Two-layer energy scheduling of electrical and thermal smart grids with energy hubs including renewable and storage units considering energy markets

• Published in: Scientific reports Vol. 15; no. 1; pp. 25079 - 20
• Main Authors: Aich, Walid, Basem, Ali, Sawaran Singh, Narinderjit Singh, Mausam, Kuwar, Hussein, Zahraa Abed, Dixit, Saurav, Ali, Naim Ben, Said, Lotfi Ben, Rajhi, Wajdi, Mostafazadeh, Loghman
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 11.07.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 639/4077/4079; 639/4077/909; Alternative energy sources; Costs; Economic two-layer energy management; Economics; Electricity; Emissions; Energy industry; Energy management; Energy resources; Energy storage; Flexibility; Genetic algorithms; Humanities and Social Sciences; Hybrid solver; Hydrogen and compressed air storages; Industrial plant emissions; Linear programming; multidisciplinary; Optimization; Problem solving; Profitability; Real-time and day-ahead markets; Renewable energy; Renewable energy hub; Renewable resources; Science; Science (multidisciplinary); Thermal energy; Hydrogen and compressed air storages; Real-time and day-ahead markets; Hybrid solver; Economic two-layer energy management; Renewable energy hub
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-09960-6

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.