With technological advancements, consumer meters have turned into storage facilities with an enormous quantum of useful information, which can enable utilities to significantly improve their operational efficiency. Meter data acquisition systems (MDAS) and meter data management systems (MDMS) help in utilising the information from meters. These systems are used to determine outcomes using data analytics algorithms, which can help utilities in undertaking demand response, identifying meter tampering, overseeing blackouts, ensuring theft protection, and so forth.
Further, smart meter data can be leveraged to improve interactions and create a more personalised and valuable experience, which helps retain and attract customers. It can also be used to generate actionable insights, which can increase the revenue potential and enable new products and services. This can be achieved by advising customers on how to manage and reduce their energy bill based on usage. Utilities can redesign bills to incorporate detailed usage information, make comparisons, and give some personalised tips. Utilities can also analyse the breakdown of customers’ usage to see what devices and appliances are using the maximum electricity. Data can be used to enable programmes to manage peak usage. Further, meter data and analytics can assist in demand forecasting as well as energy trading.
MDMS plays a key role in interpreting meaningful trends in data, besides enabling real-time event management, recognising voltage anomalies, improving consumer billing, increasing efficiency of the outage management system and many more. With the government mandating a shift to smart prepaid meters, MDMS will become a must-have application for smart meter planning and deployment.
With MDMS, meter reading can be improved, thereby reducing equipment and labour costs of utilities. It also helps in the reduction of operating costs for several field-related services like collection, connection/disconnection, cut-ins, rereads, filed tests and investigations. In addition, there would be a reduction in complaints, enquiries, cancellations, rebilling, etc. at call centres. A better outage management system can be put in place with the help of MDMS as it reduces outage/ restoration and false despatch costs. Further, the installation of MDMS can help in the recovery of unaccounted-for energy, leading to revenue protection.
With larger data volume and increased frequency of data collection, data analytics lies at the core of MDMS. It is also useful for sending alerts regarding meter-based conditions such as usage pattern, events and system performance. Further, it interconnects the metering system with a broad range of enterprise applications.
MDMS helps in undertaking business analytics and deciphering meaningful trends from consumer meter data. It provides valid, complete and uniform data for improving customer service, operating the consumer portal, and undertaking distribution planning and tariff analysis. It also manages the commands from the downstream.
Further, MDMS helps in real-time event management and notifies voltage anomalies, outage/restoration and tampering. Besides, the data gathered from the system helps in undertaking historical/ predictive analysis. This helps in maintaining a secure, comprehensive control point of information to achieve the business objectives. The availability of accurate information from MDMS helps meet user expectations and enhances consumer satisfaction. It also helps in improving the operations of a discom through improved asset management and quick response to power quality disruption.
Key considerations for MDMS selection
Several factors need to be considered by utilities during the selection of MDMS. The MDMS application should fulfil the user requirements and have a user-friendly interface, allowing them to easily access meter data, export data to any third-party system, as well as analyse data and communicate with head-end system (HES)/meters on a real-time basis. In addition, the MDMS application should be robust enough to handle a large quantum of meter data (almost 80 million records per day per 100,000 meters), with the efficient use of compute power and memory (highest compression ratio at database level), which will ensure less disk space.
The application should enable quick integration with the customer information system and HES. It is preferable that integration is done using CIM 2.0 as it complies with all the cybersecurity standards. There should be minimal use of third-party tools (such as database replication and queuing applications) to reduce the licensing cost. Also, it should be easy to maintain and customise with resources available in the open market. Once these things are in place, knowledge transfer becomes easy and implementation of MDMS becomes faster.
On the technological front, the application should be developed on a stable platform like Java or .Net. Further, since the number of meter and reads is huge, the queuing technologies have to be understood very well by utilities. Also, the database should be of enterprise version like Oracle, MS-SQL or Postgre, etc. Lastly, the application should be flexible enough to host the application both on premises and on the cloud.
The main objective of MDAS is to acquire data from meters within the distribution system and consumer meters for system performance monitoring and decision-making, network analysis, system planning, monitoring of consumer energy usage for billing and customer relationship management, and detection of tampering and outages. Broadly, MDAS comprises a communication server application, which establishes communication with the modem associated with the data concentrator unit and processes the data sent by the device. Further, the communication server reads the raw data received by the communication server application and converts it into useful meter data. Through a web-based user application, users can log in and view their meter reading. Besides, a utility dashboard can be used as an interface for supervisory activities.
MDAS remotely acquires interface meter data through automatic meter reading from the selected meters. MDAS undertakes real-time and historical data acquisition, and performs supervisory functions such as processing, monitoring, analysis and diagnostics. With no human intervention, MDAS acquires data pertaining to operational parameters; helps in accurate billing; generates management information system reports for proper planning, monitoring and decision support; and performs corrective actions after receiving directions from the management.
Issues and challenges
One of the key challenges in MDMS im-plementation is its integration with utilities’ IT infrastructure. Utilities often have concerns regarding the integration of MDMS with multiple HES in case of different communication technologies, and handling of non-communicating and legacy meters. The absence of standard interfaces between HES and MDMS also poses an issue during implementation.
Currently, there are no standard theft analysis products available in the Indian market. The products are more matured with respect to the US and European markets. Also, there is a differential licensing price for smart and non-smart meters. The use cases and scenarios vary largely across Indian utilities. Lastly, the understanding of technology and acceptance from end users are crucial for the success of any MDMS implementation project. n
With inputs from a presentation by Subhadip Raychaudhuri, HOD, Smart Metering, Tata Power Delhi Distribution Limited, at a recent Power Line virtual conference