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Determination of microgrid stability index based on measured electrical parameters and Mamdani fuzzy inference system

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Abstract

Issues related to stable microgrid (MG) operation often pose challenges for engineers and researchers, including energy management, power quality maintenance, and the effect of perturbations. Therefore, continuous measurement and monitoring of the system’s stability level require special attention. Thus, motivated and focused on addressing these issues, this paper presents a novel decision-making methodology for determining the MG stability index (MGSI) to comprehensively measure the MG stability level. The proposed index is based on continuously measuring fundamental MG parameters, including voltage, frequency, battery state of charge (SOC), and total harmonic distortion. The influence of these parameters on system stability is considered using 256 rules in a Mamdani-based fuzzy inference system. The proposed methodology is evaluated through seven case studies considering different modes of MG operation, types of loads, and availability of power sources and simulated using MATLAB Simulink software. The results demonstrate variations in MGSI for different investigated scenarios, such that a grid-connected MG system with high renewable output and high battery SOC, along with critical/noncritical load, exhibits the best MGSI compared to an islanded MG system with low renewable output, low battery SOC, and an inductive load that shows least MGSI. Additionally, to demonstrate the credibility of the fuzzy-based controller in complex decision-making, a comparative analysis is performed with the analytic hierarchy process under the multi-criteria decision-making technique. The paper also includes sensitivity analysis that reflects the system’s sensitivity to different input sources and robustness analysis of the controller.

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References

  1. Global Alliance for Buildings and Construction, International Energy Agency and the United Nations Environment Programme, “2019 Global status report for buildings and construction: Towards a zero-emission, efficient and resilient buildings and construction sector,” (2019)

  2. Sharma S, Verma A, Panigrahi BK (2021) Robustly coordinated distributed voltage control through residential demand response under multiple uncertainties. IEEE Trans Ind Appl

  3. Access to Electricity. Available at: https://ourworldindata.org/energy-access#:~:text=Citation Summary, to%20clean%20fuels%20for%20cooking. Accessed: July 2022.

  4. Energy Reports. Available at: https://mnre.gov.in/. Accessed: July 2022.

  5. UNEP, IEA. U.N. Environment and International Energy Agency: Towards a Zero-Emission, Efficient, and Resilient Buildings and Construction Sector.; 2017. http://www.globalabc.org.

  6. Government Publications Office, International Energy Outlook 2016, with Projections To 2040. Government Printing Office, 2016.

  7. Kaushal J, Basak P (2018) A novel approach for determination of power quality monitoring index of an A.C. microgrid using fuzzy inference system. Iran J Sci Technol Trans Electr Eng 42(4):429–450

    Article  Google Scholar 

  8. Iqbal S, Habib S, Ali M, Shafiq A, ur Rehman A, Ahmed EM, Khurshaid T, Kamel S (2022) The impact of V2G charging/discharging strategy on the microgrid environment considering stochastic methods. Sustainability 14(20):13211, 1–22

  9. Iqbal S, Xin A, Jan M, Abdelbaky M, Rehman H, Salman S, Rizvi S, Aurangzeb M (2020) Aggregation of EVs for primary frequency control of an industrial microgrid by implementing grid regulation & charger controller. IEEE Access 8:141977–141989

    Article  Google Scholar 

  10. Mehta S, Basak P (2021) A comprehensive review on control techniques for stability improvement in microgrids. Int Trans Electr Energy Syst 31(4):1–28

    Article  Google Scholar 

  11. Ueda Y, Kurokawa K, Tanabe T, Kitamura K, Sugihara H (2008) Analysis results of output power loss due to the grid voltage rise in grid-connected photovoltaic power generation systems. IEEE Trans Ind Electron 55(7):2744–2751

    Article  Google Scholar 

  12. Iqbal S, Xin A, Jan MU, Abdelbaky MA, Rehman HU, Salman S, Aurangzeb M, Rizvi SAA, Shah NA (2020) Improvement of power converters performance by an efficient use of dead time compensation technique. Appl Sci 10(9):3121, 1–19

  13. Alkahtani AA, Alfalahi STY, Athamneh AA et al (2020) Power quality in microgrids including supraharmonics: issues, standards, and mitigations. IEEE Access 8:127104–127122

    Article  Google Scholar 

  14. Ahmed M, Meegahapola L, Vahidnia A, Datta M (2020) Stability and control aspects of microgrid architectures-a comprehensive review. IEEE Access 8:144730–144766

    Article  Google Scholar 

  15. Farrokhabadi M, Lagos D, Wies RW et al (2020) Microgrid stability definitions, analysis, and examples. IEEE Trans Power Syst 35(1):13–29

    Article  ADS  Google Scholar 

  16. Chandak S, Rout PK (2021) The implementation framework of a microgrid: a review. Int J Energy Res 45(3):3523–3547

    Article  Google Scholar 

  17. Vandoorn TL, Meersman B, Degroote L, Renders B, Vandevelde L (2011) A control strategy for islanded microgrids with DC-link voltage control. IEEE Trans Power Del 26(2):703–713

    Article  Google Scholar 

  18. Wang L, Lin CY, Prokhorov AV (2018) Stability analysis of a microgrid system with a hybrid offshore wind and ocean energy farm fed to a power grid through an HVDC link. IEEE Trans Ind Appl 54(3):2012–2022

    Article  Google Scholar 

  19. Chauhan RK, Chauhan K, Guerrero JM (2018) Controller design and stability analysis of grid connected D.C. microgrid. J Renew Sustain Energy 10(3):1–10

    Article  Google Scholar 

  20. Navarro-Rodriguez A, Garcia P, Georgious R, Garcia J (2019) Adaptive active power sharing techniques for D.C. and A.C. voltage control in a hybrid DC/AC microgrid. IEEE Trans Ind Appl 55(2):1106–1116

    Article  Google Scholar 

  21. Dong M, Li L, Nie Y, Song D, Yang J (2019) Stability analysis of a novel distributed secondary control considering communication delay in D.C. microgrids. IEEE Trans Smart Grid 10:6690–6700

    Article  Google Scholar 

  22. Karimi M, Wall P, Mokhlis H, Terzija V (2017) A new centralized adaptive Underfrequency load shedding controller for microgrids based on a distribution state estimator. IEEE Trans Power Del 32:370–380

    Article  Google Scholar 

  23. Kayalvizhi S, Vinod Kumar DM (2017) Load frequency control of an isolated micro grid using fuzzy adaptive model predictive control. IEEE Access 5:16241–16251

    Article  Google Scholar 

  24. Agundis-Tinajero G, Segundo-Ramírez J, Visairo-Cruz N, Savaghebi M, Guerrero JM, Barocio E (2019) Power flow modeling of islanded A.C. microgrids with hierarchical control. Int J Electr Power Energy Syst 105:28–36

    Article  Google Scholar 

  25. Majumder R, Chaudhuri B, Ghosh A, Majumder R, Ledwich G, Zare F (2010) Improvement of stability and load sharing in an autonomous microgrid using supplementary droop control loop. IEEE Trans Power Syst 25(2):796–808

    Article  ADS  Google Scholar 

  26. Tummuru NR, Manandhar U, Ukil A, Gooi HB, Kollimalla SK, Naidu S (2019) Control strategy for AC-DC microgrid with hybrid energy storage under different operating modes. Int J Electr Power Energy Syst 104:807–816

    Article  Google Scholar 

  27. Hamzeh M, Mokhtari H, Karimi H (2013) A decentralized self-adjusting control strategy for reactive power management in an islanded multi-bus MV microgrid. Can J Electr Comput Eng 36(1):18–25

    Article  Google Scholar 

  28. Guan Y, Vasquez JC, Guerrero JM, Wang Y, Feng W (2015) Frequency stability of hierarchically controlled hybrid photovoltaic-battery-hydropower microgrids. IEEE Trans Ind Appl 51(6):4729–4742

    Article  Google Scholar 

  29. Hossain E, Perez R, Nasiri A, Bayindir R (2018) Stability improvement of microgrids in the presence of constant power loads. Int J Electr Power Energy Syst 96:442–456

    Article  Google Scholar 

  30. Suganthi L, Iniyan S, Samuel AA (2015) Applications of fuzzy logic in renewable energy systems—a review. Renew Sust Energ Rev 48:585–607

    Article  Google Scholar 

  31. Asghar F, Talha M, Kim SH (2018) Fuzzy logic-based intelligent frequency and voltage stability control system for standalone microgrid. Int Trans Electr Energy Syst 28(4):1–14

    Article  Google Scholar 

  32. Abazari A, Soleymani MM, Babaae M, Ghafouri M, Monsef H, Beheshti M (2020) High penetrated renewable energy sources-based AOMPC for microgrid’s frequency regulation during weather changes, time-varying parameters and generation unit collapse. IET Gener Transm Distrib 14(22):5164–5182

    Article  Google Scholar 

  33. Abazari A, Dozein M, Monsef H (2018) An optimal Fuzzy-logic based frequency control strategy in a high wind penetrated power system. J Franklin Inst 355(14):6262–6285

    Article  Google Scholar 

  34. Abazari A, Dozein M, Monsef H, Wu B (2019) Wind turbine participation in micro-grid frequency control through self-tuning, adaptive fuzzy droop in de-loaded area. IET Smart Grid 2(2):301–308

    Article  Google Scholar 

  35. Song Y, Sahoo S, Yang Y, Blaabjerg F (2023) Probabilistic risk evaluation of microgrids considering stability and reliability. IEEE Trans Power Electron 38(8):10302–10312

    Article  ADS  Google Scholar 

  36. Ceballos C, Londono S, Florez J (2023) Stability analysis framework for isolated microgrids with energy resources integrated using voltage source converters. Results Eng 19:1–10

    Google Scholar 

  37. Chen K, Baran M (2023) Robust controller for community microgrids for stability improvement in islanded mode. IEEE Trans Power Syst 38(3):2472–2484

    Article  ADS  Google Scholar 

  38. Diaz N, Guinjoan F, Quesada G, Luna A, Guerrero J (2023) Fuzzy-based cooperative interaction between stand-alone microgrids interconnected through VSC-based multiterminal converter. Int J Electr Power Energy Syst 152:1–12

    Article  Google Scholar 

  39. Leng M, Zhou G, Xu G, Sahoo S, Liu X, Zhou Q, Yin Y, Blaabjerg F (2023) Small-signal stability assessment and interaction analysis for bipolar DC microgrids. IEEE Trans Power Electron 38(4):5524–5537

    Article  ADS  Google Scholar 

  40. Khosravi N et al (2023) A novel control approach to improve the stability of hybrid AC/DC microgrids. Appl Energy 344:1–13

    Article  Google Scholar 

  41. Jameel A, Gulzar M (2023) Load frequency regulation of interconnected muli-source multi-area power system with penetration of electric vehicles aggregator model. Electr Eng 1–18

  42. Abubakr H et al (2023) Inclusion of V2G and power system stabilizer for residential microgrid applications. In: 2023 IEEE conference on power electronics and renewable energy (CPERE), pp 1–6

  43. Han S, Lee B, Kim S, Moon Y (2009) Development of voltage stability index using synchro-phasor based data. In: Transm Distrib Conf Expo Asia Pacific, T D Asia, pp 1–4

  44. Al-Khishali MJ (2012) Implementation of a stability index for power system using energy balance criteria. In: 2012 IEEE Electr Power Energy Conf EPEC 2012, pp 237–42.

  45. Hassan R, Wang H, Zane R (2019) Continuous stability monitoring of D.C. microgrids using controlled injection. In: Conf Proc—IEEE Appl Power Electron Conf Expo—APEC, pp 1357–64.

  46. Pérez-Londoño S, Rodríguez LF, Olivar G (2014) A simplified voltage stability index (SVSI). Int J Electr Power Energy Syst 63:806–813. https://doi.org/10.1016/j.ijepes.2014.06.044

    Article  Google Scholar 

  47. Iqbal S, Xin A, Jan MU, Salman S, Zaki AuM, Rehman HU, Shinwari MF, Abdelbaky MA (2020) V2G strategy for primary frequency control of an industrial microgrid considering the charging station operator. Electronics 9(4):549, 1–21

  48. Mehta S, Basak P. A novel design, economic assessment, and fuzzy-based technical validation of an islanded microgrid: a case study on load model of Kibber village in himachal pradesh. In: Hindawi—Int Trans Electr Energy Syst. https://doi.org/10.1155/2022/9639253.

  49. Mohammadzadeh A, Rathinasamy S (2020) Energy management in photovoltaic battery hybrid systems: a novel type-2 fuzzy control. Int J Hydrog Energy 45(41):20970–20982

    Article  CAS  Google Scholar 

  50. Roy AK, Biswal GR, Basak P (2020) An integrated rule-based power management and dynamic feed-forward low voltage ride through scheme for a grid-connected hybrid energy system. J Renew Sustain Energy 12(5):1–18

    Article  Google Scholar 

  51. Mehta S, Basak P (2023) Cascaded dual fuzzy logic controller for stable microgrid operation mitigating effects of natural uncertainty in solar and wind energy sources. e-Prime Adv Electr Eng Electron Energy 5:100. https://doi.org/10.1016/j.prime.2023.100215

    Article  Google Scholar 

  52. IEEE Guide for Identifying and Improving Voltage Quality in Power Systems. In: IEEE Std 1250-2011 (Revision of IEEE Std 1250-1995), pp.1–70, 31 March 2011

  53. Central Electricity Regulatory Commission (CERC)—Staff Paper, New Delhi. Available at: http://www.cercind.gov.in/, 2011. Accessed: July, 2022

  54. IEEE Recommended Practice and Requirements for Harmonic Control in Electric Power Systems. In: IEEE Std 519-2014 (Revision of IEEE Std 519-1992), pp.1–29, 11 June 2014

  55. Rahman H, Raza M, Afsar P, Alharbi A, Ahmad S, Alyami H (2021) Multi-criteria decision making model for application maintenance offshoring using analytic hierarchy process. Appl Sci 11(8):1–25

    Google Scholar 

  56. Asadabadi M, Chang E, Saberi M (2019) Are MCDM methods useful? A critical review of Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP). Cogent Eng 6(1):1–11

    Article  Google Scholar 

  57. Xi Y, Li Q (2022) Improved AHP model and neural network for consumer finance credit risk assessment. Adv Multimedia 1–10

  58. Lin C-L, Fan C-L, Chen B-K (2022) Hybrid analytic hierarchy process–artificial neural network model for predicting the major risks and quality of taiwanese construction projects. Appl Sci 12(15):7790, 1–19

  59. Kaya İ, Çolak M, Terzi F (2018) Use of MCDM techniques for energy policy and decision-making problems: a review. Int J Energy Res 42:2344–2372

    Article  Google Scholar 

  60. Medjoudj R, Aissani D, Haim KD (2013) Power customer satisfaction and profitability analysis using multi-criteria decision making methods. Int J Electr Power Energy Syst 45:331–339

    Article  Google Scholar 

  61. Eberlein S, Rudion K (2021) Small-signal stability modelling, sensitivity analysis and optimization of droop controlled inverters in LV microgrids. Int J Electr Power Energy Syst 125:1–14

    Article  Google Scholar 

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Authors Statement Sahil Mehta: Conceptualization, Methodology, Software, Data curation, Writing- Original draft preparation, Visualization, Investigation Prasenjit Basak: Conceptualization, Methodology, Supervision, Writing- Reviewing and Editing,

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Correspondence to Prasenjit Basak.

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Mehta, S., Basak, P. Determination of microgrid stability index based on measured electrical parameters and Mamdani fuzzy inference system. Electr Eng 106, 581–601 (2024). https://doi.org/10.1007/s00202-023-02002-2

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