Skip to main content

Advertisement

SpringerLink
Log in

Intelligent energy management control for independent microgrid

  • Published:
Sādhanā Aims and scope Submit manuscript

Abstract

This work presents a new adaptive scheme for energy management in an independent microgrid. The proposed energy management system has been developed to manage the utilization of power among the hybrid resources and energy storage system in order to supply the load requirement based on multi-agent system (MAS) concept and predicted renewable powers and load powers. Auto regressive moving average models have been developed for predicting the wind speed, atmospheric temperature, irradiation, and connected loads. The structure proposed in this paper includes renewable sources as primary source and storage system as secondary source. A wind generator and solar PV array system together acts as primary source, which supplies power to the local load most of the time in this energy management strategy. When they fail to meet the load demand, the secondary source present in the system will assist the primary source and help to attain the goal of satisfying load demand without interruption. If the primary source and secondary source together are not able to meet the load demand then load shedding will be executed according to the priority set. Thus the developed MAS algorithm co-ordinates the hybrid system components and achieves energy management among renewable energy sources, storage units, and load under varying environmental conditions and varying loads. STATCOM based compensation has been implemented to balance the reactive power demand and to mitigate the voltage fluctuations and harmonics on the AC bus. The proposed microgrid has been simulated with MAS concept in Matlab/Simulink environment. The results presented in this paper show cases the effectiveness of the proposed energy management controller.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20
Figure 21
Figure 22

Similar content being viewed by others

References

  1. Lagorse J, Paire D and Miraoui A 2010 A multi-agent system for energy management of distributed power sources. Renew. Energy 35(1): 174–182

    Article  Google Scholar 

  2. Lagorse J, Simoes M and Miraoui A 2009 A multiagent fuzzy-logic-based energy management of hybrid systems. IEEE Trans. Ind. Appl. 45(6): 2123–2129

    Article  Google Scholar 

  3. Carmeli M, Castelli-Dezza F, Mauri M, Marchegiani G and Rosati D 2012 Control strategies and configurations of hybrid distributed generation systems. Renew. Energy 41: 294–305

    Article  Google Scholar 

  4. Dou C, Liu B and Guerrero J 2014 Event-triggered hybrid control based on multi-agent system for microgrids. IET Gener. Transm. Distrib. 8(12): 1987–1997

    Article  Google Scholar 

  5. Dou X, Quan X, Wu Z, Hu M, Yang K, Yuan J and Wang M 2014 Hybrid multi-agent control in microgrids: framework, models and implementations based on IEC 61850. Energies 8(1): 31–58

    Article  Google Scholar 

  6. Reza Miveh M, Fadli Rahmat M and Asghar Ghadimi A 2015 Power quality improvement in autonomous microgrids using multi-functional voltage source inverters: A comprehensive review. J. Power Electron. 15(4): 1054–1065

    Article  Google Scholar 

  7. Aung H, Khambadkone A, Srinivasan D and Logenthiran T 2010 Agent-based intelligent control for real-time operation of a microgrid. In: IEEE joint international conference on power electronics, drives and energy systems. New Delhi, India. 1–6

    Google Scholar 

  8. Mao M, Jin P, Hatziargyriou N and Chang L 2014 Multiagent-based hybrid energy management system for microgrids. IEEE Trans. Sustain. Energy 5(3): 938–946

    Google Scholar 

  9. Abdoul K M, Mamadou L N, Mounirou N and Papa A N 2014 Operation optimal dynamics of a hybrid electrical system: Multi-agent approach. Proc. Comput. Sci. (Elsevier) 36: 454–461

    Article  Google Scholar 

  10. Gi-Gab Y, Won-Pyo H and Ki-Hong L 2011 A multiagent-based hybrid power control and management of distributed power sources. J. Korean Inst. Illumin. Electr. Install. Eng. 25(8): 70–81

  11. Jiang Z 2008 Agent-based power sharing scheme for active hybrid power sources. J. Power Sources 177(1): 231–238

    Article  Google Scholar 

  12. Haruni A, Negnevitsky M and Haque M 2013 A novel operation and control strategy for a standalone hybrid renewable power system. IEEE Trans. Sustain. Energy 4(2): 402–413

    Article  Google Scholar 

  13. Huang R, Huang T and Gadh R 2012 Solar generation prediction using the ARMA model in a laboratory-level micro-grid. In: IEEE Third International Conference on Smart Grid Communications, Tainan City, Taiwan: IEEE 528–533

  14. Chang W 2014 A literature review of wind forecasting methods. J. Power Energy Eng. 2: 161–168

    Article  Google Scholar 

  15. Chandra A, Singh B, Singh B and Al-Haddad K 2000 An improved control algorithm of shunt active filter for voltage regulation, harmonic elimination, power-factor correction, and balancing of nonlinear loads. IEEE Trans. Power Electron. 15(3): 495–507

    Article  Google Scholar 

  16. Garcia-Cerrada A, Pinzon-Ardila O, Feliu-Batlle V, Sánchez P R and González P G 2007 Application of a repetitive controller for a three-phase active power filter. IEEE Trans. Power Electron. 22(1): 237–246

    Article  Google Scholar 

  17. George S and Agarwal V 2007 A DSP based optimal algorithm for shunt active filter under nonsinusoidal supply and unbalanced load conditions. IEEE Trans. Power Electron. 22(2): 593–601

    Article  Google Scholar 

  18. Luo A, Peng S and Wu C and Shuai Z 2012 Power electronic hybrid system for load balancing compensation and frequency-selective harmonic suppression. IEEE Trans. Ind. Electron. 59(2): 723–732

    Article  Google Scholar 

  19. Singh B and Solanki J 2009 An implementation of an adaptive control algorithm for a three-phase shunt active filter. IEEE Trans. Ind. Electron. 56(8): 2811–2820

    Article  Google Scholar 

  20. Mehta G and Singh S 2013 Power quality improvement through grid integration of renewable energy sources. IETE J. Res. 59(3): 210–218

    Article  Google Scholar 

  21. Rinchin W, Toko C and Sarsing G 2015 Power quality analysis of hybrid renewable energy system. Cogent Eng. 2(1): 1–17

    Article  Google Scholar 

  22. Tsao-Tsung M 2012 Power quality enhancement in micro-grids using multifunctional DG inverters. In: The International Multiconference of Engineers and Computer Scientists, Vol II, Hong Kong

  23. Vasanthavalli C and Vellaisamy S 2015 Optimization technique for power quality improvement using DSTATCOM. Int. J. Sci. Res. Publ. 4(4): 1–7

    Google Scholar 

  24. Guerrero J, Poh C, Tzung-Lin L and Chandorkar M 2013 Advanced control architectures for intelligent microgrid-part II: power quality, energy storage, and AC/DC microgrids. IEEE Trans. Ind. Appl. 60(4): 1263–1270

    Google Scholar 

  25. Fatemeh D and Hassan B 2015 Multi-agent systems in control engineering: A survey. J. Control Sci. Eng. 2009: 1–12

    Google Scholar 

  26. Shu-yun J and Ziang C 2010 Design and implementation of MAS in renewable energy power generation system. In: IEEE Ninth International Conference on Machine Learning and Cybernetics, pp. 22–25. Qingdao, China

    Google Scholar 

  27. Zeng J, Wu J, Liu J, Gao L and Li M 2008 An agent–based approach to renewable energy management in eco–building. In: IEEE International Conference on Sustainable Energy Technologies, pp. 45–50. Singapore

  28. Mohammad A and Shivraj S 2014 Analysis and comparison of various control strategy of hybrid power generation a review. In: 1st International Conference on Nonconventional Energy, pp. 184–189. Kalyani, India: IEEE

  29. Bogaraj T and Kanakaraj J 2012 Development of MATLAB/Simulink models for PV and wind systems and review on control strategies for hybrid energy systems. Int. Rev. Model. Simul. 5(4): 1701–1709

    Google Scholar 

  30. Kalantar M and Mousavi G S 2010 Dynamic behavior of a stand-alone hybrid power generation system of wind turbine, microturbine, solar array and battery storage. Appl. Energy 87(10): 3051–3064

    Article  Google Scholar 

  31. Dursun E and Kilic O 2012 Comparative evaluation of different power management strategies of a stand-alone PV/Wind/PEMFC hybrid power system. Int. J. Electr. Power 34(1): 81–89

    Article  Google Scholar 

  32. Roche R, Idoumghar L, Blunier B and Miraoui A 2011 Optimized fuel cell array energy management using multi-agent systems. In: IEEE Industry Applications Society Annual Meeting, pp. 1–8. Orlando, Florida, USA

    Google Scholar 

  33. Natsheh E, Natsheh A and Albarbar A 2013 Intelligent controller for managing power flow within standalone hybrid power systems. IET Sci. Meas. Technol. 7(4): 191–200

    Article  Google Scholar 

  34. Milligan M, Schwartz M and Wan Y 2003 Statistical wind power forecasting models: Results for U.S. wind farms. In: Windpower. Austin, USA

  35. Serban I and Marinescu C 2008 Power quality issues in a stand-alone microgrid based on renewable energy. Revue Roumaine Des Sciences Techniques – Serie Électrotechnique Et Énergétique. 53(3): 285–293

  36. Natesan C, Ajithan S, Palani P and Prabaakaran K 2014 Survey on microgrid: Power quality improvement techniques. ISRN Renew. Energy 2014: 1–7

    Article  Google Scholar 

  37. Satyanarayana I, Ballala S, Rama Krishnan V B and Kesani M 2015 Multilevel D-STATCOM for linear and nonlinear loads to compensate reactive and active power during operation of distribution systems. Int. J. Res. Eng. Appl. Sci. 5(8): 98–105

    Google Scholar 

  38. The MathWorks Inc. 2015a STATCOM. Available from: http://in.mathworks.com/help/physmod/sps/examples/d-statcom-detailed-model.html?searchHighlight=STATCOM (accessed 20 September 2015)

  39. The MathWorks Inc. 2015b Time-series model identification MathWorks. Available from: http://in.mathworks.com/help/ident/ug/bq54wup.html (accessed 22 September 2015)

Download references

Author information

Authors and Affiliations

  1. Electrical and Electronics Engineering Department, PSG College of Technology, Coimbatore, 641004, India

    T Bogaraj & J Kanakaraj

Authors
  1. T Bogaraj
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J Kanakaraj
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to T Bogaraj.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bogaraj, T., Kanakaraj, J. Intelligent energy management control for independent microgrid. Sādhanā 41, 755–769 (2016). https://doi.org/10.1007/s12046-016-0515-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12046-016-0515-6

Keywords

Search

Navigation