Short-term optimal operation of phase shifting soft open point with high accuracy loss model in unbalanced distribution networks

Abstract

This paper examines the issues associated with unbalanced operations in distribution networks (DNs), which arise from inconsistencies in loads, resources, and configurations. This is particularly relevant in the context of peer-to-peer (P2P) trading, which may introduce security vulnerabilities and exacerbate existing imbalances. To improve the secure and efficient operation of DNs, we propose a short-term optimal operation model that integrates P2P transactions and emphasizes the physical layer of trading. The study assesses the effectiveness of a developed phase shifting-soft open point (PS-SOP) in enhancing operational flexibility and quantifies the related losses, including conduction and switching losses associated with semiconductor switches. By employing a deep neural network (DNN), these losses are converted into linear constraints suitable for incorporation into the convex optimization framework. An AC optimal power flow model is constructed to identify optimal power transfer sequences, which is framed as a mixed-integer linear programming problem to evaluate the PS-SOP's influence on voltage imbalance reduction, loss minimization, and facilitation of P2P transactions. Numerical simulations are conducted on two IEEE test networks to validate the proposed method's efficacy. For scenarios involving multi-terminal PS-SOP, all P2P transactions are successfully executed in the IEEE 13-bus network, and a 97.55 % authorization rate is achieved in the IEEE 123-bus network. The SOP loss derived from the DNN exhibits a negligible discrepancy of 2.54 % compared to nonlinear loss formulations, underscoring the model's precision and dependability.