Garg Ankit, Niazi K R, Tiwari Shubham, Sharma Sachin, Rawat Tanuj
Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, India.
Department of AFE, IIASA, Laxenburg, Vienna, Austria.
Sci Rep. 2025 Jul 17;15(1):25936. doi: 10.1038/s41598-025-10404-4.
This paper presents a structured approach for the efficient operation of multi-carrier energy systems under the uncertainty of renewable energy sources. As the penetration of wind and solar energy increases, managing the resulting variability becomes critical to maintaining both economic efficiency and operational flexibility. To address this, a two-stage multi objective optimization framework is proposed. In the first stage, the objective is to minimize daily operational costs while incorporating the uncertain behavior of renewables using a scenario-based stochastic approach. The second stage focuses on simultaneously enhancing system flexibility by maximizing the available capacities for both electrical and thermal energy generation and improving green house emissions. To evaluate system adaptability, two performance indicators are introduced: the Average Energy Generation Flexibility Index (AEGFI) and the Average Thermal Generation Flexibility Index (ATGFI). The optimization model is solved using the Modified Water Evaporation algorithm. Sensitivity analyses are also conducted to explore the effects of fluctuations in gas and electricity prices on system performance. The proposed model is applied to a generalized multi-carrier energy system. Simulation results demonstrate significant improvements in flexibility, with AEGFI and ATGFI increasing by 27.43% and 39.91%, respectively. Overall, the framework offers a comprehensive solution to balance cost-effectiveness and flexibility in energy systems with high shares of renewables.
本文提出了一种结构化方法,用于在可再生能源不确定性条件下实现多载体能源系统的高效运行。随着风能和太阳能渗透率的提高,管理由此产生的可变性对于维持经济效率和运营灵活性至关重要。为解决这一问题,提出了一个两阶段多目标优化框架。在第一阶段,目标是将每日运营成本降至最低,同时使用基于场景的随机方法纳入可再生能源的不确定行为。第二阶段专注于通过最大化电能和热能发电的可用容量来同时提高系统灵活性,并改善温室气体排放。为评估系统适应性,引入了两个性能指标:平均发电灵活性指数(AEGFI)和平均热发电灵活性指数(ATGFI)。使用改进的水蒸发算法求解优化模型。还进行了敏感性分析,以探讨天然气和电价波动对系统性能的影响。所提出的模型应用于一个广义多载体能源系统。仿真结果表明灵活性有显著提高,AEGFI和ATGFI分别提高了27.43%和39.91%。总体而言,该框架为在可再生能源占比高的能源系统中平衡成本效益和灵活性提供了全面的解决方案。