A novel dynamic simulation approach for Gas-Heat-Electric coupled system

Guan, Aobo; Zhou, Suyang; Gu, Wei; Liu, Zhong; Liu, Hengmen

doi:10.1016/j.apenergy.2022.118999

Cited by 11 publications

(6 citation statements)

References 23 publications

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“…This paper investigates the water heat system in the quality adjustment mode where the mass flow in the pipeline is constant and the water supply temperature at the heat source is adjustable to meet the heating demand. The model and solving strategy are described in [17], which is introduced briefly in this section. For more details, the reader is referred to [17].…”

Section: Model Of Heat Networkmentioning

confidence: 99%

“…The model and solving strategy are described in [17], which is introduced briefly in this section. For more details, the reader is referred to [17].…”

Section: Model Of Heat Networkmentioning

confidence: 99%

“…As a result, the mass is uniformly distributed on both the sides of the entire device and there will not be any delay effect. The model of the heat exchanger can be represented by Equations ( 14) to (17).…”

Section: Water-water Heat Exchanger (Wwhx)mentioning

confidence: 99%

“…This paper adopts EB as the heat source, the combined heat plant (CHPs) and heat pumps can replace it under different energy efficiency ratio (EER) requirements, which will not influence the result of this paper. As the mainstream dynamic simulation method of IES, which is in detail introduced in [17], an event-driven strategy based on the decomposition method is first adopted to divide the HECS into several submodules. Then the simulation sequence is determined based on the events and once a module is solved, the parameters will be passed to the next module.…”

Section: Simulation Time-step Optimal Determinationmentioning

confidence: 99%

“…Reference [15] discretised the PDE of the heat network via the Wendorff difference scheme while researchers in [16] combined the difference method with the system decomposition method to further reduce the computational complexity. In summary, the mainstream idea for the simulation of highly coupled IES is to decouple the whole system into submodules and discretise the PDE with an appropriate difference scheme, which is introduced in [17] in detail. In this method, the accuracy and speed of the simulation are mainly dependent on differential time-step and a suitable time-step is essential for the simulation efficiency.…”

Section: Introductionmentioning

confidence: 99%

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A data‐driven time‐step determination approach for dynamic simulation of heat‐electric coupled system

Guan

Zhou

et al. 2022

IET Renewable Power Gen

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Dynamic simulation of heat-electric coupled system (HECS) can accurately describe the operating condition of networks and equipment of the system, where the space-time step plays a crucial role in the simulation accuracy and speed. To optimally determine the simulation space-time step, this paper identifies the main influencing parameters for simulation elapsed time by distance correlation (DC) analysis and establishes a simulation time prediction model with stepwise regression (SR) method. Meanwhile, the quadratic curve fitting (QCF) method is adopted to obtain the simulation error prediction model. A simulation space-time step optimal determination model considering the specific simulation requirements is further introduced to improve the simulation efficiency based on the aforementioned models. To validate the proposed methodologies, comprehensive analysis of various study cases on a 20-node testbed is presented. The results demonstrate that the average error in elapsed time and error prediction by the proposed model is merely 0.2426 s and 4.84% compared with the real data.

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Section: Model Of Heat Networkmentioning

confidence: 99%

“…The model and solving strategy are described in [17], which is introduced briefly in this section. For more details, the reader is referred to [17].…”

Section: Model Of Heat Networkmentioning

confidence: 99%

Section: Water-water Heat Exchanger (Wwhx)mentioning

confidence: 99%

Section: Simulation Time-step Optimal Determinationmentioning

confidence: 99%