Energy Management in Hybrid Microgrid using Artificial Neural Network, PID, and Fuzzy Logic Controllers



Abstract Views
1124

Downloads
945

Citations

Share

##plugins.themes.bootstrap3.article.sidebar##

Submitted Feb 1, 2022
Published Apr 11, 2022
10.24018/ejece.2022.6.2.414

Abstract viewed = 1124 times

Table of Contents

##plugins.themes.bootstrap3.article.main##

Microgrids are described as linking many power sources (renewable energy and traditional sources) to meet the load consumption in real-time. Because renewable energy sources are intermittent, battery storage systems are required, typically used as a backup system. Indeed, an energy management strategy (EMS) is required to govern power flows across the entire Microgrid. In recent research, various methods have been proposed for controlling the micro-grids, especially voltage and frequency control. This study introduces a microgrid system, an overview of local control in Microgrid, and an efficient EMS for effective microgrid operations using three smart controllers for optimal microgrid stability. We design the Microgrid, which is made up of renewable solar generators and wind sources, Li-ion battery storage system, backup electrical grids, and AC/DC loads, taking into account all of the functional needs of a microgrid EMS and microgrid stability. In addition, the battery energy storage is managed through the performance control of battery charging and discharging using an efficiency controller. The proposed system control is based on the optimum supply of loads through the available renewable sources and the battery State of Charge (SOC). The simulation results using Matlab Simulink show the performance of the three techniques (PID, ANN, and FL) proposed for microgrid stability.

Downloads

Download data is not yet available.

References

  1. Argyrou MC, Christodoulides P, Kalogirou SA. Energy storage for electricity generation and related processes: Technologies appraisal and grid scale applications. Renewable and Sustainable Energy Reviews, 2018 Oct;94:804?21.
     Google Scholar
  2. Hirsch A, Parag Y, Guerrero J. Microgrids: A review of technologies, key drivers, and outstanding issues. Renewable and Sustainable Energy Reviews [Internet].
     Google Scholar
  3. Kumar J, Agarwal A, Agarwal V. A review on overall control of DC microgrids. Journal of Energy Storage, 2019 Feb;21:113?38.
     Google Scholar
  4. Ansari S, Chandel A, Tariq M. A Comprehensive Review on Power Converters Control and Control Strategies of AC/DC Microgrid. IEEE Access, 2021;9:17998?8015.
     Google Scholar
  5. Sahoo SK, Sinha AK, Kishore NK. Control Techniques in AC, DC, and Hybrid AC?DC Microgrid: A Review. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2018 Jun;6(2):738?59.
     Google Scholar
  6. Shafiee Q, Dragicevic T, Vasquez JC, Guerrero JM. Hierarchical Control for Multiple DC-Microgrids Clusters. IEEE Transactions on Energy Conversion, 2014 Dec;29(4):922?33.
     Google Scholar
  7. Dawood F, Shafiullah G, Anda M. Stand-Alone Microgrid with 100% Renewable Energy: A Case Study with Hybrid Solar PV-Battery-Hydrogen. Sustainability, 2020;Mar 6;12(5):2047.
     Google Scholar
  8. Justo JJ, Mwasilu F, Lee J, Jung J-W. AC-microgrids versus DC-microgrids with distributed energy resources: A review. Renewable and Sustainable Energy Reviews, 2013 Aug;24:387?405.
     Google Scholar
  9. Roslan MF, Hannan MA, Ker PJ, Uddin MN. Microgrid control methods toward achieving sustainable energy management. Applied Energy, 2019;Apr;240:583?607.
     Google Scholar
  10. Ali W, Ulasyar A, Mehmood MU, Khattak A, Imran K, Zad HS, et al. Hierarchical Control of Microgrid Using IoT and Machine Learning Based Islanding Detection. IEEE Access, 2021;9:103019?31.
     Google Scholar
  11. Arfeen ZA, Khairuddin AB, Larik RM, Saeed MS. Control of distributed generation systems for microgrid applications: A technological review. International Transactions on Electrical Energy Systems, 2019;Jul 9;29(9).
     Google Scholar
  12. Gayen PK, Roy Chowdhury P, Dhara PK. An improved dynamic performance of bidirectional SEPIC-Zeta converter based battery energy storage system using adaptive sliding mode control technique. Electric Power Systems Research, 2018;Jul;160:348?61.
     Google Scholar
  13. Ramya KC, Jegathesan V. Comparison of PI and PID Controlled Bidirectional DC-DC Converter Systems. International Journal of Power Electronics and Drive Systems (IJPEDS), 2016;Mar 1;7(1):56.
     Google Scholar
  14. Chen Z, Yuan X, Ji B, Wang P, Tian H. Design of a fractional order PID controller for hydraulic turbine regulating system using chaotic non-dominated sorting genetic algorithm II. Energy Conversion and Management, 2014;Aug;84:390?404.
     Google Scholar
  15. Zhang Y, Gatsis N, Giannakis GB. Robust Energy Management for Microgrids With High-Penetration Renewables. IEEE Transactions on Sustainable Energy, 2013;Oct;4(4):944?53.
     Google Scholar
  16. Mahmoud MS, Alyazidi NM, Abouheaf MI. Adaptive intelligent techniques for microgrid control systems: A survey. International Journal of Electrical Power & Energy Systems, 2017;Sep;90:292?305.
     Google Scholar
  17. Shtessel Y, Taleb M, Plestan F. A novel adaptive-gain supertwisting sliding mode controller: Methodology and application. Automatica, 2012;May;48(5):759?69.
     Google Scholar
  18. Dragicevic T, Novak M. Weighting Factor Design in Model Predictive Control of Power Electronic Converters: An Artificial Neural Network Approach. IEEE Transactions on Industrial Electronics, 2019;Nov;66(11):8870?80.
     Google Scholar
  19. Bakar NNA, Hassan MY, Sulaima MF, Mohd Nasir MN, Khamis A. Microgrid and load shedding scheme during islanded mode: A review. Renewable and Sustainable Energy Reviews, 2017;May;71:161?9.
     Google Scholar
  20. Boujoudar Y, Azeroual M, El Moussaoui H, Lamhamdi T. Intelligent controller based energy management for stand?alone power system using artificial neural network. International Transactions on Electrical Energy Systems, 2020;Aug 9;30(11).
     Google Scholar
  21. Madhiarasan M, Deepa SN. A novel criterion to select hidden neuron numbers in improved back propagation networks for wind speed forecasting. Applied Intelligence, 2015;Dec 4;44(4):878?93.
     Google Scholar
  22. Hern?ndez L, Baladr?n C, Aguiar JM, Carro B, S?nchez-Esguevillas A, Lloret J. Artificial neural networks for short-term load forecasting in microgrids environment. Energy, 2014;Oct;75:252?64.
     Google Scholar
  23. Chettibi N, Mellit A, Sulligoi G, Massi Pavan A. Adaptive Neural Network-Based Control of a Hybrid AC/DC Microgrid. IEEE Transactions on Smart Grid, 2016;1?13.
     Google Scholar
  24. Baghaee HR, Mirsalim M, Gharehpetian GB, Talebi HA. A Decentralized Power Management and Sliding Mode Control Strategy for Hybrid AC/DC Microgrids including Renewable Energy Resources. IEEE Transactions on Industrial Informatics, 2017;1?1.
     Google Scholar
  25. Lopez-Garcia TB, Coronado-Mendoza A, Dom?nguez-Navarro JA. Artificial neural networks in microgrids: A review. Engineering Applications of Artificial Intelligence, 2020;Oct;95:103894.
     Google Scholar
  26. Mendel JM. Fuzzy logic systems for engineering: a tutorial. Proceedings of the IEEE, 1995;Mar;83(3):345?77.
     Google Scholar
  27. ALHAJ OMAR F. performance comparison of pid controller and fuzzy logic controller for water level control with applying time delay. Konya journal of engineering sciences, 2021;dec 4;858?71.
     Google Scholar
  28. Ray PK, Paital SR, Mohanty A, Eddy FYS, Gooi HB. A robust power system stabilizer for enhancement of stability in power system using adaptive fuzzy sliding mode control. Applied Soft Computing, 2018;Dec;73:471?81.
     Google Scholar
  29. Garc?a P, Torreglosa JP, Fern?ndez LM, Jurado F. Optimal energy management system for stand-alone wind turbine/ photovoltaic/ hydrogen/ battery hybrid system with supervisory control based on fuzzy logic. International Journal of Hydrogen Energy, 2013;Nov;38(33):14146?58.
     Google Scholar
  30. Chen Y-K, Wu Y-C, Song C-C, Chen Y-S. Design and Implementation of Energy Management System with Fuzzy Control for DC Microgrid Systems. IEEE Transactions on Power Electronics, 2013;Apr;28(4):1563?70.
     Google Scholar
  31. Arcos-Aviles, D., Pascual, J., Marroyo, L., Sanchis, P., & Guinjoan, F. (2016). Fuzzy logic-based energy management system design for residential grid-connected microgrids. IEEE Transactions on Smart Grid, 9(2), 530-543.
     Google Scholar
  32. Jafari M, Malekjamshidi Z, Zhu J, Khooban M-H. A Novel Predictive Fuzzy Logic-Based Energy Management System for Grid-Connected and Off-Grid Operation of Residential Smart Microgrids. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2020;Jun;8(2):1391?404.
     Google Scholar
  33. Li Y, Sun Z, Han L, Mei N. Fuzzy Comprehensive Evaluation Method for Energy Management Systems Based on an Internet of Things. IEEE Access, 2017;5:21312?22.
     Google Scholar