Abstract--Electrical weapons and combat systems integrated into ships create challenges for their power systems. The main challenge is operation under high-power ramp rate loads, such as rail-guns and radar systems. When operated, these load devices may exceed the ships generators in terms of power ramp rate, which may drive the system to instability. Thus, electric ships require integration of energy storage devices in coordination with the power generators to maintain the power balance between distributed resources and load devices. In order to support the generators by using energy storage systems, an energy management scheme must be deployed to ensure load demand is met. This paper proposes and implements an energy management scheme based on model predictive control to optimize the coordination between the energy storage and the power generators under high-power ramp rate conditions. The simulation and experimental results validate the proposed technique in a reduced scale, notional electric ship power system. Index Terms-Ship power systems, DC microgrids, energy management, power control, and predictive control.
NOMENCLATURE
There are tradeoffs between current sharing among distributed resources and DC bus voltage stability when conventional droop control is used in DC microgrids. As current sharing approaches the setpoint, bus voltage deviation increases. Previous studies have suggested using secondary control utilizing linear controllers to overcome drawbacks of droop control. However, linear control design depends on an accurate model of the system. The derivation of such a model is challenging because the noise and disturbances caused by the coupling between sources, loads, and switches in microgrids are under-represented. This under-representation makes linear modeling and control insufficient. Hence, in this paper, we propose a robust adaptive control to adjust droop characteristics to satisfy both current sharing and bus voltage stability. First, the time-varying models of DC microgrids are derived. Second, the improvements for the adaptive control method are presented. Third, the application of the enhanced adaptive method to DC microgrids is presented to satisfy the system objective. Fourth, simulation and experimental results on a microgrid show that the adaptive method precisely shares current between two distributed resources and maintains the nominal bus voltage. Last, the comparative study validates the effectiveness of the proposed method over the conventional method.
This article focuses on the optimal operation management challenges in hybrid AC-DC microgrids considering optimal feeder switching, renewable energy sources, and plug-in electric vehicles. In comparison with the traditional hybrid AC-DC microgrid concept, the reconfigurable hybrid AC-DC microgrids provide more flexibility for better supporting consumers and reducing the operation costs through the remotely controlled switches. In addition, the reconfigurable structure of the hybrid microgrid along with the vehicle-to-grid technology supports the high penetration of plug-in electric vehicles by changing their role from only consuming loads into mobile storages. The proposed problem is prepared as a constrained multi-objective problem optimizing both cost and emission objectives. Due to the high complication and nonlinearity of the problem, an effective optimization algorithm called the theta-crow search algorithm is developed to solve the problem optimally. Also, a stochastic framework based on the point estimate method is used to model the high uncertainties of the problem. The high reliable and satisfying performance of the new method is shown on a test AC-DC microgrid.
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