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Engineering and Technology OPEN ACCESS

Monte Carlo Simulation in Renewable Energy Planning: A Comprehensive Review and Novel Framework for Uncertainty Quantification

Sahil Shah
NextEra Analytics, Inc., USA Juno Beach, USA
DOI Crossref DOI 10.37547/tajet/Volume07Issue06-04
tajet 2025
VOL. 7 / NO. 06 JUNE
FULL TEXT PP. 24-45
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VOLUME 7
ISSUE 06
YEAR 2025
PAGES 24-45
RESEARCH OVERVIEW

Abstract

The integration of renewable energy sources into modern power systems presents significant challenges due to inherent uncertainties in resource availability, demand fluctuations, and technical performance. Monte Carlo simulation has emerged as a powerful tool for addressing these uncertainties in renewable energy planning and optimization. This paper presents a comprehensive review of Monte Carlo applications across solar, wind, and hybrid renewable energy systems over the past two decades. Through systematic analysis of 75+ peer-reviewed publications, we identify key methodological trends, implementation challenges, and emerging opportunities. The review reveals that while Monte Carlo methods have been extensively applied to single-source renewable systems, significant gaps exist in addressing correlated uncertainties across hybrid configurations and real-time operational scenarios. We propose a novel unified framework that integrates machine learning-enhanced sampling techniques with traditional Monte Carlo approaches to improve computational efficiency while maintaining accuracy. The framework addresses five critical uncertainty dimensions: resource variability, demand stochasticity, equipment degradation, market price fluctuations, and grid integration constraints. Case studies demonstrate that the proposed framework reduces computational time by 40-60% compared to traditional methods while improving prediction accuracy by 15-25%. This review provides researchers and practitioners with a structured approach to implementing Monte Carlo simulations for renewable energy planning under uncertainty, contributing to more robust and economically viable renewable energy deployment strategies.

RESEARCH TOPICS

Keywords

Monte Carlo Simulation Renewable Energy Planning Uncertainty Quantification Hybrid Energy Systems Stochastic Optimization Energy Forecasting

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References

  1. International Renewable Energy Agency (IRENA), "Renewable Power Generation Costs in 2023," Abu Dhabi, 2024
  2. J. Widén, N. Carpman, V. Castellucci, et al., "Variability assessment and forecasting of renewables: A review for solar, wind, wave and tidal resources," Renewable and Sustainable Energy Reviews, vol. 44, pp. 356-375, 2015
  3. Lave, M., Kleissl, J., & Arias-Castro, E. (2011b). High-frequency irradiance fluctuations and geographic smoothing. Solar Energy, 86(8), 2190–2199. https://doi.org/10.1016/j.solener.2011.06.031
  4. Pinson, P. (2013b). Wind Energy: Forecasting challenges for its operational management. Statistical Science, 28(4). https://doi.org/10.1214/13-sts445
  5. Billinton, R., & Li, W. (1994b). Reliability assessment of electric power systems using Monte Carlo methods. In Springer eBooks. https://doi.org/10.1007/978-1-4899-1346-3
  6. Papaefthymiou, G., & Kurowicka, D. (2008b). Using copulas for modeling stochastic dependence in power system uncertainty analysis. IEEE Transactions on Power Systems, 24(1), 40–49. https://doi.org/10.1109/tpwrs.2008.2004728
  7. Hocaoğlu, F. O., Gerek, Ö. N., & Kurban, M. (2008b). Hourly solar radiation forecasting using optimal coefficient 2-D linear filters and feed-forward neural networks. Solar Energy, 82(8), 714–726. https://doi.org/10.1016/j.solener.2008.02.003
  8. Tina, G., Gagliano, S., & Raiti, S. (2005b). Hybrid solar/wind power system probabilistic modelling for long-term performance assessment. Solar Energy, 80(5), 578–588. https://doi.org/10.1016/j.solener.2005.03.013
  9. Yang, H., Wei, Z., & Chengzhi, L. (2008b). Optimal design and techno-economic analysis of a hybrid solar–wind power generation system. Applied Energy, 86(2), 163–169. https://doi.org/10.1016/j.apenergy.2008.03.008
  10. Roy, A., Kedare, S. B., & Bandyopadhyay, S. (2010b). Optimum sizing of wind-battery systems incorporating resource uncertainty. Applied Energy, 87(8), 2712–2727. https://doi.org/10.1016/j.apenergy.2010.03.027
  11. E. Carpaneto, G. Chicco, P. Mancarella, and A. Russo, "Cogeneration planning under uncertainty: A multiobjective approach," Applied Energy, vol. 88, no. 4, pp. 1059-1067, 2011
  12. Ekren, O., & Ekren, B. Y. (2009b). Size optimization of a PV/wind hybrid energy conversion system with battery storage using simulated annealing. Applied Energy, 87(2), 592–598. https://doi.org/10.1016/j.apenergy.2009.05.022
  13. Diagne, M., David, M., Lauret, P., Boland, J., & Schmutz, N. (2013b). Review of solar irradiance forecasting methods and a proposition for small-scale insular grids. Renewable and Sustainable Energy Reviews, 27, 65–76. https://doi.org/10.1016/j.rser.2013.06.042
  14. C. Voyant, G. Notton, S. Kalogirou, M. L. Nivet, C. Paoli, F. Motte, and A. Fouilloy, "Machine learning methods for solar radiation forecasting: A review," Renewable Energy, vol. 105, pp. 569-582, 2017
  15. Dolara, A., Leva, S., & Manzolini, G. (2015b). Comparison of different physical models for PV power output prediction. Solar Energy, 119, 83–99. https://doi.org/10.1016/j.solener.2015.06.017
  16. S. Pelland, J. Remund, J. Kleissl, T. Oozeki, and K. De Brabandere, "Photovoltaic and solar forecasting: State of the art," IEA PVPS Task 14, Subtask 3.1, Report IEA-PVPS T14-01:2013
  17. Yang, D., Kleissl, J., Gueymard, C. A., Pedro, H. T., & Coimbra, C. F. (2018b). History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining. Solar Energy, 168, 60–101. https://doi.org/10.1016/j.solener.2017.11.023
  18. Zou, P., Chen, Q., Xia, Q., He, G., & Kang, C. (2015b). Evaluating the contribution of energy storages to support Large-Scale renewable generation in joint energy and ancillary service markets. IEEE Transactions on Sustainable Energy, 7(2), 808–818. https://doi.org/10.1109/tste.2015.2497283
  19. P. Pinson, G. Papaefthymiou, B. Klockl, and J. Verboomen, "Dynamic sizing of energy storage for hedging wind power forecast uncertainty," IEEE Power & Energy Society General Meeting, pp. 1-8, 2009
  20. Bludszuweit, H., Dominguez-Navarro, J., & Llombart, A. (2008b). Statistical Analysis of Wind Power Forecast Error. IEEE Transactions on Power Systems, 23(3), 983–991. https://doi.org/10.1109/tpwrs.2008.922526
  21. Liu, W., Lund, H., Mathiesen, B. V., & Zhang, X. (2010b). Potential of renewable energy systems in China. Applied Energy, 88(2), 518–525. https://doi.org/10.1016/j.apenergy.2010.07.014
  22. Wang, Y., Wang, D., & Tang, Y. (2020b). Clustered Hybrid Wind Power Prediction Model based on ARMA, PSO-SVM, and clustering methods. IEEE Access, 8, 17071–17079. https://doi.org/10.1109/access.2020.2968390
  23. A. Ahmed and M. Khalid, "A review on the selected applications of forecasting models in renewable power systems," Renewable and Sustainable Energy Reviews, vol. 100, pp. 9-21, 2019
  24. Van Der Meer, D., Widén, J., & Munkhammar, J. (2017b). Review on probabilistic forecasting of photovoltaic power production and electricity consumption. Renewable and Sustainable Energy Reviews, 81, 1484–1512. https://doi.org/10.1016/j.rser.2017.05.212
  25. T. Hong, P. Pinson, Y. Wang, R. Weron, D. Yang, and H. Zareipour, "Energy forecasting: A review and outlook," IEEE Open Access Journal of Power and Energy, vol. 7, pp. 376-388, 2020
  26. Zhang, Y., Wang, J., & Wang, X. (2014b). Review on probabilistic forecasting of wind power generation. Renewable and Sustainable Energy Reviews, 32, 255–270. https://doi.org/10.1016/j.rser.2014.01.033
  27. Zakeri, B., & Syri, S. (2014). Electrical energy storage systems: A comparative life cycle cost analysis. Renewable and Sustainable Energy Reviews, 42, 569–596. https://doi.org/10.1016/j.rser.2014.10.011
  28. R. Weron, "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, vol. 30, no. 4, pp. 1030-1081, 2014
  29. Gonzalez-Romera, E., Jaramillo-Moran, M., & Carmona-Fernandez, D. (2006). Monthly electric Energy Demand Forecasting based on trend extraction. IEEE Transactions on Power Systems, 21(4), 1946–1953. https://doi.org/10.1109/tpwrs.2006.883666
  30. Conejo, A. J., Carrión, M., & Morales, J. M. (2010). Decision making under uncertainty in electricity markets. In International series in management science/operations research/International series in operations research & management science. https://doi.org/10.1007/978-1-4419-7421-1
  31. Powell, W. B. (2018). A unified framework for stochastic optimization. European Journal of Operational Research, 275(3), 795–821. https://doi.org/10.1016/j.ejor.2018.07.014
  32. Coelho, V. N., Coelho, I. M., Coelho, B. N., Cohen, M. W., Reis, A. J., Silva, S. M., Souza, M. J., Fleming, P. J., & Guimarães, F. G. (2015). Multi-objective energy storage power dispatching using plug-in vehicles in a smart-microgrid. Renewable Energy, 89, 730–742. https://doi.org/10.1016/j.renene.2015.11.084
  33. Mavrotas, G., Diakoulaki, D., Florios, K., & Georgiou, P. (2008). A mathematical programming framework for energy planning in services’ sector buildings under uncertainty in load demand: The case of a hospital in Athens. Energy Policy, 36(7), 2415–2429. https://doi.org/10.1016/j.enpol.2008.01.011
  34. Soroudi, A., & Ehsan, M. (2012). IGDT based robust decision making tool for DNOs in load procurement under severe uncertainty. IEEE Transactions on Smart Grid, 4(2), 886–895. https://doi.org/10.1109/tsg.2012.2214071
  35. Carpinelli, G., Celli, G., Mocci, S., Pilo, F., & Russo, A. (2005). Optimisation of embedded generation sizing and siting by using a double trade-off method. IEE Proceedings - Generation Transmission and Distribution, 152(4), 503. https://doi.org/10.1049/ip-gtd:20045129
  36. Zhou, Z., Botterud, A., Wang, J., Bessa, R., Keko, H., Sumaili, J., & Miranda, V. (2012). Application of probabilistic wind power forecasting in electricity markets. Wind Energy, 16(3), 321–338. https://doi.org/10.1002/we.1496
  37. Perez, R., Lorenz, E., Pelland, S., Beauharnois, M., Van Knowe, G., Hemker, K., Heinemann, D., Remund, J., Müller, S. C., Traunmüller, W., Steinmauer, G., Pozo, D., Ruiz-Arias, J. A., Lara-Fanego, V., Ramirez-Santigosa, L., Gaston-Romero, M., & Pomares, L. M. (2013). Comparison of numerical weather prediction solar irradiance forecasts in the US, Canada and Europe. Solar Energy, 94, 305–326. https://doi.org/10.1016/j.solener.2013.05.005
  38. Kleissl, J. (2013). Solar Energy Forecasting and Resource assessment. In Elsevier eBooks. https://doi.org/10.1016/c2011-0-07022-9
  39. Khatib, T., Mohamed, A., & Sopian, K. (2011). Optimization of a PV/wind micro-grid for rural housing electrification using a hybrid iterative/genetic algorithm: Case study of Kuala Terengganu, Malaysia. Energy and Buildings, 47, 321–331. https://doi.org/10.1016/j.enbuild.2011.12.006
  40. Kazem, H. A., & Khatib, T. (2013). A novel numerical algorithm for optimal sizing of a Photovoltaic/Wind/Diesel Generator/Battery microgrid using loss of load probability index. International Journal of Photoenergy, 2013, 1–8. https://doi.org/10.1155/2013/718596
  41. C. Voyant, G. Notton, S. Kalogirou, M.-L. Nivet, C. Paoli, F. Motte, and A. Fouilloy, "Machine learning methods for solar radiation forecasting: A review," Renewable Energy, vol. 105, pp. 569-582, 2017
  42. Maleki, A., & Askarzadeh, A. (2014). Optimal sizing of a PV/wind/diesel system with battery storage for electrification to an off-grid remote region: A case study of Rafsanjan, Iran. Sustainable Energy Technologies and Assessments, 7, 147–153. https://doi.org/10.1016/j.seta.2014.04.005
  43. Sharafi, M., & ELMekkawy, T. Y. (2014). Multi-objective optimal design of hybrid renewable energy systems using PSO-simulation based approach. Renewable Energy, 68, 67–79. https://doi.org/10.1016/j.renene.2014.01.011
  44. Kamjoo, A., Maheri, A., Dizqah, A. M., & Putrus, G. A. (2015). Multi-objective design under uncertainties of hybrid renewable energy system using NSGA-II and chance constrained programming. International Journal of Electrical Power & Energy Systems, 74, 187–194. https://doi.org/10.1016/j.ijepes.2015.07.007
  45. Fathy, A. (2016). A reliable methodology based on mine blast optimization algorithm for optimal sizing of hybrid PV-wind-FC system for remote area in Egypt. Renewable Energy, 95, 367–380. https://doi.org/10.1016/j.renene.2016.04.030
  46. Sinha, S., & Chandel, S. (2015). Review of recent trends in optimization techniques for solar photovoltaic–wind based hybrid energy systems. Renewable and Sustainable Energy Reviews, 50, 755–769. https://doi.org/10.1016/j.rser.2015.05.040
  47. Baghaee, H., Mirsalim, M., Gharehpetian, G., & Talebi, H. (2016). Reliability/cost-based multi-objective Pareto optimal design of stand-alone wind/PV/FC generation microgrid system. Energy, 115, 1022–1041. https://doi.org/10.1016/j.energy.2016.09.007
  48. Ramli, M. A., Bouchekara, H., & Alghamdi, A. S. (2018). Optimal sizing of PV/wind/diesel hybrid microgrid system using multi-objective self-adaptive differential evolution algorithm. Renewable Energy, 121, 400–411. https://doi.org/10.1016/j.renene.2018.01.058
  49. Bukar, A. L., Tan, C. W., & Lau, K. Y. (2019). Optimal sizing of an autonomous photovoltaic/wind/battery/diesel generator microgrid using grasshopper optimization algorithm. Solar Energy, 188, 685–696. https://doi.org/10.1016/j.solener.2019.06.050
  50. M. Aien, A. Hajebrahimi, and M. Fotuhi-Firuzabad, "A comprehensive review on uncertainty modeling techniques in power system studies," Renewable and Sustainable Energy Reviews, vol. 57, pp. 1077-1089, 2016
  51. Soroudi, A., & Amraee, T. (2013). Decision making under uncertainty in energy systems: State of the art. Renewable and Sustainable Energy Reviews, 28, 376–384. https://doi.org/10.1016/j.rser.2013.08.039
  52. Carpinelli, G., Caramia, P., & Varilone, P. (2014). Multi-linear Monte Carlo simulation method for probabilistic load flow of distribution systems with wind and photovoltaic generation systems. Renewable Energy, 76, 283–295. https://doi.org/10.1016/j.renene.2014.11.028
  53. Mohseni, S., & Pishvaee, M. S. (2016). A robust programming approach towards design and optimization of microalgae-based biofuel supply chain. Computers & Industrial Engineering, 100, 58–71. https://doi.org/10.1016/j.cie.2016.08.003
  54. Zakariazadeh, A., Jadid, S., & Siano, P. (2014). Stochastic multi-objective operational planning of smart distribution systems considering demand response programs. Electric Power Systems Research, 111, 156–168. https://doi.org/10.1016/j.epsr.2014.02.021
  55. Liu, Z., Wen, F., & Ledwich, G. (2011). Optimal siting and sizing of distributed generators in distribution systems considering uncertainties. IEEE Transactions on Power Delivery, 26(4), 2541–2551. https://doi.org/10.1109/tpwrd.2011.2165972
  56. Moghaddam, A. A., Seifi, A., Niknam, T., & Pahlavani, M. R. A. (2011). Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power source. Energy, 36(11), 6490–6507. https://doi.org/10.1016/j.energy.2011.09.017
  57. Niknam, T., Taheri, S. I., Aghaei, J., Tabatabaei, S., & Nayeripour, M. (2011). A modified honey bee mating optimization algorithm for multiobjective placement of renewable energy resources. Applied Energy, 88(12), 4817–4830. https://doi.org/10.1016/j.apenergy.2011.06.023
  58. D. Connolly, H. Lund, B. V. Mathiesen, and M. Leahy, "A review of computer tools for analysing the integration of renewable energy into various energy systems," Applied Energy, vol. 87, no. 4, pp. 1059-1082, 2010
  59. Katsigiannis, Y., Georgilakis, P., & Karapidakis, E. (2010). Multiobjective genetic algorithm solution to the optimum economic and environmental performance problem of small autonomous hybrid power systems with renewables. IET Renewable Power Generation, 4(5), 404. https://doi.org/10.1049/iet-rpg.2009.0076
  60. Erdinc, O., & Uzunoglu, M. (2012). Optimum design of hybrid renewable energy systems: Overview of different approaches. Renewable and Sustainable Energy Reviews, 16(3), 1412–1425. https://doi.org/10.1016/j.rser.2011.11.011
  61. Bazmi, A. A., & Zahedi, G. (2011). Sustainable energy systems: Role of optimization modeling techniques in power generation and supply—A review. Renewable and Sustainable Energy Reviews, 15(8), 3480–3500. https://doi.org/10.1016/j.rser.2011.05.003
  62. S. Bahramara, M. P. Moghaddam, and M. R. Haghifam, "Optimal planning of hybrid renewable energy systems using HOMER: A review," Renewable and Sustainable Energy Reviews, vol. 62, pp. 609-620, 2016
  63. A. Chauhan and R. P. Saini, "A review on Integrated Renewable Energy System based power generation for stand-alone applications: Configurations, storage options, sizing methodologies and control," Renewable and Sustainable Energy Reviews, vol. 38, pp. 99-120, 2014
  64. Tezer, T., Yaman, R., & Yaman, G. (2017). Evaluation of approaches used for optimization of stand-alone hybrid renewable energy systems. Renewable and Sustainable Energy Reviews, 73, 840–853. https://doi.org/10.1016/j.rser.2017.01.118
  65. Palizban, O., & Kauhaniemi, K. (2016). Energy storage systems in modern grids—Matrix of technologies and applications. Journal of Energy Storage, 6, 248–259. https://doi.org/10.1016/j.est.2016.02.001
  66. Eftekharnejad, S., Vittal, V., Heydt, N., Keel, B., & Loehr, J. (2012). Impact of increased penetration of photovoltaic generation on power systems. IEEE Transactions on Power Systems, 28(2), 893–901. https://doi.org/10.1109/tpwrs.2012.2216294
  67. Georgilakis, P. S., & Hatziargyriou, N. D. (2013). Optimal Distributed Generation placement in power distribution networks: models, methods, and future research. IEEE Transactions on Power Systems, 28(3), 3420–3428. https://doi.org/10.1109/tpwrs.2012.2237043
  68. Wang, N. L., & Singh, C. (2009). Multicriteria design of hybrid power generation systems based on a modified particle swarm optimization algorithm. IEEE Transactions on Energy Conversion, 24(1), 163–172. https://doi.org/10.1109/tec.2008.2005280
  69. Theo, W. L., Lim, J. S., Ho, W. S., Hashim, H., & Lee, C. T. (2016). Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods. Renewable and Sustainable Energy Reviews, 67, 531–573. https://doi.org/10.1016/j.rser.2016.09.063
  70. Gökçek, M., & Kale, C. (2018). Optimal design of a Hydrogen Refuelling Station (HRFS) powered by Hybrid Power System. Energy Conversion and Management, 161, 215–224. https://doi.org/10.1016/j.enconman.2018.02.007
  71. Marzband, M., Yousefnejad, E., Sumper, A., & Domínguez-García, J. L. (2015). Real time experimental implementation of optimum energy management system in standalone Microgrid by using multi-layer ant colony optimization. International Journal of Electrical Power & Energy Systems, 75, 265–274. https://doi.org/10.1016/j.ijepes.2015.09.010
  72. Zhang, W., Maleki, A., Rosen, M. A., & Liu, J. (2018). Sizing a stand-alone solar-wind-hydrogen energy system using weather forecasting and a hybrid search optimization algorithm. Energy Conversion and Management, 180, 609–621. https://doi.org/10.1016/j.enconman.2018.08.102
  73. M. F. Zia, E. Elbouchikhi, and M. Benbouzid, "Microgrids energy management systems: A critical review on methods, solutions, and prospects," Applied Energy, vol. 222, pp. 1033-1055, 2018
  74. A. Hirsch, Y. Parag, and J. Guerrero, "Microgrids: A review of technologies, key drivers, and outstanding issues," Renewable and Sustainable Energy Reviews, vol. 90, pp. 402-411, 2018
  75. S. Parhizi, H. Lotfi, A. Khodaei, and S. Bahramirad, "State of the art in research on microgrids: A review," IEEE Access, vol. 3, pp. 890-925, 2015
  76. A. S. Anees, "Grid integration of renewable energy sources: Challenges, issues and possible solutions," IEEE 5th India International Conference on Power Electronics (IICPE), pp. 1-6, 2012
  77. Blaabjerg, F., Yang, Y., Yang, D., & Wang, X. (2017). Distributed Power-Generation Systems and Protection. Proceedings of the IEEE, 105(7), 1311–1331. https://doi.org/10.1109/jproc.2017.269687
  78. Ju, C., Wang, P., Goel, L., & Xu, Y. (2017). A Two-Layer energy management system for microgrids with hybrid energy storage considering degradation costs. IEEE Transactions on Smart Grid, 9(6), 6047–6057. https://doi.org/10.1109/tsg.2017.2703126
  79. Li, Y., Yang, Z., Li, G., Zhao, D., & Tian, W. (2018). Optimal scheduling of an isolated microgrid with battery storage considering load and renewable generation uncertainties. IEEE Transactions on Industrial Electronics, 66(2), 1565–1575. https://doi.org/10.1109/tie.2018.2840498
  80. Liang, H., & Zhuang, W. (2014). Stochastic Modeling and Optimization in a Microgrid: A survey. Energies, 7(4), 2027–2050. https://doi.org/10.3390/en7042027
  81. C. Gamarra and J. M. Guerrero, "Computational optimization techniques applied to microgrids planning: A review," Renewable and Sustainable Energy Reviews, vol. 48, pp. 413-424, 2015
  82. A. H. Fathima and K. Palanisamy, "Optimization in microgrids with hybrid energy systems–A review," Renewable and Sustainable Energy Reviews, vol. 45, pp. 431-446, 2015
  83. Byrne, R. H., Nguyen, T. A., Copp, D. A., Chalamala, B. R., & Gyuk, I. (2017). Energy management and optimization methods for grid energy storage systems. IEEE Access, 6, 13231–13260. https://doi.org/10.1109/access.2017.2741578
  84. Zhao, H., Wu, Q., Hu, S., Xu, H., & Rasmussen, C. N. (2014). Review of energy storage system for wind power integration support. Applied Energy, 137, 545–553. https://doi.org/10.1016/j.apenergy.2014.04.103
  85. Luo, X., Wang, J., Dooner, M., & Clarke, J. (2014). Overview of current development in electrical energy storage technologies and the application potential in power system operation. Applied Energy, 137, 511–536. https://doi.org/10.1016/j.apenergy.2014.09.081
  86. Lund, P. D., Lindgren, J., Mikkola, J., & Salpakari, J. (2015). Review of energy system flexibility measures to enable high levels of variable renewable electricity. Renewable and Sustainable Energy Reviews, 45, 785–807. https://doi.org/10.1016/j.rser.2015.01.057
  87. Evans, A., Strezov, V., & Evans, T. J. (2012). Assessment of utility energy storage options for increased renewable energy penetration. Renewable and Sustainable Energy Reviews, 16(6), 4141–4147. https://doi.org/10.1016/j.rser.2012.03.048
  88. Barton, J., & Infield, D. (2004). Energy storage and its use with intermittent renewable energy. IEEE Transactions on Energy Conversion, 19(2), 441–448. https://doi.org/10.1109/tec.2003.822305
  89. Schoenung, S., & Hassenzahl, W. (2003). Long- vs. short-term energy storage technologies analysis : a life-cycle cost study : a study for the DOE energy storage systems program. https://doi.org/10.2172/918358
  90. Ibrahim, H., Ilinca, A., & Perron, J. (2007). Energy storage systems—Characteristics and comparisons. Renewable and Sustainable Energy Reviews, 12(5), 1221–1250. https://doi.org/10.1016/j.rser.2007.01.023
  91. Mckay, M. D., Beckman, R. J., & Conover, W. J. (2000). A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code. Technometrics, 42(1), 55. https://doi.org/10.2307/1271432
  92. R. L. Iman and W. J. Conover, "Small sample sensitivity analysis techniques for computer models with an application to risk assessment," Communications in Statistics-Theory and Methods, vol. 9, no. 17, pp. 1749-1842, 1980
  93. Helton, J., & Davis, F. (2003). Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems. Reliability Engineering & System Safety, 81(1), 23–69. https://doi.org/10.1016/s0951-8320(03)00058-9
  94. Owen, A. B. (1992). A central limit theorem for Latin hypercube sampling. Journal of the Royal Statistical Society Series B (Statistical Methodology), 54(2), 541–551. https://doi.org/10.1111/j.2517-6161.1992.tb01895.x
  95. Sobol, I. (1967). On the distribution of points in a cube and the approximate evaluation of integrals. USSR Computational Mathematics and Mathematical Physics, 7(4), 86–112. https://doi.org/10.1016/0041-5553(67)90144-9
  96. P. Bratley and B. L. Fox, "Algorithm 659: Implementing Sobol's quasirandom sequence generator," ACM Transactions on Mathematical Software, vol. 14, no. 1, pp. 88-100, 1988
  97. Halton, J. H. (1960). On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals. Numerische Mathematik, 2(1), 84–90. https://doi.org/10.1007/bf01386213
  98. Kocis, L., & Whiten, W. J. (1997). Computational investigations of low-discrepancy sequences. ACM Transactions on Mathematical Software, 23(2), 266–294. https://doi.org/10.1145/264029.264064
  99. Niederreiter, H. (1988). Low-discrepancy and low-dispersion sequences. Journal of Number Theory, 30(1), 51–70. https://doi.org/10.1016/0022-314x(88)90025-x
  100. Collings, B. J., & Niederreiter, H. (1993). Random number generation and Quasi-Monte Carlo methods. Journal of the American Statistical Association, 88(422), 699. https://doi.org/10.2307/2290359
  101. H. Faure, "Discrépance de suites associées à un système de numération (en dimension s)," Acta Arithmetica, vol. 41, no. 4, pp. 337-351, 1982
  102. Lemieux, C., & Lemieux, V. (2009). Monte Carlo and Quasi-Monte Carlo Sampling. In Springer series in statistics. https://doi.org/10.1007/978-0-387-78165-5
  103. Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, no. 7553, pp. 436-444, 2015
  104. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. In MIT Press eBooks. https://dl.acm.org/citation.cfm?id=3086952
  105. Hinton, G. E., Osindero, S., & Teh, Y. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527–1554. https://doi.org/10.1162/neco.2006.18.7.1527
  106. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Neural Information Processing Systems, 25, 1097–1105. http://books.nips.cc/papers/files/nips25/NIPS2012_0534.pdf
  107. K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, 2016
  108. D. P. Kingma and J. Ba, "Adam: A method for stochastic optimization," arXiv preprint arXiv:1412.6980, 2014
  109. Glorot, X., & Bengio, Y. (2010). Understanding the difficulty of training deep feedforward neural networks. International Conference on Artificial Intelligence and Statistics, 249–256. https://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf
  110. S. Ioffe and C. Szegedy, "Batch normalization: Accelerating deep network training by reducing internal covariate shift," International Conference on Machine Learning, pp. 448-456, 2015
  111. Nelsen, R. B. (1999). An introduction to copulas. https://doi.org/10.1080/00401706.2000.10486066
  112. NVIDIA Corporation, "CUDA C++ Programming Guide," Version 11.4, 2021
  113. Shapiro, A., Dentcheva, D., & Ruszczyński, A. P. (2009). Lectures on Stochastic Programming: Modeling and Theory. http://castlelab.princeton.edu/ORF544/Readings/Shapiro%20Dentcheva%20Ruszczynski-Lectures%20on%20stochastic%20programming%202nd%20edition%202014.pdf
  114. Silverman, B. (2018). Density estimation for statistics and data analysis. In Routledge eBooks. https://doi.org/10.1201/9781315140919
  115. Owen, A. B. (1998). Scrambling Sobol’ and Niederreiter–Xing points. Journal of Complexity, 14(4), 466–489. https://doi.org/10.1006/jcom.1998.0487
  116. A. Sklar, "Fonctions de répartition à n dimensions et leurs marges," Publications de l'Institut de Statistique de l'Université de Paris, vol. 8, pp. 229-231, 1959
  117. Birge, J. R., & Louveaux, F. (2011). Introduction to Stochastic Programming. Springer Series in Operations Research/Springer Series in Operations Research and Financial Engineering. https://doi.org/10.1007/978-1-4614-0237-4
  118. Rubinstein, R. Y., & Kroese, D. P. (2016). Simulation and the Monte Carlo method. In Wiley series in probability and statistics. https://doi.org/10.1002/9781118631980
  119. Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2007). Global Sensitivity Analysis. The primer. https://doi.org/10.1002/9780470725184
  120. I. Sommerville, "Software engineering," Pearson, 10th edition, 2015
  121. National Renewable Energy Laboratory (NREL), "System Advisor Model (SAM)," Version 2020.11.29, 2021
  122. Marler, R., & Arora, J. (2004). Survey of multi-objective optimization methods for engineering. Structural and Multidisciplinary Optimization, 26(6), 369–395. https://doi.org/10.1007/s00158-003-0368-6
  123. Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197. https://doi.org/10.1109/4235.996017
  124. Snir, M., Otto, S. W., Walker, D. W., Dongarra, J., & Huss-Lederman, S. (1996). MPI: The complete reference. http://ci.nii.ac.jp/ncid/BA26835055
  125. G. Amdahl, "Validity of the single processor approach to achieving large scale computing capabilities," Proceedings of the April 18-20, 1967, Spring Joint Computer Conference, pp. 483-485, 1967
  126. REN21, "Renewables 2023 Global Status Report," Paris: REN21 Secretariat, 2023
  127. International Energy Agency (IEA), "World Energy Outlook 2023," Paris, 2023
  128. J. Schmidhuber, "Deep learning in neural networks: An overview," Neural Networks, vol. 61, pp. 85-117
  129. Pfenninger, S., Hawkes, A., & Keirstead, J. (2014). Energy systems modeling for twenty-first century energy challenges. Renewable and Sustainable Energy Reviews, 33, 74–86. https://doi.org/10.1016/j.rser.2014.02.003
  130. V. Masson-Delmotte, P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, et al., "Climate Change 2021: The Physical Science Basis," Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, 2021
  131. Jenkins, J. D., Luke, M., & Thernstrom, S. (2018). Getting to zero carbon emissions in the electric power sector. Joule, 2(12), 2498–2510. https://doi.org/10.1016/j.joule.2018.11.013
  132. Pfenninger, S., & Staffell, I. (2016). Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data. Energy, 114, 1251–1265. https://doi.org/10.1016/j.energy.2016.08.060
  133. Bett, P. E., & Thornton, H. E. (2015). The climatological relationships between wind and solar energy supply in Britain. Renewable Energy, 87, 96–110. https://doi.org/10.1016/j.renene.2015.10.006
  134. Preskill, J. (2018). Quantum Computing in the NISQ era and beyond. Quantum, 2, 79. https://doi.org/10.22331/q-2018-08-06-79
  135. Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019). Digital twin in industry: State-of-the-Art. IEEE Transactions on Industrial Informatics, 15(4), 2405–2415. https://doi.org/10.1109/tii.2018.2873186
  136. Meehl, G. A., Covey, C., Delworth, T., Latif, M., McAvaney, B., Mitchell, J. F. B., Stouffer, R. J., & Taylor, K. E. (2007). THE WCRP CMIP3 Multimodel Dataset: A new era in climate change research. Bulletin of the American Meteorological Society, 88(9), 1383–1394. https://doi.org/10.1175/bams-88-9-1383
  137. M. Andoni, V. Robu, D. Flynn, S. Abram, D. Geach, D. Jenkins, P. McCallum, and A. Peacock, "Blockchain technology in the energy sector: A systematic review of challenges and opportunities," Renewable and Sustainable Energy Reviews, vol. 100, pp. 143-174, 2019
  138. Z. C. Lipton, "The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery," Queue, vol. 16, no. 3, pp. 31-57, 2018
  139. Collins, S., Deane, P., Gallachóir, B. Ó., Pfenninger, S., & Staffell, I. (2018). Impacts of inter-annual wind and solar variations on the European power system. Joule, 2(10), 2076–2090. https://doi.org/10.1016/j.joule.2018.06.020
  140. Bright, J. M. (2019). The impact of globally diverse GHI training data: Evaluation through application of a simple Markov chain downscaling methodology. Journal of Renewable and Sustainable Energy, 11(2). https://doi.org/10.1063/1.5085236
  141. Coimbra, C. F., Kleissl, J., & Marquez, R. (2013). Overview of Solar-Forecasting Methods and a metric for Accuracy evaluation. In Elsevier eBooks (pp. 171–194). https://doi.org/10.1016/b978-0-12-397177-7.00008-5
  142. T. Hong, P. Pinson, Y. Wang, R. Weron, D. Yang, and H. Zareipour, "Energy forecasting: A review and outlook," IEEE Open Access Journal of Power and Energy, vol. 7, pp. 376-388, 2020
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