One of the key focus areas of power distribution utilities is revamping the metering infrastructure with the deployment of smart metering solutions that improve operational efficiency and optimise processes. Smart meter data can be accessed through a web portal, any time and anywhere, and can be used to offer value-added services to customers. The meter data has to be stored, processed, visualised and shared with other market players to deliver these services.
A meter data management system (MDMS) helps in consolidating data from meters, analysing the data trends from meter readings and resolving the issues of meter tampering, outages, theft, etc. Data analytics is one of the primary objectives of implementing MDMS. The embedded analytics in an MDMS processes meter readings, ensures that the data is current and valid, and issues alerts in case of configured conditions of interest. Meanwhile, extended analytics can be undertaken to leverage valid data for complex analysis by combining core MDM business logic with additional correlations critical to the utility.
MDMS
MDMS is a critical component of advanced metering infrastructure (AMI). It helps realise the full potential of smart meters. It also eases IT integration of AMI, and facilitates the distribution of meter data across the utility enterprise by presenting the volumes of data retrieved from the fields as manageable and familiar information. By consolidating meter data from multiple systems into one MDMS, utilities can set consistent validation routines to evaluate the performance of their AMI systems.
Broadly, MDMS imports the data, then validates, cleans and processes it before making it available for billing and analysis. MDMS also performs long-term data storage and management functions for the vast quantities of data delivered by smart metering systems. This data primarily consists of electricity usage and events that are imported from head-end servers, which manage the data collection in AMI and automatic meter reading systems.
MDMS offers a user-friendly interface and provides automated meter data management. It provides a clearly arranged dashboard and intuitive user prompting guides for convenient troubleshooting. Apart from this, MDMS has a range of other valuable capabilities such as cost-effective planning for periods of high electricity demand, seamless management of distributed generation sources, anticipation of system outages, identification of particular systems that are at risk of failure and monitoring of service theft.
One of the key considerations in the selection of MDMS is that the system must fulfil user requirements. For example, the graphical user interface should be user friendly. It should be easy for users to access meter data through MDMS for analysis. In addition, the application should be able to handle a large quantum of meter data (around 80 million records per day per 100,000 meters); receive data from third-party head-end systems (HES); communicate with automatic end-to-end smart metering processes; establish real-time communication with meters, HES and other downstream systems like custom integration solutions (CIS) and outage management systems; use integration available with CIS and HES; and comply with cybersecurity standards.
In addition, MDMS should fit easily in the utility’s existing IT infrastructure. It should be flexible enough to host the application both on premises and on the cloud. There should be minimal use of third-party tools (such as database replication, queuing applications) to reduce the licensing cost.
Meter data analytics
Data collected by the MDMS 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 faults, energy audit, peak demand identification, consumer profile, etc.
Another upcoming area where analytics is being used is forecasting. This includes forecasting data on 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.
Smart meter data helps in improving customer experience. It can be leveraged to improve interactions, and create a more personalised and valuable experience that helps retain and attract customers. This can be done by personalised energy management advice (on how to manage and reduce energy bills based on electricity usage), marketing, campaigns to use real-time usage information, usage visualisations and comparisons (digital portal/app to view energy usage and compare it to others), usage-based notifications and alerts, customised billing period, etc.
Apart from this, smart meter data helps in optimising revenue. Smart meter data can offer actionable imsights that can be used to increase the revenue potential of the utility, and enable new products and services. These include enhanced customer segmentation, digital pre-payment audits, load disaggregation and analytics (analytics that break down customers usage to see what devices and appliances are using electricity), energy demand management programmes to manage peak usage like peak pricing, etc. Smart meter data also helps in increasing operational efficiency.
Utilities’ retail operations can be enhanced by leveraging smart meter data. This will help reduce cost and enable better revenue collection. This can be achieved by theft analytics (analysing energy usage and identifying customers who may have tampered with the meter or stolen power), outage notification and alerts (sending customer notifications when electricity is out along with restoration time and status updates), optimised demand forecasting, workforce optimisation, network optimisation, etc.
Distribution utilities are increasingly adopting smart metering solutions to enhance consumer experience and improve operational efficiency. A case in point is Tata Power (Mumbai), which has recently, in June 2021, initiated smart metering for its consumers. It has already completed over 7,000 smart meter installations. With smart meters, Tata Power has enabled its customers with valuable data analytics tools. Customers can now view and optimise their electricity consumption in near-real time through the customer portal and mobile app. Further, smart meters update consumers on their monthly usage and allow them to compare their consumption with the average monthly consumption of their peers.
Issues and challenges
One of the key issues and challenges in the deployment of smart metering solutions is the integration of data generated from smart meters with the HES, MDMS, and billing and collection software. The entire process has to take place seamlessly through the communication backbone and has to be automated through cloud-based services. Since most operating platforms being used by utilities are based on different software, getting them all on a standard platform is a challenge. Besides, with the growing interconnectedness and access to granular power consumption data, there is an increasing threat to cybersecurity and data privacy. Ensuring cybersecurity, building capacity and raising awareness is essential. The recent countrywide lockdown due to the Covid-19 pandemic has impacted the timeline for smart meter installation.
Conclusion
Meter data is emerging as the greatest asset of utilities. As utilities adopt MDMS to manage their ever-increasing meter data, the industry is witnessing its revolutionary impact on operational efficiencies, customer service, energy forecasting, distribution system reliability, regulatory compliance, etc. Selecting the right MDMS solution is not only important to generate an optimum return on investments, but is also critical for achieving the transformational changes that will allow utilities to survive in an ever-changing business landscape. Besides, there is a need to ensure reliability and interoperability within the system, synergy among various metering components and security against cyberthreats. n
Based on a presentation by Subhadip Raychaudhuri, HOD (Smart Metering), Tata Power Delhi Distribution, and Anurag Johri, Senior Principal (Utilities), Accenture, at a recent Power Line conference