Insights

Not all the electricity generated at a power plant reaches customers - a certain percentage is lost as it travels through transmission lines and transformers, often as heat. These technical system losses are an expected part of any power grid, but excessive losses can be a sign of inefficiency (for example, aging infrastructure or suboptimal grid configuration) and translate to higher costs and wasted energy. In many countries, transmission and distribution losses range from around 5% in advanced grids to over 15% in developing systems. Even a 1-2 percentage point reduction in losses can save enormous amounts of energy and money; for a large utility, that could equate to powering tens of thousands of additional homes without generating more electricity. An AI-first solution for reducing system losses focuses on grid optimization . Machine learning algorithms can analyze real-time grid data to recommend adjustments in network configuration. For instance, AI can manage voltage levels through techniques like Conservation Voltage Reduction (CVR), optimizing the voltage supplied to consumers to reduce losses

while maintaining service quality. During periods of fluctuating demand, AI can dynamically re-route power flow in the grid to avoid overloading certain lines (which causes more resistance losses) and instead utilize under-loaded lines - balancing the network more efficiently than what manual control typically achieves. AI can also help prioritize infrastructure upgrades by pinpointing which lines or transformers are causing the highest losses, allowing utilities to target investments where they’ll have the greatest impact. Although there’s no specific product named in our catalog for loss reduction, utilities can utilize Digital Twin models of their grid built on platforms like Azure Digital Twins . By simulating the electric network under different conditions, the digital twin - powered by AI - can find the optimal settings and configurations to minimize losses. Coupling this with sensor data and automation via Azure IoT devices means the recommendations can be implemented in real time. Through this continuous fine-tuning of the grid, utilities are able to noticeably lower their System losses % , delivering more power to customers with the same generation output and achieving cost and energy savings that benefit both the company and its consumers. Contact us at info@acclero.ai for demos and discussions.

Contact us at info@acclero.ai for demos and discussions.