Distribution network planning based on double deep Q‐network with self‐adjusting parameters

Abstract To address the challenge of low adaptability in distribution network planning caused by significant regional differences in electricity consumption, this paper proposes a distribution network planning method based on a self‐adjusting parameter double deep Q‐network (SAP‐DDQN). First, considering the disparities in electricity consumption across different locations, distribution areas are classified according to load density. For each type of distribution area, appropriate calibration criteria are selected, and indicator models are established covering reliability, economics and flexibility criteria. Subsequently, key indicators under each criterion are extracted using the analytic hierarchy process and kernel principal component analysis. A deep reinforcement learning model for distribution network planning is then developed to achieve rapid optimization of the network configuration. Finally, the effectiveness of the proposed method is validated using a 24‐node distribution network and an actual 84‐node urban distribution network in China. Test results demonstrate that the proposed method can accurately select the optimal network configuration for each distribution area and provide a specific planning scheme.