Population in Karachi and KE’s serviced territories is growing with a fast pace. Segmented demand forecasting requires accounting for varying consumers, their profile and usage patterns to enable targeted planning and resource allocation. How can this process be automated using AI, what can be done to reduce manual work?

As renewable energy’s share in the generation mix rises, variability in output poses threats to grid stability—leading to frequency deviations, voltage fluctuations, and potential curtailments. Battery Energy Storage Systems (BESS) can capture surplus energy and release it when generation dips, ensuring smoother power delivery. Yet, large-scale BESS deployment introduces its own set of hurdles in terms of cost, lifecycle management, and grid interoperability. How can we design and implement robust BESS solutions that maintain power quality, mitigate intermittency, and deliver reliable service under ever-increasing renewable penetration?

Transformer failures disrupt power distribution, lead to unplanned outages, increase maintenance costs, and reduce customer satisfaction. Predicting and preventing transformer-level failures requires a data-driven approach to monitor equipment health, identify failure patterns, and enable proactive maintenance. How can we leverage predictive analytics and automation to reduce unexpected PMT failures while minimizing manual interventions?

KE’s power distribution network is rapidly integrating Distributed Energy Resources (DERs), primarily Photovoltaic (PV) systems. To ensure grid stability and reliability, it’s crucial to accurately analyze & forecast PV penetration levels across different geographical areas and socioeconomic segments for timely capacity enhancement or shifting strategies, mitigating potential grid congestion and power quality issues. Additionally, KEL grid faces the challenge of managing significant solar (Grid-tied/Off-Grid) PV penetration, especially under variable cloud conditions. Load masking, a phenomenon where PV generation obscures recognizing the true load demand, can intensify this challenge. Design and develop an automated tool to analyze & forecast when any distribution transformer will reach PV penetration threshold by analyzing factors like geographical location, social class, income level, electrical parameters of Distribution Transformers (DTs) and all other relevant variables? How can we accurately predict the net load on an asset (Transformer/Feeder), accounting for the full impact of solar PV generation, to effectively plan load relief strategies?

Ensuring the reliability and efficiency of transmission infrastructure is critical for uninterrupted power supply. Traditional monitoring methods often face challenges in detecting faults, assessing line conditions, and responding to potential failures in real-time. With expanding grids and increasing demand, manual inspections alone are no longer sufficient.

To improve grid resilience and operational efficiency, how can K-Electric implement smart monitoring solutions—leveraging IoT sensors, drones, and GIS mapping to enable real-time fault detection, predictive maintenance, and enhanced transmission line management?