The Effects of the Model Predictive Controller Compared to the LQR Controller on the Optimal Distribution of the Suitable Load and the Creation of Balance in Microgrids

Sayedi, I.; Fatehi, M.H.; Simab, M.

doi:10.22061/jecei.2021.8194.489

Document Type : Review paper

Authors

¹ Department of Electrical Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran

² Department of Electrical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran

https://doi.org/10.22061/jecei.2021.8194.489

Abstract

Background and Objectives: Distributed generation (DG) sources are modeled using an ideal DC voltage source connected to the microgrid via voltage source converters (VSCs). Model predictive control presents a distinct method for energy processing.
Methods: In this method, the electric power converter is considered a power amplifier with a discrete and nonlinear structure. Therefore, unlike linear control methods, the discrete and nonlinear nature of the converter is considered in this method. In this paper, the distributed model predictive controller was selected from among different methods of load allocation among DG sources due to its more advantages compared to the linear quadratic regulator (LQR) controller.
Results: It has been Proposed that we could obtain better results in predictive control, utilizing similarity transform in the state matrix and its modification. In this research, all the simulations have been performed in the MATLABSimpower environment of MATLAB software.
Conclusion: Moreover, to demonstrate the superior performance of the model predictive controller compared to the LQR controller, both performance modes of the microgrid, namely the grid-connected and islanding modes, have been considered.

Keywords

20.1001.1.23223952.2022.10.1.19.2

Main Subjects

Distributed systems

Open Access

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit: http://creativecommons.org/licenses/by/4.0/

Publisher’s Note

JECEI Publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Publisher

Shahid Rajaee Teacher Training University

References

[1] K. Rajesh, S. Dash, R. Rajagopa, R. Sridhar, "A review on control of ac microgrid," Renewable Sustainable Energy Rev., 71: 814-819, 2019.

[2] M.Liangyu, Y. Kwang, G. Yinping , "An improved predictive optimal controller with elastic search space for steam temperature control of large-scale supercritical power unit," in Proc. 51st IEEE Conference on Decision and Control (CDC), 2012 .

[3] W.Tong, G. Huijun, Q. Jianbin, "A combined adaptive neural network and nonlinear model predictive control for multirate networked industrial process control," IEEE Trans. Neural Networks Learn. Syst., 27(2): 416-425, 2015.

[4] P. Buduma, G. Panda, "LQR based control method for grid connected and islanded DG system," Int. J. Emerging Electr. Power Syst., 19(5), 2018.

[5] M. Alhasheem, F. Blaabjerg, P. Davari, "Performance assessment of grid forming converters using different finite control set model predictive control (FCS-MPC) algorithms," Appl. Sci., 9(17): 1-14, 2019.

[6] W.R. Sultana, S.K. Sahoo, S. Sukchai, S. Yamuna, D.A .Venkatesh, "Review on state of art development of model predictive control for renewable energy applications," Renew Sustain Energy Rev., 76: 391-406, 2017.

[7] C. Jiang, G. Du, F. Du, Lei Y, "A fast model predictive control with fixed switching frequency based on virtual space vector for three-phase inverters," in Proc of the IEEE International Power Electronics and Application Conference and Exposition, PEAC, Shenzhen, China, 2018.

[8] M. Aguirre, S. Kouro, C.A. Rojas, J. Rodriguez, J. Leon, "Switching frequency regulation for FCS-MPCBased on a period control approach," IEEE Trans. Ind. Electron, 65(7): 5764-5773, 2018.

[9] H. Yang, R. Tu, K. Wang, J. Lei, W. Wang, S. Feng, C.A. Wei, "Hybrid predictive control for a current source converter in an aircraft DC microgrid," Energies, 12(21): 4025, 2019.

[10] T. Qingfang, X. Ruiqi, "Integral sliding mode-based model predictive current control with low computational amount for three-level neutral-point-clamped inverter-Fed PMSM drives," IEEE Trans. Energy Convers., 35: 2249–2260, 2020.

[11] W. Wang, C. Liu, S. Liu, H. Zhao, "Model predictive torque control for dual three-phase PMSMs with simplified deadbeat solution and discrete space-vector modulation," IEEE Trans. Energy Convers., 36(2): 1491–1499, 2021.

[12] T. Dragiˇcevi´c, X. Lu, J.C. Vasquez, "DC microgrids-part II: A review of power architectures, applications, and standardization issues," IEEE Trans. Power Electron., 31: 3528–3549, 2016.

[13] T. Dragiˇcevi´c, X. Lu, J.C. Vasquez, J.M. Guerrero, "DC microgrids-part I: A review of control strategies and stabilization techniques," IEEE Trans. Power Electron., 31: 4876–4891, 2016.

[14] T. Dragiˇcevi´c, X. Lu, J.C. Vasquez, "DC microgrids-part II: a review of power architectures, applications, and standardization issues," IEEE Trans. Power Electron., 31: 3528–3549, 2016.

[15] N. Melanie, C. Jones, M. Morari, "Real-time suboptimal model predictive control using a combination of explicit MPC and online optimization," IEEE Control Syst. Soc., 56(7): 1524-1534, 2011.

[16] M.Khooban, N. vafaman, T. Niknam, T. Dragicevic, F. Blaabjerg, "Model-predictive control based on Takagi-Sugeno fuzzy model for electrical vehicles delayed model," IET Electr. Power Appl., 11(5): 918–934, 2017.

[17] M. Gillespie, C. Best, E. Townsend, et al., "Learning nonlinear dynamic models of soft robots for model predictive control with neural networks ," in Proc. IEEE International Conference on Soft Robotics (RoboSoft), 2018.

[18] G. Romos, E. Lucas, C. Fernández, "Model predictive control performance optimized by genetic algorithm," Model Predictive Control, Springer , 93-108, 2019.

[19] G. Pathak, B. Singh, B. Panigrahi, "Three-phase four-wire wind-diesel based microgrid," Power Systems (ICPS), in Proc. IEEE 6th International Conference on Power Systems (ICPS), 2016.

[20] X. Luo, J. Wang, M. Dooner, j. Clarke, "Overview of current development in electrical energy storage technologies and the application potential in power system operation ,” Appl. Energy, 137: 511-536, 2015.

[21] U. Krishnan, S. Mija, E. Cheriyan, "State space modelling, analysis and optimization of microgrid droop controller," in Proc. 6th International Conference on Computer Applications In Electrical Engineering-Recent Advances (CERA), 2017.

[22] J. Lee, H. Moon, K. Lee, "An improved finite set model predictive control based on discrete space vector modulation methods for grid-connected three-level voltage source inverter," IEEE J. Emerging Sel. Top. Power Electron., 6(4): 1744–1760, 2018.

[23] J. Rohten, J. Muñoz, E. Pulido, J. Silva, "Very low sampling frequency model predictive control for power converters in the mediumand high-power range applications ," Energies, 14(1): 199, 2021.

[24] M. Ghanbarian, M. Nayeripour, A. Rajaei, F. Jamshidi, "Model predictive control of distributed generation micro-grids in island and grid connected operation under balanced and unbalanced conditions ," J. Renewable Sustainable Energy, 9(4), 2017.

[25] T. Morstyn, B. Hredzak, V. Agelidis, "Cooperative multi-agent control of heterogeneous storage devices distributed in a dcmicrogrid," IEEE Trans. Power Syst., 31: 2974–2986, 2016.

[26] Y. Shan, H. Jiefeng, L. Zilin, M. Josep, "A model predictive control for renewable energy based AC microgrids without any PID regulators," IEEE Trans. Power Electron., 33(11): 9122–9126, 2018.

[27] Y. Wang, X. Wang, Y. Xie, F. Wang, "deadbeat model predictive torque control with discrete space vector modulation for PMSM drivers," IEEE Trans. Ind. Electron., 64(5): 3537–3547, 2017.

[28] T. Elsayed, A. Mohamed, O. Mohamed, "DC microgrids and distribution systems: An overview," Electric. Power Syst. Res., 119: 407–417, 2014.

[29] R.N. Beres, X. Wang, M. Liserre, F. Blaabjerg, C.L. Bak, “A review of passive power filters for three-phase grid-connected voltage-source converters,” IEEE J. Emerg. Sel. Top. Power Electron., 4: 54–69, 2016.

[30] F. Donoso, R. C´ardenas, H. S´aez, "finite-set model-predictive control strategies for a 3L-NPC inverter operating with fixed switching frequency," EEE Trans. Ind. Electron., 65 (5): 3954-3965, 2018.

LETTERS TO EDITOR

Journal of Electrical and Computer Engineering Innovations (JECEI) welcomes letters to the editor for the post-publication discussions and corrections which allows debate post publication on its site, through the Letters to Editor. Letters pertaining to manuscript published in JECEI should be sent to the editorial office of JECEI within three months of either online publication or before printed publication, except for critiques of original research. Following points are to be considering before sending the letters (comments) to the editor.

[1] Letters that include statements of statistics, facts, research, or theories should include appropriate references, although more than three are discouraged.

[2] Letters that are personal attacks on an author rather than thoughtful criticism of the author’s ideas will not be considered for publication.

[3] Letters can be no more than 300 words in length.

[4] Letter writers should include a statement at the beginning of the letter stating that it is being submitted either for publication or not.

[5] Anonymous letters will not be considered.

[6] Letter writers must include their city and state of residence or work.

[7] Letters will be edited for clarity and length.

Name *

Email Address *

Affiliation *

Comments *

Security Code *

Journal of Electrical and Computer Engineering Innovations (JECEI)

The Effects of the Model Predictive Controller Compared to the LQR Controller on the Optimal Distribution of the Suitable Load and the Creation of Balance in Microgrids

References

References

Send comment about this article

Volume 10, Issue 1
January 2022
Pages 231-242

The Effects of the Model Predictive Controller Compared to the LQR Controller on the Optimal Distribution of the Suitable Load and the Creation of Balance in Microgrids

References

References

Send comment about this article

Volume 10, Issue 1January 2022Pages 231-242

Volume 10, Issue 1
January 2022
Pages 231-242