Heikal Aya M A, Aleem Shady H E Abdel, El-Sehiemy Ragab A, Abdelaziz Almoataz Y
Electrical Power & Machines Department, Faculty of Engineering, Ain Shams University, Cairo, 11517, Egypt.
Department of Electrical Engineering, Faculty of Engineering, Science Valley Academy, El-Obour City, Egypt.
Sci Rep. 2025 Jul 26;15(1):27197. doi: 10.1038/s41598-025-12358-z.
Energy hubs (EHs) are considered a promising solution for multi-energy resources, providing advanced system efficiency and resilience. However, their operation is often challenged by the need for techno-economic trade-offs and the uncertainties related to supply and demand. This research presents a multi-objective optimizing framework for EH operations tackling these techno-economic aspects under uncertainty. Utilizing artificial neural networks (ANN)-based active learning (AL), the proposed approach dynamically enhances the model's capability to achieve optimal scheduling and planning while considering complex, fluctuating energy demands and system constraints. The optimization approach under uncertainty provides robust predictive abilities across various scenarios, allowing the system to optimize energy management effectively, enhancing operational efficiency while minimizing overall energy losses, costs, and emissions. Results demonstrate significant improvements in system reliability, cost efficiency, and flexible operation, validating the effectiveness of ANN-based AL to optimize EHs management and ensure sustainable operation complexities. The AL algorithm enhances the ANN model's predictive ability, resulting in a 57.9% decrease in operating costs and a 0.010682 loss of energy supply probability (LESP) value. It ensures energy efficiency while sustaining system flexibility, adapting to frequent load dynamics and intermittent renewable energy supply. The algorithm minimizes electrical and thermal deviations, achieving a balance of flexible operation with efficient energy management. Despite uncertainties and intermittent renewable energy supply, the AL optimizes renewables utilization and demand adjustments, reducing energy losses, costs, and emissions by 80.3The optimized system achieves an output of 13,687.8 kW per day. The system's implementation is performed using MATLAB R2023b software, ensuring precision and efficiency.
能源枢纽（EHs）被认为是一种解决多能源资源问题的有前景的方案，可提供先进的系统效率和恢复力。然而，其运行常常受到技术经济权衡需求以及与供需相关的不确定性的挑战。本研究提出了一个针对EHs运行的多目标优化框架，以应对不确定性下的这些技术经济方面。利用基于人工神经网络（ANN）的主动学习（AL），所提出的方法在考虑复杂、波动的能源需求和系统约束的同时，动态增强模型实现最优调度和规划的能力。不确定性下的优化方法在各种场景中提供强大的预测能力，使系统能够有效地优化能源管理，提高运行效率，同时将总体能源损失、成本和排放降至最低。结果表明，系统在可靠性、成本效率和灵活运行方面有显著改善，验证了基于ANN的AL在优化EHs管理和确保可持续运行复杂性方面的有效性。AL算法增强了ANN模型的预测能力，使运营成本降低了57.9%，能源供应损失概率（LESP）值降低到0.010682。它在维持系统灵活性的同时确保能源效率，适应频繁的负荷动态和间歇性可再生能源供应。该算法将电气和热偏差降至最低，实现了灵活运行与高效能源管理的平衡。尽管存在不确定性和间歇性可再生能源供应，AL仍能优化可再生能源利用和需求调整，将能源损失、成本和排放降低80.3%。优化后的系统每天实现13,687.8千瓦的输出。该系统的实现使用MATLAB R2023b软件进行，确保了精度和效率。
