Two-layer intelligent planning model of active distribution network based on simulated annealing algorithm and multi-objective function

Traditional power distribution network planning methods face challenges such as single-objective optimization and the use of algorithms that are prone to becoming trapped in local optima, making it difficult to satisfy the multi-dimensional and complex requirements of modern distribution systems. To address these issues, this paper proposes a two-layer intelligent planning model based on simulated annealing and multi-objective functions, designed for active distribution networks (ADNs). The upper layer aims to minimize the total cost of the ADN, while the lower layer focuses on reducing voltage deviation and maintaining power system stability, leveraging the global search capability of the simulated annealing algorithm. Experimental results demonstrate that the proposed model reduces total costs by approximately 15 % to 30.8 %, with an average cost reduction per node of about 27 %, while effectively maintaining voltage deviations within the range of 0.98 to 1.03 per unit. The model successfully overcomes the limitations of single-objective optimization and poor algorithmic convergence in active distribution network/system (ADN/ADS) planning, exhibiting excellent performance in both cost efficiency and voltage stability enhancement, and offering an innovative solution for large-scale ADS planning.