Electrical Engineering

, Volume 101, Issue 2, pp 409–420 | Cite as

Online generalized droop-based demand response for frequency control in islanded microgrids

  • Farshid HabibiEmail author
  • Qobad Shafiee
  • Hassan Bevrani
Original Paper


Frequency stability, as one of the most important issues in the modern power grids, requires more efficient control methods due to the increasing complexity of the power system, high penetration of distributed generation sources as well as high electrical energy consumption. The challenges become more critical in the case of islanded microgrids (MGs), due to existing no traditional ancillary services of the upstream electric power network. Thus, the modern power grids, such as MGs, need advanced regulation methods to keep the generation-consumption balancing. Demand response (DR) is the recently introduced control approach which guarantees continuous contribution of controllable loads in the system frequency control. In this paper, a new online droop-based DR, generalized droop control (GDC), is introduced to apply in islanded MGs frequency control. An artificial neural network is used for online tuning of droop coefficients in the presented GDC framework. The proposed control approach changes controllable active and reactive loads, using a set of equations based on satisfying dynamics. To evaluate the effectiveness of the proposed control method, several scenarios are simulated in which changes of the system frequency and voltage are studied. Results show significant damping of power–frequency fluctuation and a desirable performance of the closed-loop system.


Demand response Droop control Frequency control Microgrid Artificial neural network 



This work is supported by the Smart/Micro Grids Research Center at the University of Kurdistan, Sanandaj, Kurdistan, Iran.


  1. 1.
    Hatziargyriou Nikos (2014) Microgrid: architectures and control. Wiley, HobokenGoogle Scholar
  2. 2.
    Bevrani H, Francois B, Ise T (2017) Microgrid dynamics and control. Wiley, HobokenCrossRefGoogle Scholar
  3. 3.
    Kundur P, Paserba J, Ajjarapu V et al (2004) Definition and classification of power system stability. IEEE Trans Power Syst 19(3):1387–1401CrossRefGoogle Scholar
  4. 4.
    Reddy SS (2017) Optimizing energy and demand response programs using multi-objective optimization. Electr Eng 99(1):397–406CrossRefGoogle Scholar
  5. 5.
    Schweppe FC, Tabors RD, Kirtley JL et al (1980) Homeostatic utility control. IEEE Trans Power Appar Syst 99(3):1151–1163CrossRefGoogle Scholar
  6. 6.
    Dehghanpour K, Afsharnia S (2015) Electrical demand side contribution to frequency control in power systems: a review on technical aspects. Renew Sustain Energy Rev 41:1267–1276CrossRefGoogle Scholar
  7. 7.
    Doğan A, Alçı M (2018) Real-time demand response of thermostatic load with active control. Electr Eng 100(4):2649–2658CrossRefGoogle Scholar
  8. 8.
    Shareef H, Ahmed MS, Mohamed A, Al Hassan E (2018) Review on home energy management system considering demand responses, smart technologies, and intelligent controllers. IEEE Access 6:24498–24509CrossRefGoogle Scholar
  9. 9.
    Deng R, Yang Z, Chow MY, Chen J (2015) A survey on demand response in smart grids: mathematical models and approaches. IEEE Trans Ind Inform 11(3):570–582CrossRefGoogle Scholar
  10. 10.
    Guo Y, Pan M, Fang Y (2012) Optimal power management of residential customers in the smart grid. IEEE Trans Parallel Distrib Syst 23(9):1593–1606CrossRefGoogle Scholar
  11. 11.
    Pourmousavi SA, Nehrir MH (2014) Introducing dynamic demand response in the LFC model. IEEE Trans Power Syst 29(4):1562–1572CrossRefGoogle Scholar
  12. 12.
    Klobasa M (2010) Analysis of demand response and wind integration in Germany’s electricity market. IET Renew Power Gener 4(1):55–63CrossRefGoogle Scholar
  13. 13.
    Saele H, Grande OS (2011) Demand response from household customers: experiences from a pilot study in Norway. IEEE Trans Smart Grid 2(1):90–97CrossRefGoogle Scholar
  14. 14.
    Chuan L, Ukil A (2015) Modeling and validation of electrical load profiling in residential buildings in Singapore. IEEE Trans Power Syst 30(5):2800–2809CrossRefGoogle Scholar
  15. 15.
    Vedady Moghadam MR, Ma RTB, Zhang R (2014) Distributed frequency control in smart grids via randomized demand response. IEEE Trans Smart Grid 5(6):2798–2809CrossRefGoogle Scholar
  16. 16.
    Pourmousavi SA, Nehrir MH (2012) Real-time central demand response for primary frequency regulation in microgrids. IEEE Trans Smart Grid 3(4):1988–1996CrossRefGoogle Scholar
  17. 17.
    Abbasi E (2017) Coordinated primary control reserve by flexible demand and wind power generation. In: 2017 IEEE power & energy society innovative smart grid technologies conference (ISGT), pp 1–5Google Scholar
  18. 18.
    Jiang H, Lin J, Song Y et al (2014) Demand side frequency control scheme in an isolated wind power system for industrial aluminum smelting production. IEEE Trans Power Syst 29(2):844–853CrossRefGoogle Scholar
  19. 19.
    Rezaei N, Kalantar M (2015) Stochastic frequency-security constrained energy and reserve management of an inverter interfaced islanded microgrid considering demand response programs. Int J Electr Power Energy Syst 69:273–286CrossRefGoogle Scholar
  20. 20.
    Molina-García A, Bouffard F, Kirschen DS (2011) Decentralized demand-side contribution to primary frequency control. IEEE Trans Power Syst 26(1):411–419CrossRefGoogle Scholar
  21. 21.
    Gouveia C, Moreira J, Moreira CL, Pecas Lopes JA (2013) Coordinating storage and demand response for microgrid emergency operation. IEEE Trans Smart Grid 4(4):1898–1908CrossRefGoogle Scholar
  22. 22.
    Xu Z, Østergaard J, Togeby M (2011) Demand as frequency controlled reserve. IEEE Trans Power Syst 26(3):1062–1071CrossRefGoogle Scholar
  23. 23.
    Westermann D, John A (2007) Demand matching wind power generation with wide-area measurement and demand-side management. IEEE Trans Energy Convers 22(1):145–149CrossRefGoogle Scholar
  24. 24.
    Huang K-Y, Chin H-C, Huang Y-C (2004) A model reference adaptive control strategy for interruptible load management. IEEE Trans Power Syst 19(1):683–689CrossRefGoogle Scholar
  25. 25.
    Navid-Azarbaijani N (1996) Realizing load reduction functions by aperiodic switching of load groups. IEEE Trans Power Syst 11(2):721–727CrossRefGoogle Scholar
  26. 26.
    Medina J, Muller N, Roytelman I (2010) Demand response and distribution grid operations: opportunities and challenges. IEEE Trans Smart Grid 1(2):193–198CrossRefGoogle Scholar
  27. 27.
    Rezkalla M, Pertl M, Marinelli M (2018) Electric power system inertia: requirements, challenges and solutions. Electr Eng 100(4):2677–2693CrossRefGoogle Scholar
  28. 28.
    Rezaei N, Kalantar M (2015) Smart microgrid hierarchical frequency control ancillary service provision based on virtual inertia concept: an integrated demand response and droop controlled distributed generation framework. Energy Convers Manag 92:287–301CrossRefGoogle Scholar
  29. 29.
    Bevrani H, Habibi F, Babahajyani P et al (2012) Intelligent frequency control in an AC microgrid: online PSO-based fuzzy tuning approach. IEEE Trans Smart Grid 3(4):1935–1944CrossRefGoogle Scholar
  30. 30.
    Shafiee Q, Dragičević T, Vasquez JC, Guerrero JM (2014) Hierarchical control for multiple DC-microgrids clusters. IEEE Trans Energy Convers 29(4):922–933CrossRefGoogle Scholar
  31. 31.
    Bevrani H (2014) robust power system frequency control, 2nd edn. Springer, New YorkzbMATHGoogle Scholar
  32. 32.
    Babahajiani P, Shafiee Q, Bevrani H (2018) Intelligent demand response contribution in frequency control of multi-area power systems. IEEE Trans Smart Grid 9(2):1282–1291CrossRefGoogle Scholar
  33. 33.
    Zhang L, Good N, Mancarella P (2019) Building-to-grid flexibility: modelling and assessment metrics for residential demand response from heat pump aggregations. Appl Energy 233–234:709–723CrossRefGoogle Scholar
  34. 34.
    Morren J, De Haan SWH, Ferreira JA (2006) Contribution of DG units to primary frequency control. Eur Trans Electr Power 16(5):507–521CrossRefGoogle Scholar
  35. 35.
    Akkari S, Petit M, Dai J, Guillaud X (2016) Interaction between the voltage-droop and the frequency-droop control for multi-terminal HVDC systems. IET Gener Transm Distrib 10(6):1345–1352CrossRefGoogle Scholar
  36. 36.
    Cingoz F, Elrayyah A, Sozer Y (2015) Optimized droop control parameters for effective load sharing and voltage regulation in DC microgrids. Electr Power Compon Syst 43(8–10):879–889CrossRefGoogle Scholar
  37. 37.
    De Brabandere K, Bolsens B, Van den Keybus J et al (2007) A voltage and frequency droop control method for parallel inverters. IEEE Trans Power Electron 22(4):1107–1115CrossRefGoogle Scholar
  38. 38.
    Sharma R, Suhag S (2019) Virtual impedance based phase locked loop for control of parallel inverters connected to islanded microgrid. Comput Electr Eng 73:58–70CrossRefGoogle Scholar
  39. 39.
    Karami A, Mahmoodi Galougahi K (2019) Improvement in power system transient stability by using STATCOM and neural networks. Electr Eng. CrossRefGoogle Scholar
  40. 40.
    Cichocki A, Unbehauen R (1993) Neural networks for optimization and signal processing. Wiley, HobokenzbMATHGoogle Scholar
  41. 41.
    Wu B (1992) An introduction to neural networks and their applications in manufacturing. J Intell Manuf 3(6):391–403CrossRefGoogle Scholar
  42. 42.
    Gupta M, Jin L, Homma N (2004) Static and dynamic neural networks: from fundamentals to advanced theory. Wiley, HobokenGoogle Scholar
  43. 43.
    Kleineidam G, Krasser M, Reischböck M (2016) The cellular approach: smart energy region Wunsiedel. Testbed for smart grid, smart metering and smart home solutions. Electr Eng 98(4):335–340CrossRefGoogle Scholar
  44. 44.
    Bevrani H, Shokoohi S (2013) An intelligent droop control for simultaneous voltage and frequency regulation in Islanded microgrids. IEEE Trans Smart Grid 4(3):1505–1513CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electrical Engineering, Smart/Micro Grids Research Center (SMGRC)University of KurdistanSanandajIran

Personalised recommendations