India’s efforts to digitalise its electric infrastructure are being closely observed by stakeholders worldwide. The attractiveness stems from the infinite outcomes and, currently, whatever successes it has had in the past decade are just the tip of the iceberg, with hundreds of potential use cases in states like Bihar, Uttar Pradesh, Assam, Madhya Pradesh, and Maharashtra that will soon become benchmarks for many countries to adopt.
Numerous companies are working to digitise India’s electric infrastructure. Some of them have achieved limited success, while others have ticked almost all the boxes of performance. It includes, amongst others, execution and technology.
The Government of India’s focused efforts are helping the energy sector undergo a significant transformation, driven by the digitalisation of the grid, the integration of renewable energy sources, the rise of decentralised power generation, and reforms focused on consumer needs.
Some of us in the industry are working under the motto “power to empower.” It is a demonstration that Software can give utilities and consumers the power to make informed decisions and take actionable insights.
The fact that India’s energy landscape reflects a complex interplay of rising demand, varied resource availability, and shifting strategic priorities makes digitisation not only key but critical to the sector’s growth. The significant transition from oil and gas to electric distribution poses a substantial challenge for India, aimed at reducing reliance on oil and gas.
This transition offers numerous advantages beyond mere energy efficiency, including the management of non-technical losses, such as revenue leakage, which totals $10 billion each year in India due to theft and meter tampering.
The government is also driving smart meter installation through the Revamped Distribution Sector Scheme (RDSS) funding, but the challenge lies in the discoms fully realising the value of this adoption. The intelligent metering of consumers, along with system metering (which encompasses feeder and distribution transformer metering), enables automatic measurement of energy flows, energy accounting, and auditing without human intervention.
By implementing smart metering and the associated advanced metering infrastructure (AMI), the installation of communicable system meters at both the feeder and distribution transformer levels will enable precise energy accounting, thereby helping identify regions vulnerable to theft and those experiencing significant energy losses.
By leveraging intelligence obtained from smart meter data through an AI framework, CEOs, CIOs, CFOs, and their respective teams are now able to transform these insights into actionable strategies. This process entails forming strong partnerships across business units that integrate utilities’ extensive data resources with artificial intelligence (AI) capabilities to identify new opportunities for efficiency and growth, as well as to provide improved options and services for consumers.
At present, most utilities have made considerable investments in smart meters, collecting millions of data points daily.
By applying advanced machine learning and data analytics, AI can improve energy efficiency, strengthen grid resilience, and enable more intelligent and effective resource utilisation. Strategies for energy efficiency and innovations in smart grids powered by AI could generate up to $1.3 trillion in economic value by 2030.
As Artificial Intelligence (AI) adoption accelerates across sectors, the demand for reliable, scalable, and sustainable AI infrastructure has become increasingly critical.
The primary beneficiary of the advancement in electric distribution and the digital India initiatives will be the grid, as it transitions from a legacy system to a modern one. The smart meter data can provide insights into consumption patterns and appliances, enabling comparisons between current utility usage statements and archaic documents that make it difficult for consumers to understand bill fluctuations, leading to disputes.
An AI-driven infrastructure could provide consumers with real-time insights into their consumption, inefficient appliances, and reasons for bill spikes, greatly benefiting the consumer market, primarily e-commerce, m-commerce, and mobility-first markets like India.
A precise forecast of power demand, bolstered by behind-the-meter intelligence from each household, has the potential to lower peak power-purchasing costs for distribution companies, which currently represent their most significant operational expenditure.
Artificial intelligence-driven software solutions may facilitate the redistribution of load from high-capacity assets (such as transformers operating at 98% capacity) to those with lower capacity (50%), which could result in significant savings, potentially amounting to millions of dollars, on urgent upgrades to grid assets.
To effectively address the high-bill challenge, technology leaders must grasp the essential difference between the two categories of AI:
Horizontal AI: These are versatile tools intended for use across various sectors—consider automated customer service bots. They excel at general tasks and broad business intelligence, but they lack the in-depth contextual understanding of the energy sector.
Vertical AI: Specifically designed for utilities. Vertical AI solutions incorporate extensive energy company context within the utility’s own cloud infrastructure, facilitating predictive insights for the grid, tailored customer interactions, and enhanced operational risk management.
Vertical AI serves as the crucial link. It converts fragmented utility data into actionable intelligence that propels comprehensive use cases, from the call centre to grid reliability.
The views and opinions expressed in this article are the author’s own, and do not necessarily reflect those held by pv magazine.
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