Integrated energy systems (IES) are under development to accommodate the widespread penetration of renewable energy into power systems. The universal utilization of coupling components makes the connection between power grids and heat grids even closer. This paper applies energy flow calculations to planning problems and explores a method for selecting coupling components in the integrated electrical and heating systems. This process is based on the analysis of energy flow distribution and the comprehensive indexes, which include operation cost and the penalty cost of wind curtailment and solar energy curtailment. Besides, a selection method of coupling components installation locations based on energy flow calculations is provided. At last, an IES with 5 electrical buses and 5 heating nodes, and a 9-bus electrical power system with a 31-node heating transmission system are chosen to study the specific application problems in detail as well as to elucidate the effectiveness of the proposed method. It turned out that the relevant coupling components and their positions can be selected according to the indexes obtained by integrated electrical-hydraulic-thermal calculation. This proposed method is conducive to the planning of integrated energy systems.

Energy Flow

Electric potential energy

Thermal energy

10.17775/cseejpes.2019.00420

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Citations (3)

An improved convolutional neural network with load range discretization for probabilistic load forecasting

Energy (2020)

Qian Huang Jinghua Li Mengshu Zhu

Probabilistic Forecasting

10.1016/j.energy.2020.117902

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Citations (57)

Evaluating the accuracy and reliability of compound‐specific carbon isotopic analysis using gas chromatography‐combustion‐isotope ratio mass spectrometry with the addition of a reduction furnace

Rapid Communications in Mass Spectrometry (2022)

Yunning Cao Hu Liu Jing Hu Zheng Wang Mengshu Zhu

Gas chromatography-combustion-isotope ratio mass spectrometry (GC/C/IRMS) is widely used for compound-specific carbon isotopic analysis. However, current isotopic analysis systems utilize the GC IsoLink combustion reactor, and independent reduction furnaces are not implemented. Therefore, whether this limitation in furnace use affects the precision of compound-specific carbon isotopic analysis needs to be evaluated.We attempted to add a separate reduction furnace to the GC IsoLink interface and compared the δ13 C values of n-alkanes (including C and H elements), fatty acid methyl ester (including C, H, and O elements), caffeine (USGS61 and USGS62, including C, H, O, and N elements), and 9-ethylcarbazole (including C, H, and N elements) before and after the addition of the reduction furnace using the GC IsoLink combustion reactor.For n-alkanes and fatty acid methyl esters, the δ13 C differences between the measured values and their standard values were basically falling within 0.5‰ whether or not an independent reduction furnace was added. However, for the nitrogen-containing compounds (caffeine and 9-ethylcarbazole), the δ13 C differences between the measured values and their standard values were much larger without an independent reduction furnace (1.0-3.71‰ for USGS61, 1.78-2.19‰ for USGS62, and 0.39-1.13‰ for 9-ethylcarbazole) than with a reduction furnace (-0.31-0.68‰ for USGS61, -0.44-0.06‰ for USGS62, and -0.04-0.25‰ for 9-ethylcarbazole).The addition of an independent reduction furnace had no significant effect on the δ13 C of n-alkanes and fatty acid methyl esters, but it had a significant effect on the δ13 C of nitrogen-containing compounds. It is suggested that GC IsoLink needs an independent reduction furnace to effectively eliminate the interference of NOx on CO2 isotopic determination to improve the accuracy of δ13 C for nitrogen-containing compounds.

Isotope-ratio mass spectrometry

Carbon fibers

Isotope Analysis

10.1002/rcm.9450

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Citations (0)

Gradient descent iterative method for energy flow of integrated energy system considering multiple modes of compressors

Energy Conversion and Management (2020)

Jinghua Li Yujin Huang Mengshu Zhu

Energy Flow

Constant (computer programming)

10.1016/j.enconman.2020.112534