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.

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

International Renewable Energy Agency (IRENA), "Renewable Power Generation Costs in 2023," Abu Dhabi, 2024

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

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

Pinson, P. (2013b). Wind Energy: Forecasting challenges for its operational management. Statistical Science, 28(4). https://doi.org/10.1214/13-sts445

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Kleissl, J. (2013). Solar Energy Forecasting and Resource assessment. In Elsevier eBooks. https://doi.org/10.1016/c2011-0-07022-9

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

H. Faure, "Discrépance de suites associées à un système de numération (en dimension s)," Acta Arithmetica, vol. 41, no. 4, pp. 337-351, 1982

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

Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, no. 7553, pp. 436-444, 2015

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. In MIT Press eBooks. https://dl.acm.org/citation.cfm?id=3086952

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

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

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

D. P. Kingma and J. Ba, "Adam: A method for stochastic optimization," arXiv preprint arXiv:1412.6980, 2014

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

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

Nelsen, R. B. (1999). An introduction to copulas. https://doi.org/10.1080/00401706.2000.10486066

NVIDIA Corporation, "CUDA C++ Programming Guide," Version 11.4, 2021

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

Silverman, B. (2018). Density estimation for statistics and data analysis. In Routledge eBooks. https://doi.org/10.1201/9781315140919

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

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

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

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

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

I. Sommerville, "Software engineering," Pearson, 10th edition, 2015

National Renewable Energy Laboratory (NREL), "System Advisor Model (SAM)," Version 2020.11.29, 2021

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

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

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

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

REN21, "Renewables 2023 Global Status Report," Paris: REN21 Secretariat, 2023

International Energy Agency (IEA), "World Energy Outlook 2023," Paris, 2023

J. Schmidhuber, "Deep learning in neural networks: An overview," Neural Networks, vol. 61, pp. 85-117

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

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

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

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

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

Preskill, J. (2018). Quantum Computing in the NISQ era and beyond. Quantum, 2, 79. https://doi.org/10.22331/q-2018-08-06-79

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

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

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

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

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

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

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

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