Integrated energy hub optimization in microgrids: Uncertainty-aware modeling and efficient operation

Abstract

This paper introduces a novel approach that addresses the intricate challenges associated with energy hubs, focusing on diverse issues in the transmission and production of energy, particularly within the realms of gas and electricity networks. A key emphasis is placed on the profitability of energy hubs, navigating through uncertainties heightened by the presence of varied energy carriers. The proposed model features an energy hub with electrical inputs and outputs encompassing natural gas, electricity, and thermal energy. Diverse sources contribute to electricity supply, including bilateral contracts, power basin purchases, and generation via a cogeneration unit. Thermal heat is provided by a heating furnace, alongside a simultaneous cooling, heating, and power production unit. Addressing market price uncertainties, the model incorporates probabilistic considerations for the next day's electricity prices. Notably, the model encompasses the intricacies of electric vehicle rechargeable modes, treating them as a bidirectional source for both energy production and consumption, contributing to effective demand-side management. To tackle the complexity inherent in these dynamics, a novel optimization method based on crow search is proposed, enhancing local and global search capabilities. The efficacy of the proposed method is demonstrated through a comprehensive application on a micro-energy grid, considering four distinct cases on a typical summer day. The analysis reveals that Case 2, incorporating PV and WT, significantly outperforms Case 1, with a 10.8 % surplus in electricity generation. Furthermore, the proposed Crow Search Optimization algorithm exhibits a 50 % improvement over alternative methods, showcasing its efficacy in achieving optimal solutions for microgrid performance.