Principles of Smart Grids on the generation electrical and thermal energy and control of heat consumption within the District Heating Networks

Neuman, P.; Pokorný, Michal; Weiglhofer, W.

doi:10.3182/20140824-6-za-1003.00553

Cited by 7 publications

(2 citation statements)

References 6 publications

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“…The behavior and characteristics of the DH network system are influenced by its various parameters [4]. The dynamic features of DH consumers and networks usually influence the operational effect of the DH system heavily because of the time delays in the DH system [5].…”

Section: Introductionmentioning

confidence: 99%

An Influencing Parameters Analysis of District Heating Network Time Delays Based on the CFD Method

Zhao

2019

Energies

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With the expansion of cities, district heating (DH) networks are playing an increasingly important role. The energy consumption due to the time delay caused by the transport of the medium in the DH network is enormous, especially in large networks. The study of time delay is necessary for the operation and optimization of DH networks. Compared with previous studies of constant flow rates and ideal pipeline (without regard to branches, elbows, variable pipe diameters, etc), this paper simulates a DH network in Tianjin University, China, by establishing the actual engineering model in a Computational Fluid Dynamics (CFD) method to analyze the time delay. The CFD model has great advantages in terms of computational cost and application range compared to theoretical calculations. The peak-valley method was used to verify the correctness of the time delay simulation model. Results show that the time delay calculated by the CFD model is consistent with the actual time delay obtained from the measured data. Based on this model, the parameters that affect the time delay are furtherly analyzed. Four key parameters, including flow rate, pipe length, pipe diameter, and water supply temperature are summarized. The results show that the flow rate, pipe length and pipe diameter have a great influence on the time delay of the DH network, while the temperature has little effect on the time delay. The time delay of the DH network system has a significant impact and can provide services for optimal control of the system.

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Section: Introductionmentioning

confidence: 99%

An Influencing Parameters Analysis of District Heating Network Time Delays Based on the CFD Method

Zhao

2019

Energies

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“…Smart Grids (SGs) can be deemed as rather complex, large systems, in which smart control is one of the necessary conditions for SG realization (Neuman, 2011(Neuman, , 2012(Neuman, , 2014. In late 1990's, the main issues related to Distributed Grids (DG), nowadays referred to as MicroGrids, were widely considered by the working groups of the International Council on Large Electric Systems (CIGRE) and the International Conference and Exhibition on Electricity Distribution (CIRED) in their review reports (Chowdhury et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Study of Closed-Loop Model Reference Adaptive Control of Smart MicroGrid with QNU and Recurrent Learning

Malý¹,

Vesely²,

Beneš³

et al. 2017

AMS

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An adaptive quadratic polynomial neural unit (QNU) controller for optimization of a conventional Smart Microgrid control loop is studied and proposed. The parameters associated with the studied grid plants are considered to be known in this study, with the fact that the load is unknown and time-variant. A sample-by-sample real-time recurrent learning algorithm of an additional QNU controller is derived, with its performance tested and discussed as a result of this paper.

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Combination of air-based high-temperature heat and power storage system with an Organic Rankine Cycle for an improved electricity efficiency

Arabkoohsar

2020

Applied Thermal Engineering

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Principles of Smart Grids on the generation electrical and thermal energy and control of heat consumption within the District Heating Networks

Cited by 7 publications

References 6 publications

An Influencing Parameters Analysis of District Heating Network Time Delays Based on the CFD Method

An Influencing Parameters Analysis of District Heating Network Time Delays Based on the CFD Method

Study of Closed-Loop Model Reference Adaptive Control of Smart MicroGrid with QNU and Recurrent Learning

Combination of air-based high-temperature heat and power storage system with an Organic Rankine Cycle for an improved electricity efficiency

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