Comparison of Hybrid DC-AC Microgrid Energy Management System Using Neural Network and Vector-Decoupled Algorithm Driven by Horse Herd Optimization

International Journal of Electrical and Electronics Engineering

© 2024 by SSRG - IJEEE Journal

Volume 11 Issue 8

Year of Publication : 2024

Authors : Sreenivasula Reddy Pilli, Venkata Siva Krishna Rao Gadi

10.14445/23488379/IJEEE-V11I8P114

How to Cite?

Sreenivasula Reddy Pilli, Venkata Siva Krishna Rao Gadi, "Comparison of Hybrid DC-AC Microgrid Energy Management System Using Neural Network and Vector-Decoupled Algorithm Driven by Horse Herd Optimization," SSRG International Journal of Electrical and Electronics Engineering, vol. 11,  no. 8, pp. 152-167, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I8P114

Abstract:

This research paper presents a comparative analysis of Energy Management Systems (EMS) in hybrid DC-AC microgrids, focusing on the application of a neural network vector-decoupled algorithm driven by Horse Herd Optimization (HHO). The objective is to assess the performance and competence of this innovative approach compared to traditional methods in controlling power flow and optimizing energy utilization within microgrid environments. The study begins by outlining the structure and components of a hybrid DC-AC microgrid scheme, emphasizing the incorporation of non-conventional resources, Battery, and the grid. Key components such as converters, inverters, and control mechanisms are discussed to provide a complete empathetic of the scheme. The core focus of this research lies in the comparison between conventional energy management techniques and the proposed neural network vector-decoupled algorithm enhanced by HHO. The neural network algorithm facilitates real-time decision-making and adaptive control, optimizing power flow and enhancing system stability. HHO further enhances the algorithm's efficiency by leveraging the collective intelligence of a simulated horse herd, mimicking their behavior of communication and collaboration for optimization. Simulation studies are done using MATLAB/Simulink to justify the efficacy of the suggested approach. Performance metrics such as system stability, power quality, energy losses, and economic considerations are analyzed and compared against baseline models and existing methodologies. The results demonstrate significant improvements in energy management efficiency, grid stability, and cost-effectiveness with the neural network vector-decoupled algorithm driven by HHO. The paper concludes with insights into the practical implications and future research directions for advancing energy management systems in hybrid DC-AC microgrids.

Keywords:

Hybrid DC-AC microgrid, Energy Management System (EMS), Neural network, Vector-decoupled algorithm, Horse Herd Optimization.

References:

[1] Sathesh Murugan, Mohana Jaishankar, and Kamaraj Premkumar, “Hybrid DC-AC Microgrid Energy Management System Using an Artificial Gorilla Troops Optimizer Optimized Neural Network,” Energies, vol. 15, no. 21, pp. 1-19, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[2] S. Sahaya Elsi, F. Michael Raj, and S. Prince Mary, “Hybrid DC-AC Microgrid Energy Management System Using Grey Wolf Optimized Neural Network,” Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6885-6899, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Enrique Rodriguez-Diaz et al., “Real-Time Energy Management System for a hybrid AC/DC Residential Microgrid,” 2017 IEEE Second International Conference on DC Microgrids (ICDCM), Nuremburg, Germany, pp. 256-261, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Sohail Sarwar et al., ”Major Challenges Towards Energy Management and Power Sharing in a Hybrid AC/DC Microgrid: A Review,” Energies, vol. 15, no. 23, pp. 1-30, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Kyung-Min Kang et al., “Energy Management Method of Hybrid AC/DC Microgrid Using Artificial Neural Network,” Electronics, vol. 10, no. 16, pp. 1-29, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Joao Pedro Carvalho Silveira et al., ”Power Management of Energy Storage System with Modified Interlinking Converters Topology in Hybrid AC/DC Microgrid,” International Journal of Electrical Power & Energy Systems, vol. 130, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Hassan Abouobaida et al., “Energy Management and Control Strategy of DC Microgrid Based Hybrid Storage System,” Simulation Modelling Practice and Theory, vol. 124, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[8] R. Rajasekaran, and P. Usha Rani, “Bidirectional DC-DC Converter for Microgrid in Energy Management System,” International Journal of Electronics, vol. 108, no. 2, pp. 322-343, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Bin Wei et al., “Temporally Coordinated Energy Management for AC/DC Hybrid Microgrid Considering Dynamic Conversion Efficiency of Bidirectional AC/DC Converter,” IEEE Access, vol. 8, pp. 70878-70889, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Yunwei Ryan Li, Farzam Nejabatkhah, and Hao Tian, Smart Hybrid AC/DC Microgrids: Power Management, Energy Management, and Power Quality Control, John Wiley & Sons, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Indragandhi V. et al., “Multi-Objective Optimization and Energy Management in Renewable Based AC/DC Microgrid,” Computers & Electrical Engineering, vol. 70, pp. 179-198, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Moudud Ahmed et al., ”A Novel Hybrid AC/DC Microgrid Architecture with a Central Energy Storage System,” IEEE Transactions on Power Delivery, vol. 37, no. 3, pp. 2060-2070, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Zhongwen Li et al., “A Novel Two-Stage Energy Management of Hybrid AC/DC Microgrid Considering Frequency Security Constraints,” International Journal of Electrical Power & Energy Systems, vol. 146, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Zhengwei Qu et al., “Energy Management Strategy of AC/DC Hybrid Microgrid Based on Solid-State Transformer,” IEEE Access, vol. 10, pp. 20633-20642, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Ahmad Aziz Al Alahmadi et al., ”Hybrid Wind/PV/Battery Energy Management-Based Intelligent Non-Integer Control for Smart DC-Microgrid of Smart University,” IEEE Access, vol. 9, pp. 98948-98961, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Yahui Wang et al., “Hybrid AC/DC Microgrid Architecture with Comprehensive Control Strategy for Energy Management of Smart Building,” International Journal of Electrical Power & Energy Systems, vol. 101, pp. 151-161, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Tae-Gyu Kim et al., “Hybrid AC/DC Microgrid Energy Management Strategy Based on Two-Step ANN,” Energies, vol. 16, no. 4, pp. 1-23, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Tawfiq M. Aljohani, Ahmed F. Ebrahim, and Osama Mohammed, “Hybrid Microgrid Energy Management and Control Based on Metaheuristic-Driven Vector-Decoupled Algorithm Considering Intermittent Renewable Sources and Electric Vehicles Charging Lot,” Energies, vol. 13, no. 13, pp. 1-19, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Mohamed Amine Hartani et al., “Proposed Frequency Decoupling-Based Fuzzy Logic Control for Power Allocation and State-of-Charge Recovery of Hybrid Energy Storage Systems Adopting Multi-Level Energy Management for Multi-DC-Microgrids,” Energy, vol. 278, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Ramin Nourollahi, Kazem Zare, and Sayyad Nojavan, “Energy Management of Hybrid AC-DC Microgrid under Demand Response Programs: Real-Time Pricing versus Time-of-Use Pricing,” Demand Response Application in Smart Grids, pp. 75-93, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Moein Manbachi, “Energy Management Systems for Hybrid AC/DC Microgrids: Challenges and Opportunities,” Operation of Distributed Energy Resources in Smart Distribution Networks, pp. 303-348, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Amit Kumar Rajput, and J.S. Lather, “Energy Management of a DC Microgrid with Hybrid Energy Storage System Using PI and ANN Based Hybrid Controller,” International Journal of Ambient Energy, vol. 44, no. 1, pp. 703-718, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Omar Azeem et al., “A Comprehensive Review on Integration Challenges, Optimization Techniques and Control Strategies of Hybrid AC/DC Microgrid,” Applied Sciences, vol. 11, no. 14, pp. 1-32, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Sivasankar Gangatharan et al., “A Novel Battery Supported Energy Management System for the Effective Handling of Feeble Power in Hybrid Microgrid Environment,” IEEE Access, vol. 8, pp. 217391-217415, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[25] J. Vinothkumar, and R. Thamizhselvan, “Efficient Power Management and Control Strategy of Hybrid Renewable Energy System in Microgrid,” International Journal on Applied Physics and Engineering, vol. 2, pp. 106-127, 2023.
[CrossRef] [Google Scholar] [Publisher Link]