A recently published study introduces a groundbreaking two-layer energy management method designed to significantly enhance the operational and economic performance of energy hubs within electrical and thermal smart grids. This novel approach, detailed in Scientific Reports, enables energy hubs to actively and profitably participate in both day-ahead and real-time energy markets, demonstrating an 18% improvement in economic performance and a 18-27% enhancement in operational efficiency compared to traditional power flow studies.

The proposed framework operates on a sophisticated two-layer coordination model. The initial layer focuses on optimizing the management of diverse energy sources and storage equipment in direct collaboration with the energy hub operator. This ensures efficient internal resource allocation and system balancing. The second layer then extends this optimization to the interaction between the hub operator and the broader grid operator, facilitating seamless integration and adherence to grid-wide operational constraints.

The methodology employs a two-stage formulation to address distinct market dynamics. The first stage tackles day-ahead operation, utilizing a bi-level optimization strategy. At the upper level, the objective is to minimize the overall energy cost of the smart grids while adhering to optimal power flow constraints. Concurrently, the lower level aims to maximize the energy hub’s profit within the day-ahead market, subject to the operational limitations of its integrated sources and storage systems. The second stage mirrors this structure for real-time scheduling, but with a finer time resolution and an objective focused on minimizing flexibility costs for the upper level.

To manage the inherent complexity of bi-level optimization, the researchers applied the Karush–Kuhn–Tucker (KKT) method, transforming the problem into a more tractable single-objective model. Furthermore, the study effectively addresses the pervasive uncertainties associated with load fluctuations, market price volatility, and the intermittent nature of renewable energy generation by employing the unscented transformation technique. The problem-solving was executed using a hybrid optimization solver, combining the strengths of artificial bee colony and honey-bee mating optimization methods, which proved instrumental in achieving the reported performance gains.

This research underscores the critical role of advanced energy management systems in the evolving landscape of smart grids. By providing a robust framework for integrating renewable energy sources and storage units, it offers a pathway for energy hubs to not only bolster grid stability but also to significantly improve their market profitability and operational flexibility, paving the way for more resilient and economically viable clean energy ecosystems.