Multi-Objective Optimal Scheduling Technology for Honeycomb Active Distribution Networks Based on Information Entropy

In the context of changes to the single-source radial network structure of active distribution networks, there exists the problem of inaccurate prediction of multi-user target loads, leading to significant power scheduling errors and high energy losses. To mitigate losses in distribution network systems and enhance the scheduling capabilities of distributed generation, a multi-objective optimal scheduling technology for honeycomb active distribution networks based on information entropy is proposed. By constructing a multi-objective optimal scheduling model for honeycomb active distribution networks, information entropy is utilized to quantify the uncertainty of different scheduling schemes. A multi-objective load scheduling model for distribution networks is designed, and a self-adaptive particle swarm optimization algorithm based on distribution entropy is adopted to propose the solution method and process for the model. Experimental results show that after applying this technology, the distribution network system loss is reduced, the operational stability of the distribution network is improved, and flexible scheduling and efficient utilization of distributed generation are achieved.