A Review of Fuzzy-Based Residential Grid-Connected Microgrid Energy Management Strategies for Grid Power Profile Smoothing

  • Diego Arcos-AvilesEmail author
  • Francesc Guinjoan
  • Julio Pascual
  • Luis Marroyo
  • Pablo Sanchis
  • Rodolfo Gordillo
  • Paúl Ayala
  • Martin P. Marietta
Part of the Energy, Environment, and Sustainability book series (ENENSU)


Residential grid-connected microgrids (MG) comprising renewable generation and storing capability are constrained to grid-operator requirements which include, among others, a smooth and bounded grid power profile. These requirements attempt to mitigate a high unpredictability on the electrical power exchanged between the grid and the MG and affect the design of the MG Energy Management System (EMS). This chapter reviews several energy management strategies based on Fuzzy-Logic Controllers (FLC) designed in the last years to smooth the grid power profile of a residential grid-connected MG. Two MG power architectures are considered. Both include wind and PV solar renewable generation and non-controllable domestic electrical loads. The first architecture assumes a battery charger/inverter as the only controllable element whereas the second one also considers a thermal load as an additional controllable element. The chapter presents a fuzzy logic approach to design the control strategies of the microgrid EMS. The strategies are designed under two scenarios, the first one assuming that forecast of generation and consumption is not available and the second one using MG forecasted data. Simulation and experimental results are provided to highlight and compare the features of all the strategies in terms of their power profile smoothing capability.


Distributed power generation Renewable power Energy management Power forecasting Fuzzy logic control Microgrids Power smoothing 


  1. Ally C, Bahadoorsingh S, Singh A, Sharma C (2015) A review and technical assessment integrating wind energy into an island power system. Renew Sustain Energy Rev 51:863–874CrossRefGoogle Scholar
  2. Ang KH, Chong G, Li Y (2005) PID control system analysis, design, and technology. IEEE Trans Control Syst Technol 13(4):559–576Google Scholar
  3. Anuphappharadorn S, Sukchai S, Sirisamphanwong C, Ketjoy N (2014) Comparison the economic analysis of the battery between lithium-ion and lead-acid in PV stand-alone application. Energy Procedia 56:352–358CrossRefGoogle Scholar
  4. Arcos-Aviles D (2016a) Energy management strategies based on fuzzy logic control for grid-tied domestic electro-thermal microgrid. Universitat Politècnica de CatalunyaGoogle Scholar
  5. Arcos-Aviles D, Guinjoan F, Barricarte J, Marroyo L, Sanchis P, Valderrama H (2012) Battery management fuzzy control for a grid-tied microgrid with renewable generation. In: IECON 2012—38th annual conference on IEEE industrial electronics society, pp 5607–5612Google Scholar
  6. Arcos-Aviles D, Vega C, Guinjoan F, Marroyo L, Sanchis P (2014a) Fuzzy logic controller design for battery energy management in a grid connected electro-thermal microgrid. In: 2014 IEEE 23rd international symposium on industrial electronics (ISIE), pp 2014–2019Google Scholar
  7. Arcos-Aviles D, Espinosa N, Guinjoan F, Marroyo L, Sanchis P (2014b) Improved fuzzy controller design for battery energy management in a grid connected microgrid. In: IECON 2014—40th annual conference of the IEEE industrial electronics society, pp 2128–2133Google Scholar
  8. Arcos-Aviles D, Pascual J, Marroyo L, Sanchis P, Guinjoan F, Marietta MP (2015) Optimal fuzzy logic EMS design for residential grid-connected microgrid with hybrid renewable generation and storage. In: 2015 IEEE 24th international symposium on industrial electronics (ISIE), pp 742–747Google Scholar
  9. Arcos-Aviles D, Guinjoan F, Marietta MP, Pascual J, Marroyo L, Sanchis P (2016b) Energy management strategy for a grid-tied residential microgrid based on Fuzzy Logic and power forecasting. In: IECON 2016—42nd annual conference of the IEEE industrial electronics society, pp 4103–4108Google Scholar
  10. Arcos-Aviles D, Pascual J, Guinjoan F, Marroyo L, Sanchis P, Marietta MP (2017a) Low complexity energy management strategy for grid profile smoothing of a residential grid-connected microgrid using generation and demand forecasting. Appl Energy 205:69–84CrossRefGoogle Scholar
  11. Arcos-Aviles D, Sotomayor D, Proano JL, Guinjoan F, Marietta MP, Pascual J, Marroyo L, Sanchis P (2017b) Fuzzy energy management strategy based on microgrid energy rate-of-change applied to an electro-thermal residential microgrid. In: 2017 IEEE 26th international symposium on industrial electronics (ISIE), pp 99–105Google Scholar
  12. Arcos-Aviles D, Pascual J, Marroyo L, Sanchis P, Guinjoan F (2018) Fuzzy logic-based energy management system design for residential grid-connected microgrids. IEEE Trans Smart Grid 9(2):530–543CrossRefGoogle Scholar
  13. Asmus P, Lauderbaugh A, Lawrence M (2013) Market data: microgrids. Campus / Institutional, Military, and Remote MicrogridsGoogle Scholar
  14. Barricarte JJ, Martín IS, Sanchis P, Marroyo L (2011) Energy management strategies for grid integration of microgrids based on renewable energy sources. In: 10th International conference on sustainable energy technologies, pp 4–7Google Scholar
  15. Black J, Larson R (2007) Strategies to overcome network congestion in infrastructure systems. J Ind Syst Eng 1(2):97–115Google Scholar
  16. Chen Y-H, Lu S-Y, Chang Y-R, Lee T-T, Hu M-C (2013) Economic analysis and optimal energy management models for microgrid systems: a case study in Taiwan. Appl Energy 103:145–154CrossRefGoogle Scholar
  17. Chong WT, Hew WP, Yip SY, Fazlizan A, Poh SC, Tan CJ, Ong HC (2014) The experimental study on the wind turbine’s guide-vanes and diffuser of an exhaust air energy recovery system integrated with the cooling tower. Energy Convers Manag 87:145–155CrossRefGoogle Scholar
  18. Comodi G, Giantomassi A, Severini M, Squartini S, Ferracuti F, Fonti A, Nardi Cesarini D, Morodo M, Polonara F (2015) Multi-apartment residential microgrid with electrical and thermal storage devices: experimental analysis and simulation of energy management strategies. Appl Energy 137:854–866CrossRefGoogle Scholar
  19. Danish Wind Industry Association (2003) Wind energy reference manual part 1: wind energy concepts. http://drømstø web/en/stat/unitsw.htm#roughness
  20. European Commission (2018) Clean energy for all Europeans. Accessed 11 Aug 2018
  21. Fathima AH, Palanisamy K (2015) Optimization in microgrids with hybrid energy systems—a review. Renew Sustain Energy Rev 45:431–446CrossRefGoogle Scholar
  22. Foley AM, Leahy PG, Marvuglia A, McKeogh EJ (2012) Current methods and advances in forecasting of wind power generation. Renew Energy 37(1):1–8CrossRefGoogle Scholar
  23. Fossati JP, Galarza A, Martín-Villate A, Echeverría JM, Fontán L (2015) Optimal scheduling of a microgrid with a fuzzy logic controlled storage system. Int J Electr Power Energy Syst 68:61–70CrossRefGoogle Scholar
  24. Guasch D, Silvestre S (2003) Dynamic battery model for photovoltaic applications. Prog Photovolt Res Appl 11(3):193–206CrossRefGoogle Scholar
  25. Hanna R, Kleissl J, Nottrott A, Ferry M (2014) Energy dispatch schedule optimization for demand charge reduction using a photovoltaic-battery storage system with solar forecasting. Sol Energy 103:269–287CrossRefGoogle Scholar
  26. Hatziargyriou N (2014) Microgrids: architectures and control. Wiley, Chichester, UKGoogle Scholar
  27. Kim Seul-Ki, Jeon Jin-Hong, Cho Chang-Hee, Ahn Jong-Bo, Kwon Sae-Hyuk (2008) Dynamic modeling and control of a grid-connected hybrid generation system with versatile power transfer. IEEE Trans Ind Electron 55(4):1677–1688CrossRefGoogle Scholar
  28. Lasseter RH (2002) MicroGrids. IEEE Power Eng Soc Winter Meet 1:305–308CrossRefGoogle Scholar
  29. Lorenzo E (2011) Energy collected and delivered by PV modules. In: Luque A, Hegedus S (eds) Handbook of photovoltaic science and engineering. Wiley, Chichester, UK, pp 984–1042CrossRefGoogle Scholar
  30. Lü X, Lu T, Kibert CJ, Viljanen M (2014) A novel dynamic modeling approach for predicting building energy performance. Appl Energy 114:91–103CrossRefGoogle Scholar
  31. Mahmud MA, Hossain MJ, Pota HR, Nasiruzzaman ABM (2011) Voltage control of distribution networks with distributed generation using reactive power compensation. In: IECON 2011—37th annual conference of the IEEE industrial electronics society, pp 985–990Google Scholar
  32. Manwell JF, McGowan JG, Rogers AL (2009) Wind energy explained: theory, design and application. Wiley, Chichester, UK, pp 23–87CrossRefGoogle Scholar
  33. Marcos J, de la Parra I, García M, Marroyo L (2014) Control strategies to smooth short-term power fluctuations in large photovoltaic plants using battery storage systems. Energies 7(10):6593–6619CrossRefGoogle Scholar
  34. Masters CL (2002) Voltage rise: the big issue when connecting embedded generation to long 11 kV overhead lines. Power Eng J 16(1):5–12MathSciNetCrossRefGoogle Scholar
  35. Mathew S (2006) Wind energy: fundamentals, resource analysis and economics. Springer, Berlin, pp 11–88CrossRefGoogle Scholar
  36. Meteogalicia. Servidor THREDDS de MeteoGalicia. Accessed 05 July 2018
  37. Mohamed A, Mohammed O (2013) Real-time energy management scheme for hybrid renewable energy systems in smart grid applications. Electr Power Syst Res 96:133–143CrossRefGoogle Scholar
  38. Niknam T, Azizipanah-Abarghooee R, Narimani MR (2012) An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation. Appl Energy 99:455–470CrossRefGoogle Scholar
  39. Olatomiwa L, Mekhilef S, Ismail MS, Moghavvemi M (2016) Energy management strategies in hybrid renewable energy systems: a review. Renew Sustain Energy Rev 62:821–835CrossRefGoogle Scholar
  40. Parisio A, Rikos E, Tzamalis G, Glielmo L (2014) Use of model predictive control for experimental microgrid optimization. Appl Energy 115:37–46CrossRefGoogle Scholar
  41. Parissis O-S, Zoulias E, Stamatakis E, Sioulas K, Alves L, Martins R, Tsikalakis A, Hatziargyriou N, Caralis G, Zervos A (2011) Integration of wind and hydrogen technologies in the power system of Corvo island, Azores: a cost-benefit analysis. Int J Hydrogen Energy 36(13):8143–8151CrossRefGoogle Scholar
  42. Pascual J, Sanchis P, Marroyo L (2014) Implementation and control of a residential electrothermal microgrid based on renewable energies, a hybrid storage system and demand side management. Energies 7(1):210–237CrossRefGoogle Scholar
  43. Pascual J, Barricarte J, Sanchis P, Marroyo L (2015) Energy management strategy for a renewable-based residential microgrid with generation and demand forecasting. Appl Energy 158:12–25CrossRefGoogle Scholar
  44. Passino K, Yurkovich S (1998) Fuzzy control. Addisson-Wesley, Menlo Park, CAzbMATHGoogle Scholar
  45. Schnitzer D, Lounsbury DS, Carvallo JP, Deshmukh R, Apt J, Kammen DM (2014) Microgrids for rural electrification: a critical review of best practices based on seven case studiesGoogle Scholar
  46. Serraji M, Boumhidi J, Nfaoui EH (2015) MAS energy management of a microgrid based on fuzzy logic control. Intell. Syst. Comput. Vis. (ISCV) 2015:1–7Google Scholar
  47. Shinji T, Sekine T, Akisawa A, Kashiwagi T, Fujita G, Matsubara M (2008) Reduction of power fluctuation by distributed generation in micro grid. Electr Eng Japan 163(2):22–29CrossRefGoogle Scholar
  48. Tascikaraoglu A, Boynuegri AR, Uzunoglu M (2014) A demand side management strategy based on forecasting of residential renewable sources: a smart home system in Turkey. Energy Build 80:309–320CrossRefGoogle Scholar
  49. Tazvinga H, Zhu B, Xia X (2015) Optimal power flow management for distributed energy resources with batteries. Energy Convers Manag 102:104–110CrossRefGoogle Scholar
  50. Tuballa ML, Abundo ML (2016) A review of the development of Smart Grid technologies. Renew Sustain Energy Rev 59:710–725CrossRefGoogle Scholar
  51. Vamos C, Craciun M (2012) Noise Smoothing. In: Vamos C, Craciun M (eds) Automatic trend estimation. Springer Netherlands, Dordrecht, pp 43–59CrossRefGoogle Scholar
  52. Velik R, Nicolay P (2014) Grid-price-dependent energy management in microgrids using a modified simulated annealing triple-optimizer. Appl Energy 130:384–395CrossRefGoogle Scholar
  53. Xue X, Wang S, Sun Y, Xiao F (2014) An interactive building power demand management strategy for facilitating smart grid optimization. Appl Energy 116:297–310CrossRefGoogle Scholar
  54. Yang C, Thatte AA, Xie L (2015) Multitime-scale data-driven spatio-temporal forecast of photovoltaic generation. IEEE Trans Sustain Energy 6(1):104–112CrossRefGoogle Scholar
  55. Yoo J, Park B, An K, Al-Ammar EA, Khan Y, Hur K, Kim JH (2012) Look-ahead energy management of a grid-connected residential PV system with energy storage under time-based rate programs. Energies 5(12):1116–1134CrossRefGoogle Scholar
  56. Zhao B, Zhang X, Chen J, Wang C, Guo L (2013) Operation optimization of standalone microgrids considering lifetime characteristics of battery energy storage system. IEEE Trans Sustain Energy 4(4):934–943CrossRefGoogle Scholar
  57. Zhou H, Bhattacharya T, Tran D, Siew TST, Khambadkone AM (2011) Composite energy storage system involving battery and ultracapacitor with dynamic energy management in microgrid applications. IEEE Trans Power Electron 26(3):923–930CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Departamento de Eléctrica y ElectrónicaUniversidad de las Fuerzas Armadas ESPESangolquíEcuador
  2. 2.Department of Electronics EngineeringEscuela Técnica Superior de Ingenieros de Telecomunicación de Barcelona, Universitat Politècnica de CatalunyaBarcelonaSpain
  3. 3.Department of Electrical and Electronics EngineeringPublic University of Navarre (UPNa) Edificio de los PinosPamplonaSpain

Personalised recommendations