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Recurrent neural network for combined economic and emission dispatch

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Abstract

Recently, the combined economic and emission dispatch (CEED) problem, which aims to simultaneously decrease fuel cost and reduce environmental emissions of power systems, has been a widespread concern. To improve the utilization efficiency of primary energy, combined heat and power (CHP) units are likely to play an important role in the future. The goal of this study is to propose an approach to solve the CEED problems in a CHP system which consists of eight power generators (PGs), two CHP units and one heat only unit. Owing to the existence of power loss in power transmission line and the non-convex feasible region of CHP units, the proposed problem is a nonlinear, multi-constraints, non-convex multi-objectives (MO) optimization problem. To deal with it, a recurrent neural network (RNN) combined with a novel technique is developed. It means that the feasible region is separated into two convex regions by using two binary variables to search for different regions. In the frame of the neurodynamic optimization, existence and convergence of the dynamic model are analyzed. It shows that the convergence solution obtained by RNN is the optimal solution of CEED problem. Numerical simulation results show that the proposed algorithm can generate solutions efficiently.

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Acknowledgements

This work was supported in part by the Natural Science Foundation of China under Grant 61403313, Grant 61773320, in part by the Fundamental Research Funds for the Central Universities under Grant XDJK2016B017, in part by the China Post-Doctoral Science Foundation under Grant 2016M600144.

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Authors and Affiliations

  1. Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China

    Ting Deng & Xing He

  2. School of Automation, Huazhong University of Science and Technology, Hubei, 430074, China

    Zhigang Zeng

Authors
  1. Ting Deng
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  2. Xing He
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  3. Zhigang Zeng
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Corresponding author

Correspondence to Xing He.

Appendix

Appendix

Table 4 Data for 10-PG units [39, 40]
Full size table
Table 5 Data for the two incorporated CHP units [39, 40]
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Table 6 Data for the one incorporated heat only unit [39, 40]
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The transmission loss formula coefficients of ten-unit system are [41]:

$$B=\left[ {\begin{array}{l} \text{0.000049 0.000014 0.000015 0.000015 0.000016 0.000017 0.000017 0.000018 0.000019 0.000020} \\ \text{0.000014 0.000045 0.000016 0.000016 0.000017 0.000015 0.000015 0.000016 0.000018 0.000018} \\ \text{0.000015 0.000016 0.000039 0.000010 0.000012 0.000012 0.000014 0.000014 0.000016 0.000016} \\ \text{0.000015 0.000016 0.000010 0.000040 0.000014 0.000010 0.000011 0.000012 0.000014 0.000015} \\ \text{0.000016 0.000017 0.000012 0.000014 0.000035 0.000011 0.000013 0.000013 0.000015 0.000016} \\ \text{0.000017 0.000015 0.000012 0.000010 0.000011 0.000036 0.000012 0.000012 0.000014 0.000015} \\ \text{0.000017 0.000015 0.000014 0.000011 0.000013 0.000012 0.000038 0.000016 0.000016 0.000018} \\ \text{0.000018 0.000016 0.000014 0.000012 0.000013 0.000012 0.000016 0.000040 0.000015 0.000016} \\ \text{0.000019 0.000018 0.000016 0.000014 0.000015 0.000014 0.000016 0.000015 0.000042 0.000019} \\ \text{0.000020 0.000018 0.000016 0.000015 0.000016 0.000015 0.000018 0.000016 0.000019 0.000044} \end{array}} \right] $$

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Deng, T., He, X. & Zeng, Z. Recurrent neural network for combined economic and emission dispatch. Appl Intell 48, 2180–2198 (2018). https://doi.org/10.1007/s10489-017-1072-3

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