Stochastic simulation-optimisation framework for the design and assessment of renewable energy systems under uncertainty

G.-K. Sakki, I. Tsoukalas, P. Kossieris, C. Makropoulos, and A. Efstratiadis, Stochastic simulation-optimisation framework for the design and assessment of renewable energy systems under uncertainty, Renewable and Sustainable Energy Reviews, 168, 112886, doi:10.1016/j.rser.2022.112886, 2022.



As the share of renewable energy resources rapidly increases in the electricity mix, the recognition, representation, quantification, and eventually interpretation of their uncertainties become important. In this vein, we propose a generic stochastic simulation-optimization framework tailored to renewable energy systems (RES), able to address multiple facets of uncertainty, external and internal. These involve the system’s drivers (hydrometeorological inputs) and states (by means of fuel-to-energy conversion model parameters and energy market price), both expressed in probabilistic terms through a novel coupling of the triptych statistics, stochastics and copulas. Since the most widespread sources (wind, solar, hydro) exhibit several common characteristics, we first introduce the formulation of the overall modelling context under uncertainty, and then offer uncertainty quantification tools to put in practice the plethora of simulated outcomes and resulting performance metrics (investment costs, energy production, revenues). The proposed framework is applied to two indicative case studies, namely the design of a small hydropower plant (particularly, the optimal mixing of its hydro-turbines), and the long-term assessment of a planned wind power plant. Both cases reveal that the ignorance or underestimation of uncertainty may hide a significant perception about the actual operation and performance of RES. In contrast, the stochastic simulation-optimization context allows for assessing their technoeconomic effectiveness against a wide range of uncertainties, and as such provides a critical tool for decision making, towards the deployment of sustainable and financially viable RES.

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Our works referenced by this work:

1. D. Koutsoyiannis, C. Makropoulos, A. Langousis, S. Baki, A. Efstratiadis, A. Christofides, G. Karavokiros, and N. Mamassis, Climate, hydrology, energy, water: recognizing uncertainty and seeking sustainability, Hydrology and Earth System Sciences, 13, 247–257, doi:10.5194/hess-13-247-2009, 2009.
2. G. Tsekouras, and D. Koutsoyiannis, Stochastic analysis and simulation of hydrometeorological processes associated with wind and solar energy, Renewable Energy, 63, 624–633, doi:10.1016/j.renene.2013.10.018, 2014.
3. A. Efstratiadis, Y. Dialynas, S. Kozanis, and D. Koutsoyiannis, A multivariate stochastic model for the generation of synthetic time series at multiple time scales reproducing long-term persistence, Environmental Modelling and Software, 62, 139–152, doi:10.1016/j.envsoft.2014.08.017, 2014.
4. I. Tsoukalas, A. Efstratiadis, and C. Makropoulos, Stochastic periodic autoregressive to anything (SPARTA): Modelling and simulation of cyclostationary processes with arbitrary marginal distributions, Water Resources Research, 54 (1), 161–185, WRCR23047, doi:10.1002/2017WR021394, 2018.
5. I. Tsoukalas, Modelling and simulation of non-Gaussian stochastic processes for optimization of water-systems under uncertainty, PhD thesis, 339 pages, Department of Water Resources and Environmental Engineering – National Technical University of Athens, December 2018.
6. I. Tsoukalas, P. Kossieris, and C. Makropoulos, Simulation of non-Gaussian correlated random variables, stochastic processes and random fields: Introducing the anySim R-Package for environmental applications and beyond, Water, 12 (6), 1645, doi:10.3390/w12061645, 2020.
7. N. Mamassis, A. Efstratiadis, P. Dimitriadis, T. Iliopoulou, R. Ioannidis, and D. Koutsoyiannis, Water and Energy, Handbook of Water Resources Management: Discourses, Concepts and Examples, edited by J.J. Bogardi, T. Tingsanchali, K.D.W. Nandalal, J. Gupta, L. Salamé, R.R.P. van Nooijen, A.G. Kolechkina, N. Kumar, and A. Bhaduri, Chapter 20, 617–655, doi:10.1007/978-3-030-60147-8_20, Springer Nature, Switzerland, 2021.
8. A. Efstratiadis, I. Tsoukalas, and D. Koutsoyiannis, Generalized storage-reliability-yield framework for hydroelectric reservoirs, Hydrological Sciences Journal, 66 (4), 580–599, doi:10.1080/02626667.2021.1886299, 2021.
9. L. Katikas, P. Dimitriadis, D. Koutsoyiannis, T. Kontos, and P. Kyriakidis, A stochastic simulation scheme for the long-term persistence, heavy-tailed and double periodic behavior of observational and reanalysis wind time-series, Applied Energy, 295, 116873, doi:10.1016/j.apenergy.2021.116873, 2021.
10. G.-F. Sargentis, P. Siamparina, G.-K. Sakki, A. Efstratiadis, M. Chiotinis, and D. Koutsoyiannis, Agricultural land or photovoltaic parks? The water–energy–food nexus and land development perspectives in the Thessaly plain, Greece, Sustainability, 13 (16), 8935, doi:10.3390/su13168935, 2021.
11. G.-K. Sakki, I. Tsoukalas, and A. Efstratiadis, A reverse engineering approach across small hydropower plants: a hidden treasure of hydrological data?, Hydrological Sciences Journal, 67 (1), 94–106, doi:10.1080/02626667.2021.2000992, 2022.
12. K.-K. Drakaki, G.-K. Sakki, I. Tsoukalas, P. Kossieris, and A. Efstratiadis, Day-ahead energy production in small hydropower plants: uncertainty-aware forecasts through effective coupling of knowledge and data, Advances in Geosciences, 56, 155–162, doi:10.5194/adgeo-56-155-2022, 2022.

Our works that reference this work:

1. A. Roxani, A. Zisos, G.-K. Sakki, and A. Efstratiadis, Multidimensional role of agrovoltaics in era of EU Green Deal: Current status and analysis of water-energy-food-land dependencies, Land, 12 (5), 1069, doi:10.3390/land12051069, 2023.
2. A. Zisos, G.-K. Sakki, and A. Efstratiadis, Mixing renewable energy with pumped hydropower storage: Design optimization under uncertainty and other challenges, Sustainability, 15 (18), 13313, doi:10.3390/su151813313, 2023.
3. A. Efstratiadis, and G.-K. Sakki, The water-energy nexus as sociotechnical system under uncertainty, Elgar Encyclopedia of Water Policy, Economics and Management, edited by P. Kountouri and A. Alamanos, Chapter 64, 279–283, doi:10.4337/9781802202946.00071, 2024.

Other works that reference this work (this list might be obsolete):

1. Woon, K. S., Z. X. Phuang, J. Taler, P. S. Varbanov, C. T. Chong, J. J. Klemeš, and C. T. Lee, Recent advances in urban green energy development towards carbon neutrality, Energy, 126502, doi:10.1016/, 2022.
2. Angelakis, A., Reframing the high-technology landscape in Greece: Empirical evidence and policy aspects, International Journal of Business & Economic Sciences Applied Research, 15(2), 58-70, doi:10.25103/ijbesar.152.06, 2022.
3. Kim, J., M. Qi, J. Park, and I. Moon, Revealing the impact of renewable uncertainty on grid-assisted power-to-X: A data-driven reliability-based design optimization approach, Applied Energy, 339, 121015, doi:10.1016/j.apenergy.2023.121015, 2023.
4. Yin, S., L. Chen, and H. Qin, Reduced space optimization-based evidence theory method for response analysis of space-coiled acoustic metamaterials with epistemic uncertainty, Mathematical Problems in Engineering, 2023, 9937158, doi:10.1155/2023/9937158, 2023.
5. Qu, K., H. Zhang, X. Zhou, F. Causone, X. Huang, X. Shen, and X. Zhu, Optimal design of building integrated energy systems by combining two-phase optimization and a data-driven model, Energy and Buildings, 295, 113304, doi:10.1016/j.enbuild.2023.113304, 2023.
6. Wang, Z., W. Zhang, H. Fan, C. Zhang, Y. Zhao, and Z. Huang, An uncertainty-tolerant robust distributed control strategy for building cooling water systems considering measurement uncertainties, Journal of Building Engineering, 76, 107162, doi:10.1016/j.jobe.2023.107162, 2023.
7. Caputo, A. C., A. Federici, P. M. Pelagagge, and P. Salini, Offshore wind power system economic evaluation framework under aleatory and epistemic uncertainty, Applied Energy, 350, 121585, doi:10.1016/j.apenergy.2023.121585, 2023.
8. Liu, J., Y. Li, Y. Ma, R. Qin, X. Meng, and J. Wu, Two-layer multiple scenario optimization framework for integrated energy system based on optimal energy contribution ratio strategy, Energy, 285, 128673, doi:10.1016/, 2023.
9. Wang, Q., and L. Zhao, Data-driven stochastic robust optimization of sustainable utility system, Renewable and Sustainable Energy Reviews, 188, 113841, doi:10.1016/j.rser.2023.113841, 2023.
10. Ahmed, S., T. Li, P. Yi, and R. Chen, Environmental impact assessment of green ammonia-powered very large tanker ship for decarbonized future shipping operations, Renewable and Sustainable Energy Reviews, 188, 113774, doi:10.1016/j.rser.2023.113774, 2023.
11. Maitra, S., V. Mishra, and S. Kundu, A novel approach with Monte-Carlo simulation and hybrid optimization approach for inventory management with stochastic demand, arXiv e-prints, 2023.
12. Al Hasibi, R. A., and A. Haris, An analysis of the implementation of a hybrid renewable-energy system in a building by considering the reduction in electricity price subsidies and the reliability of the grid, Clean Energy, 7(5), 1125-1135, doi:10.1093/ce/zkad053, 2023.
13. Caputo, A. C., A. Federici, P. M. Pelagagge, and P. Salini, Scenario analysis of offshore wind-power systems under uncertainty, Sustainability, 15(24), 16912, doi:10.3390/su152416912, 2023.
14. Li, Y., F. Wu, X. Song, L. Shi, K. Lin, and F. Hong, Data-driven chance-constrained schedule optimization of cascaded hydropower and photovoltaic complementary generation systems for shaving peak loads, Sustainability, 15(24), 16916, doi:10.3390/su152416916, 2023.
15. Kim, S., Y. Choi, J. Park, D. Adams, S. Heo, and J. H. Lee, Multi-period, multi-timescale stochastic optimization model for simultaneous capacity investment and energy management decisions for hybrid Micro-Grids with green hydrogen production under uncertainty, Renewable and Sustainable Energy Reviews, 190(A), 114049, doi:10.1016/j.rser.2023.114049, 2024.
16. García-Merino, J. C., C. Calvo-Jurado, and E. García-Macías, Sparse polynomial chaos expansion for universal stochastic kriging, Journal of Computational and Applied Mathematics, 444, 115794, doi:10.1016/, 2024.
17. Hasanien, H. M., I. Alsaleh, Z. Ullah, and A. Alassaf, Probabilistic optimal power flow in power systems with renewable energy integration using enhanced walrus optimization algorithm, Ain Shams Engineering Journal, 15(3), 102663, doi:10.1016/j.asej.2024.102663, 2024.
18. Gómez-Beas, R., E. Contreras, M. J. Polo, and C. Aguilar, Stochastic flow analysis for optimization of the operationality in run-of-river hydroelectric plants in mountain areas, Energies, 17(7), 1705, doi:10.3390/en17071705, 2024.
19. Chang, K.-H., and T.-L. Chen, Simulation learning and optimization: Methodology and applications, Asia-Pacific Journal of Operational Research, doi:10.1142/S0217595924400086, 2024.
20. Leng, R., Z. Li, and Y. Xu, Joint planning of utility-owned distributed energy resources in an unbalanced active distribution network considering asset health degradation, IEEE Transactions on Smart Grid, doi:10.1109/TSG.2024.3365974, 2024.
21. Kim, S., J. Park, S. Heo, and J. H. Lee, Green hydrogen vs Green ammonia: A hierarchical optimization-based integrated temporal approach for comparative techno-economic analysis of international supply chains, Journal of Cleaner Production, 142750, doi:10.1016/j.jclepro.2024.142750, 2024.

Tagged under: Determinism vs. stochasticity, Renewable energy, Stochastics, Uncertainty