Double layer real-time scheduling of active distribution network based on improved particle swarm optimization algorithm

The active distribution network forms a double-layer complex structure in the case of double-layer input. Due to different input sources, using a single linear scheduling method can lead to multiple constraint environments, which in turn affects the scheduling results. To address this issue, this paper proposes a dual layer real-time scheduling strategy for active distribution networks based on an improved particle swarm optimization algorithm. Build a dual layer active distribution network real-time scheduling model, including voltage regulation layer and schedulable unit control layer. In the voltage regulation layer, by introducing penalty terms to optimize the objective function, it ensures that the voltage of each node is maintained within the safe operating range, and adjusts the active distribution network voltage based on the daily prediction of distributed energy output and uncontrollable loads. At the schedulable unit control layer, we have designed an active distribution network operation scheme that meets the load demand with the goal of optimizing the operating cost of the active distribution network. Collaborative scheduling is achieved between the two layers through instruction flow and information flow, and an improved particle swarm optimization algorithm is used to optimize and solve the dual layer real-time scheduling model of active distribution network, thereby obtaining the best scheduling results. The experimental results show that the algorithm can ensure that the node voltage meets the safety margin, improve the utilization rate of distributed energy, reduce the operating cost of active distribution network, and reasonably allocate the active power of the transferable load of nodes.