Application of new optimisation model for multi-chiller system consumption

Erlin, Tian; Ebadi, Abdol Ghaffar; Mavaluru, Dinesh; Kadyrov, Marsel; Ponomarev, Andrey; Druzhinin, Zumrat

doi:10.1080/01430750.2019.1621201

Cited by 1 publication

(2 citation statements)

References 25 publications

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“…However, the system energy performance showed the opposite trend. Saeedi et al [20] and Tian et al [23] used this method to optimize chiller loading with uncertain cooling demand. According to their results, the energy consumption of chillers with the most conservative solution was 8.5% higher than that without uncertainty.…”

Section: Robust Optimal Control (Roc)mentioning

confidence: 99%

“…For example, by optimizing cooling water temperature, Huang et al [14] reduced the total system energy demand by 9.7%. Secondly, the optimization can help to enhance the control performance (e.g., stability and robustness) [17][18][19][20][21][22][23]. For example, using a multiplexed optimization method, Sun et al [18] improved the system control stability with a 50.0% reduction in tracking error of setpoints.…”

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confidence: 99%

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A review of optimization approaches for controlling water-cooled central cooling systems

Jia

Wei

Liu

2021

Building and Environment

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Section: Robust Optimal Control (Roc)mentioning

confidence: 99%

mentioning

confidence: 99%

A review of optimization approaches for controlling water-cooled central cooling systems

Jia

Wei

Liu

2021

Building and Environment

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Modelling multi-stage energy optimization in the smart hub energy system with hybrid demand management and local supply by Electric Vehicles

jafari,

Najafi,

Pirkolachahi

et al. 2024

Preprint

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The interdependence of energy with social sustainability, economic viability, and environmental impact has led energy operators to conduct thorough investigations into energy scheduling issues. These investigations have prompted the development of new strategies for energy generation, such as the utilization of energy carriers including electrical, gas, and thermal loads. This article presents a model for multi-stage energy optimization in a smart system like energy hub, with a focus on hybrid demand management at the consumer level. The proposed multi-stage energy optimization approach aims to address demand management in the upper stage, while also minimizing energy generation costs and emission pollution in the lower stage. The hybrid demand management is proposed considering two demand response programs like optimal shifting and interrupting of the thermal and electrical demands. The modeling of emission pollution and energy generation costs in the lower stage is done by multi-criteria optimization. As well, local supply strategy is implemented via electric vehicles (EVs) in lower stage by consumers to supply self-electrical demand. The proposed two-stage multi-criteria optimization by using GAMS software is solved. On the other hand, multi-criteria optimization is handled by a modified epsilon-constraint method and max-min fuzzy decision approach in the lower stage of optimization. To validate the mentioned approach, three scenarios are compared, and the results clearly illustrate the energy optimization for minimizing the energy costs and emissions achieved through the presented approach.

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Application of new optimisation model for multi-chiller system consumption

Cited by 1 publication

References 25 publications

A review of optimization approaches for controlling water-cooled central cooling systems

A review of optimization approaches for controlling water-cooled central cooling systems

Modelling multi-stage energy optimization in the smart hub energy system with hybrid demand management and local supply by Electric Vehicles

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