Data processing: database and file management or data structures – Database design – Data structure types
Reexamination Certificate
1998-05-21
2001-03-06
Alam, Hosain T. (Department: 2771)
Data processing: database and file management or data structures
Database design
Data structure types
C702S062000, C340S870030, C370S449000
Reexamination Certificate
active
06199068
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates generally to an automated meter reading (AMR) system, and more particularly to an AMR server within the automated reading system which collects, loads and manages data from energy meters, and processes and stores meter data for routing to end users and business systems.
ACRONYMS AND KEYWORDS
The written description provided herein contains acronyms and keywords to describe the various system components and services. Although known, use of several of the acronyms and keywords is not standardized in the art. For the purposes of the written description herein, acronyms and keywords are defined as follows:
ACID—Atomicity, Consistency, Isolation, Durability
AMPS—Analog Mobile Phone System
AMR—Automated Meter Reading
API—Application Program Interface
BOM—Bill of Material
C&I—Commercial and Industrial
CIS—Customer Information System
CDS—Cell Directory Service
CDMA—Code Division Multiplexed Access
CDPD—Cellular Digital Packet Data
CM—Communications Manager
CORBA—Common Object Request Broker Architecture
CPU—Central Processing Unit
CRUDLE—Create, Read, Update, Delete, List, and Exists
CSR—Customer Service Representative
CURDLE—Create, Update, Read, Delete, List and Exist
DAO—Data Access Object
DCE—Distributed Computing Environment
DFS—Distributed File Service
DSS—Distributed Security Service
DTS—Distributed Time Service
ESCO—Non-Grid and Non-Commodity Energy Services Companies
ESP—Energy Service Provider
GUI—Graphical User Interface
IDL—Interface Definition Language
ISO—Independent System Operator
LAN—Local Area Network
LECRUD—List, Exist, Create, Read, Update and Delete
MDMA—Meter Data Management Agent
OMS—Outage Management System
OO—Object Oriented
PM—Wholesale Power Market Services
PSTN—Public Switched Telephone Network
PX—Power Exchange
RDBMS—Relational Database Management System
RF—Radio Frequency
RM—Resource Managers
RPC—Remote Procedure Call
RPU—Real Time Processor Unit
RQS—Recoverable Queuing System
RSP—Remote Stored Procedure
RTG—Remote Terminal Gateway
RTU—Remote Telemetry Unit
SC—Schedule Coordinator
SCADA—Supervisory Control and Data Acquisition
SFS—Structured File System
SNMP—Simple Network Management Protocol
SOE—Sequence of Events
TDMA—Time Division Multiple Access
TM—Transaction Manager
TOU—Time of Use
UDC—Utility Distribution Company
UPC—Universal Protocol Converter
VEE—Validation, Editing, and Estimation
WAN—Wide Area Network
WFM—Work Flow Manager
BACKGROUND OF THE INVENTION
The reading of electrical energy has historically been accomplished with human meter readers that came on-site to the customers' premises and manually documented the readings. Over time, manual meter reading has been enhanced with walk-by or drive-by reading systems that utilize radio communications between the meters and a meter reading device. The information that these walk-by and drive-by systems collected increased, but still the functions provided by the communication systems were limited.
More recently, over the last few years, there has been a concerted effort to automate meter reading by installing fixed networks that allow data to flow from the meter to a host computer system without human intervention, such systems have been referred to in the art as Automated Meter Reading (AMR) systems. AMR systems have gained interest because there are approximately 150 million installed meters, of which 17 million are considered to be “hard-to-read” because of location, etc. A limitation in these conventional AMR systems is that they typically use only one type of communication infrastructure to gather data. For example, the AMR system may receive data from meters via one of a fixed proprietary RF communications infrastructure, the public switched telephone network or power line transmission. This one-infrastructure communication of data has led to the development of incompatible AMR systems that are tied to that particular communications infrastructure, utilize proprietary devices and protocols, and have unacceptably low data rates. Such implementations are also lacking because RF coverage is limited, and public switched telephone network and power line transmission solutions require relatively long periods of time to communicate data from the meter.
In addition to the limitations regarding communication infrastructures, conventional AMR systems are not easily adaptable to changing requirements of both the energy provider and the energy consumer. For example, while most meters measure energy monthly in kWh or Time-of-Use (TOUT), rising consumer demand for daily reads of kWh or TOU, load profile metering along with demand, outage, power quality and tamper monitoring capabilities will render conventional systems obsolete. For example, conventional AMR systems collect data via a pulsed input, and over a period of time to determine energy usage or may create a load profile. These systems, however, are not capable of reading data from newly developing intelligent meters that provide load profile information and the like to the AMR system.
A further limitation of the conventional AMR system is that they do not accommodate the requirements of end-user systems (e.g., billing systems, energy management systems and supervisory control systems). Theses systems are typically standalone systems, separate from the metering system. One of the primary reasons that the requirements of end-user systems are not met is because of the above-mentioned limitations that conventional AMR systems were designed as proprietary systems rather than open systems. These systems generally output the meter data in a raw format that is not compatible with the end-user systems and that must be converted for use. Thus, conventional AMR systems do not perform validation, editing and estimation of the output data, and require a relatively high amount of manual intervention to transfer data from the AMR system to end users for further processing.
Yet another limitation of conventional AMR systems is that metering data has been captured and managed using traditional mainframe or two-tiered client/server architectures. While mainframe and client/server solutions have been up to the present relatively successful in addressing the needs of utilities and their customers, AMR Systems are becoming far too large and complex for conventional technologies because of the amount of data flowing in and out of the system (e.g., it may be necessary to store and process data from daily or hourly meter reads from millions of meters). As data requirements steadily increase in an automated meter reading system, traditional mainframe and two-tiered architectures (non-distributed systems) experience limitations in memory, CPU capabilities, and storage capacity because a growing amount of data traffic over the network leads to bottlenecks that result in performance limitations as data is shipped between the database and the client, and records in the database can become locked when client programs need to lock data to use it. Upgrading these systems to increase the load capability and performance requires bringing the system down. In addition, the cost of maintenance and upgrade of these systems increases as companies attempt to solve client/server performance problems and scalability issues by purchasing bigger and faster machines.
In addition to limitations noted-above in conventional AMR systems, perhaps the greatest limitation of the existing AMR systems is that the electric utility marketplace is moving towards deregulation. Under deregulation, utility customers will be able to choose their electric service providers. As a result, the deregulated marketplace has created many new business entities, which will place additional demands on AMR systems. For example, in California, a Meter Data Management Agent (MDMA) has been created which is responsible for collecting and publishing the data required for billing. Further, the MDMA requires that settlement quality data be provided as the MDMA publishes data to multiple business entities, including the ESP, the UDC and potentially other ancillary servi
ABB Power T&D Company Inc.
Alam Hosain T.
Shah Sanjiv
Woodcock Washburn Kurtz Mackiewicz & Norris
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