[
Al-Shetwi, A. Q., Hannan, M. A., Jern, K. P., Mansur, M. and Mahlia, T. M. I. (2020). Grid-Connected Renewable Energy Sources: Review of the Recent Integration Requirements and Control Methods. Journal of Cleaner Production, 253, pp. 119831.10.1016/j.jclepro.2019.119831
]Search in Google Scholar
[
Arefifar, S. A. and Alam, M. S. (2019). Energy Management in Power Distribution Systems: Review, Classification, Limitations and Challenges. IEEE Access, 7, pp. 92979–93001.10.1109/ACCESS.2019.2927303
]Search in Google Scholar
[
Baltensperger, D., Buechi, A., Segundo Sevilla, F. R. and Korba, P. (2017). Optimal Integration of Battery Energy Storage Systems and Control of Active Power Curtailment for Distribution Generation. IFAC-PapersOnLine, 50(1), pp. 8856–8860.10.1016/j.ifacol.2017.08.1542
]Search in Google Scholar
[
Benadli, R., Bjaoui, M., Khiari, B. and Sellami, A. (2021). Sliding Mode Control of Hybrid Renewable Energy System Operating in Grid Connected and Stand-Alone Mode. Power Electronics and Drives, 6(41), pp. 144–166.10.2478/pead-2021-0009
]Search in Google Scholar
[
Bird, L., Lew, D., Milligan, M., Carlini, E. M., Estanqueiro, A., Flynn, D., Gomez-Lazaro, E., Holttinen, H., Menemenlis, N., Orths, A. and Eriksen, P. B. (2016). Wind and Solar Energy Curtailment: A Review of International Experience. Renewable and Sustainable Energy Reviews, 65, pp. 577–586.10.1016/j.rser.2016.06.082
]Search in Google Scholar
[
Bremen, L. V. (2010). Large-Scale Variability of Weather Dependent Renewable Energy Sources. In: A. Troccoli, eds., Management of Weather and Climate Risk in the Energy Industry. NATO Science for Peace and Security Series C: Environmental Security. Springer, Dordrecht, pp. 189–206.10.1007/978-90-481-3692-6_13
]Search in Google Scholar
[
Colson, C. M. and Nehrir, M. H. (2011). Algorithms for Distributed Decision-Making for Multi-Agent Microgrid Power Management. In: 2011 IEEE power and Energy Society General Meeting. Detroit, MI, USA.10.1109/PES.2011.6039764
]Search in Google Scholar
[
El Bourakadi, D., Yahyaouy, A. and Boumhidi, J. (2020). Multi-Agent System Based on the Extreme Learning Machine and Fuzzy Control for Intelligent Energy Management in Microgrid. Journal of Intelligent Systems, 29(1), pp. 877–893.10.1515/jisys-2018-0125
]Search in Google Scholar
[
Hesse, H. C., Schimpe, M., Kucevic, D. and Jossen, A. (2017). Lithium-Ion Battery Storage for the Grid - A Review of Stationary Battery Storage System Design Tailored for Applications in Modern Power Grids. Energies, 10(12), pp. 2107.10.3390/en10122107
]Search in Google Scholar
[
Hossain, R., Maung, A., Than Oo, A. and Ali, A. S. (2013). A Hybrid Machine Learning using Mamdani Type Fuzzy Inference System (FIS) for Solar Power Prediction. Annals of Fuzzy Sets, Fuzzy Logic and Fuzzy Systems, 2(3), pp. 73–113.
]Search in Google Scholar
[
IEA. (2021). Renewables 2021 Analysis and forecast to 2026. International Energy Agency Publications. Available at: https://www.iea.org/reports/renewables-2021 [Accessed January 2022].
]Search in Google Scholar
[
Li, J., Wei, W. and Xiang, J. (2012). A Simple Sizing Algorithm for Stand-Alone PV/Wind/Battery Hybrid Microgrids. Energies, 5(12), pp. 5307–5323.10.3390/en5125307
]Search in Google Scholar
[
Mohamed, A., Refaat, S. S. and Abu-Rub, H. (2019). A Review on Big Data Management and Decision-Making in Smart Grid. Power Electronics and Drives, 4(39), pp. 1–13.10.2478/pead-2019-0011
]Search in Google Scholar
[
Park, S. W., Cho, K. S., Hoefter, G. and Son, S. Y. (2022). Electric Vehicle Charging Management Using Location-Based Incentives for Reducing Renewable Energy Curtailment Considering the Distribution System. Applied Energy, 305, pp. 117680.10.1016/j.apenergy.2021.117680
]Search in Google Scholar
[
Rabbani, M. G., Devotta, J. B. X. and Elangovan, S. (1997). A Fuzzy Set Theory Based Control of Superconductive Magnetic Energy Storage Unit to Improve Power System Dynamic Performance. Electric Power Systems Research, 40(2), pp. 107–114.10.1016/S0378-7796(96)01139-X
]Search in Google Scholar
[
Ren, Y., Yao, X., Liu, D., Qiao, R., Zhang, L., Zhang, K., Jin, K., Li, H., Ran, Y. and Li, F. (2022). Optimal Design of Hydro-Wind-PV Multi-Energy Complementary Systems Considering Smooth Power Output. Sustainable Energy Technologies and Assessments, 50, pp. 101832.10.1016/j.seta.2021.101832
]Search in Google Scholar
[
Shams, M. H., Niaz, H., Na, J., Anvari-Moghaddam, A. and Liu, J. J. (2021) Machine Learning-Based Utilization of Renewable Power Curtailments Under Uncertainty by Planning of Hydrogen Systems and Battery Storages. Journal of Energy Storage, 41, pp. 103010.10.1016/j.est.2021.103010
]Search in Google Scholar
[
Skfuzzy 0.2 documentation. (2022). Available at: https://pythonhosted.org/scikit-fuzzy/index.html [Accessed January 2022].
]Search in Google Scholar
[
Song, F., Yu, Z., Zhuang, W. and Lu, A. (2021). The Institutional Logic of Wind Energy Integration: What can China Learn from the United States to Reduce Wind Curtailment? Renewable and Sustainable Energy Reviews, 137, pp. 110440.
]Search in Google Scholar
[
Steiner, A., Köhler, C., Metzinger, I., Braun, A., Zirkelbach, M., Ernst, D., Tran, P. and Ritter, B. (2017). Critical Weather Situations for Renewable Energies – Part A: Cyclone Detection for Wind Power. Renewable Energy, 101, pp. 41–50.10.1016/j.renene.2016.08.013
]Search in Google Scholar
[
Teo, T. T., Logenthiran, T., Woo, W. L. and Abidi, K. (2016). Fuzzy Logic Control of Energy Storage System in Microgrid Operation. In: 2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia). Melbourne, VIC, Australia.
]Search in Google Scholar
[
Vargas, L. S., Bustos-Turu G. and Larran, F. (2015). Wind Power Curtailment and Energy Storage in Transmission Congestion Management Considering Power Plants Ramp Rates. IEEE Transactions on Power Systems, 30(5), pp. 2498–2506.10.1109/TPWRS.2014.2362922
]Search in Google Scholar
[
Wang, Y., Song, F., Ma, Y., Zhang, Y., Yang, J., Liu, Y., Zhang, F., Zhu, J. (2020). Research on Capacity Planning and Optimization of Regional Integrated Energy System Based on Hybrid Energy Storage System. Applied Thermal Engineering, 180, pp. 115834.10.1016/j.applthermaleng.2020.115834
]Search in Google Scholar
[
Wu, K. and Zhou, H. (2014). A Multi-Agent-Based Energy-Coordination Control System for Grid-Connected Large-Scale Wind–Photovoltaic Energy Storage Power-Generation Units. Solar Energy, 107, pp. 245–259.10.1016/j.solener.2014.05.012
]Search in Google Scholar
, 