ArticleScientific reports2025
Two-layer energy scheduling of electrical and thermal smart grids with energy hubs including renewable and storage units considering energy markets.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A hybrid transformer-PPO framework for multi-objective energy management in renewable-based microgrids.Scientific reports · 2026Article
- Optimized scheduling of integrated energy systems: a multi-dimensional electricity, hydrogen, ammonia, heat synergy approach using the LSDBO-WOA algorithm.Scientific reports · 2026Article
- Enhancing battery thermal management system using the emulsion of encapsulated phase change material and water in a mini helical tube.Scientific reports · 2025Article
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Authors and funding
10 authors.
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Abstract
This paper presents a two-layer energy management method designed for the operation of hubs within electrical and thermal smart grids. These energy hubs actively participate in both day-ahead and real time energy markets. Two-layer approach involves coordination at two distinct levels. In the first layer, the focus is on managing sources and storage equipment in collaboration with the hub operator. In the second layer, attention shifts to the interaction between the hub operator and the grid operator. The framework follows a two-stage formulation, where the first stage addresses the day-ahead operation model and the second stage pertains to real-time scheduling. In the first stage, a bi-level optimization strategy is employed. The upper level seeks to minimize the energy cost of smart grids while adhering to optimal power flow constraints, whereas the lower level aims to maximize hubs' profit in the day-ahead energy market subject to the operational constraints of sources and storage systems represented in an energy hub model. The second stage mirrors this problem structure but uses a smaller time step and adopts the flexibility cost minimization as objective for the upper level. To simplify the bi-level optimization problem into a single-objective model, the Karush-Kuhn-Tucker (KKT) method is applied. Uncertainties of load, price of market, and renewable energy generation are modeled using the unscented transformation technique. Problem-solving is undertaken using a hybrid optimization solver that combines artificial bee colony and honey-bee mating optimization methods. Simulation results highlight the effectiveness of this approach, demonstrating its capability to enhance both economic and technical performance. Specifically, hubs achieve significant profitability and operational flexibility, leading to an 18% improvement in economic performance and a 18-27% enhancement in operational efficiency compared to traditional power flow studies.
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