The robotic mobile fulfillment system (RMFS), with wide application in warehousing and logistics, requires many robots powered by electricity, which significantly impacts energy consumption. This paper investigates the energy consumption in the RMFS under a classic e-business environment, which classifies the orders into regular orders and expedited orders. We evaluate the impact of three dynamic priority policies (the earliest deadline first policy, waiting time-dependent policy, and weighted waiting time first policy) on throughput time and energy consumption. This paper proposes multi-class semi-open queuing network models (SOQN) with dynamic priority policies to investigate energy consumption. We validate the accuracy of the analytical models by simulation models. This paper makes the following contributions: (1) In methodology, we propose new methods to solve the SOQN with dynamic priority policies. (2) In operational planning and control, we are among the earliest to investigate the impact of dynamic priority policies on order throughput time and energy consumption in an RMFS. (3) In design optimization, we propose a decision tool to optimize the robot number for realizing the required throughput time with minimal energy consumption. Our model can also decide the optimal warehouse shape to minimize energy consumption. (4) In system analysis, we estimate the energy consumption per transaction in an RMFS, providing logistics managers insights into energy saving of warehouses.