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[en] Highlights: • Isolated oil drilling rig microgrid power flows are analyzed over 30 days. • Rule-based diesel generator scheduling is proposed to reduce fuel consumption. • A battery energy storage is parameterized and used for peak load leveling. • The effectiveness of proposed hybrid microgrid is verified by simulations. • Return-of-investment might be expected within 20% of battery system lifetime. - Abstract: This paper presents the development of a rule-based energy management control strategy suitable for isolated diesel power-plants equipped with a battery energy storage system for peak load shaving. The proposed control strategy includes the generator scheduling strategy and peak load leveling scheme based on current microgrid active and reactive power requirements. In order to investigate the potentials for fuel expenditure reduction, 30 days-worth of microgrid power flow data has been collected on an isolated land-based oil drilling rig powered by a diesel generator power-plant, characterized by highly-variable active and reactive load profiles due to intermittent engagements and disengagements of high-power electric machinery such as top-drive, draw-works and mud-pump motors. The analysis has indicated that by avoiding the low-power operation of individual generators and by providing the peak power requirements (peak shaving) from a dedicated energy storage system, the power-plant fuel efficiency may be notably improved. An averaged power flow simulation model has been built, comprising the proposed rule-based power flow control strategy and the averaged model of a suitably sized battery energy storage system equipped with grid-tied power converter and state-of-charge control system. The effectiveness of the proposed rule-based strategy has been evaluated by means of computer simulation analysis based on drilling rig microgrid active and reactive power data recorded during the 30 day period. The analysis has indicated that fuel consumption of thus modified drilling rig diesel generator power-plant can be reduced by about 12% compared to current practice in the field relying on human operator-based decision making, which would also result in proportional reduction of carbon-dioxide emissions. Finally, the analysis has also shown that the return-of-investment period for the considered battery energy storage system might be between one and two years depending on the power-plant utilization (duty) ratio.