Energy Management and Control of Hybrid AC/DC Microgrid Using Fuzzy Logic Controller
Authors
Assistant Professor, Department of Electrical & Electronics Engineering, Annamalai University, Chidambaram, Tamil Nadu (India)
Assistant Professor, Department of Electrical & Electronics Engineering, Annamalai University, Chidambaram, Tamil Nadu (India)
Article Information
DOI: 10.51584/IJRIAS.2026.11080104
Subject Category: Engineering & Technology
Volume/Issue: 11/8 | Page No: 1420-1430
Publication Timeline
Submitted: 2026-08-31
Accepted: 2026-09-05
Published: 2026-09-12
Abstract
In a microgrid system, an energy management system is necessary to control the flow of power and energy between sources and loads and to provide high quality, safe, sustainable, and environment-friendly energy to consumers.it is a hybrid electrical system, low or medium-voltage, that includes small energy sources based on renewable resources as primary resources to provide high-quality electricity for small consumers, mostly operated in rural areas that are separated or isolated. To ensure the efficient Power Management and control, this article proposes an efficient Hybrid DC/AC microgrid using four renewable energy sources such as solar, wind, Biomass Generator and Lithium-ion batteries were chosen as storage system. The existence of both AC and DC microgrids has led to a new concept of hybrid AC/DC microgrids tied by an Interlinking Converter (ILC). It comprises a DC grid and AC grid interlinked by a bidirectional DC/AC converter. In addition, to provide higher voltages, the Multi-Input Booster (MIB) DC-DC converters are used as a power converter in between load and source to enforce and increase the PV depending on the voltage output signal. Further extract maximum power from the solar PV system, perturb and observe algorithm-based power point tracking control mechanism is proposed DC link voltage of ILC is regulated usually by DC side control in load sharing among sources in the DC microgrid. Adding up, to overcome the load fluctuation problem in a microgrid, this article introduced a Mamdani type 2 PID-fuzzy controller. Simulation Results reveal that the proposed type-II fuzzy PID controller can balance the power generation and demand properly and control both system frequency and tie-line power effectively
Keywords
Microgrid, Photovoltaic, Battery, Wind, Interlinking Converter, Multi-Input Booster, Mamdani type 2 PID-fuzzy controller.
Downloads
References
1. Bouchekara, H.R.E.H., Javaid, M.S., Shaaban, Y.A., Shahriar, M.S., Ramli, M.A.M. and Latreche, Y., 2021. Decomposition based multiobjective evolutionary algorithm for PV/Wind/Diesel Hybrid Microgrid System design considering load uncertainty. Energy Reports, 7, pp.52-69. [Google Scholar] [Crossref]
2. Gamil, M.M., Senjyu, T., Masrur, H., Takahashi, H. and Lotfy, M.E., 2022. Controlled V2Gs and battery integration into residential microgrids: Economic and environmental impacts. Energy Conversion and Management, 253, p.115171. [Google Scholar] [Crossref]
3. Gui, Y., Han, R., Guerrero, J.M., Vasquez, J.C., Wei, B. and Kim, W., 2021. Large-Signal stability improvement of DC-DC converters in DC microgrid. IEEE Transactions on Energy Conversion, 36(3), pp.2534-2544. [Google Scholar] [Crossref]
4. Jabeur, R., Boujoudar, Y., Azeroual, M., Aljarbouh, A. and Ouaaline, N., 2022. Microgrid energy management system for smart home using multi-agent system. International Journal of Electrical & Computer Engineering (2088- 8708), 12(2). [Google Scholar] [Crossref]
5. Kaysal, A., Köroğlu, S. and Yüksel, O.Ğ.U.Z., Self-tuning Fuzzy PID Controller Design and Energy Management in DC Microgrid: Standalone and Grid Connected Mode. Celal Bayar University Journal of Science, 18(1), pp.41-51. [Google Scholar] [Crossref]
6. 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] [Crossref]
7. Fathy, A., Alanazi, T.M., Rezk, H. and Yousri, D., 2022. Optimal energy management of micro-grid using sparrow search algorithm. Energy Reports, 8, pp.758-773. [Google Scholar] [Crossref]
8. 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] [Crossref]
9. Hu, J.; Shan, Y.; Guerrero, J.M.; Ioinovici, A.; Chan, K.W.; Rodriguez, J. Model predictive control of microgrids–An overview. Renewable and Sustainable. Energy Rev. 2021, 136, 110422. [Google Scholar] [Crossref]
10. Meng, L.; Sanseverino, E.R.; Luna, A.; Dragicevic, T.; Vasquez, J.C.; Guerrero, J.M. Microgrid supervisory controllers and energy management systems: A literature review. Renew. Sustain. Energy Rev. 2016, 60, 1263–1273. [Google Scholar] [Crossref]
Metrics
Views & Downloads
Similar Articles
- The Impact Of UI/UX Design on User Trust and Task Completion in Civic Tech Platforms
- Solar Cell Photovoltaic Model Shell Sp 75
- Development of an Intelligent Traffic Management System to Address Visibility Obstruction at Urban Intersections: A Case Study of Ibadan Metropolis
- Optimum Placement of Facts Devices on an Interconnected Power Systems Using Particle Swarm Optimisation Technique
- Assessing Construction Transformation and Implication on Future Production Flow System