The global network of pipelines constitutes a strategic backbone for the world economy, enabling safe and efficient transportation of energy products. These pipelines serve distinct functions in the energy supply chain: gas pipelines support emerging cleaner energy carriers; multi-product pipelines provide versatility in transporting refined liquid fuels; and oil pipelines remain dominant for crude oil delivery. Energy management across the pipeline value chain emphasizes efficiency optimization, cost reduction, and sustainability through real-time monitoring, data analytics, integrated systems, and technological innovations spanning operations, maintenance, and emission control. Despite their critical role, petroleum depots remain relatively understudied, particularly in developing and Sub-Saharan African contexts. This review synthesizes insights from over 100 studies on energy-efficient pumping, predictive control, digitalization, and socio-technical energy management in depots. Analysis of these studies highlights recurring operational and infrastructural issues that constrain energy efficiency in depots. The challenges include irregular truck-loading schedules, frequent pump cycling, aging equipment, power-supply instability, manual operator interventions, and policy-driven constraints. The reviewed studies demonstrate that anticipatory, multi-layer control strategies integrating short-horizon flow forecasting, hybrid model predictive control, and cyber–physical–human–institutional system representations outperform reactive approaches in mitigating energy losses and operational variability. Site-specific calibration and phased deployment emerge as pragmatic pathways for implementing advanced energy optimization under the constrained conditions typical of real-world petroleum depots.

Globally, downstream petroleum operations face increasing pressure to optimize energy use and reduce carbon emissions, with electrically driven pumping systems representing the dominant site-level electricity load. This study investigates why anticipated energy savings from a variable frequency drive (VFD) retrofit were not realized in a Kenyan petroleum depot. High-resolution PLC-SCADA data at 1 s intervals were analysed over a six-month period using a custom Pump-Arm Modelling framework, reconstructing pump operation and energy consumption as a function of loading arm demand, runtime, and control mode. Results show that, despite VFD installation, pump speeds remained effectively fixed, and energy consumption was governed by discrete loading arm demand rather than speed modulation. Eight months billing data confirmed that post-retrofit energy intensity increased, from an average of 0.493 to 0.555 kWh/m³, demonstrating that expected efficiency improvements were not achieved. Stratified analysis further showed that idle running due to manual overrides accounted for approximately 3% of total depot electricity consumption. To contextualize this performance gap, idealized affinity-law scaling was applied to observed partial-load intervals, revealing unrealized energy savings potential of approximately 92 MWh, equivalent to a depot-level savings potential of 28.4% ± 1.4%. The findings indicate that limited energy savings were attributable to open-loop control architecture, partial digital integration, and legacy equipment constraints rather than VFD hardware limitations, highlighting the need for demand-aware, closed-loop energy optimization in retrofitted petroleum depots.