Dynamic Optimal Energy Flow in the Heat and Electricity Integrated Energy System

Yao, Shuai; Gu, Wei; Lu, Shuai; Pan, Guangsheng; He, Di

doi:10.1109/tste.2020.2988682

Cited by 73 publications

(36 citation statements)

References 27 publications

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“…By controlling the mass flow rate at the primary side, the temperature of the outlet water at the secondary side can be kept constant. The relationship between the primary side mass flow and the outlet water temperature can be illustrated by Equation (18).…”

Section: Water-water Heat Exchanger (Wwhx)mentioning

confidence: 99%

“…Therefore, to obtain an optimal simulation time-step, the relationship between simulation time-step and simulation elapsed time and error should be fully discussed. The qualitative relationship is first mentioned in [18] and the effect of different time-steps on the number of iterations is briefly investigated in [19] while the authors in [20] state that the simulation error will decrease with the small simulation time-step, which is further used in the security check of district heating network. Lu et al focus on the solution accuracy of coupling equipment in [21] and Zheng et al study how to achieve the precise simulation of heat network in [22], which both emphasis on the importance of simulation time-step.…”

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: Water-water Heat Exchanger (Wwhx)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Guan

Zhou

et al. 2022

IET Renewable Power Gen

Self Cite

View full text Add to dashboard Cite

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“…PDE solvers lay the foundation of quasi-dynamic EFC and fall into three categories: 1) finite difference methods (FDMs); 2) analytical methods (AMs); 3) Semi-discrete methods (SDMs). In the first category, PDEs are converted into AEs by difference scheme with different accuracy and stability properties, which include: 1) implicit upwind scheme [7]; 2) the modified characteristic line methods [7], [8]; 3) Yao's scheme [9]. However, little attention is paid to the dissipative and dispersive error analysis of these difference schemes, which investigate whether the dacaying rate and propagating speed of solutions of difference equations match that of the original PDEs [10].…”

Section: Coefficientsmentioning

confidence: 99%

Non-iterative Calculation of Quasi-Dynamic Energy Flow in the Heat and Electricity Integrated Energy Systems

Yu¹,

Gu²

2021

Preprint

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Energy flow calculation plays a vital role in the analysis, operation, and control of heat and electricity integrated energy systems (HE-IESs). The quasi-dynamic energy flow models of HE-IESs are essentially partial differential equations (PDEs) (governing thermal inertia) and non-linear algebraic equations (AEs) (governing temperature, hydraulics, and power flow), which present a complicated system of partial differential algebraic equations (PDAEs). To solve the non-linear PDAEs in an efficient, accurate and robust way, we propose a novel noniterative semi-analytical method based on differential transformation (DT). Firstly, we define a new DT framework under per unit systems to avoid the numerical problems due to inconsistent base quantities of district heating systems and electrical power systems, and inappropriate base time. Secondly, we discretize the spatial derivative of the PDEs using a semi-discrete difference scheme which converts the PDEs into ordinary differential equations (ODEs). The scheme has total variation decreasing property and hence helps eliminate dissipative and dispersive errors that are often overlooked in the existing literature. Then, we employ the newly defined DT framework as the time-marching solver of the ODEs and further extend DT to non-linear AEs, deriving high-order explicit closed-form solutions of all the variables. In addition, to further improve the convergence and computation efficiency, we propose an adaptive time window strategy based on local truncation error estimation. Case studies in a real DHS and an HE-IES with 225 heating nodes and 118 electrical buses show that the proposed method outperforms the finite-differencebased Newton-Raphson iterative solver in efficiency, accuracy, and robustness.

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“…One is the "top-down" modeling method. It performs overall modeling and load identification and prediction for air conditioning load groups [2]. These models are widely used in large power grid electromechanical transient and small disturbance simulation and analysis.…”

Section: Introductionmentioning

confidence: 99%

BIM Building HVAC Energy Saving Technology Based on Fractional Differential Equation

Nie

Sedki

2022

Applied Mathematics and Nonlinear Sciences

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This article uses BIM technology to simulate HVAC in a commercial building. And use fractional differential equations to verify the set temperature, humidity, and other related parameters. The experiment takes an air-conditioned room model as the research object and uses the thermodynamic characteristics to establish a dynamic mathematical model of differential equations. Research shows that the multiple electric heat pump equipment aggregation groups discussed in this article are a good resource for user-side demand response. The air-side flow rate disturbance of the building HVAC has the greatest impact on the heat transfer of the surface cooler. As the air velocity increases, the rate of increase in heat exchange will decrease.

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Dynamic Optimal Energy Flow in the Heat and Electricity Integrated Energy System

Cited by 73 publications

References 27 publications

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

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

Non-iterative Calculation of Quasi-Dynamic Energy Flow in the Heat and Electricity Integrated Energy Systems

BIM Building HVAC Energy Saving Technology Based on Fractional Differential Equation

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