Multi-objective Optimal Scheduling of Integrated Energy Systems Based On Distributed Neurodynamic Optimization

被引：0
|
作者：
Huang B.-N. [1 ]
Wang Y. [1 ]
Li Y.-S. [2 ]
Liu X.-R. [1 ]
Yang C. [3 ]
机构：
[1] College of Information Science and Engineering, Northeastern University, Shenyang
[2] Department of Electrical and Computer Engineering, University of Denver, Denver
[3] Information and Communication Branch of State Grid Liaoning Electric Power Co., Ltd., Shenyang
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2022年 / 48卷 / 07期
关键词：
distributed multi-objective optimization; Integrated energy systems (IES); neural dynamics; non-convex; recurrent neural networks (RNNs);
D O I：
10.16383/j.aas.c200168
中图分类号：
学科分类号：
摘要：
This paper studies the distributed multi-objective optimized scheduling problem of integrated energy systems (IES) based on neurodynamic optimization. Firstly, IES component units (including load) are treated as independent decision-making entities, considering their fuel cost and emission cost, and taking into account the transmission loss between multi-energy devices, an IES multi-objective multi-objective optimized scheduling model is proposed, which can be described as a non-convex multi-objective optimization problem. Secondly, in order to solve such problems, a distributed multi-objective optimization algorithm based on the neurodynamics system is proposed. This algorithm is based on the dynamic weight neural network model, which can solve the inseparability inequality constraint problem. The algorithm has the advantages of small computational burden, fast convergence speed and easy hardware implementation. The simulation results show that the proposed method can simultaneously optimize the two conflicting objectives of cost and emission of the integrated energy systems, and obtain the whole Pareto front, which can effectively reduce the pollutant discharge and integrated operation costs of the integrated energy systems. © 2022 Science Press. All rights reserved.
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收藏
页码：1718 / 1736
页数：18
相关论文
共 26 条
  • [1] Tang Hao, Liu Chang, Yang Ming, Tang Bi-Qiang, Xu Dan, Lv Kai, Learning-based optimization of active distribution system dispatch in industrial park considering the peak operation demand of power grid, Acta Automatica Sinica, 47, 10, pp. 2449-2463, (2021)
  • [2] Jiang Zhao-Yu, Jia Qing-Shan, Guan Xiao-Hong, A review of multi-temporal-and-spatial-scale wind power forecasting method, Acta Automatica Sinica, 45, 1, pp. 51-71, (2019)
  • [3] Liu Jian-Gang, Yang Sheng-Jie, Distributed cooperative control of DC micro-grid systems with capacitive loads, Acta Automatica Sinica, 46, 6, pp. 1283-1290, (2020)
  • [4] Dey B, Roy S K, Bhattacharyya B., Solving multi-objective economic emission dispatch of a renewable integrated microgrid using latest bio-inspired algorithms, Engineering Science and Technology, an International Journal, 22, 1, (2019)
  • [5] Qiu H F, Gu W, Xu Y L, Yu W W, Pang G S, Liu P X., Trilevel mixed-integer optimization for two-stage microgrid dispatch with multi-uncertainties, IEEE Transactions on Power Systems, 35, 5, pp. 3636-3647, (2020)
  • [6] Chen C L, Lee T Y, Jan R M, Lu C L., A novel direct search approach for combined heat and power dispatch, International Journal of Electrical Power and Energy Systems, 43, pp. 766-773, (2012)
  • [7] Bahrami S, Sheikhi A., From demand response in smart grid toward integrated demand response in smart energy hub, IEEE Transactions on Smart Grid, 7, 2, pp. 650-658, (2016)
  • [8] Alkano D, Scherpen J M A, Chorfi Y., Asynchronous distributed control of biogas supply and multi-energy demand, IEEE Transactions on Automation Science and Engineering, 14, 2, (2017)
  • [9] Li Y S, Zhang H G, Liang X D, Huang B N., Event-triggered-based distributed cooperative energy management for multi-energy systems, IEEE Transactions on Industrial Informatics, 15, 4, pp. 2008-2022, (2019)
  • [10] Yi Z K, Xu Y L, Hu J F, Chow M Y, Sun H B., Distributed neurodynamic-based approach for economic dispatch in an integrated energy system, IEEE Transactions on Industrial Informatics, 16, 4, pp. 2245-2257, (2020)
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