Self-Adaptive Evaluation of Hybrid AC/DC Distribution Networks with Multi-Energy Complementary Systems

The hybrid AC/DC distribution networks with multienergy complementary systems (HDN-MECS) have become a powerful supporting platform for the vertical integration of single energy and the horizontal integration of multiple energy. However, the complex composition, diverse operating scenarios, and strong spatiotemporal coupling have all posed major challenges for the comprehensive evaluation of HDN-MECS operating effectiveness. To this end, a self-adaptive evaluation framework is proposed in this paper based on the unique characteristics of HDN-MECS. Taking maximum entropy criterion (MEC) as an objective function, this paper proposes a two-stage interval analytic hierarchy process (IAHP), and solves the problem with a hybrid algorithm of improved particle swarm optimization (IPSO) and chaos optimization (CO). A self-adaptive interval analytic hierarchy process (SAIAHP) is developed to weight performance indicators under the framework, which aims to enable dynamic, robust evaluation considering human expert inputs. A case study forecasting verifies the accuracy and applicability of the SAIAHP method.