Open Access
Issue
BIO Web Conf.
Volume 97, 2024
Fifth International Scientific Conference of Alkafeel University (ISCKU 2024)
Article Number 00066
Number of page(s) 12
DOI https://doi.org/10.1051/bioconf/20249700066
Published online 05 April 2024
  • Hameed, A.R., Aftan, A.O. and Kudher, N.A., 2023, September. A structured review of MPPT techniques for photovoltaic systems. In American Institute of Physics Conference Series (Vol. 2804, No. 1, p. 050034). [Google Scholar]
  • T. T. Teo, T. Logenthiran, and W. L. Woo, “Forecasting of photovoltaic power using extreme learning machine,” in 2015 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA), 3-6 Nov. 2015 2015, pp. 1–6, DOI: 10.1109/ISGT-Asia.2015.7387113. [Google Scholar]
  • B. N. Alhasnawi, B. H. Jasim, P. Siano, and J. M. Guerrero, “A novel real-time electricity scheduling for home energy management system using the internet of energy,” Energies, vol. 14, no. 11, pp. 3191, 2021, doi: https://doi.org/10.3390/en14113191. [CrossRef] [Google Scholar]
  • S. Garip and S. Ozdemir, “Optimization of PV and battery energy storage size in grid-connected microgrid,” Applied Sciences, vol. 12, no. 16, pp. 8247, 2022. [CrossRef] [Google Scholar]
  • T. Zhang, H. B. Gooi, S. Chen, and T. Goh, “Cost-effectiveness studies of the BESSs participating in frequency regulation,” in 2015 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA), 3-6 Nov. 2015 2015, pp. 1–6, DOI: 10.1109/ISGT-Asia.2015.7387077. [Google Scholar]
  • J. J. Kelly and P. G. Leahy, “Sizing Battery Energy Storage Systems: Using Multi-Objective Optimization to Overcome the Investment Scale Problem of Annual Worth,” IEEE Transactions on Sustainable Energy, vol. 11, no. 4, pp. 2305–2314, 2020, DOI: 10.1109/TSTE.2019.2954673. [CrossRef] [Google Scholar]
  • C. U. o. T. Giorgos Georgiou, “A novel grid-connected microgrid energy management system with optimal sizing using hybrid grey wolf and cuckoo search optimization algorithm,” frontiers 2022. [Google Scholar]
  • T. Kerdphol, Y. Qudaih, and Y. Mitani, “Battery energy storage system size optimization in microgrid using particle swarm optimization,” 2014: IEEE, pp. 1 -6, DOI: 10.1109/ISGTEurope.2014.7028895. [Google Scholar]
  • K. S. El-Bidairi, H. D. Nguyen, S. D. G. Jayasinghe, T. S. Mahmoud, and I. Penesis, “A hybrid energy management and battery size optimization for standalone microgrids: A case study for Flinders Island, Australia,” Energy conversion and management, Vol. 175, pp. 192–212, 2018, doi: https://doi.org/10.1016/j.enconman.2018.08.076. [CrossRef] [Google Scholar]
  • Y. Yang, S. Bremner, C. Menictas, and M. Kay, “Battery energy storage system size determination in renewable energy systems: A review,” Renewable and Sustainable Energy Reviews, Vol. 91, pp. 109–125, 2018/08/01/ 2018, doi: https://doi.org/10.1016/j.rser.2018.03.047. [CrossRef] [Google Scholar]
  • J. Fedjaev, S.-A. Amamra, and B. Francois, “Linear programming based optimization tool for day ahead energy management of a lithium-ion battery for an industrial microgrid,” 2016: IEEE, pp. 406–411, DOI: 10.1109/EPEPEMC.2016.7752032. [Google Scholar]
  • T. Kerdphol, K. Fuji, Y. Mitani, M. Watanabe, and Y. Qudaih, “Optimization of a battery energy storage system using particle swarm optimization for stand-alone microgrids,” International Journal of Electrical Power & Energy Systems, Vol. 81, pp. 32–39, 2016/10/01/ 2016, doi: https://doi.org/10.1016/j.ijepes.2016.02.006. [CrossRef] [Google Scholar]
  • M. Moghimi, R. Garmabdari, S. Stegen, and J. Lu, “Battery energy storage cost and capacity optimization for university research center,” 2018: IEEE, pp. 1–8, DOI: 10.1109/ICPS.2018.8369968. [Google Scholar]
  • S. Chen, T. Zhang, H. B. Gooi, R. D. Masiello, and W. Katzenstein, “Penetration Rate and Effectiveness Studies of Aggregated BESS for Frequency Regulation,” IEEE Transactions on Smart Grid, vol. 7, no. 1, pp. 167–177, 2016, DOI: 10.1109/TSG.2015.2426017. [CrossRef] [Google Scholar]
  • M. D. A. Al-Falahi and M. Z. C. Wanik, “Modeling and performance analysis of hybrid power system for residential application,” 2015: IEEE, pp. 1–6. [Google Scholar]
  • Z. W. Geem and Y. Yoon, “Harmony search optimization of renewable energy charging with energy storage system,” International Journal of Electrical Power & Energy Systems, Vol. 86, pp. 120–126, 2017. [CrossRef] [Google Scholar]
  • K. S. Nimma, M. D. A. Al-Falahi, H. D. Nguyen, S. D. G. Jayasinghe, T. S. Mahmoud, and M. Negnevitsky, “Grey Wolf Optimization-Based Optimum Energy-Management and Battery-Sizing Method for Grid-Connected Microgrids,” Energies, vol. 11, no. 4, pp. 847, 2018. [Online]. Available: https://www.mdpi.com/1996-1073/11/4/847. [CrossRef] [Google Scholar]
  • R. Mohanty and A. Pradhan, “Protection of DC and hybrid AC-DC microgrids with ring configuration,” in 2017 7th International Conference on Power Systems (ICPS), 2017: IEEE, pp. 607–612. [CrossRef] [Google Scholar]
  • F. Katiraei, R. Iravani, N. Hatziargyriou, and A. Dimeas, “Microgrids management,” IEEE power and energy magazine, vol. 6, no. 3, pp. 54–65, 2008. [CrossRef] [Google Scholar]
  • S. M. Nosratabadi, R.-A. Hooshmand, and E. Gholipour, “A comprehensive review on microgrid and virtual power plant concepts employed for distributed energy resources scheduling in power systems,” Renewable and Sustainable Energy Reviews, Vol. 67, pp. 341–363, 2017. [CrossRef] [Google Scholar]
  • M. D. A. Al-falahi, S. D. G. Jayasinghe, and H. Enshaei, “A review on recent size optimization methodologies for standalone solar and wind hybrid renewable energy system,” Energy Conversion and Management, Vol. 143, pp. 252–274, 2017/07/01/ 2017, doi: https://doi.org/10.1016/j.enconman.2017.04.019. [CrossRef] [Google Scholar]
  • B. Bahmani-Firouzi and R. Azizipanah-Abarghooee, “Optimal sizing of battery energy storage for micro-grid operation management using a new improved bat algorithm,” International Journal of Electrical Power & Energy Systems, Vol. 56, pp. 42–54, 2014/03/01/ 2014, doi: https://doi.org/10.1016/j.ijepes.2013.10.019. [CrossRef] [Google Scholar]
  • R. G. Allwyn, A. Al-Hinai, and V. Margaret, “A comprehensive review on energy management strategy of microgrids,” Energy Reports, Vol. 9, pp. 5565–5591, 2023, DOI: 10.1016/j.egyr.2023.04.360. [CrossRef] [Google Scholar]
  • B. Zhou et al., “Smart home energy management systems: Concept, configurations, and scheduling strategies,” Renewable and Sustainable Energy Reviews, Vol. 61, pp. 30–40, 2016. [CrossRef] [Google Scholar]
  • M. W. Khan, J. Wang, M. Ma, L. Xiong, P. Li, and F. Wu, “Optimal energy management and control aspects of distributed microgrid using multi-agent systems,” Sustainable Cities and Society, Vol. 44, pp. 855–870, 2019. [CrossRef] [Google Scholar]
  • X. He, X. Fang, and J. Yu, “Distributed energy management strategy for reaching cost-driven optimal operation integrated with wind forecasting in multimicrogrids system,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 49, no. 8, pp. 1643–1651, 2019. [CrossRef] [Google Scholar]
  • D. E. Olivares et al., “Trends in microgrid control,” IEEE Transactions on smartGridd, vol. 5, no. 4, pp. 1905–1919, 2014. [Google Scholar]
  • N. Wu and H. Wang, “Deep learning adaptive dynamic programming for real time energy management and control strategy of micro-grid,” Journal of cleaner production, Vol. 204, pp. 1169–1177, 2018. [CrossRef] [Google Scholar]
  • X. Xing, L. Xie, and H. Meng, “Cooperative energy management optimization based on distributed MPC in grid-connected microgrids community,” International Journal of Electrical Power & Energy Systems, Vol. 107, pp. 186–199, 2019. [CrossRef] [Google Scholar]
  • H. Fontenot and B. Dong, “Modeling and control of building-integrated microgrids for optimal energy management-a review,” Applied Energy, Vol. 254, p. 113689, 2019. [CrossRef] [Google Scholar]
  • Ilic-Spong, M., Christensen, J., Eichorn, K., 1988. Secondary voltage control using pilot point information. IEEE Trans. Power Syst. 3, 660–668. [CrossRef] [Google Scholar]
  • Li, J., Liu, Y., Wu, L., 2016. Optimal operation for community-based multi-party microgrid in grid-connected and islanded modes. IEEE Trans. Smart Grid 9, 756–765. [Google Scholar]
  • Rahim, S., Javaid, N., Khan, R.D., Nawaz, N., Iqbal, M., 2019. A convex optimization based decentralized real-time energy management model with the optimal integration of microgrid in smart grid. J. Clean. Prod. 236, 117688. [CrossRef] [Google Scholar]
  • Olivares, D.E., Mehrizi-Sani, A., Etemadi, A.H., Cañizares, C.A., Iravani, R., Kazerani, M., et al., 2014. Trends in microgrid control. IEEE Trans. Smart Grid 5, 1905–1919 [CrossRef] [Google Scholar]
  • Agnoletto, E.J., De Castro, D.S., Neves, R.V., Machado, R.Q., Oliveira, V.A., 2019. An optimal energy management technique using the e-constraint method for grid-tied and stand-alone battery-based microgrids. IEEE Access 7, 165928–165942 [CrossRef] [Google Scholar]
  • C. K. Nayak, K. Kasturi, and M. R. Nayak, “Economical management of microgrid for optimal participation in electricity market,” Journal of Energy Storage, Vol. 21, pp. 657–664, 2019. [CrossRef] [Google Scholar]
  • M. A. Husted, B. Suthar, G. H. Goodall, A. M. Newman, and P. A. Kohl, “Coordinating microgrid procurement decisions with a dispatch strategy featuring a concentration gradient,” Applied Energy, Vol. 219, pp. 394–407, 2018. [CrossRef] [Google Scholar]
  • M. Petrollese, L. Valverde, D. Cocco, G. Cau, and J. Guerra, “Real-time integration of optimal generation scheduling with MPC for the energy management of a renewable hydrogen-based microgrid,” Applied Energy, Vol. 166, pp. 96–106, 2016. [CrossRef] [Google Scholar]
  • A. A. Moghaddam, A. Seifi, and T. Niknam, “Multi-operation management of a typical micro-grids using Particle Swarm Optimization: A comparative study,” Renewable and Sustainable Energy Reviews, 16, no. 2, pp. 1268–1281, 2012/02/01/ 2012, doi: https://doi.org/10.1016/j.rser.2011.10.002. [CrossRef] [Google Scholar]
  • A. Hassan, Y. M. Al-Abdeli, M. Masek, and O. Bass, “Optimal sizing and energy scheduling of grid-supplemented solar PV systems with battery storage: Sensitivity of reliability and financial constraints,” Energy, Vol. 238, p. 121780, 2022/01/01/ 2022, doi: https://doi.org/10.1016/j.energy.2021.121780. [CrossRef] [Google Scholar]
  • J. Ahmad, M. Tahir, and S. K. Mazumder, “Dynamic economic dispatch and transient control of distributed generators in a microgrid,” IEEE Systems Journal, vol. 13, no. 1, pp. 802–812, 2018. [Google Scholar]
  • S. Leonori, M. Paschero, F. M. Frattale Mascioli, and A. Rizzi, “Optimization strategies for Microgrid energy management systems by Genetic Algorithms,” Applied Soft Computing, Vol. 86, p. 105903, 2020/01/01/ 2020, doi: https://doi.org/10.1016/j.asoc.2019.105903. [CrossRef] [Google Scholar]
  • J. Zhao, H. S. Ramadan, and M. Becherif, “Metaheuristic-based energy management strategies for fuel cell emergency power unit in electrical aircraft,” International Journal of Hydrogen Energy, vol. 44, no. 4, pp. 2390–2406, 2019. [CrossRef] [Google Scholar]
  • M. A. Shaheen, H. M. Hasanien, R. A. Turky, M. Ćalasan, A. F. Zobaa, and S. H. Abdel Aleem, “Opf of modern power systems comprising renewable energy sources using improved chgs optimization algorithm,” Energies, vol. 14, no. 21, pp. 6962, 2021. [CrossRef] [Google Scholar]
  • C. G. Marcelino, J. V. C. Avancini, C. A. D. M. Delgado, E. F. Wanner, S. Jiménez-Fernández, and S. Salcedo-Sanz, “Dynamic Electric Dispatch for Wind Power Plants: A New Automatic Controller System Using Evolutionary Algorithms,” Sustainability, vol. 13, no. 21, pp. 11924, 2021. [Online]. Available: https://www.mdpi.com/2071-1050/13/21/11924. [CrossRef] [Google Scholar]
  • M. Nemati, K. Bennimar, S. Tenbohlen, L. Tao, H. Mueller, and M. Braun, “Optimization of microgrids short term operation based on an enhanced genetic algorithm,” in 2015 IEEE Eindhoven PowerTech, 29 June-2 July 2015 2015, pp. 1–6, DOI: 10.1109/PTC.2015.7232801. [Google Scholar]
  • R.-K. Kim, M. B. Glick, K. R. Olson, and Y.-S. Kim, “MILP-PSO Combined Optimization Algorithm for an Islanded Microgrid Scheduling with Detailed Battery ESS Efficiency Model and Policy Considerations,” Energies, vol. 13, no. 8, pp. 1898, 2020. [Online]. Available: https://www.mdpi.com/1996-1073/13/8/1898. [CrossRef] [Google Scholar]
  • L. Mellouk, M. Ghazi, A. Aaroud, M. Boulmalf, D. Benhaddou, and K. Zine-Dine, “Design and energy management optimization for hybrid renewable energy system- case study: Laayoune region,” Renewable Energy, Vol. 139, pp. 621–634, 2019/08/01/ 2019, doi: https://doi.org/10.1016/j.renene.2019.02.066. [CrossRef] [Google Scholar]
  • K. Y. Lee and J. B. Park, “Application of Particle Swarm Optimization to Economic Dispatch Problem: Advantages and Disadvantages,” in 2006 IEEE PES Power Systems Conference and Exposition, 29 Oct.-1 Nov. 2006 2006, pp. 188–192, DOI: 10.1109/PSCE.2006.296295. [CrossRef] [Google Scholar]
  • G. Aghajani and N. Ghadimi, “Multi-objective energy management in a micro-grid,” Energy Reports, Vol. 4, pp. 218–225, 2018/11/01/ 2018, doi: https://doi.org/10.1016/j.egyr.2017.10.002. [CrossRef] [Google Scholar]
  • J. Koskela, A. Rautiainen, and P. Järventausta, “Using electrical energy storage in residential buildings - Sizing of battery and photovoltaic panels based on electricity cost optimization,” Applied Energy, Vol. 239, pp. 1175–1189, 2019/04/01/ 2019, doi: https://doi.org/10.1016/j.apenergy.2019.02.021. [CrossRef] [Google Scholar]
  • A. Aftan, “A New Optimization Method for Improving the Performance of Photovoltaic System,” 2022 4th International Conference on Current Research in Engineering and Science Applications (ICCRESA), Baghdad, Iraq, 2022, pp. 355–359, DOI: 10.1109/ICCRESA57091.2022.10352491. [CrossRef] [Google Scholar]
  • Aftan, A.O., Sadiq, M.S., Alnasrawi, M., Aljanabi, M. and Jumaa, F.A., 2020, July. Low Complexity Rate Compatible Puncturing For Future Communication network. In IOP Conference Series: Materials Science and Engineering (Vol. 881, No. 1, p. 012145). IOP Publishing. [CrossRef] [Google Scholar]
  • C. Yohwan and K. Hongseok, “Optimal scheduling of energy storage system for self-sustainable base station operation considering battery wear-out cost,” in 2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN), 5-8 July 2016 2016, pp. 170–172, DOI: 10.1109/ICUFN.2016.7537010. [CrossRef] [Google Scholar]
  • M. Moghimi, R. Garmabdari, S. Stegen, and J. Lu, “Battery energy storage cost and capacity optimization for university research center,” in 2018 IEEE/IAS 54th Industrial and Commercial Power Systems Technical Conference (I&CPS), 7-10 May 2018 2018, pp. 1–8, DOI: 10.1109/ICPS.2018.8369968. [Google Scholar]
  • U. G. K. Mulleriyawage and W. X. Shen, “Optimally sizing of battery energy storage capacity by operational optimization of residential PV-Battery systems: An Australian household case study,” Renewable Energy, Vol. 160, pp. 852–864, 2020/11/01/ 2020, doi: https://doi.org/10.1016/j.renene.2020.07.022. [CrossRef] [Google Scholar]
  • Hayder Khairi Kadhim, Ahmed Obaid Aftan, Ahmed Ghanim Wadday; Performance improvement of the solar PV system-based phase change material: A review. AIP Conf. Proc. 22 December 2023; 2977 (1): 020021. [CrossRef] [Google Scholar]
  • Hayder Khairi, Ahmed Obaid Aftan, Ahmed Ghanim Wadday. (Improving the Performance of a Photovoltaic Panel Using Locally Manufactured Phase Change Materials). Accepted in 2nd ICASDG-Tabriz-Iran and will be published in American Institute of Physics Conference Series (2024). [Google Scholar]
  • S. Grillo, A. Pievatolo, and E. Tironi, “Optimal Storage Scheduling Using Markov Decision Processes,” IEEE Transactions on Sustainable Energy, vol. 7, no. 2, pp. 755–764, 2016, DOI: 10.1109/TSTE.2015.2497718. [CrossRef] [Google Scholar]
  • N. K. Paliwal, A. K. Singh, N. K. Singh, and P. Kumar, “Optimal sizing and operation of battery storage for economic operation of hybrid power system using artificial bee colony algorithm,” International Transactions on Electrical Energy Systems, 29, no. 1, p. e2685, 2019. [CrossRef] [Google Scholar]
  • Z. W. Geem, J. H. Kim, and G. V. Loganathan, “A new heuristic optimization algorithm: harmony search,” Simulation, vol. 76, no. 2, pp. 60–68, 2001. [CrossRef] [Google Scholar]
  • L. Urbanucci, “Limits and potentials of Mixed Integer Linear Programming methods for optimization of polygeneration energy systems,” Energy Procedia, Vol. 148, pp. 1199–1205, 2018/08/01/ 2018, doi: https://doi.org/10.1016/j.egypro.2018.08.021. [Google Scholar]
  • B. R. Mistry and A. Desai, “Privacy preserving heuristic approach for association rule mining in distributed database,” in 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), 19-20 March 2015 2015, pp. 1–7, DOI: 10.1109/ICIIECS.2015.7192972. [Google Scholar]
  • Aftan, A., 2018. Multiple Parallel Concatenated Gallager Codes and Their Applications (Doctoral dissertation, University of Sheffield). [Google Scholar]
  • Aftan, A., Benaissa, M. and Behairy, H., 2018, June. Efficient coding method of multiple parallel concatenated gallager codes for WiMAX. In 2018 Wireless Advanced (WiAd) (pp. 1–6). IEEE. [Google Scholar]
  • E. Gerhardt and H. M. Gomes, “Artificial bee colony (ABC) algorithm for engineering optimization problems,” 2012, Vol. 11, 4 ed. [Google Scholar]

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