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AMI SYSTEM USING DLMS
Nirmal Thaliyil
nirmal@kalkitech.com
Kalki Communication Technologies Limited
Abstract-Advanced Metering Infrastructure is
the key terminology which is in spot light
especially in Energy Industry for last couple of
years. Power sector reforms happening in most
of the countries, like APDRP in India, has
been focused on distribution loss optimisation
and thereby reducing the AT&C losses for
improving the profit of the utilities. The need
for communicable and possibly smart meters
with open communication protocol with smart
communication infrastructure and scalable
head end system has attained significance.
Only a scalable, flexible and open end to end
Smart metering system will offer long term
benefits, not only for system designers,
operators and maintainers, but also to
customers. This paper identifies some of the
essential requirements for building large scale
AMI system.
Keywords-AMI/AMR, DLMS, Smart Meters
I. INTRODUCTION
Study shows that by 2030 total energy
consumption in India will be 950,000 MW
[1]. India has a unique problem of having
the highest amount of aggregate technical
and commercial losses in power
distribution. Generation plants coming on
stream has received a boost with UMPP's,
but still we are behind target in our 5 Year
plans. Without action, any energy shortage
will become markedly worse. Key to
managing the situation is our ability to
accurately measure usage, thereby taking
action on losses and thefts. Measurement
is one of the first steps toward effective
management and refining consumer
behavior. This paper highlights some of
the advantages of DLMS/COSEM
standard suite (IEC 62056 / EN 13757-1)
which is one of the most widely accepted
international standards for utility meter
data exchange in AMI system.
II. SMART METERS
Smart Meters are advanced meters that
collects energy-use data and transmit and
receive data to/from the utility system.
Electric energy-use will be recorded on
every hour or 15 minutes this depends
upon the regulatory requirement. Use of
smart meters enables users and suppliers to
make use of following functionalities:
a. Time of Day usage pattern and storage
at discrete intervals from 15 minutes, 1
hour, to specific time blocks within the
day. These data can be used for
consumption information to central
database for various purposes like
billing, ABT, and analysis purposes
using wired or wireless communication
infrastructure.
b. Demand response refers to the
reduction of customer energy usage at
time of peak usage in-order to help
address system reliability, reflect
market conditions and pricing and
support infrastructure optimisation or
deferral.
c. Improved accuracy of forecasting
energy demand at different times of the
day. Setting flexible tariffs that
measure consumption over time or
otherwise called as Dynamic pricing.
Different types of dynamic pricing are:
Time of usage pricing, Critical peak
pricing, and Real time pricing. Critical
peak pricing and Real time pricing are
of two types: day ahead pricing or
hour-ahead pricing. Same smart meters
can be used for multiple suppliers
whether they are following pre-paid or
credit based billing.
2
d. Two way interface metering for
renewable integration and roof-top
solar and other types of generation.
III.COMMUNICATION PROTOCOL -
DLMS COSEM
There are a lot of standard metering
protocols which can carry out these
functionalities. The most commonly used
standard for meter data exchange is the
FLAG protocol, standardized for the
purposes of electricity metering as IEC
61107 by IEC TC 13 and DLMS/COSEM.
Other protocols widely used are Euridis
protocol (used mainly in France) over
twisted pairs, standardized by IEC TC 13
as IEC 62056-31 for electricity metering,
MBUS standardized by CEN TC 294 as
EN1434-3 (future EN 13757-2 and -3),
IEC 60870-5-102 for transmission of
integrated totals, in and between
transmission and distribution stations,
standardized by IEC TC 57, in North
America the ANSI C12.18 (optical port),
C12.19 (utility tables) and C12.21
(communication through telephone
modems).
DLMS/COSEM stands out from the list
and are particularly suited to meet the
needs of the liberalized energy markets
because of the following advantages:
COSEM stands for Companion
Specification for Energy Metering which
provides a layer for processing application
layer request and responses in a
communication profile independent
manner. This enables COSEM application
layer to support many communication
profiles for example HDLC for serial,
Transport layer for IPV4 for TCP/IP
networks.
Data layer defines interface classes for
different kinds of data. Interface classes
are broadly classified into four types: data
storage, time and event bound control,
access control, and communication
channel set-up. Data storage classification
includes data(IC: 1) registers(IC: 3),
demand registers(IC: 5), generic
profiles(IC: 7), utility tables(IC: 21),
register table(IC: 61) etc. while time and
event bound control classification defines
interface classes for clock (IC: 8), single
action schedule(IC: 22), special days table
(IC: 11), script table (IC: 9), schedule(IC:
10) etc. This model supports for future
expansion by allowing manufacture
specific instances, attributes, methods
without changing the lower layer services.
The specific classes can be used to
“instantiate” multiple “instances” in
typical OOPS terminology and each
instances of same classes will be unique
identification code called OBIS codes.
OBIS codes are standardized identification
system that identifies all data severed by
meter application. Coding format is a six
digit sequence denoted as a.b.c.d.e.f, in
which each digit has its own significance
[2].
DLMS/COSEM standard helps users to
use a generic driver which enables to
communicate with various meter types
from different manufactures. It is to be
noted that for using other old protocols
like IEC61107, a specific driver is required
for each type of meters.
This unique combination of features, not
available in any other metering protocols
known presently, supports the business
processes of the various stakeholders on
the liberalized markets, supports
innovation and competition, and
drastically reduces system life cycle costs.
IV.COMMUNICATION
Choosing the communication
infrastructure is another key aspect for
making a stable AMI system, which is
reliable and scalable. As far as
communication options are concerned
there are various methodologies defined
below:
3
a. Wired Communication
(Ethernet/serial) adoption
b. Cellular technologies
c. RF
d. Power line
There are technological and commercial
advantages and disadvantages for all the
options mentioned above. Wired system
will be more reliable and scalable.
However, the cost will be much higher
compared to other technologies. Selection
of the wireless technology depends upon
various other factors like location,
distance, geographical layout etc. But here
again, the protocol selected for AMI
purpose will be dependent on the
communication media selected by the
utility.
One of the strengths of DLMS/COSEM is
that application modeling is well separated
from the communication profiles.
Therefore, application data can be easily
transported over various media. The
DLMS / COSEM standard suite has been
developed based on two strong and proven
concepts: object modeling of application
data and the Open Systems
Interconnection (OSI) model. This allows
covering the widest possible range of
applications and communication media. As
the interface model is completely
independent from the communication
media, a wide choice of media can be
used, without ever changing the model and
the data management application of the
data collecting system. While today, serial
interfaces are supported, using
DLMS/COSEM over the Internet is
already planned. At this time the protocol
stack defined in the Green book allows to
use DLMS/COSEM through direct
connection via an optical or electrical port,
via switched or leased telephone lines and
over the GSM/GPRS network.
As the COSEM application layer is
separated from the lower layers, it is easy
to define any lower layer protocols, based
on OSI to support any communication
media.
V. SMART METER DATA
ACQUISITION
Meter data acquisition system includes two
major components, Metering head end
system and Meter data management
system. Metering head end system is a
combination of software and hardware for
collecting data from huge number of
meters/data concentrators installed in the
field using push/pull mechanism. Its sole
responsibility is to handle the connection
with the remote nodes and collect the data
and provide it to the meter data
management system. It is also expected to
provide statistics and reporting
information on the meter data collection,
and alarms any events that require manual
intervention, like the meter being faulty;
modem faulty etc., Meter data
management is the application software
which provides long term storage and
management which include the data
validation, reporting, notification etc. Data
stored will be used by the enterprise
application for energy management,
billing, outage management, and customer
service system or by any application which
helps utility to stream line its business
process.
An AMI system typically will have to
manage from medium to large pool of
meters, and could easily go up to 1-10
Million meters. The most critical issue in
such AMI systems is the scalability and
distributed network capability of the
system. High scalability can be achieved
by designing a system with N-tier
architecture. Since application as a whole
runs sequentially, a single N-tier
application with additional CPU would not
solve the scalability issue. Bottle neck in
one of the tier might cost a lot in
performance and scalability. However this
limitation can be overcome using
distributed architecture.
4
Here we propose a distributed message
queue based architecture, with distributed
meter reading modules, and a scalable
messaging interface that concentrates the
data at a central location. In Message
queuing architecture each and every major
components of head end system are
segregated in to separate application and
interconnected using a central message
queue, which allows loose coupling
between the disparate systems. This also
makes the system simple to maintain.
Messages are queued asynchronously
between applications and systems, thereby
enabling each application to run in same
hardware or in different hardware.
Multiple providers can post messages to a
queue and there can be multiple message
consumers attached to the single queue.
Messages can be successfully submitted to
a queue even if the messages consumers
for that queue are not running or are
unreachable, thereby enabling the total
reliability of the system. Each application
components can act as both provider and
consumer, thereby enabling two way
communications for smart grid
requirement.
A simple metering head end system
consists of a central message queue, meter
data reading module, and a central
database for storing configuration details
and data collected. Meter data reading
(MRM) module will handle collection of
data from the meter. This can be a single
application with multiple protocols or
separate application which is having
different protocol drivers. MRM will be
deployed based on the requirement of
number of meter data to be collected and
frequency of data collection etc. MRM
will be assigned with adequate number of
meters that it can handle which also
depends upon the hardware performance
capability. A predefined, performance
evaluated deployment can be resorted to,
or the MRM can be configured to report
it's running state and load on the system,
and the Administrator module can re-
assign the meters to a less loaded system.
Administrator can also prioritize each
queue depending on the requirement, for
example demand scan and
connection/disconnection from meter data
management system will be configured for
high priority queue, so that MRM can
suspend all other activities and take care of
the high priority commands. User will be
able to configure each data under different
priority so that consumer can process the
high priority queue before moving to the
low priority data. This feature helps utility
to provide priority for the demand
response/ remote switch off of the loads in
case of emergency.
Distributed system invariably tend to be
heterogeneous, that is composed of several
operating system, running application
developed in several languages. Another
advantage of head end system using
distributed architecture is that it’s very
easy to incorporate meter reading software
from the meter manufactures like MIOS
using customizable MRM adapter for
proprietary protocols. Typical MIOS
MRM adapter has two major components,
one for reading the meter data and other
for parsing the CDF file and pushing the
data to database using message queue.
Distributed head end system can run in
single hardware having single OS, single
hardware using multiple OS in a
virtualized environment and in multiple
hardware. Virtualization is software
technology which uses single server
hardware and divides it in to different
virtual machines, which enables user to
install multiple OS in a single machine and
run different components of head-end in
separate OS of same machine. Main
advantages of virtualization are server
consolidation, reduced power and cooling,
ease of deployment and administrator, and
high availability. Even some of the
processor available in the market from
5
major vendors supports hardware-assisted
virtualization/accelerated virtualization in
which each OS executes in complete
isolation thereby improving the
performance of the whole system.
However in case of head end components
to run in distributed hardware
environment, network should be designed
in such a manner that the data transfer
between different components in head end
system should not saturate the network and
proper firewall or encryption mechanism
for data should be provided for additional
security.
MDM software is intended to provide all
the features and functionality utilities need
for meter-to-bill value chain, settlement
and forecasting. Smart MDM will support
all aspects of C&I, roll-out support,
validation, analysis, balance reports,
settlement and preparation of data for
billing along with allowing utilities for
configure regulatory requirements and
strategies. Software will also have web-
services based on SOA for integrating to
CIS and back office system. MDM system
will have flexible tool for handling
calculation based on a time series, either
metered or as a result of pre-calculations
and calculated data will be stored or
exported to third party systems like CIS,
GIS, SCADA, ABT or accounting
systems[4].
VI.CONCLUSION
Utility who finds out new analysis
strategies with the meter data, has found
themselves’ in the transforming process of
adopting a smarter AMI solution for
increasing the efficiency and savings over
a period of time. Smart AMI System
ensures to deliver a scalable, open and
common standard for metering. This will
enable utilities and end users to save time,
and money by better control over the
energy use and costs.
REFERENCE
1. Pathway for energy and climate
changes.pdf
2. DLMS standard documents
3. Powel Meter Data Management
System, Powel ASA
ABBREVIATION
ABT - Availability Based Tariff
AMI - Automated Meter
Infrastructure
AMR - Automated Meter Reading
APDRP - Accelerated Power
Development and Reform
Program
CIS - Customer information
system
GIS - Geographic Information
System
COSEM - Companion Specification for
Electricity Metering
CDF - Common Data Format
DLMS - Distribution Line Message
Specification
GPRS - General Packet Radio
Service
GSM - Global System for Mobile
communication
IEC - International Electro-
technical Commission
IC - Interface Class
MRM - Meter Reading Module
MIOS - Meter Inter Interoperability
Solution
OOPS - Object oriented
programming structure
OS - Operating system
OSI - Open system
Interconnection model
MDM - Meter data Management
RF - Radio Frequency
SOA - Service Oriented
Architecture
TC - Technical committee
UMPP’S - Ultra Mega Power Plants

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Ami system using dlms

  • 1. 1 AMI SYSTEM USING DLMS Nirmal Thaliyil nirmal@kalkitech.com Kalki Communication Technologies Limited Abstract-Advanced Metering Infrastructure is the key terminology which is in spot light especially in Energy Industry for last couple of years. Power sector reforms happening in most of the countries, like APDRP in India, has been focused on distribution loss optimisation and thereby reducing the AT&C losses for improving the profit of the utilities. The need for communicable and possibly smart meters with open communication protocol with smart communication infrastructure and scalable head end system has attained significance. Only a scalable, flexible and open end to end Smart metering system will offer long term benefits, not only for system designers, operators and maintainers, but also to customers. This paper identifies some of the essential requirements for building large scale AMI system. Keywords-AMI/AMR, DLMS, Smart Meters I. INTRODUCTION Study shows that by 2030 total energy consumption in India will be 950,000 MW [1]. India has a unique problem of having the highest amount of aggregate technical and commercial losses in power distribution. Generation plants coming on stream has received a boost with UMPP's, but still we are behind target in our 5 Year plans. Without action, any energy shortage will become markedly worse. Key to managing the situation is our ability to accurately measure usage, thereby taking action on losses and thefts. Measurement is one of the first steps toward effective management and refining consumer behavior. This paper highlights some of the advantages of DLMS/COSEM standard suite (IEC 62056 / EN 13757-1) which is one of the most widely accepted international standards for utility meter data exchange in AMI system. II. SMART METERS Smart Meters are advanced meters that collects energy-use data and transmit and receive data to/from the utility system. Electric energy-use will be recorded on every hour or 15 minutes this depends upon the regulatory requirement. Use of smart meters enables users and suppliers to make use of following functionalities: a. Time of Day usage pattern and storage at discrete intervals from 15 minutes, 1 hour, to specific time blocks within the day. These data can be used for consumption information to central database for various purposes like billing, ABT, and analysis purposes using wired or wireless communication infrastructure. b. Demand response refers to the reduction of customer energy usage at time of peak usage in-order to help address system reliability, reflect market conditions and pricing and support infrastructure optimisation or deferral. c. Improved accuracy of forecasting energy demand at different times of the day. Setting flexible tariffs that measure consumption over time or otherwise called as Dynamic pricing. Different types of dynamic pricing are: Time of usage pricing, Critical peak pricing, and Real time pricing. Critical peak pricing and Real time pricing are of two types: day ahead pricing or hour-ahead pricing. Same smart meters can be used for multiple suppliers whether they are following pre-paid or credit based billing.
  • 2. 2 d. Two way interface metering for renewable integration and roof-top solar and other types of generation. III.COMMUNICATION PROTOCOL - DLMS COSEM There are a lot of standard metering protocols which can carry out these functionalities. The most commonly used standard for meter data exchange is the FLAG protocol, standardized for the purposes of electricity metering as IEC 61107 by IEC TC 13 and DLMS/COSEM. Other protocols widely used are Euridis protocol (used mainly in France) over twisted pairs, standardized by IEC TC 13 as IEC 62056-31 for electricity metering, MBUS standardized by CEN TC 294 as EN1434-3 (future EN 13757-2 and -3), IEC 60870-5-102 for transmission of integrated totals, in and between transmission and distribution stations, standardized by IEC TC 57, in North America the ANSI C12.18 (optical port), C12.19 (utility tables) and C12.21 (communication through telephone modems). DLMS/COSEM stands out from the list and are particularly suited to meet the needs of the liberalized energy markets because of the following advantages: COSEM stands for Companion Specification for Energy Metering which provides a layer for processing application layer request and responses in a communication profile independent manner. This enables COSEM application layer to support many communication profiles for example HDLC for serial, Transport layer for IPV4 for TCP/IP networks. Data layer defines interface classes for different kinds of data. Interface classes are broadly classified into four types: data storage, time and event bound control, access control, and communication channel set-up. Data storage classification includes data(IC: 1) registers(IC: 3), demand registers(IC: 5), generic profiles(IC: 7), utility tables(IC: 21), register table(IC: 61) etc. while time and event bound control classification defines interface classes for clock (IC: 8), single action schedule(IC: 22), special days table (IC: 11), script table (IC: 9), schedule(IC: 10) etc. This model supports for future expansion by allowing manufacture specific instances, attributes, methods without changing the lower layer services. The specific classes can be used to “instantiate” multiple “instances” in typical OOPS terminology and each instances of same classes will be unique identification code called OBIS codes. OBIS codes are standardized identification system that identifies all data severed by meter application. Coding format is a six digit sequence denoted as a.b.c.d.e.f, in which each digit has its own significance [2]. DLMS/COSEM standard helps users to use a generic driver which enables to communicate with various meter types from different manufactures. It is to be noted that for using other old protocols like IEC61107, a specific driver is required for each type of meters. This unique combination of features, not available in any other metering protocols known presently, supports the business processes of the various stakeholders on the liberalized markets, supports innovation and competition, and drastically reduces system life cycle costs. IV.COMMUNICATION Choosing the communication infrastructure is another key aspect for making a stable AMI system, which is reliable and scalable. As far as communication options are concerned there are various methodologies defined below:
  • 3. 3 a. Wired Communication (Ethernet/serial) adoption b. Cellular technologies c. RF d. Power line There are technological and commercial advantages and disadvantages for all the options mentioned above. Wired system will be more reliable and scalable. However, the cost will be much higher compared to other technologies. Selection of the wireless technology depends upon various other factors like location, distance, geographical layout etc. But here again, the protocol selected for AMI purpose will be dependent on the communication media selected by the utility. One of the strengths of DLMS/COSEM is that application modeling is well separated from the communication profiles. Therefore, application data can be easily transported over various media. The DLMS / COSEM standard suite has been developed based on two strong and proven concepts: object modeling of application data and the Open Systems Interconnection (OSI) model. This allows covering the widest possible range of applications and communication media. As the interface model is completely independent from the communication media, a wide choice of media can be used, without ever changing the model and the data management application of the data collecting system. While today, serial interfaces are supported, using DLMS/COSEM over the Internet is already planned. At this time the protocol stack defined in the Green book allows to use DLMS/COSEM through direct connection via an optical or electrical port, via switched or leased telephone lines and over the GSM/GPRS network. As the COSEM application layer is separated from the lower layers, it is easy to define any lower layer protocols, based on OSI to support any communication media. V. SMART METER DATA ACQUISITION Meter data acquisition system includes two major components, Metering head end system and Meter data management system. Metering head end system is a combination of software and hardware for collecting data from huge number of meters/data concentrators installed in the field using push/pull mechanism. Its sole responsibility is to handle the connection with the remote nodes and collect the data and provide it to the meter data management system. It is also expected to provide statistics and reporting information on the meter data collection, and alarms any events that require manual intervention, like the meter being faulty; modem faulty etc., Meter data management is the application software which provides long term storage and management which include the data validation, reporting, notification etc. Data stored will be used by the enterprise application for energy management, billing, outage management, and customer service system or by any application which helps utility to stream line its business process. An AMI system typically will have to manage from medium to large pool of meters, and could easily go up to 1-10 Million meters. The most critical issue in such AMI systems is the scalability and distributed network capability of the system. High scalability can be achieved by designing a system with N-tier architecture. Since application as a whole runs sequentially, a single N-tier application with additional CPU would not solve the scalability issue. Bottle neck in one of the tier might cost a lot in performance and scalability. However this limitation can be overcome using distributed architecture.
  • 4. 4 Here we propose a distributed message queue based architecture, with distributed meter reading modules, and a scalable messaging interface that concentrates the data at a central location. In Message queuing architecture each and every major components of head end system are segregated in to separate application and interconnected using a central message queue, which allows loose coupling between the disparate systems. This also makes the system simple to maintain. Messages are queued asynchronously between applications and systems, thereby enabling each application to run in same hardware or in different hardware. Multiple providers can post messages to a queue and there can be multiple message consumers attached to the single queue. Messages can be successfully submitted to a queue even if the messages consumers for that queue are not running or are unreachable, thereby enabling the total reliability of the system. Each application components can act as both provider and consumer, thereby enabling two way communications for smart grid requirement. A simple metering head end system consists of a central message queue, meter data reading module, and a central database for storing configuration details and data collected. Meter data reading (MRM) module will handle collection of data from the meter. This can be a single application with multiple protocols or separate application which is having different protocol drivers. MRM will be deployed based on the requirement of number of meter data to be collected and frequency of data collection etc. MRM will be assigned with adequate number of meters that it can handle which also depends upon the hardware performance capability. A predefined, performance evaluated deployment can be resorted to, or the MRM can be configured to report it's running state and load on the system, and the Administrator module can re- assign the meters to a less loaded system. Administrator can also prioritize each queue depending on the requirement, for example demand scan and connection/disconnection from meter data management system will be configured for high priority queue, so that MRM can suspend all other activities and take care of the high priority commands. User will be able to configure each data under different priority so that consumer can process the high priority queue before moving to the low priority data. This feature helps utility to provide priority for the demand response/ remote switch off of the loads in case of emergency. Distributed system invariably tend to be heterogeneous, that is composed of several operating system, running application developed in several languages. Another advantage of head end system using distributed architecture is that it’s very easy to incorporate meter reading software from the meter manufactures like MIOS using customizable MRM adapter for proprietary protocols. Typical MIOS MRM adapter has two major components, one for reading the meter data and other for parsing the CDF file and pushing the data to database using message queue. Distributed head end system can run in single hardware having single OS, single hardware using multiple OS in a virtualized environment and in multiple hardware. Virtualization is software technology which uses single server hardware and divides it in to different virtual machines, which enables user to install multiple OS in a single machine and run different components of head-end in separate OS of same machine. Main advantages of virtualization are server consolidation, reduced power and cooling, ease of deployment and administrator, and high availability. Even some of the processor available in the market from
  • 5. 5 major vendors supports hardware-assisted virtualization/accelerated virtualization in which each OS executes in complete isolation thereby improving the performance of the whole system. However in case of head end components to run in distributed hardware environment, network should be designed in such a manner that the data transfer between different components in head end system should not saturate the network and proper firewall or encryption mechanism for data should be provided for additional security. MDM software is intended to provide all the features and functionality utilities need for meter-to-bill value chain, settlement and forecasting. Smart MDM will support all aspects of C&I, roll-out support, validation, analysis, balance reports, settlement and preparation of data for billing along with allowing utilities for configure regulatory requirements and strategies. Software will also have web- services based on SOA for integrating to CIS and back office system. MDM system will have flexible tool for handling calculation based on a time series, either metered or as a result of pre-calculations and calculated data will be stored or exported to third party systems like CIS, GIS, SCADA, ABT or accounting systems[4]. VI.CONCLUSION Utility who finds out new analysis strategies with the meter data, has found themselves’ in the transforming process of adopting a smarter AMI solution for increasing the efficiency and savings over a period of time. Smart AMI System ensures to deliver a scalable, open and common standard for metering. This will enable utilities and end users to save time, and money by better control over the energy use and costs. REFERENCE 1. Pathway for energy and climate changes.pdf 2. DLMS standard documents 3. Powel Meter Data Management System, Powel ASA ABBREVIATION ABT - Availability Based Tariff AMI - Automated Meter Infrastructure AMR - Automated Meter Reading APDRP - Accelerated Power Development and Reform Program CIS - Customer information system GIS - Geographic Information System COSEM - Companion Specification for Electricity Metering CDF - Common Data Format DLMS - Distribution Line Message Specification GPRS - General Packet Radio Service GSM - Global System for Mobile communication IEC - International Electro- technical Commission IC - Interface Class MRM - Meter Reading Module MIOS - Meter Inter Interoperability Solution OOPS - Object oriented programming structure OS - Operating system OSI - Open system Interconnection model MDM - Meter data Management RF - Radio Frequency SOA - Service Oriented Architecture TC - Technical committee UMPP’S - Ultra Mega Power Plants