A Mixed-Integer Second-Order Cone Programming Approach for the Optimal Scheduling of Active Distribution Networks with High Penetration of Distributed Energy Resources Including Electric Vehicles

With the high penetration of distributed energy resources (DERs) including electric vehicles (EVs) in the active distribution network (ADN), the optimal scheduling of the ADN has become a crucial issue worthy of further study. Therefore, we propose a mixed-integer second-order cone programming (MISOP) approach for the optimal scheduling of the ADN with the high penetration of DERs including EVs. First, we present the basic idea of optimizing the quantities of charging EVs and discharging EVs on each bus of the ADN. Second, we propose a scenario generation and reduction method based on Latin hypercube sampling (LHS), density-based spatial clustering of applications with noise (DBSCAN), and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex>-means. Third, we propose an MISOCP approach for the optimal scheduling of the ADN. Finally, we validate our proposed approaches through case studies. The results demonstrate that the high penetration of DERs including EVs in the ADN can effectively reduce the network losses and operating costs of the ADN.