Enhanced resilience and efficiency in multi-energy systems via stochastic gradient-driven robust optimization

This paper develops an advanced framework for the operational optimization of Integrated Multi-Energy Systems (IES) that encompass electricity, gas, and heating networks. Introducing a cutting-edge Stochastic Gradient-Enhanced Distributionally Robust Optimization (DRO) approach, this study integrates deep learning models, especially Generative Adversarial Networks (GANs), to adeptly handle the inherent variability and uncertainties of renewable energy and fluctuating consumer demands. The effectiveness of this framework is rigorously tested through detailed simulations mirroring real-world urban energy consumption, renewable energy production, and market price fluctuations over an annual period. The results reveal substantial improvements in the resilience and efficiency of the grid, achieving a reduction in power distribution losses by 15% and enhancing voltage stability by 20%, markedly outperforming conventional systems. Additionally, the framework facilitates up to 25% in cost reductions during peak demand periods, significantly lowering operational costs. The adoption of stochastic gradients further refines the framework's ability to continually adjust to real-time changes in environmental and market conditions, ensuring stable grid operations and fostering active consumer engagement in demand-side management. This strategy not only aligns with contemporary sustainable energy practices but also provides scalable and robust solutions to pressing challenges in modern power network management.