Optimization Algorithm Design for Power Balance Dispatching Strategy of Active Distribution Network Based on Fuzzy Logic
The increasing penetration of distributed energy resources has introduced substantial operational uncertainties into active distribution networks, posing significant challenges to stable electromagnetic energy transmission and intelligent power dispatch. This study proposes a fuzzy logic-based power balance scheduling optimization algorithm that dynamically adjusts daily dispatch plans through a multi-input single-output fuzzy inference system. A three-input fuzzy controller is first established using net load deviation, energy storage state-of-charge deviation, and transmission line power fluctuation as input variables, while the output represents the power adjustment of dispatchable resources. To enhance adaptability under varying operating conditions, a variable-domain mechanism is incorporated to overcome the limitations of fixed membership functions. Historical operational data are further classified through fuzzy clustering, enabling scenario-oriented rule-base optimization. Simulation results on the IEEE 33-node active distribution network demonstrate that, compared with fixed-domain fuzzy control, the proposed method reduces the cumulative daily average absolute power deviation by 8.4%, decreases energy storage charge-discharge cycles by 1.3%, and improves tie-line power fluctuation variance by 6.4%. Relative to model predictive control (MPC), it achieves comparable control performance within 1.2% while requiring only 6.6% of the computational time. Robustness evaluations under communication latency and measurement noise further verify its practical applicability. The proposed algorithm provides an efficient and reliable solution for uncertainty-aware dispatching in active distribution networks and offers valuable support for intelligent electromagnetic energy management and modern power transmission systems.