The metering landscape in India is fast evolving. Utilities have started deploying smarter meters, which can track and report data on multiple parameters other than consumption. Most utilities have also installed automated meter reading (AMR) systems in some pockets to improve billing efficiency by eliminating manual reading. Meanwhile, advanced metering infrastructure (AMI) is being deployed on a pilot basis to test advanced functionalities such as two-way communication for load management.
The meter data acquisition system (MDAS) forms a crucial component of the AMR and AMI systems as it helps manage the large volume of data generated by smart metering systems, processes the information to generate meaningful results, integrates the metering systems with enterprise-wide systems, and acts as an interface for various applications within the utility. At present, most Indian utilities have either deployed MDAS or are in the process of deploying it in limited areas under the central government-sponsored Integrated Power Development Scheme.
An AMI system consists of various key components such as MDAS, smart meters, communication medium, load monitoring, demand response, load control, tamper detection, alarm handling, real-time energy audit and time-of-day tariff. The main objective of the system is to enable two-way communication between smart energy meters and the head-end system (HES). It also aims to enable remote reading, monitoring and control of meters installed at the consumer end, as well as on feeders, distribution transformers, etc. The AMI system also serves as a repository of record for all raw, validated and edited data. The data sanitised by AMI can be subscribed for higher-order analysis, as well as for billing and collection functions.
Role of MDAS in AMI
MDAS has gained significant importance and its use has become critical for realising the full potential of AMI. It transfers meter data to meter data management systems coupled with analytics on the standard data exchange model. MDAS is installed at the smart grid control centre along with servers to undertake periodic collection of information from smart meters. The process commences with the installation and connection of modems to meters in the field. The modem has to be configured with certain parameters including baud rate, make of meter, and network service provider access point name. It must be installed with the right communication cables based on the type of meter. Meter data should be successfully connected to a back-end-compatible application at the central data centre of the state over the general packet radio service (GPRS) network. The data should be sent without fail at regular intervals, hourly or daily, based on the requirement.
By capturing smart meter data and converting it into actionable points, MDAS improves the efficiency of the distribution utility and provides quality power to the consumer. It increases meter integrity for utilities by managing the collection of meter readings. Further, it allows utilities to have enhanced network visibility, which in turn improves their demand-supply planning. Through two-way communication, MDAS provides access to information on the consumption pattern of consumers and helps settle consumer bill disputes. Moreover, the implementation of MDAS leads to lower consumption and lower losses, which in turn reduces carbon emissions. It also complies with regulatory and policy requirements related to power quality and conservation.
The approach to MDAS deployment has shifted over the years (see Figure). Initial deployments involved the installation of individual HES to send data to each utility system. This often resulted in duplication of work, which was overcome by customising core business systems to accommodate the metered data. Later, utilities started deploying centralised meter data repositories to collect and store data. A centralised repository isolates utility systems from the details of the associated AMR system, thereby allowing the utilities to upgrade their AMR systems without changing their core business systems. Recently, the concept of vendor-neutral meter data warehouses has gained popularity. These warehouses are equipped with data processing capabilities and allow for interoperability at the device and head-end levels. The latest and the most preferred approach at present is the enterprise service bus model where HES communicates with the MDAS. Indian utilities can leapfrog to this model. Data from the AMR and AMI systems is conveyed to the MDAS, which transfers it to an enterprise bus. The enterprise bus then makes the data available to the utility systems.
Analytics is the discovery and communication of meaningful patterns in data, involving visualisation. The key drivers for analytics have been the increasing role of data-driven decision-making, the need to discover relationships between groups and behaviours, and the huge amount of data that is currently available. It is a gradual process that begins with descriptive analysis, which forms the base of the analytics value chain. This includes statistical analytics, visualisations of structured data and identification of patterns that can help draw insights. Based on this, diagnostic analysis is carried out using both internal and external data. Diagnostic analysis generates insights to determine the reason behind events. This is followed by predictive analytics, which identifies trends and clusters, and projects them to predict future expectations. The last stage is prescriptive analytics, which gives focused answers to specific questions. Prescriptive analytics uses simulations, scenario analysis, etc. to identify the best course of action from the given choices.
Data collected by the MDAS is used in meter data analytics to generate leads for carrying out corrective actions. Data analytics helps utilities perform online energy audits to improve operational efficiency, asset management and system planning. It also helps utilities extract and use the information embedded in meter data pertaining to meter data validation; tampering; missing information due to communication failure, meter fault; energy audit and accounting of distribution transformers; peak demand identification; consumer profile analysis; etc. Another upcoming area where analytics is being used is forecasting. This includes forecasting data such as total customer usage at the feeder or substation level, net usage reduced for distributed generation, and demand response available at the facility level for the use of new technologies such as plug-in electric vehicles.
Challenges and the way forward
The current requirement is to recognise the need for smart metering data beyond billing and use it as a strategic tool to improve customer service. Meanwhile, the development of specifications for interoperability and the regulatory mechanism to promote MDAS-enabled applications is the need of the hour.
The growing number of installed meters and the increasing data volumes pose a serious challenge to MDAS. Utilities face major difficulties in capturing data and transferring it to the data centre remotely through GPRS-based communication systems. Spectrum availability is an other key issue in the Indian context. Moreover, the data should be insensitive to external interference and should be encrypted to avoid any losses.
The development of standardised specifications for interoperability will simplify the usage of MDAS. MDAS should also be made capable of handling the storage and distribution of non-billing data as well as managing tamper alarms and demand-response events. Further, the simplification of interfacing of MDAS with other IT systems in the utility is required so that the same platform can be leveraged to develop key applications such as outage management system and revenue protection. There is also a need to ensure the highest meter data security standards to enable efficient competition among the various players.
Integration of MDAS with the smart grid ecosystem is important and if not planned adequately, it may lead to usability and costs issues. Thus, a holistic IT vision with adequate server capability, is paramount. In addition to this, an enabling policy push from the government and a tariff increase from the regulator can significantly alter the distribution scenario.
Based on inputs from a presentation by Mitul Thapliyal, Senior Principal and Leader, Energy, Utilities and Smart Cities, Infosys, at a recent Power Line conference