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Research on exergy flow analysis and operation optimization of integrated energy system

火用 可用能 节点(物理) 流量(数学) 功率(物理) 适应性 能量流 工艺工程 环境科学 工程类 能量(信号处理) 计算机科学 联轴节(管道) 数学优化 分布(数学) 数学 热力学第二定律 功率流研究 流出 能源消耗 数学模型 参数统计 电力系统
作者
Peng Li,Xinwu Lei,Haipeng Yin,Hetao Su,Chang Zhou,Zhao Wensheng,Zhonghe Han
出处
期刊:Journal of Renewable and Sustainable Energy [American Institute of Physics]
卷期号:17 (5)
标识
DOI:10.1063/5.0292180
摘要

The integrated energy system (IES) is of great importance for increasing energy efficiency and attaining sustainable development. Currently, there are many studies on the IES, while few of them pay close attention to the variation of exergy in the IES. Additionally, the exergy flow distribution of the system cannot be determined using the classic black box model, which only analyses the input and output of exergy. A power flow model for an IES is established based on the multi-energy coupling law. Also, the Newton−Raphson method is adopted to solve the power flow model, then the power flow distribution of the IES is obtained. Moreover, an exergy flow analysis approach for IES is proposed to address the inadequacies of conventional exergy analysis. To confirm the accuracy and adaptability of the suggested method, an example is constructed in accordance with the usual IES structure. A node in the calculation system is taken as an example. The sum of the exergy flows into the node is 548.22 kW, the sum of the exergy flows out of the node is 544.2 kW, and the exergy loss is 4.02 kW. The exergy loss is tantamount to the gap between the exergy flow of input and outflow nodes, which is consistent with Kirchhoff's law (KL). Similar conclusions can be derived from the analysis of other nodes in the system. Simulation results indicate that the proposed method effectively characterizes the distribution of exergy flow within an IES. Overall and local exergy efficiency of the system can both be expressed using the exergy flow distribution. To perform multi-objective optimization for the system, exergy efficiency and conventional indicators are combined. This method can evaluate the system performance more comprehensively, reveal the system defects or inefficient links that have not been detected by traditional indicators, and provide ideas for innovative optimization schemes.

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