Innovative distribution network design using GAN-based distributionally robust optimization for DG planning

Abstract

The integration of renewable energy sources and the increasing demand for reliable power have posed significant challenges in the design and operation of distribution networks under uncertain conditions. The inherent variability in renewable energy generation and fluctuating consumer load demand requires advanced strategies for Distributed Energy Resources (DERs) allocation and sizing to enhance grid resilience and operational efficiency. This article introduces an innovative framework for optimizing distribution network design under these uncertainties. The approach integrates deep learning-assisted Distributionally Robust Optimization (DRO) with Generative Adversarial Networks (GANs) to dynamically model and manage the inherent variability in renewable sources and demand fluctuations. Employing a combination of nonlinear optimization techniques and advanced statistical methods, the framework robustly optimizes network configurations to minimize losses and improve voltage stability. The model's efficacy is rigorously tested on the IEEE 33-bus system, achieving a 15% reduction in power distribution losses and a 20% improvement in voltage stability compared to traditional models. Utilizing open-source computational tools, the method not only boosts operational reliability and efficiency but also adapts effectively to the increasing integration of volatile renewable energy sources. These results underscore the framework's potential as a scalable and robust solution for modern power network design challenges.