Development of a fuel management model for a multi-source district heating system under multi-uncertainty and multi-dimensional constraints

Fu, Dianzheng; Zhuang, Zheng; Gui, Jun; Xiao, Rui; Huang, Guohe; Li, Y. P.

doi:10.1016/j.enconman.2017.10.002

Cited by 12 publications

(3 citation statements)

References 41 publications

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“…Moretti et al [6] investigate the issues concerning multisource energy systems in general by proposing a robust optimization method for their day-ahead scheduling. Nonetheless, comprehensive studies regarding the control of these networks in the framework of power grid flexibility are still unavailable or at an early stage [7].…”

Section: Introductionmentioning

confidence: 99%

Robust control of a cogeneration plant supplying a district heating system to enable grid flexibility

et al. 2021

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In recent years, the flexibility of energy systems has become essential due to the growing penetration of renewable energy sources. The producers and consumers can enhance this flexibility by enabling a given amount of power that they can produce or consume in every condition. This is made available to the grid operator to globally optimize the dispatch management and to stabilize the grid. However, this can interfere with the operation of production units such as cogeneration plants, which also have to meet thermal demand. Therefore, producers and consumers require smart controllers to comply with grid operator requests at any time. This paper proposes a robust control strategy based on Model Predictive Control, which manages distribution networks and production plants by considering the uncertainty of the requirements for flexibility from the grid operator. The simulation case study is the district heating network of a school complex supplied by a Combined Heat and Power plant and a Thermal Energy Storage tank. The robustness of the proposed optimization is investigated by simulating several scenarios with different degrees of uncertainty about the request for electricity from the grid operator. The results show that the plant operator is able to comply with the electricity requirements to different extents depending on the degree of uncertainty and on system design choices. These considerations make it possible to improve the plant design and production planning from the perspective of grid flexibility.

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Section: Introductionmentioning

confidence: 99%

Robust control of a cogeneration plant supplying a district heating system to enable grid flexibility

et al. 2021

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“…Such methods exploit Artificial Intelligence (AI) and statistical methods to learn the degradation patterns of devices and predict the remaining useful life (RUL) of the device [7]. Data-driven methods can be applied to the scenario which is difficult to build analytical model [8], they are effective to transform noisy data into logical information for remaining useful life estimation. The proposed EMD-TCN framework in this paper is a kind of data-driven method.…”

Section: Introductionmentioning

confidence: 99%

Empirical Mode Decomposition and Temporal Convolutional Networks for Remaining Useful Life Estimation

Yang

Yao

et al. 2019

Int J Parallel Prog

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Remaining useful life (RUL) prediction plays an important role in guaranteeing safe operation and reducing maintenance cost in modern industry. In this paper, we present a novel deep learning method for RUL estimation based on time empirical mode decomposition (EMD) and temporal convolutional networks (TCN). The proposed framework can effectively reveal the non-stationary characteristics of bearing degradation signals and acquire time-series degradation signals which namely intrinsic mode functions through empirical mode decomposition. Furthermore, the feature information is used as the input to convolution layer and trained by TCN to predict remaining useful life. The proposed EMD-TCN model structure maintains a superior result compared to several state-of-the-art convolutional algorithms on public data sets. Experimental results show that the average score of EMD-TCN model is improved by 10-20% than traditional convolutional algorithms.

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“…Such methods exploit Artificial Intelligence (AI) and statistical methods to learn the degradation patterns of devices, and predict the Remaining Useful Life (RUL) of the device [8]. Data-driven methods can be applied to the scenario which is uneasy to build analytical model [9].…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Estimation by Empirical Mode Decomposition and Ensemble Deep Convolution Neural Networks

Yao

Yang

et al. 2019

2019 IEEE International Conference on Prognostics and Health Management (ICPHM)

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Bearing remaining useful life (RUL) prediction plays a key role in guaranteeing safe operation and reducing maintenance costs. In this paper, we present a novel deep learning method for RUL estimation approach through time Empirical Mode Decomposition (EMD) and Convolutional Neural Network (CNN). EMD can reveal the nonstationary property of bearing degradation signals effectively. After acquiring time-series degradation signals, namely Intrinsic Mode Functions (IMF), we can utilize the featured information as the input of Convolution layer of models. Here, we introduce an EMD-CNN model structure, which keeps the global and local information synchronously compared to a traditional CNN. In order to get a more accurate prediction, an ensemble model with several weighting methods are proposed, where the experiment indicates an improvement of performance.

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Development of a fuel management model for a multi-source district heating system under multi-uncertainty and multi-dimensional constraints

Cited by 12 publications

References 41 publications

Robust control of a cogeneration plant supplying a district heating system to enable grid flexibility

Robust control of a cogeneration plant supplying a district heating system to enable grid flexibility

Empirical Mode Decomposition and Temporal Convolutional Networks for Remaining Useful Life Estimation

Remaining Useful Life Estimation by Empirical Mode Decomposition and Ensemble Deep Convolution Neural Networks

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