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Intelligent multi-agent system for DC microgrid energy coordination control

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper, an energy coordination control method based on intelligent multi-agent systems (MAS) is proposed for energy management and voltage control of a DC microgrid. The structure of the DC microgrid is designed to realize the mathematical modeling of photovoltaic cells, fuel cells and batteries. A two-layer intelligent MAS is designed for energy coordination control: grid-connection and islanding of a DC microgrid is combined with energy management of PV cells, fuel cells, loads and batteries. In the hidden layer and the output layer of the pro-posed neural network there are 17 and 8 neurons, respectively, and the “logsig” activation function is used for the neurons in the network. Eight kinds of feature quantities and 13 different actions are taken as the input and output parameters of the neural network from the micro-source and the load, and the as the control center agent’s decision-makers. The feasibility of the proposed intelligent multi-agent energy coordination control strategy is verified by MATLAB/Simulink simulation, and three types of examples are analyzed after increasing the load. The simulation results show that the proposed scheme exhibits better performance than the traditional approaches.
Rocznik
Strony
741--748
Opis fizyczny
Bibliogr. 21 poz., rys., tab.
Twórcy
  • Faculty of Electrical, Biomedical and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
autor
  • Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
  • Faculty of Electrical, Biomedical and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
autor
  • Faculty of Electrical, Biomedical and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Bibliografia
  • [1] N. Hatziargyriou, H. Asano, R. Iravani, and C. Marnay, “Micro-grids,” IEEE Power and Energy Magazine,5(4) 78‒94, July–August 2007.
  • [2] P. Komarnicki, “Energy Storage Systems: Power Grid and Energy Market Use Cases,” Archives of Electrical Engineering, Journal of PAS, 65(3) 495‒511, Sep. 2016.
  • [3] D. Gao, J. Jiang, and S. Qiao, “Comparing the Use of Two Kinds of Droop Control Under Microgrid Islanded Operation Mode,” Archives of Electrical Engineering, Journal of PAS, 62(2) 321‒331, Jun. 2013.
  • [4] R. Olfati-Saber, J.A. Fax, and R. M. Murray, “Consensus and Cooperation in Networked Multi-Agent Systems,” Proceedings of the IEEE,95(1) 215‒233, Jan. 2007
  • [5] A.H. Sayed, “Adaptive Networks,” Proceedings of the IEEE,102(4) 460‒497, Apr. 2014.
  • [6] J. Chen and A.H. Sayed, “Diffusion Adaptation Strategies for Distributed Optimization and Learning Over Networks,” IEEE Transactions on Signal Processing,60(8) 4289‒4305, Aug. 2012.
  • [7] J. Chen and A. H. Sayed, “Distributed Pareto Optimization via Diffusion Strategies,” IEEE Journal of Selected Topics in Signal Processing,7(2) 205‒220, Apr. 2013.
  • [8] S.-Y. Tu and A. H. Sayed, “On the Influence of Informed Agents on Learning and Adaptation Over Networks,” IEEE Transactions on Signal Processing,61(6) 1339‒1356, Mar. 2013.
  • [9] V. Salehi, A. Mohamed, A. Mazloomzadeh, and O. A. Moham-med, “Laboratory-Based Smart Power System, Part I: Design and System Development,” IEEE Transactions on Smart Grid,3(3) 1394‒1404, Sept. 2012.
  • [10] V. Salehi, A. Mohamed, A. Mazloomzadeh, and O.A. Moham-med, “Laboratory-Based Smart Power System, Part II: Control, Monitoring, and Protection,” IEEE Transactions on Smart Grid,3(3) 1405‒1417, Sept. 2012.
  • [11] W. Shi, X. Xie, C.-C. Chu, and R. Gadh, “Distributed Optimal Energy Management in Microgrids,” IEEE Transactions on Smart Grid,6(3) 1137‒1146, May 2015.
  • [12] A. Parisio, E. Rikos, and L. Glielmo, “A Model Predictive Control Approach to Microgrid Operation Optimization,” IEEE Transactions on Control Systems Technology,22(5) 1813‒1827, Sept. 2014.
  • [13] M.B. Shadmand and R.S. Balog, “Multi-Objective Optimization and Design of Photovoltaic-Wind Hybrid System for Community Smart DC Microgrid,” IEEE Transactions on Smart Grid,5(5) 2635‒2643, Sept. 2014.
  • [14] L. Meng, T. Dragicevic, J.M. Guerrero, and J.C. Vasquez, “Dynamic Consensus Algorithm based Distributed Global Efficiency Optimization of a Droop Controlled DC Microgrid,” IEEE International Energy Conference (ENERGYCON) pp. 1276‒1283, 13‒16 May 2014.
  • [15] Y. Xu and Z. Li, “Distributed Optimal Resource Management based on the Consensus Algorithm in a Microgrid,” IEEE Trans-actions on Industrial Electronics,62(4) 2584‒2592, Apr. 2015.
  • [16] G. Hug, S. Kar, and C. Wu, “Consensus + Innovations Approach for Distributed Multiagent Coordination in a Microgrid,” IEEE Transactions on Smart Grid,6(4) 1893‒1903, July 2015.
  • [17] W. Chen, A.M. Bazzi, J. Hare, and S. Gupta “Real-Time Integrated Model of a Micro-Grid with Distributed Clean Energy Generators and their Power Electronics,” Applied Power Electronics Conference and Exposition (APEC), IEEE, 20‒24 Mar. 2016.
  • [18] C.D. Fuentes, C.A. Rojas, H. Renaudineau, S. Kouro, M.A. Perez, and T. Meynard, “Experimental Validation of a Single DC Bus Cascaded H-Bridge Multilevel Inverter for Multistring Photo-voltaic Systems,” IEEE Transactions on Industrial Electronics64(2) 930‒934, Feb. 2017.
  • [19] L. Valverde, C. Bordons, and F. Rosa, “Integration of Fuel Cell Technologies in Renewable-Energy-Based Microgrids Optimizing Operational Costs and Durability,” IEEE Transactions on Industrial Electronic,63(1) 167‒177, Jan. 2016.
  • [20] B. Farzanegan, S.D. Banadaki, and M.-B. Menhaj, “Direct Arti-\ficial Neural Network Control of Single Link Flexible Joint,” 4th International Conference on Control, Instrumentation, and Automation (ICCIA), IEEE,27‒28 Jan. 2016.
  • [21] M. Elsied, A. Oukaour, H. Gualous, and R. Hassan, “Energy Management and Optimization in Microgrid System based on Green Energy,” Energy, Elsevier, 84(C) 139‒151, 2015
Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
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