Cover
Vol. 16 No. Special Issue (2020)

Published: June 30, 2020

Pages: 73-85

Conference Article

Adaptive Energy Management System for Smart Hybrid Microgrids

Abstract

The energy management will play an important role in the future smart grid by managing loads in an intelligent way. Energy management programs, realized via House Energy Management systems (HEMS) for smart cities, provide many benefits; consumers enjoy electricity price savings, and utility operates at reduced peak demand. This paper proposed an adaptive energy management system for islanded mode and grid-connected mode. In this paper, a hybrid system that includes distribution electric grid, photovoltaics, and batteries are employed as energy sources in the residential of the consumer in order to meet the demand. The proposed system permits coordinated operation of distributed energy resources to concede necessary active power and additional service whenever required. This paper uses home energy management system which switches between the distributed energy and the grid power sources. The home energy management system incorporates controllers for maximum power point tracking, battery charge and discharge and inverter for effective control between different sources depending upon load requirement and availability of sources at maximum powerpoint. Also, in this paper, the Maximum Power Point Tracking (MPPT) technique is applied to the photovoltaic station to extract the maximum power from hybrid power system during variation of the environmental conditions. The operation strategy of energy storage systems is proposed to solve the power changes from photovoltaics and houses loads fluctuations locally, instead of reflecting those disturbances to the utility grid. Furthermore, the energy storage systems energy management scheme will help to achieve the peak reduction of the houses daily electrical load demand. The simulation results have verified the effectiveness and feasibility of the introduced strategy and the capability of the proposed controller for a hybrid microgrid operating in different modes.

References

  1. M. H. Kapourchali and M. Sepehry, "Fault Detector and Switch Placement in Cyber-Enabled Power Distribution Network," IEEE Transactions on Smart Grid, Vol.9, No.2, pp-980-992, 2018.
  2. M. Tabari, A. Yazdani, "An Energy Management Strategy for A DC Distribution System For Power System Integration of Plugin Electric Vehicles". IEEE Transaction on Smart Grid, Vol.7, pp. 659- 668, 2016.
  3. A. Rajabi, L. Li, J. Zhang, and J. Zhu, "Aggregation of Small Loads for Demand Response Programs— Implementation And Challenges: A Review", 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), pp. 1-6,2017.
  4. Ma Xiandong, Wang Yifei, Qin Jianrong, "Generic model of a community-based microgrid integrating wind turbines, photovoltaic and CHP generations", Appl energy, vol.112, pp.147-582, 2013.
  5. Alonso Monica, Amaris Hortensia, Alvarez-Ortega Carlos. "Integration of Renewable Energy Sources In Smart Grids By Means Of Evolutionary Optimization Algorithms", Expert Systems with Applications, vol.39, pp.22-55, 2015.
  6. Zhang Yu, Gatsis N, Giannakis GB, "Robust Energy Management For Microgrid With High-Penetration Renewables". IEEE Transactions on Sustainable Energy, vol.4, pp.45-53, 2015.
  7. Rouholamini, M.; Mohammadian, M. "Heuristic-Based Power Management of A Grid-Connected Hybrid Energy System Combined With Hydrogen Storage", Journal of Renewable Energy, vol.89, pp.12-24, 2016.
  8. Almada, J.B.; Leão, R.P.S.; Sampaio, R.F.; Barroso, G.C. A Centralized and Heuristic Approach For Energy Management of an AC Microgrid " , Journal of Renewable Sustainable Energy Review, vol.45, pp.67- 87, 2016.
  9. Merabet, A.; Tawfique Ahmed, K.; Ibrahim, H.; Beguenane, R.; Ghias, A.M.Y.M, "Energy Management and Control System for Laboratory Scale Microgrid BasedWind-PV-Battery", IEEE Transactions on Sustainable Energy, vol.8, 2017.
  10. Farzin, H.; Fotuhi-Firuzabad, M.; Moeini-Aghtaie, M. "Stochastic Energy Management of Microgrids during Unscheduled Islanding Period", IEEE Transactions on Industrial Informatics, vol.13, pp.1079 - 1087, 2017.
  11. Battistelli, C.; Agalgaonkar, Y.P.; Pal, B.C. "Probabilistic Dispatch of Remote Hybrid Microgrids Including Battery Storage and Load Management" IEEE Transactions on Smart Grid, vol.8, pp.1305 - 1317, 2017.
  12. Basu, A. K., Chowdhury, S. P., Chowdhury, S, & Paul, "Microgrids: Energy Management by Strategic Deployment of DERs—A Comprehensive Survey", Renewable and Sustainable Energy Reviews, vol.15, pp. 4348–4356, 2011.
  13. Molderink, A., Bakker, V., Bosman, M. G. C., Hurink, J. L., & Smit, G. J. M, "Management and Control of Domestic Smart Grid Technology", IEEE Transactions on Smart Grid, vol. 2, pp. 109–119, 2010.
  14. Chen, C., Duan, S., Cai, T., Liu, B., & Hu, G. (2011). "Smart Energy Management System for Optimal Microgrid Economic Operation" IET Renewable Power Generation, vol.5, pp. 258-265, 2012.
  15. Lujano-Rojas, J. M., Monteiro, C., Dufo-López, R., & Bernal-Agustín, J. L, "Optimum Load Management Strategy for Wind/Diesel/Battery Hybrid Power Systems", Journal of Renewable Energy, vol. 44, pp. 288–295, 2013.
  16. Palma-Behnke, R., Benavides, C., Lanas, F., Severino, B., Reyes, L., Llanos, J., & Saez, D , "A Microgrid Energy Management System Based On The Rolling Horizon Strategy", IEEE Transactions on Smart Grid, vol. 4, pp. 996–1006, 2013. Alhasnawi & Jasim | 85
  17. S. Jamalaldin, S.Hakim and H.Razak, "Damage Identification Using Experimental Modal Analysis and Adaptive Neuro-Fuzzy Interface System (ANFIS)", Topics in Modal Analysis, Conference Proceedings of the Society for Experimental Mechanics Series 30, Vol.5, pp.399-405, 2012.
  18. B. Tarek, D. Said, and M.Benbouzid, "Maximum Power Point Tracking Control for Photovoltaic System Using Adaptive Neuro-Fuzzy", IEEE, 8th International Conference and Exhibition on Ecological Vehicles and Renewable Energies (EVER), pp. 1-7, 2013.
  19. M. Mahdavi, Li Li, J. Zhu and S.Mekhilef, "An Adaptive Neuro-Fuzzy Controller for Maximum Power Point Tracking of Photovoltaic Systems", IEEE, 2015, TENCON 2015-2015 IEEE Region10 Conference, pp.1-6, 2015.
  20. M. Villalva, J. Gazoli, E. Ruppert, "Modeling and Circuit-Based Simulation of Photovoltaic Arrays", Brazilian Journal of Power Electronics, Vol. 14, No. 1, pp. 35-45, 2009.
  21. K.ElNounou, "Design of GASugeno Fuzzy Controller for Maximum Power Point and Sun Tracking in Solar Array Systems", Master Thesis, The Islamic University, Gaza, 2013.
  22. Fathima AH, Palanisamy K. "Optimization in Microgrids with Hybrid Energy Systems – A Review". Renewable & Sustainable Energy Reviews, Vol.45, pp. 431–46, 2015.