Nonlinear active distribution network optimization for improving the renewable energy power quality and economic efficiency: a multi-objective bald eagle search algorithm

The active distribution network (ADN) integrated optimization is the optimization of the system using three optimization techniques: reconfiguration, reactive power optimization and optimal configuration of distributed generation. This study establishes an optimization model of the renewable energy ADN and proposes a novel multi-objective bald eagle search algorithm to optimize the model. This study integrates three techniques of network reconfiguration, reactive power optimization and optimal configuration of distributed power sources to establish a comprehensive optimization model of ADN, which aims to reduce ADN energy losses, improve active distribution network power quality and economic efficiency. The contributions are as follows: (1) a multi-objective bald eagle search algorithm is proposed to deal with the complex nonlinear ADN integrated optimization problem, and its superiority is verified; (2) an ADN integrated optimization model is established; and (3) multiple scenarios are established in the IEEE33 node system for comparison and validation to verify the effectiveness of the proposed model. The results show that the proposed model can effectively improve the ADN operation with power loss, node voltage deviation and system cost reduced by 86.34%, 83.12% and 30.27%. The proposed algorism improves the power quality of ADN and promotes economic production and sustainable energy development.