Home Real Time Simulation of Modified Bias Based Load Disturbance Rejection Controller for Frequency Regulation of Islanded Micro-Grid
Article
Licensed
Unlicensed Requires Authentication

Real Time Simulation of Modified Bias Based Load Disturbance Rejection Controller for Frequency Regulation of Islanded Micro-Grid

  • Badal Kumar , Shuma Adhikari , Subir Datta ORCID logo EMAIL logo and Nidul Sinha
Published/Copyright: September 20, 2019

Abstract

The paper presents a real time modelling of self-reliant isolated grid comprising both governable and ungovernable sources. The frequency of this system must be maintained to its desired value by reducing its deviation occurred when there is a mismatch between the load demand and the actual power generation. In order to achieve it, the actual power output of the micro-grid is required to regulate. Therefore, an attempt is made in this paper to design a modified bias (MB) with LDR (load disturbance rejection) based PID (proportional integral derivative) Controller and it can also be called droop based controller (DBC). Simulation of the micro-grid system with proposed MB-LDR based control scheme is successfully done in Real Time Simulation platform using the digital simulator of OPAL-RT OP4510. Simulation results of the system for different conditions are presented and analysis is given in details. Thereafter, comparative performance of the results obtained using proposed MB-LDR based controller with that of LDR and classical controllers is made. Results show that the proposed MB-LDR based controller gives better response as compare to other two methods in terms of peak transient deviation, settling time and number of oscillations.

Acknowledgement

We would like to extend our heartfelt gratitude to TEQIP-III NIT Manipur for Procuring OPAL-RT Loop Simulator which enabled us to validate all the system resposes in real time platform.

Appendix

A Nominal parameters of the microgrid [15]

fsys = 50 Hz, Pbase = 1 MVA, D = 0.012 MW/Hz, H = 5 s, Tdg= 2 s, Tdt= 20 s, Tfc = 4 s, Tae = 0.2 s, TBESS = 0.1 s, Tfw = 0.1, Kdg=Kdt=Kae=Kfc=KBESS=Kfw=1.

B Nominal parameters of the microgrid [15]

Wind power source (300 KW), Solar Power source (300 KW), load (600 KW), Diesel Generator (400 KW), Fuel Cell (200 KW), Aqua Electrolyzer(100 KW), Battery(30 KWh) and Fly Wheel(30 KWh).

References

[1] Kroposki B, Lasseter R, Ise T, Morozumi S, Papathanassiou S, Hatziargyriou N. Making microgrid work. IEEE Power Energy Mag. 2008;6:40–53.10.1109/MPE.2008.918718Search in Google Scholar

[2] Chowdhury SP, Crossley P, Chowdhury S, Clarke E. Microgrids and active distribution networks. London: IET, 200910.1049/PBRN006ESearch in Google Scholar

[3] Mallesham G, Mishra S, Jha AN. Ziegler-Nichols based controller parameters tuning for load frequency control in a microgrid. 2011 International Conference on Energy, Automation and Signal. Bhubaneswar, Odisha, 2011:1–8.Search in Google Scholar

[4] Lasseter RH. Microgrids. Proc IEEE Power Eng Soc Winter Meeting. Jan 2002;1:305–8.10.1109/PESW.2002.985003Search in Google Scholar

[5] Nandar CS. Robust PI control of smart controllable load for frequency stabilization of microgrid power system. Int J Renewable Energy. 2013;56:16–23.10.1016/j.renene.2012.10.032Search in Google Scholar

[6] Debbarma S, Bhattacharya M, Meena BK, Datta A. Frequency control of autonomous hybrid power system using smart controllable load. 2015 International Conference on Robotics, Automation, Control and Embedded Systems (RACE). Chennai, 2015:1–7.Search in Google Scholar

[7] Sattar A, Muyeen SM, Al-Durra A, Caruana C, Musleh AS. Experimental study and performance evaluation of the renewable energy conversion systems under realistic grid conditions using RTDS. 2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia). Melbourne, VIC, 2016:412–1710.1109/ISGT-Asia.2016.7796421Search in Google Scholar

[8] Sekhar PC, Mishra S. Storage free smart energy management for frequency control in a diesel-pv-fuel cell-based hybrid AC microgrid. IEEE Trans Neural Networks Learn Syst. 2016;27:1657–71.10.1109/TNNLS.2015.2428611Search in Google Scholar PubMed

[9] Mondal A, Illindala MS. Improved frequency regulation in an islanded mixed source microgrid through coordinated operation of DERs and smart loads. IEEE Trans Ind Appl. 2018;54:112–20.10.1109/TIA.2017.2761825Search in Google Scholar

[10] Majumder R, Chaudhuri B, Ghosh A, Majumder R, Ledwich G, Zare F. Improvement of stability and load sharing in an autonomous microgrid using supplementary droop control loop. IEEE PES General Meeting. Minneapolis, MN, 2010:1–1.10.1109/PES.2010.5589665Search in Google Scholar

[11] Diaz G, Gonzalez-Moran C, Gomez-Aleixandre J, Diez A. Scheduling of droop coefficients for frequency and voltage regulation in isolated microgrids. IEEE Trans Power Sys. 2010;25:489–96.10.1109/TPWRS.2009.2030425Search in Google Scholar

[12] Mishra S, Mallesham G, Sekhar PC. Biogeography based optimal state feedback controller for frequency regulation of a smart microgrid. IEEE Trans Smart Grid. March 2013;4:628–37.10.1109/TSG.2012.2236894Search in Google Scholar

[13] Schiffer J, Ortega R, Astolfi A, Raisch J, Sezi T. Conditions for stability of droop-controlled inverter-based microgrids. Int J Autom. 2014;50:2457–69.10.1016/j.automatica.2014.08.009Search in Google Scholar

[14] Elrayyah A, Cingoz F, Sozer Y. Smart loads management using droop-based control in integrated microgrid systems. IEEE J Emerg Sel Top Power Electron. 2017;5:1142–53.10.1109/JESTPE.2017.2666786Search in Google Scholar

[15] Kumar B, Bhongade S. Load disturbance rejection based PID controller for frequency regulation of a microgrid. 2016 Biennial International Conference on Power and Energy Systems: Towards Sustainable Energy (PESTSE). Bangalore, 2016:1–610.1109/PESTSE.2016.7516459Search in Google Scholar

[16] Gao W, Zheglov V, Wang G, Mahajan SM. PV - wind - fuel cell - electrolyzer micro-grid modeling and control in real time digital simulator. 2009 International Conference on Clean Electrical Power. Capri, 2009:29–34.Search in Google Scholar

[17] Meng X, Yang C, Lin K, Zhang F, Wu J, Shen J. Research on photovoltaic power system of microgrid based on real-time simulation. 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2). Beijing, 2017:1–510.1109/EI2.2017.8245676Search in Google Scholar

[18] Xiaodong Y, Yan Z, Weiping Z. Real-time simulation and research on photovoltaic power system based on RT-LAB. Open Fuels Energy Sci J. 2015;8:183–8.10.2174/1876973X01508010183Search in Google Scholar

[19] Wanik MZ, Bousselham A, Elrayyah A. Real-time simulation modeling for PV-battery based microgrid system. 2016 IEEE International Conference on Power System Technology (POWERCON). Wollongong, NSW, 2016:1–6.Search in Google Scholar

Received: 2019-02-23
Revised: 2019-08-14
Accepted: 2019-08-30
Published Online: 2019-09-20

© 2019 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 3.10.2025 from https://www.degruyterbrill.com/document/doi/10.1515/ijeeps-2019-0053/html
Scroll to top button