Data-Driven Distributionally Robust Optimization for Day-Ahead Operation Planning of a Smart Transformer-Based Meshed Hybrid AC/DC Microgrid Considering the Optimal Reactive Power Dispatch

Abstract

Smart Transformer (ST)-based Meshed Hybrid AC/DC Microgrids (MHMs) present a promising solution to enhance the efficiency of conventional microgrids (MGs) and facilitate higher integration of Distributed Energy Resources (DERs), simultaneously managing active and reactive power dispatch. However, MHMs face challenges in resource management under uncertainty and control of electronic converters linked to the ST and DERs, complicating the pursuit of optimal system performance. This paper introduces a Data-Driven Distributionally Robust Optimization (DDDRO) approach for day-ahead operation planning in ST-based MHMs, focusing on minimizing network losses, voltage deviations, and operational costs by optimizing the reactive power dispatch of DERs. The approach accounts for uncertainties in photovoltaic generator (PVG) output and demand. The Column-and-Constraint Generation (C&CG) algorithm and the Duality-Free Decomposition (DFD) method are employed. The initial mixed-integer non-linear planning problem is also reformulated into a mixed-integer (MI) Second-Order Cone Programming (SOCP) problem using second-order cone relaxation and a positive octagonal constraint method. Simulation results on a connected MHM system validate the model’s efficacy and performance. The study also highlights the advantages of the meshed MG structure and the positive impact of integrating the ST into MHMs, leveraging the multi-stage converter’s flexibility for optimal energy management under uncertain conditions.

Keywords

• AC/DC microgrid
• Data-Driven Distributionally Robust Optimization
• Duality-Free Decomposition
• Meshed Hybrid Microgrids
• uncertainty
• Smart Transformer