refers to the statistical relationship between two random variables to predict

the outcome. Commonly used for forecasting purposes, regression examines the causal

relationship between two variables. An example is using regression to analyse the relationship

between equipment conditions in the field, such as a prediction of transformer failure, based on

the demand from meters associated with it.

Usually, more than one technique might be required to solve the problem. For example, to

develop a relationship between device status and outage, a combination of correlation, factor

analysis and regression will be required to obtain the correct results.

Improving Outage Management through Meter Events

Smart meter events such as last gasp and power restore that provide meter off/on status can be

used for improving outage management. Being near-real-time, these events have an advantage

over outage information coming from customers and field staff. Event information generated by

smart meters is raw data with duplicate traps and high volume due to:

Momentary outages and restoration-related events.

Communication and network interface issue-related events

Events due to planned outages, outages at the lateral, feeder or transformer level, customer

disconnects, etc.

Hence, it is practically not possible for outage management systems3 to process raw event data

in the same way as they currently process inputs from SCADA systems, customers and field staff.

Many utilities realized this when they integrated event information from head end systems

(HES) directly into their outage management systems.

In order to effectively use events data, an event processing and analytics engine is required. This

engine needs to have the capabilities of logical filtration based on uniqueness of events,

momentary and existing outages and capabilities of association based on physical network

hierarchies. It also needs to have pattern analysis or regression capabilities to predict the

outages.

A multistage event processing and analytics framework identifies confirmed cases of outages

that can be passed to the outage management system for restoration (see Figure).

Stage 1: A set of conditions is used to filter duplicates from last-gasp events to identify unique cases of outage events. Such events are then correlated with power-restore events to remove the cases of momentary outages (outages with duration of less than 60 seconds).

Further, inputs from other systems such as CIS and WMS are considered to segregate

outage events that have occurred due to existing planned maintenance, meter exchange

or customer disconnect. The remaining outage events are considered as realized events.

Event Processing and Analytics Framework

Stage 2: In this stage, the meter-level realized events from Stage 1 are escalated to a higher level

of device hierarchies (lateral, feeder, transformer, etc.) and compared with other device inputs

using association rules and conditions to identify an outage incident. These cases of outage are

considered to be probable cases that need to be tested further.

Stage 3: During this stage, the probable cases of outages from Stage 2 are verified using remote

meter ping functionality, and only confirmed outage incidents results are communicated to the

outage management system for further action.

The event processing and analytics engine needs to be integrated into the utilities system

landscape, comprising the head end system, CIS, meter data management (MDM), WMS,

distribution automation and SCADA (see Figure 2). This will enable effective outage

management and crew optimization by focusing on “real” outage events from smart meters.

The benefits of this approach include:

Early and accurate outage detection, leading to improvement in power system reliability indices such as CAIDI, SAIDI, etc.

Early detection of momentary and planned outages to help avoid costly field visits.

Outage and restoration verification to avoid costly field crew movement.

Improved intelligence due to inputs from applications such as CIS, WMS and SCADA .

Smart Meter Event Processing: Business Context Diagram

Smart Meter Event Processing Solution

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Our Utilities Practice has designed a smart meter event processing (SMEP) solution for

improving the outage management process. The SMEP solution is configurable to meet

dynamic business requirements and is based on multistage processing and analytics.

Our SMEP solution is designed to provide the functionality required to process huge

volumes of real-time outage meter events data. The following are the key features of the

SMEP solution:

Near-real-time processing of a high volume of meter event data.

Business rules-based engine to configure the algorithms and rules to process the events.

Dynamic and flexible control based on requirements from other utility systems.

Business process management to effectively route and manage events/incidents.

Integration with other utility applications for validation, association and correlation.

Visualization and dashboarding tools.

In addition to the above features, SMEP has been designed using the event-driven architecture (EDA). EDA helps orchestrate the generation, detection and consumption of meter events, as well as the responses evoked by them. It helps effectively manage events and communication with various application processes using messaging (see Figure 3).

Conclusion: From Data to Insights

The concept of leveraging meter events data to gain business insights is at an early stage. To

effectively convert raw data into meaningful insights, utilities need to build state-of-the-art

methods in logical and predictive reasoning with data management capabilities. The theory

of integrating and exploiting logical and statistical data relationships is quite new; most

utilities are still at an early stage of the maturity curve, primarily reporting on and

dashboarding the smart meter analytics they gather.

Analytics need a combination of sound business and statistical capabilities, which many

utilities lack. Statistical capabilities include knowledge of statistical methods, statistical tools

such as SAS and an ability to provide statistical inferences.

Hence, utilities need to have a two-pronged approach. In the short to medium term, utilities

can build solutions largely on logical techniques where they have sufficient development

experience and can leverage vendors and partners that provide statistical capabilities.

For the longer term, utilities need to take a holistic approach toward analytics, keeping in

mind the needs of the enterprise and leveraging various sources of information (not limited

to meter read or event data) based on the assessment of the current state of process and

Page 16 of 17

people skills. They should consider various approaches, including building analytics skills

through a Center of Excellence for Analytics or developing collaborative models with

vendors specializing in analytics.

Future of a Smart Meter: - “Smart Home”

A smart home is a home that is equipped with special structured wiring to enable occupants

to remotely control or program an array of automated home electronic devices by entering

a single command. For example, a homeowner on vacation can use a Touchtone phone to

arm a home security system, control temperature gauges, switch appliances on or off,

control lighting, program a home theatre or entertainment system, and perform many other

tasks.

The field of home automation is expanding rapidly as electronic technologies converge. The

home network encompasses communications, entertainment, security, convenience, and

information systems. A technology known as Power line Carrier Systems (PCS) is used to

Page 17 of 17

send coded signals along a home's existing electric wiring to programmable switches, or

outlets. These signals convey commands that correspond to "addresses" or locations of

specific devices, and that control how and when those devices operate. A PCS transmitter,

for instance, can send a signal along a home's wiring, and a receiver plugged into any

electric outlet in the home could receive that signal and operate the appliance to which it is

attached. This embedded control protocol for digital communication between smart devices

consists of a two-wire bus line that is installed along with normal electrical wiring. The Insta-

bus line links all appliances to a decentralized communication system and functions like a

telephone line over which appliances can be controlled.