Reservoir optimization to produce maximum power generation under climatic conditions based on the improved bat optimization algorithm (IBOA)

References

• Abera, F. F., D. Asfaw, A. Engida, and A. Melesse. 2018. Optimal operation of hydropower reservoirs under climate change: The case of Tekeze reservoir, Eastern Nile. Water 10 (3):273. doi:10.3390/w10030273.
   Web of Science ®Google Scholar
• Aghajani, G., and N. Ghadimi. 2018. Multi-objective energy management in a micro-grid. Energy Reports 4:218–25. doi:10.1016/j.egyr.2017.10.002.
   Web of Science ®Google Scholar
• Altamirano Del Carmen, M., F. Estrada, and C. Gay-García. 2021. A new method for assessing the performance of general circulation models based on their ability to simulate the response to observed forcing. Journal of Climate 34 (13):5385–402. doi:10.1175/JCLI-D-20-0510.1.
   Web of Science ®Google Scholar
• Cai, W., R. Mohammaditab, G. Fathi, K. Wakil, A. G. Ebadi, and N. Ghadimi. 2019. Optimal bidding and offering strategies of compressed air energy storage: A hybrid robust-stochastic approach. Renewable Energy 143:1–8. doi:10.1016/j.renene.2019.05.008.
   Web of Science ®Google Scholar
• Chong, K. L., S. H. Lai, A. N. Ahmed, W. Z. Wan Jaafar, and A. El-Shafie. 2021. Optimization of hydropower reservoir operation based on hedging policy using Jaya algorithm. Applied Soft Computing 106:107325. doi:10.1016/j.asoc.2021.107325.
   Web of Science ®Google Scholar
• CMDSC. China Meteorological Data Service Centre. Available from: http://data.cma.cn/en.
   Google Scholar
• Dallison, R. J., S. D. Patil, and A. P. Williams. 2021. Impacts of climate change on future water availability for hydropower and public water supply in Wales. UK Journal of Hydrology: Regional Studies 36:100866.
   Google Scholar
• Dasgupta, K., B. Mandal, P. Dutta, J. K. Mandal, and S. Dam. 2013. A genetic algorithm (ga) based load balancing strategy for cloud computing. Procedia Technology 10:340–47. doi:10.1016/j.protcy.2013.12.369.
   Google Scholar
• De Gois Barbosa, A., A. B. Celeste, and L. A. Mendes. 2021. Influence of Inflow nonstationarity on the multipurpose optimal operation of hydropower plants using nonlinear programming. Water Resources Management 35 (8):1–25. doi:10.1007/s11269-021-02812-8.
   Web of Science ®Google Scholar
• Duan, F., F. Song, S. Chen, M. Khayatnezhad, and N. Ghadimi. 2022. Model parameters identification of the PEMFCs using an improved design of crow search algorithm. International Journal of Hydrogen Energy 47 (79):33839–49. doi:10.1016/j.ijhydene.2022.07.251.
   Web of Science ®Google Scholar
• Ebrahimian, H., S. Barmayoon, M. Mohammadi, and N. Ghadimi. 2018. The price prediction for the energy market based on a new method. Economic Research-Ekonomska Istraživanja 31 (1):313–37. doi:10.1080/1331677X.2018.1429291.
   Web of Science ®Google Scholar
• Eslami, M., H. A., Moghadam, H., Zayandehroodi. 2019. A New Formulation to Reduce the Number of Variables and Constraints to Expedite SCUC in Bulky Power Systems. Proc. Natl. Acad. Sci., India, Sect. A Phys. Sci. 89:311–21. doi:10.1007/s40010-017-0475-1.
   Google Scholar
• Fan, X., H. Sun, Z. Yuan, Z. Li, R. Shi, and N. Razmjooy. 2020. Multi-objective optimization for the proper selection of the best heat pump technology in a fuel cell-heat pump micro-CHP system. Energy Reports 6:325–35. doi:10.1016/j.egyr.2020.01.009.
   Web of Science ®Google Scholar
• Fei, X., R. Xuejun, and N. Razmjooy. 2019. Optimal configuration and energy management for combined solar chimney, solid oxide electrolysis, and fuel cell: A case study in Iran. energy sources, part A: Recovery. Utilization, and Environmental Effects 1–21. doi:10.1080/15567036.2019.1680770.
   Google Scholar
• Ghadimi, N. 2015. An adaptive neuro‐fuzzy inference system for islanding detection in wind turbine as distributed generation. Complexity 21 (1):10–20. doi:10.1002/cplx.21537.
   Web of Science ®Google Scholar
• Ghadimi, N., M. Sedaghat, K. K. Azar, B. Arandian, G. Fathi, and M. Ghadamyari. 2023. An innovative technique for optimization and sensitivity analysis of a PV/DG/BESS based on converged Henry gas solubility optimizer: A case study. IET Generation, Transmission & Distribution 00:1–15. doi:https://doi.org/10.1049/gtd2.12773.
   Google Scholar
• Gheydi, M., A. Nouri, and N. Ghadimi. 2016. Planning in microgrids with conservation of voltage reduction. IEEE Systems Journal 12 (3):2782–90. doi:10.1109/JSYST.2016.2633512.
   Web of Science ®Google Scholar
• Ghiasi, M., N. Ghadimi, and E. Ahmadinia. 2019. An analytical methodology for reliability assessment and failure analysis in distributed power system. SN Applied Sciences 1 (1):44. doi:10.1007/s42452-018-0049-0.
   Web of Science ®Google Scholar
• Ghiasi, M., T. Niknam, Z. Wang, M. Mehrandezh, M. Dehghani, and N. Ghadimi. 2023. A comprehensive review of cyber-attacks and defense mechanisms for improving security in smart grid energy systems: Past, present and future. Electric Power Systems Research 215:108975. doi:10.1016/j.epsr.2022.108975.
   Google Scholar
• Ghiasi, M., Z. Wang, M. Mehrandezh, S. Jalilian, and N. Ghadimi. 2023. Evolution of smart grids towards the Internet of energy: Concept and essential components for deep decarbonisation. IET Smart Grid 6 (1):86–102. doi:10.1049/stg2.12095.
   Google Scholar
• Gollou, A. R., and N. Ghadimi. 2017. A new feature selection and hybrid forecast engine for day-ahead price forecasting of electricity markets. Journal of Intelligent & Fuzzy Systems 32 (6):4031–45. doi:10.3233/JIFS-152073.
   Web of Science ®Google Scholar
• Haguma, D., R. Leconte, P. Côté, S. Krau, and F. Brissette. 2014. Optimal hydropower generation under climate change conditions for a northern water resources system. Water Resources Management 28 (13):4631–44. doi:10.1007/s11269-014-0763-3.
   Web of Science ®Google Scholar
• Han, E., and N. Ghadimi. 2022. Model identification of proton-exchange membrane fuel cells based on a hybrid convolutional neural network and extreme learning machine optimized by improved honey badger algorithm. Sustainable Energy Technologies and Assessments 52:102005. doi:10.1016/j.seta.2022.102005.
   Web of Science ®Google Scholar
• Hunt, J. D., A. Nascimento, C. S. T. Caten, F. M. C. Tomé, P. S. Schneider, A. L. R. Thomazoni, N. J. D. Castro, R. Brandão, M. A. V. D. Freitas, J. S. C. Martini, et al. 2022. Energy crisis in Brazil: Impact of hydropower reservoir level on the river flow. Energy 239:121927. doi:10.1016/j.energy.2021.121927.
   Web of Science ®Google Scholar
• Jiang, W., Wang X, Huang H, Zhang D, Ghadimi N. 2022. Optimal economic scheduling of microgrids considering renewable energy sources based on energy hub model using demand response and improved water wave optimization algorithm. Journal of Energy Storage 55:105311. doi:10.1016/j.est.2022.105311.
   Web of Science ®Google Scholar
• Karamnejadi Azar, K., Kakouee A, Mollajafari M, Majdi A, Ghadimi N, Ghadamyari M. 2022. Developed design of battle royale optimizer for the optimum identification of solid oxide fuel cell. Sustainability. 14(16):9882. doi:10.3390/su14169882.
   Web of Science ®Google Scholar
• Liu, Y., W. Wang, and N. Ghadimi. 2017. Electricity load forecasting by an improved forecast engine for building level consumers. Energy 139:18–30. doi:10.1016/j.energy.2017.07.150.
   Web of Science ®Google Scholar
• Marahatta, S., U. Bhattarai, L. P. Devkota, and D. Aryal. 2022. Unravelling the water-energy-economics-continuum of hydroelectricity in the face of climate change. International Journal of Energy and Water Resources 6 (3):1–13. doi:10.1007/s42108-021-00174-w.
   Google Scholar
• Minville, M., F. Brissette, S. Krau, and R. Leconte. 2009. Adaptation to climate change in the management of a Canadian water-resources system exploited for hydropower. Water Resources Management 23 (14):2965–86. doi:10.1007/s11269-009-9418-1.
   Web of Science ®Google Scholar
• Mirjalili, S., S. M. Mirjalili, and A. Lewis. 2014. Grey wolf optimizer. Advances in Engineering Software 69:46–61. doi:10.1016/j.advengsoft.2013.12.007.
   Web of Science ®Google Scholar
• Ndeketeya, A., and M. Dundu. 2021. Application of HEC-HMS model for evaluation of rainwater harvesting potential in a semi-arid city. Water Resources Management 35 (12):4217–32. doi:10.1007/s11269-021-02941-0.
   Web of Science ®Google Scholar
• Niu, W. -J., and Z. -K. Feng. 2021. Evaluating the performances of several artificial intelligence methods in forecasting daily streamflow time series for sustainable water resources management. Sustainable Cities and Society 64:102562. doi:10.1016/j.scs.2020.102562.
   Web of Science ®Google Scholar
• Rezaie, M., Akbari E, Ghadimi N, Razmjooy N, Ghadamyari M. 2022. Model parameters estimation of the proton exchange membrane fuel cell by a modified golden jackal optimization. Sustainable Energy Technologies and Assessments 53:102657. doi:10.1016/j.seta.2022.102657.
   Web of Science ®Google Scholar
• Simon, D. 2008. Biogeography-based optimization. IEEE Transactions on Evolutionary Computation 12 (6):702–13. doi:10.1109/TEVC.2008.919004.
   Web of Science ®Google Scholar
• Tariku, T. B., K. E. Gan, X. Tan, T. Y. Gan, H. Shi, and A. Tilmant. 2021. Global warming impact to river basin of blue nile and the optimum operation of its multi-reservoir system for hydropower production and irrigation. The Science of the Total Environment 767:144863. doi:10.1016/j.scitotenv.2020.144863.
   PubMed Web of Science ®Google Scholar
• Wilson, J. 2021. Two exploratory uses for general circulation models in climate science. Perspectives on Science 29 (4):493–509. doi:10.1162/posc_a_00380.
   Google Scholar
• Yang, Z., Liu Q, Zhang L, Dai J, Razmjooy N. 2020. Model parameter estimation of the PEMFCs using improved barnacles mating optimization algorithm. Energy 212:118738. doi:10.1016/j.energy.2020.118738.
   Web of Science ®Google Scholar
• Ye, H., G. Jin, W. Fei, and N. Ghadimi. 2020. High step-up interleaved dc/dc converter with high efficiency. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 1–20. doi:10.1080/15567036.2020.1716111.
   Web of Science ®Google Scholar
• Yin, Z., and N. Razmjooy. 2020. PEMFC identification using deep learning developed by improved deer hunting optimization algorithm. International Journal of Power and Energy Systems 40 (2):2020.203–0189. doi:10.2316/J.2020.203-0189.
   Web of Science ®Google Scholar
• Yuan, Z., W. Wang, H. Wang, and N. Ghadimi. 2020. Probabilistic decomposition‐based security constrained transmission expansion planning incorporating distributed series reactor. IET Generation, Transmission & Distribution 14 (17):3478–87. doi:10.1049/iet-gtd.2019.1625.
   Web of Science ®Google Scholar
• Yu, D., and N. Ghadimi. 2019. Reliability constraint stochastic UC by considering the correlation of random variables with Copula theory. IET Renewable Power Generation 13 (14):2587–93. doi:10.1049/iet-rpg.2019.0485.
   Web of Science ®Google Scholar
• Zhang, J., M. Khayatnezhad, and N. Ghadimi. 2022. Optimal model evaluation of the proton-exchange membrane fuel cells based on deep learning and modified African vulture optimization algorithm. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 44 (1):287–305. doi:10.1080/15567036.2022.2043956.
   Web of Science ®Google Scholar
• Zhao, G., H. Gao, and S. -C. Kao. 2021. The implications of future climate change on the blue water footprint of hydropower in the contiguous US. Environmental Research Letters 16 (3):034003. doi:10.1088/1748-9326/abd78d.
   Web of Science ®Google Scholar
• Zhao, Y., K. Xu, N. Dong, and H. Wang. 2022. Projection of climate change impacts on hydropower in the source region of the Yangtze River based on CMIP6. Journal of Hydrology 606:127453. doi:10.1016/j.jhydrol.2022.127453.
   Web of Science ®Google Scholar