Optimization of a bidirectional boost converter for nanogrid applications
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The electrical distribution system is undergoing a transformation from centralized to distributed generation, particularly in rural areas with limited grid access. This change is supported by advancements in key technologies, including distributed resources, renewable generators, electricity storage systems, and power electronic converters which enable the efficient and reliable integration of all energy systems within the nanogrid. Given the importance of the DC/DC converter in a nanogrid and the increasing requirements, optimizing its design is essential for improving the nanogrid's overall performance. This paper proposes an optimization method for a bidirectional boost converter that optimizes cost and efficiency as a function of switching frequency, current ripple, and other parameters. The optimization is performed using a genetic algorithm widely used in power electronics design optimization problems. Analytical models for cost and power losses are used to solve the optimization. Several solutions to the optimization problem are presented, dependent on the weight of the objective function, which can be customized to suit the user's preferences.
The electrical distribution system is undergoing a transformation from centralized to distributed generation, particularly in rural areas with limited grid access. This change is supported by advancements in key technologies, including distributed resources, renewable generators, electricity storage systems, and power electronic converters which enable the efficient and reliable integration of all energy systems within the nanogrid. Given the importance of the DC/DC converter in a nanogrid and the increasing requirements, optimizing its design is essential for improving the nanogrid's overall performance. This paper proposes an optimization method for a bidirectional boost converter that optimizes cost and efficiency as a function of switching frequency, current ripple, and other parameters. The optimization is performed using a genetic algorithm widely used in power electronics design optimization problems. Analytical models for cost and power losses are used to solve the optimization. Several solutions to the optimization problem are presented, dependent on the weight of the objective function, which can be customized to suit the user's preferences.