Power distribution is a critical link in the electricity value chain, interfacing with the end-customers and the only revenue source for the entire value chain. As distribution utilities continue to provide reliable and affordable electricity to all, they need to address both traditional grid challenges such as grid reliability, quality of supply, access, affordability, increasing and evolving demand, aging infrastructure, and high electricity transmission and distribution losses along with emerging challenges of renewable energy integration, urbanization, resilience, cybersecurity and data management.
In India, use of classical AI has been limited, but is picking up. Indian utilities are already a data powerhouse—generating data around energy consumption, financial transactions, customer-initiated interactions (complaint, feedback, etc.). Furthermore, with the roll-out of 250 million smart meters, the data volume generated would be impossible to manage through rule-based analytics.
Owing to its superior knowledge management capabilities (because of new age Large/Small/Tiny Language Models and their “conversational” abilities), Gen AI can be brought to bear on many areas of distribution utility operation. Broadly, the use cases will span across four areas – load forecasting and planning; grid and assets; revenue and customer; and governance and compliance
Starting with the core function of load forecasting and power supply and capacity planning, Gen AI can help analyze large and complex data sets from historical consumption, weather patterns, economic activity, demographic changes, leading to more accurate demand forecasts. Its interactive features can help build different scenarios for optimal power mix and help balance costs, environmental goals and grid stability. Similarly, Gen AI coupled with AI based capacity models, can simulate factors such as fuel prices, regulatory changes, technological advancements, and can help develop future capacity expansion pathways.
Gen AI can play a pivotal role in improved utility asset management through its integration with sensors, helping in better tracking of equipment performance and reduction in the failure rate. The real time asset monitoring and early fault detection also helps in maintenance schedule optimization. The predictive maintenance helps improve the overall asset health and performance thereby resulting in not only cost savings (through reduced O&M costs), but also improving asset reliability and equipment safety and grid reliability indices and reducing revenue loss.
On the revenue side, through Gen AI models utilities can accurately predict energy demand and subsequent energy bills for its customers, helping in better load and revenue management. Gen AI based customized customer communication can aid in recovering outstanding dues and following-up with defaulting customers. This improves the revenue predictability for the utility and better service delivery for the customer. For customers specifically, utilities can provide hyper personalized services such as personalized insights/ alerts (including in vernacular languages) based on the individual profiling and consumption patterns. This data can be leveraged on for promoting adoption of energy efficient practices, demand response interventions, including automated interventions. Analysis of the consumption patterns with an overlay of the customer demographics can help develop specialized customer personas for quick and effective service and query resolution. Select utilities in South Asia are already building solutions around these.
Last but not the least, Gen AI can also help improve governance and compliance for power discoms. It significantly enhances the ability to identify and neutralize cyber threats efficiently. By leveraging deep learning models, this technology can simulate advanced attack scenarios crucial for testing and enhancing security systems. Specific to the procurement function, Gen AI can automate search for open tenders and aid with bid drafting. It automates, optimizes and expedites bid evaluation through a standardized technical and commercial bid analysis methodology, and can help reduce unconscious bias by focusing on objective data such as historical supplier performance, bill of material/ services, etc. Learning and development is another strong use case where Gen AI can create personalized learning pathways based on individual needs, preferences, and progress, automate the creation of educational content including in vernacular languages, identify skill gaps and recommend targeted training to address these deficiencies.Gen AI is increasingly becoming accessible to all due to advancements in technology, cloud computing and open-source tools. Today, cloud platforms provide access to high performance computing resources, data storage and AI tools at a fraction of cost. Low-code and no-code AI platforms allow utility professionals to leverage AI without the need for extensive programming knowledge. Its simplified and approachable features make it beneficial for all, allowing them to leverage AI for their daily tasks as well as challenge projects. The more we explore, the more it gives back in return.
Turbocharging power distribution through Generative AI