What is it about?
Diesel-based microgrids serving oil drilling operations are subject to sharp and unpredictable load fluctuations that lead to generator underloading, elevating specific fuel consumption, and reducing system reliability. Conventional battery energy storage system (BESS) sizing methods typically depend on long-term field measurements and static load assumptions, which fail to capture the real transient nature of drilling processes. This paper develops a framework for BESS sizing that directly integrates a measurement-based machine learning load prediction model. The adopted model, built using the exponential Gaussian process regression technique, has been trained and validated on 6 months of high-resolution field measurements collected from an operating oil drilling rig. These predicted load sequences are then utilized to calculate instantaneous BESS power requirements, cumulative energy capacity, and discharge rate validation under real operational constraints. The methodology further incorporates generator switching limits, depth-of-discharge margins, battery C-Factor, and degradation safety factors to ensure practical deployment feasibility. Simulation results have shown that the highest predicted load peak of 1.546 MW, lasting 7 min, required a BESS capacity of 162 kWh. Incorporating operational constraints such as minimum generator switching intervals, an 80 % depth-of-discharge limit, and a 15 % aging safety margin, the final BESS energy requirement is determined to be 232 kWh. The proposed approach eliminates the need for long-term field data collection while providing a physically feasible, prediction-driven tool for sizing energy storage systems in dynamic industrial microgrids.
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Photo by RICARDO MENDONCA on Unsplash
Why is it important?
Overall, the developed GPR-integrated framework establishes a scalable, predictive, and physically validated foundation for intelligent BESS integration in off-grid industrial power systems, offering a practical pathway toward more efficient, cleaner, and resilient drilling operations.
Perspectives
This manuscript presented a predicated data- driven framework for determining the optimal BESS size for peak shaving in isolated diesel-based oil drilling microgrids. The methodology integrates a GPR load prediction model with an analytical sizing process that accounts for real operational dynamics and generator constraints. By utilizing predicted rather than directly measured load profiles, the proposed approach eliminates the dependency on extended field data collection while maintaining high prediction accuracy and technical reliability.
Professor Omar H. Abdalla
Helwan University
Read the Original
This page is a summary of: Field-Validated Load Modelling and Prediction-Driven BESS Sizing for Oil Drilling Microgrids, Trends in advanced sciences and technology, January 2025, Helwan University TAST Journal,
DOI: 10.62537/2974-444x.1044.
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