4
How to cite this paper
APA: Alvarez, G. (2024). Hybrid optimization model with Neural Network approach for renewable energy prediction and scheduling in large scale systems. Management Science Letters, 14(4), 247-264.
Chicago/Turabian: Alvarez, G. 2024. "Hybrid optimization model with Neural Network approach for renewable energy prediction and scheduling in large scale systems." Management Science Letters 14, no. 4 (2024): 247-264.
AMA: Alvarez, G. Hybrid optimization model with Neural Network approach for renewable energy prediction and scheduling in large scale systems. Management Science Letters. 2024;14(4):247-264.
References
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Alvarez, G. E. (2021). A multi-objective formulation of improving flexibility in the operation of electric power systems: Application to mitigation measures during the coronavirus pandemic. Energy, 227, 120471. https://doi.org/10.1016/j.energy.2021.120471
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Horváth, L., Kokoszka, P., & Rice, G. (2014). Testing stationarity of functional time series. Journal of Econometrics, 179(1), 66–82. https://doi.org/10.1016/j.jeconom.2013.11.002
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Ahmed, S. D., Al-Ismail, F. S. M., Shafiullah, M., Al-Sulaiman, F. A., & El-Amin, I. M. (2020). Grid Integration Chal-lenges of Wind Energy: A Review. IEEE Access, 8, 10857–10878. https://doi.org/10.1109/ACCESS.2020.2964896
Akella, A. K., Saini, R. P., & Sharma, M. P. (2009). Social, economical and environmental impacts of renewable energy systems. Renewable Energy, 34(2), 390–396. https://doi.org/10.1016/j.renene.2008.05.002
Alguacil, N., Membel, S., Conejo, a J., & Membel, S. (2000). Multiperiod Optimal Power Flow Using Benders Decompo-sition. Power Systems, IEEE Transactions On, 15(1), 196–201. https://doi.org/10.1109/59.852121
Alvarez, G. (2020a). Integrated scheduling from a diversity of sources applied to the Argentine electric power and natural gas systems. Computers & Chemical Engineering, 134, 106691. https://doi.org/10.1016/j.compchemeng.2019.106691
Alvarez, G. (2020b). Operation of pumped storage hydropower plants through optimization for power systems. Energy, 202, 117797. https://doi.org/10.1016/j.energy.2020.117797
Alvarez, G. (2020c). Optimization analysis for hydro pumped storage and natural gas accumulation technologies in the Argentine Energy System. Journal of Energy Storage, 31(June), 101646. https://doi.org/10.1016/j.est.2020.101646
Alvarez, G. (2022a). Integrated modeling of the peer-to-peer markets in the energy industry. International Journal of In-dustrial Engineering Computations, 13(1), 101–118. https://doi.org/10.5267/j.ijiec.2021.7.002
Alvarez, G. (2022b). Stochastic optimization considering the uncertainties in the electricity demand, natural gas infra-structures, photovoltaic units, and wind generation. Computers & Chemical Engineering, 160, 107712. https://doi.org/10.1016/j.compchemeng.2022.107712
Alvarez, G., & Blas, M. J. (2021). Optimization of electric power systems considering the environmental impact and un-certainties. 2021 Third International Sustainability and Resilience Conference: Climate Change, 416–423. https://doi.org/10.1109/IEEECONF53624.2021.9667979
Alvarez, G. E. (2021). A multi-objective formulation of improving flexibility in the operation of electric power systems: Application to mitigation measures during the coronavirus pandemic. Energy, 227, 120471. https://doi.org/10.1016/j.energy.2021.120471
Argentine Atomic Energy Commission (CNEA). (2016). Synthesis of the Wholesale Electricity Market of the Republic of Argentina. January.
Arias, A., Sanchez, J. D., & Granada, M. (2018). Integrated planning of electric vehicles routing and charging stations lo-cation considering transportation networks and power distribution systems. International Journal of Industrial Engi-neering Computations, 535–550. https://doi.org/10.5267/j.ijiec.2017.10.002
Bachy, B., & Franke, J. (2015). Modeling and optimization of laser direct structuring process using artificial neural net-work and response surface methodology. International Journal of Industrial Engineering Computations, 6(4), 553–564. https://doi.org/10.5267/j.ijiec.2015.4.003
Bagherian, M. A., & Mehranzamir, K. (2020). A comprehensive review on renewable energy integration for combined heat and power production. Energy Conversion and Management, 224, 113454. https://doi.org/10.1016/j.enconman.2020.113454
BBC. (2021). Climate change: UN to reveal landmark IPCC report findings. Science-Environment, 1.
Bharadwa, A. (2020). Wind Turbine Power Output Forecast.
Branco, P., Gonçalves, F., & Costa, A. C. (2020). Tailored Algorithms for Anomaly Detection in Photovoltaic Systems. Energies, 13(1), 225. https://doi.org/10.3390/en13010225
Breeze, P. (2014). Power Generation Technologies (2nd editio). Elsevier. https://doi.org/10.1016/C2012-0-00136-6
Brownlee, J. (2017). How to use timesteps in LSTM networks for time series forecasting. Machine Learning Mastery.
Burton, T., Jenkins, N., Sharpe, D., & Bossanyi, E. (2011). Wind Energy Handbook, Second Edition. In Wind Energy Handbook, Second Edition. John Wiley & Sons, Ltd. https://doi.org/10.1002/9781119992714
CAMMESA. (2019). Report February 2019. In Monthly report - Main variables of the month. https://doi.org/10.18356/fc4b62a8-es
CAMMESA. (2023). Generación de Renovables. Generación Por Tecnología. https://cammesaweb.cammesa.com/generacion-real/
Carrion-i-Silvestre, J. L., & Kim, D. (2019). Quasi-likelihood ratio tests for cointegration, cobreaking, and cotrending. Econometric Reviews, 38(8), 881–898. https://doi.org/10.1080/07474938.2018.1528416
Chen, C. H., Chen, N., & Luh, P. B. (2017). Head Dependence of Pump-Storage-Unit Model Applied to Generation Sched-uling. IEEE Transactions on Power Systems, 32(4), 2869–2877. https://doi.org/10.1109/TPWRS.2016.2629093
Chen, P.-Y., Chen, S.-T., Hsu, C.-S., & Chen, C.-C. (2016). Modeling the global relationships among economic growth, energy consumption and CO2 emissions. Renewable and Sustainable Energy Reviews, 65, 420–431. https://doi.org/10.1016/j.rser.2016.06.074
Erisen, B. (2018). Wind Turbine Scada Dataset. Scada Data of a Wind Turbine in Turkey. https://www.kaggle.com/datasets/berkerisen/wind-turbine-scada-dataset
Farrokhtala, A., Chen, Y., & Hu, T. (2019). The Time Element of Temporal Networks. 2019 IEEE Global Communications Conference (GLOBECOM), 1–6. https://doi.org/10.1109/GLOBECOM38437.2019.9013412
Foley, A. M., Leahy, P. G., Marvuglia, A., & McKeogh, E. J. (2012). Current methods and advances in forecasting of wind power generation. Renewable Energy, 37(1), 1–8. https://doi.org/10.1016/j.renene.2011.05.033
Hart, W. E., Watson, J.-P., & Woodruff, D. L. (2011). Pyomo: modeling and solving mathematical programs in Python. Mathematical Programming Computation, 3(3), 219–260. https://doi.org/10.1007/s12532-011-0026-8
Homan, S., Mac Dowell, N., & Brown, S. (2021). Grid frequency volatility in future low inertia scenarios: Challenges and mitigation options. Applied Energy, 290, 116723. https://doi.org/10.1016/j.apenergy.2021.116723
Horváth, L., Kokoszka, P., & Rice, G. (2014). Testing stationarity of functional time series. Journal of Econometrics, 179(1), 66–82. https://doi.org/10.1016/j.jeconom.2013.11.002
IPCC - Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. (2021). Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis.
Jangir, P., Manoharan, P., Pandya, S., & Sowmya, R. (2023). MaOTLBO: Many-objective teaching-learning-based opti-mizer for control and monitoring the optimal power flow of modern power systems. International Journal of Industri-al Engineering Computations, 14(2), 293–308. https://doi.org/10.5267/j.ijiec.2023.1.003
Kagemoto, H. (2020). Forecasting a water-surface wave train with artificial intelligence- A case study. Ocean Engineer-ing, 207, 107380. https://doi.org/10.1016/j.oceaneng.2020.107380
Kim, B., & Kim, J. (2020). Adjusting Decision Boundary for Class Imbalanced Learning. IEEE Access, 8, 81674–81685. https://doi.org/10.1109/ACCESS.2020.2991231
Kumar, M. (2020). Social, Economic, and Environmental Impacts of Renewable Energy Resources. In Wind Solar Hybrid Renewable Energy System (Vol. 151, pp. 1298–1306). IntechOpen. https://doi.org/10.5772/intechopen.89494
Kumar, R., Sharma, A. Kr., & Tewari, P. C. (2012). Markov approach to evaluate the availability simulation model for power generation system in a thermal power plant. International Journal of Industrial Engineering Computations, 3(5), 743–750. https://doi.org/10.5267/j.ijiec.2012.08.003
Li, X., Li, T., Wei, J., Wang, G., & Yeh, W. W. G. (2014). Hydro unit commitment via mixed integer linear programming: A case study of the three gorges project, China. IEEE Transactions on Power Systems, 29(3), 1232–1241. https://doi.org/10.1109/TPWRS.2013.2288933
Livieris, I. E., Stavroyiannis, S., Pintelas, E., & Pintelas, P. (2020). A novel validation framework to enhance deep learn-ing models in time-series forecasting. Neural Computing and Applications, 32(23), 17149–17167. https://doi.org/10.1007/s00521-020-05169-y
Neshat, M., Nezhad, M. M., Abbasnejad, E., Mirjalili, S., Tjernberg, L. B., Astiaso Garcia, D., Alexander, B., & Wagner, M. (2021). A deep learning-based evolutionary model for short-term wind speed forecasting: A case study of the Lillgrund offshore wind farm. Energy Conversion and Management, 236, 114002. https://doi.org/10.1016/j.enconman.2021.114002
Nuclear Energy Agency. (2011). Technical and Economic Aspects of Load Following with Nuclear Power Plants.
Orlov, A., Sillmann, J., & Vigo, I. (2020). Better seasonal forecasts for the renewable energy industry. Nature Energy, 5(2), 108–110. https://doi.org/10.1038/s41560-020-0561-5
Overbye, T. J., Cheng, X., & Sun, Y. (2004). A comparison of the AC and DC power flow models for LMP calculations. Proceedings of the 37th Annual Hawaii International Conference on System Sciences, 9. https://doi.org/10.1109/HICSS.2004.1265164
Panos, E., & Lehtilä, A. (2016). Dispatching and unit commitment features in TIMES.
Phillips, P. C. B., & Ouliaris, S. (1990). Asymptotic Properties of Residual Based Tests for Cointegration. Econometrica, 58(1), 165. https://doi.org/10.2307/2938339
Poorvaezi Roukerd, S., Abdollahi, A., & Rashidinejad, M. (2020). Uncertainty-based unit commitment and construction in the presence of fast ramp units and energy storages as flexible resources considering enigmatic demand elasticity. Journal of Energy Storage, 29, 101290. https://doi.org/10.1016/j.est.2020.101290
Rakhmonov, I. U., & Reymov, K. M. (2020). Statistical models of renewable energy intermittency. E3S Web of Confer-ences, 216, 01167. https://doi.org/10.1051/e3sconf/202021601167
Raybaut, P. (2009). Spyder-documentation.
Sahoo, A. K., Rout, A. K., & Das, D. K. (2015). Response surface and artificial neural network prediction model and opti-mization for surface roughness in machining. International Journal of Industrial Engineering Computations, 6(2), 229–240. https://doi.org/10.5267/j.ijiec.2014.11.001
Shahidehpour, M., Yamin, Hatim., & Li, Zuyi. (2003). Market Operations in Electric Power Systems. In Market Opera-tions in Electric Power Systems. Institute of Electrical and Electronics Engineers, Wiley-Interscience. https://doi.org/10.1002/047122412x
Silva, R. P., Zarpelão, B. B., Cano, A., & Junior, S. B. (2021). Time Series Segmentation Based on Stationarity Analysis to Improve New Samples Prediction. Sensors, 21(21), 7333. https://doi.org/10.3390/s21217333
Sinsel, S. R., Riemke, R. L., & Hoffmann, V. H. (2020). Challenges and solution technologies for the integration of varia-ble renewable energy sources—a review. Renewable Energy, 145, 2271–2285. https://doi.org/10.1016/j.renene.2019.06.147
Stott, B., Jardim, J., & Alsac, O. (2009). DC Power Flow Revisited. IEEE Transactions on Power Systems, 24(3), 1290–1300. https://doi.org/10.1109/TPWRS.2009.2021235
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