AI integration could revolutionize power distribution
Tamil Nadu: The implementation of artificial intelligence (AI) technology, spearheading transformation across industries in recent years, can be a potential game changer in revolutionising the power distribution system for reliable and efficient power supply. The forecast of future demands for the Tamil Nadu Power Distribution Corporation Limited (TNPDCL), the erstwhile Tangedco, was completed with the incorporation of AI-powered mathematical models that led to highly favourable outcomes.
These models assisted with the maintenance of power plants and equipment during lean periods and operating the plants at their full potential during peak demand periods, minimising private power purchases to an extent – demonstrating that AI models can be employed for the betterment of both the electrical distribution network and the consumers in diverse ways.
Generally, preventive maintenance is preferred over breakdown maintenance, even though the latter cannot be wholly prevented. By employing AI models, equipment failures can be predicted by analysing patterns in historical data. This enables proper maintenance scheduling, thereby reducing breakdowns and maintenance costs.
For a better distribution system, load forecasting is imperative as it helps to plan and optimise energy distribution and bring down operational costs. AI models can predict short and long-term power demand by reviewing past consumption data, in correlation with weather conditions, and other relevant factors.
Presently, the processes such as fault detection and isolation of faulty portions are carried out manually and are time-consuming. AI can rapidly detect and isolate faults with the use of Machine Learning (ML) algorithms by analysing real-time data and identifying anomalies, thus minimising the impact on the grid. It will enable a safer and faster response in isolating the fault and reducing outage durations. With the integration of renewable sources, AI can manage and optimise the grid and microgrids. In modern times, renewable generations were extended up to the prosumer (producer-cum-consumer) end with rooftop solar panels and wind turbines. The operation of the distribution network can be managed perfectly with the AI by the real-time adjustment of electricity flow. Considering the intermittent nature of renewable energy sources, AI can be beneficially utilised for voltage level optimisation, load balancing, and in managing distributed energy resources (DERs) such as solar panels and wind turbines.
AT&C (Aggregate Technical and commercial) loss is another vital field wherein AI can be better utilised. At the national level, the AT&C loss stands at nearly 16%, which includes electricity theft. AI, along with a smart metering system, can effectively identify all instances of theft and irregularities like misuse of tariffs by analysing consumption patterns and identifying leakage areas in the network. AL can help detect suspicious usage patterns and assist in taking appropriate actions.
The integration of AI systems can also benefit customers as they can plan their energy consumption during a particular time of day (ToD), when the power is cheaply available. The tariff rates are differently charged during ToD, so consumers can plan their usage economically, which is better for grid management.
With vast amounts of data produced by smart meters, IoT devices and sensors like breakers, advanced data visualisation tools can provide actionable insights to grid operators, enabling them to quickly make informed decisions. Also, in a modern world plagued with consistent cybersecurity threats, AI enhances cybersecurity by monitoring the network for suspicious activities and potential threats. AI can detect unusual patterns that may indicate cyberattacks and protect critical infrastructure from malicious threats.
Though the implementation of AI has a wide range of advantages, it could only be successful when properly utilised. It seems that the implementation of AI in some states was not up to the mark and failed to live up to expectations.
To effectively implement AI in electrical distribution networks, ensuring robust and reliable data collection from all relevant sources is imperative. A centralised data storing and management system must be established. The AI system must be integrated with the existing management system.
The interoperability between the two systems must be ensured to the full extent and must be adept at future network expansions, data growth, and fluctuating grid conditions.
Finally, to properly use and maintain AI systems, it is essential to effectively train the staff. Hiring data scientists, engineers, and IT professionals with an expertise in AI and machine learning is critical.
By implementing AI technologies, electrical distribution networks can become more efficient, reliable, and resilient, ultimately leading to a better service for consumers and improved operational efficiency for utility companies.
Consumer benefits
The integration of artifical intelligence systems can also benefit customers as they can plan their energy consumption during a particular time of day (ToD), when the power is cheaply available
