f Optimizing Real-Time Scheduling for Post Islanding Energy Management Using African Vulture Optimization Algorithm on Hybrid Microgrids Environment
FOREX Press I. J. of Electrical & Electronics Research
Support Open Access
  • Home/
  • IJEER-120405

Research Article |

Optimizing Real-Time Scheduling for Post Islanding Energy Management Using African Vulture Optimization Algorithm on Hybrid Microgrids Environment

Author(s): Ramya Madamaneri* and Dr Devaraju T

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 12, Issue 4

Publisher : FOREX Publication

Published : 15 October 2024

e-ISSN : 2347-470X

Page(s) : 1151-1162

DOI: https://doi.org/10.37391/IJEER.120405

Abstract

Microgrids (MG) are small-scale energy systems that use distributed energy storage and sources. Hybrid microgrids are transforming energy management by incorporating various energy resources like wind, solar, and battery storage. Effective scheduling of this resource is vital to minimize the costs and maximize energy autonomy. Advanced scheduling algorithm optimizes the operation of hybrid microgrids, which dynamically adjusts the energy consumption and generation to satisfy the demand while ensuring power balancing. This scheduling strategy has been instrumental in improving the sustainability and resilience of MGS, which paves the way for an environmentally friendly and more reliable energy future. They can operate on islanded or grid-connected modes. The optimization of hybrid MG scheduling is paramount in the field of post-island management to ensure effective energy sustainability and distribution. Using metaheuristic approaches like simulated annealing or genetic algorithms allows the finetuning of scheduling parameters to increase energy utilization while reducing environmental impact and costs. Therefore, the study presents a Real-Time Scheduling for Post Islanding Energy Management using African Vulture Optimization Algorithm (RTSPIEM-AVOA) in Hybrid microgrid environment. The RTSPIEM-AVOA approach is utilized to improve its functioning by determining the most efficient scheduling of the installed generation unit. The AVOA can handle complex optimization issues while avoiding local optima solutions because of the balance among the exploitation and exploration stages. A microturbine (MT) system, battery storage, a fuel cell (FC), photovoltaic (PV), and wind turbine (WT) make up the suggested MG system. This work looks at three scenarios: PV and WT operating at normal generation, PV and WT operating at their maximum power, and WT operating at its rated power. Consider the two objective functions of minimizing pollutant emissions and reducing operational costs. According to the experimental results, the RTSPIEM-AVOA technique outperforms other models in microgrid scheduling by efficiently optimizing it to satisfy the community's changing needs while transitioning to a greener and more sustainable energy future.

Keywords: Microgrids, African Vulture Optimization Algorithm, Renewable Energy Source, Microturbine, Photovoltaic.




Ramya Madamaneri*, Research Scholar, Mohan Babu University, India; Email: mramya2020@gmail.com

Dr Devaraju T, Professor in EEE, Mohan Babu University, India; Email: devaraj@vidyanikethan.edu

    [1] W.S. Ho, S. Macchietto, J.S. Lim, H. Hashim, Z.A. Muis, W.H. Liu, Optimal scheduling of energy storage for renewable energy distributed energy generation system, Renew Sustain Energy Rev 58 (2016) 1100–110.
    [2] Raghav, L. Phani, Seshu Kumar, R., Koteswara Raju, D., Singh, Arvind R., 2021. Optimal energy management of microgrids using quantum teaching learning based algorithm. IEEE Trans. Smart Grid 12 (6), 4834–4842.
    [3] Li, N., Su, Z., Jerbi, H., Abbassi, R., Latifi, M., Furukawa, N., 2021. Energy management and optimized operation of renewable sources and electric vehicles based on microgrid using hybrid gravitational search and pattern search algorithm. Sustainable Cities Soc. 75, 103279.
    [4] X. Kong, L. Bai, Q. Hu, F. Li, C. Wang, Day-ahead optimal scheduling method for grid-connected microgrid based on energy storage control strategy, J. Mod. Power Syst. Clean Energy 4 (4) (2016) 648–658.
    [5] Adefarati, T., Bansal, R.C., Bettayeb, M., Naidoo, R., 2021. Optimal energy management of a PV-WTG-BSS-DG microgrid system. Energy 217, 119358.
    [6] S. Rajamand, M. Shafie-khah, J.P.S. Catalao, ˜ Energy storage systems implementation and photovoltaic output prediction for cost minimization of a Microgrid, Electr. Power Syst. Res. 202 (Jan. 2022), 107596.
    [7] Kim, H.J., Kim, M.K., Lee, J.W., 2021. A two-stage stochastic p-robust optimal energy trading management in microgrid operation considering uncertainty with hybrid demand response. Int. J. Electr. Power Energy Syst. 124, 106422.
    [8] Y.R. Lee, H.J. Kim, M.K. Kim, Optimal operation scheduling considering cycle aging of battery energy storage systems on stochastic unit commitments in microgrids, Energies 14 (2) (2021).
    [9] H. Takano, R. Hayashi, H. Asano, T. Goda, Optimal sizing of battery energy storage systems considering cooperative operation with microgrid components, Energies 14 (21) (2021) 1–13.
    [10] Ghasemi, A. and Enayatzare, M., 2018. Optimal energy management of a renewable-based isolated microgrid with pumped-storage unit and demand response. Renewable energy, 123, pp.460-474.
    [11] Huy, T.H.B., Le, T.D., Van Phu, P., Park, S. and Kim, D., 2024. Real-time power scheduling for an isolated microgrid with renewable energy and energy storage system via a supervised-learning-based strategy. Journal of Energy Storage, 88, p.111506.
    [12] Zia, M.F., Nasir, M., Elbouchikhi, E., Benbouzid, M., Vasquez, J.C. and Guerrero, J.M., 2022. Energy management system for a hybrid PV-Wind-Tidal-Battery-based islanded DC microgrid: Modeling and experimental validation. Renewable and Sustainable Energy Reviews, 159, p.112093.
    [13] Rahmani, E., Mohammadi, S., Zadehbagheri, M. and Kiani, M., 2023. Probabilistic reliability management of energy storage systems in connected/islanding microgrids with renewable energy. Electric Power Systems Research, 214, p.108891.
    [14] Abdelghany, M.B., Al-Durra, A., Daming, Z. and Gao, F., 2024. Optimal multi-layer economical schedule for coordinated multiple mode operation of wind–solar microgrids with hybrid energy storage systems. Journal of Power Sources, 591, p.233844.
    [15] Mahmoud, I., Hasanien, H.M., Turky, R.A. and Omran, W.A., 2023. Energy management system for islanded multi‐microgrids using a two‐stage optimization scheme based on political optimizer. IET Renewable Power Generation, 17(7), pp.1713-1730.
    [16] Roy, K., Mandal, K.K. and Mandal, A.C., 2022. Energy management system of microgrids in grid-tied mode: A hybrid approach. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, pp.1-23.
    [17] Wu, J., Li, S., Fu, A., Cvetković, M., Palensky, P., Vasquez, J.C. and Guerrero, J.M., 2024. Hierarchical online energy management for residential microgrids with Hybrid hydrogen–electricity Storage System. Applied Energy, 363, p.123020.
    [18] Hartani, M.A., Rezk, H., Benhammou, A., Hamouda, M., Abdelkhalek, O., Mekhilef, S. and Olabi, A.G., 2023. 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, 278, p.127703.
    [19] Chen, H., Gao, L., Zhang, Z. and Li, H., 2021. Optimal energy management strategy for an islanded microgrid with hybrid energy storage. Journal of Electrical Engineering & Technology, 16, pp.1313-1325.
    [20] B.T. Geetha, P. Santhosh Kumar, B. Sathya Bama, Chiranjit Dutta, D. Vijendra Babu, Green energy aware and cluster-based communication for future load prediction in IoT, Sustainable Energy Technologies and Assessments, Vol.52,2022,102244, https://doi.org/10.1016/j.seta.2022.102244.
    [21] https://www.kaggle.com/datasets/jonathandumas/liege-microgrid-open-data
    [22] Perumal SK, Kallimani JS, Ulaganathan S, Bhargava S, Meckanizi S. Controlling energy aware clustering and multihop routing protocol for IoT assisted wireless sensor networks. Concurrency Computat Pract Exper. 2022;e7106. doi: 10.1002/cpe.71.
    [23] Paladini, V.; Donateo, T.; de Risi, A.; Laforgia, D. Super-capacitors fuel-cell hybrid electric vehicle optimization and control strategy development. Energy Convers. Manag. 2007, 48, 3001–3008.
    [24] Onori, S.; Serrao, L.; Rizzoni, G. Adaptive Equivalent Consumption Minimization Strategy for Hybrid Electric Vehicles. 2010. Available online: https://api.semanticscholar.org/CorpusID:12583370 (accessed on 25 January 2011).
    [25] Zhang, W.; Li, J.; Xu, L.; Ouyang, M. Optimization for a fuel cell/battery/capacity tram with equivalent consumption minimization strategy. Energy Convers. Manag. 2017, 134, 59–69.
    [26] adehFard, H.; Tooryan, F.; Collins, E.R.; Jin, S.; Ramezani, B. Design and optimum energy management of a hybrid renewable energy system based on efficient various hydrogen production. Int. J. Hydrogen Energy 2020, 45, 30113–30128.

Ramya Madamaneri and Dr Devaraju T (2024), Optimizing Real-Time Scheduling for Post Islanding Energy Management Using African Vulture Optimization Algorithm on Hybrid Microgrids Environment. IJEER 12(4), 1151-1162. DOI: 10.37391/IJEER.120405.