Data analytics has emerged as one of the key focus areas of discoms. It is aimed at improving their operational performance and enhancing consumer satisfaction. Utilities are increasingly undertaking data analytics to predict load growth for efficient network planning, and assess network health for predictive maintenance. Further, data analytics plays a key role in revenue protection and theft detection. By analysing consumption data, utilities can identify the anomalies in energy consumption.
Data analytics in distribution
Data analytics involves interpreting data elements and communicating their meaning to relevant stakeholders for actionable insights. It leverages the volume, velocity, variability and veracity of big data to deliver value. Big data refers to very large and complex data sets that require analysis to provide insights for modelling, monitoring and controlling purposes.
In a distribution utility, multi-variate data is analysed to derive benefits. Data pertaining to distribution transformer loading (three-phase current, voltage, active and reactive power data is available for 30-minute intervals), distribution station and feeder loading (current, voltage and power data is available for 15-minute intervals), consumer meter reading (one tab-based reading per month is available in electronic meters whereas 15-minute interval reading of V, amp, kW and kWh is available in smart meters), fault and fusing is analysed. In addition, utilities analyse data from call centres and complaint management systems (including the reason, type and resolution timeline of complaints), as well as data pertaining to bill payment and bill payment mode (online versus offline). It helps improve customer service, revenue protection, cost efficiency and manpower utilisation.
At present, hardly any type of analysis is performed on the available data by Indian utilities. The reporting of data is mosty done according to the needs of the utility. Further, the available data is not complete and hence valuable insights cannot be drawn from it.
Currently, the utilities follow a top down analytics approach wherein consultants generate reports for the management which direct the field officials for action. There is a significant lack of time, resources and tools with the field staff. As a result, there is disconnect between the on-ground situation at the field and the directives imparted through analytics reports. Utilities need for a system with both top down and bottom up information flow. Everyone in the system should be connected centrally with the field staff so that the ground information is available to all and feedback/directives for improvement should be given accordingly.
Data analytics can be applied to various areas of a discoms operations such as asset management, revenue protection, detection of electricity thefts, and loss reduction among others. For instance, predictive asset management (PAM) leverages distribution grid sensor data to identify patterns and signals before a feeder level outage. Through the use of PAM, utilities can predict when and a fault will occur due to equipment deterioration. The PAM system also gives a warning to enable preventative maintenance, avoiding outages. As result, PAM stops outages before they start with reliable outage forecasting via advanced analytics, thus also enabling targeted, more efficient asset maintenance. It can help improve utility operational performance in terms of a decrease in the System Average Interruption Duration Index.
Similarly, data analytics tools can be used for reducing non-technical losses of utilities by analysing electricity consumption details, credit-worthiness of consumers in high loss areas, and loading history of distribution transformer in theft prone pockets. Utilities are also deploying data analytics for complaint management. Through data analytics, utilities identify prospective areas of complaints and sends proactive communication to consumers regarding this. With the increasing penetration of distributed energy resources and electric vehicles, the amount of data in the grid is expected to increase significantly. In such a scenario, the need of data analytics tools, especially big data analytics will increase considerably.
The way forward
Going forward, data analytics will be one of the key trends driving the digital utilities of the future. The use of artificial intelligence (AI) and machine learning (ML) is creating value by enhancing operational efficiency. Utilities are fast adopting AI and ML solutions for HT fault prediction, reduced outage duration and optimisation of O&M costs. Internet of things and analytics-based solutions are also being deployed by utilities for predictive asset maintenance. Meanwhile, for better consumer complaint management, utilities are resorting to automating call centre operations using voicebots and resolving queries using chatbots.
To conclude, it is essential for the distribution utilities to undertake data analytics in order to assess their operational performance and identify anomalies, if any. Further, the distribution utilities must take steps to plug the gaps in network performance.
Based on a presentation by Kirit Rana, Deputy General Manager, CESC, at a recent Power Line conference