A surrogate based optimization approach for the development of uncertainty-aware reservoir operational rules: the case of Nestos hydrosystem

I. Tsoukalas, and C. Makropoulos, A surrogate based optimization approach for the development of uncertainty-aware reservoir operational rules: the case of Nestos hydrosystem, Water Resources Management, 29 (13), 4719–4734, doi:10.1007/s11269-015-1086-8, 2015.

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[English]

Operation of large-scale hydropower reservoirs is a complex problem that involves conflicting objectives, such as hydropower generation and water supply. Deriving optimal operational rules is a challenging task due to the non-linearity of the system dynamics and the uncertainty of future inflows and water demands. A common approach to derive optimal control policies is to couple simulation models with optimization algorithms. This paper in order to investigate the performance of a future reservoir and safely infer about its significance employs stochastic simulation, thus long synthetically generated time-series and a multi-objective version of the Parameterization-Simulation-Optimization (PSO) framework to develop uncertainty-aware operational rules. Furthermore, in order to handle the high computational effort that ensues from that coupling we investigate the potential of a surrogate-based multi-objective optimization algorithm, ParEGO. The PSO framework is deployed with WEAP21 water resources management model as simulation engine and MATLAB for the implementation of optimization algorithms. A comparison between NSGAII and ParEGO optimization algorithms is performed to assess the effectiveness of the proposed algorithm. The aforementioned comparison showed that ParEGO provides efficient approximations of the Pareto front while reducing the computational effort required. Finally, the potential benefit and the significance of the future reservoir is underlined.

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Our works referenced by this work:

1. D. Koutsoyiannis, A generalized mathematical framework for stochastic simulation and forecast of hydrologic time series, Water Resources Research, 36 (6), 1519–1533, doi:10.1029/2000WR900044, 2000.
2. D. Koutsoyiannis, and A. Economou, Evaluation of the parameterization-simulation-optimization approach for the control of reservoir systems, Water Resources Research, 39 (6), 1170, doi:10.1029/2003WR002148, 2003.
3. D. Koutsoyiannis, Stochastic simulation of hydrosystems, Water Encyclopedia, Vol. 4, Surface and Agricultural Water, edited by J. H. Lehr and J. Keeley, 421–430, doi:10.1002/047147844X.sw913, Wiley, New York, 2005.
4. A. Efstratiadis, D. Koutsoyiannis, and S. Kozanis, Theoretical documentation of stochastic simulation of hydrological variables model "Castalia", Integrated Management of Hydrosystems in Conjunction with an Advanced Information System (ODYSSEUS), Contractor: NAMA, Report 3, 61 pages, doi:10.13140/RG.2.2.30224.40966, Department of Water Resources, Hydraulic and Maritime Engineering – National Technical University of Athens, Athens, September 2005.
5. A. Efstratiadis, and D. Koutsoyiannis, One decade of multiobjective calibration approaches in hydrological modelling: a review, Hydrological Sciences Journal, 55 (1), 58–78, doi:10.1080/02626660903526292, 2010.
6. D. Koutsoyiannis, Hurst-Kolmogorov dynamics and uncertainty, Journal of the American Water Resources Association, 47 (3), 481–495, doi:10.1111/j.1752-1688.2011.00543.x, 2011.
7. A. Efstratiadis, D. Bouziotas, and D. Koutsoyiannis, The parameterization-simulation-optimization framework for the management of hydroelectric reservoir systems, Hydrology and Society, EGU Leonardo Topical Conference Series on the hydrological cycle 2012, Torino, doi:10.13140/RG.2.2.36437.22243, European Geosciences Union, 2012.
8. 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.
9. I. Tsoukalas, and C. Makropoulos, Multiobjective optimisation on a budget: Exploring surrogate modelling for robust multi-reservoir rules generation under hydrological uncertainty, Environmental Modelling and Software, 69, 396–413, doi:10.1016/j.envsoft.2014.09.023, 2015.

Our works that reference this work:

1. I. Tsoukalas, P. Kossieris, A. Efstratiadis, and C. Makropoulos, Surrogate-enhanced evolutionary annealing simplex algorithm for effective and efficient optimization of water resources problems on a budget, Environmental Modelling and Software, 77, 122–142, doi:10.1016/j.envsoft.2015.12.008, 2016.
2. I. Tsoukalas, C. Makropoulos, and D. Koutsoyiannis, Simulation of stochastic processes exhibiting any-range dependence and arbitrary marginal distributions, Water Resources Research, 54 (11), 9484–9513, doi:10.1029/2017WR022462, 2018.
3. I. Tsoukalas, A. Efstratiadis, and C. Makropoulos, Building a puzzle to solve a riddle: A multi-scale disaggregation approach for multivariate stochastic processes with any marginal distribution and correlation structure, Journal of Hydrology, doi:10.1016/j.jhydrol.2019.05.017, 2019.

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

1. Müller, R., and N. Schütze, Multi-objective optimization of multi-purpose multi-reservoir systems under high reliability constraints, Environmental Earth Sciences, 75:1278, doi:10.1007/s12665-016-6076-5, 2016.
2. Christelis, V., and A. G. Hughes, Metamodel-assisted analysis of an integrated model composition: an example using linked surface water – groundwater models, Environmental Modelling and Software, doi:10.1016/j.envsoft.2018.05.004, 2018.

Tagged under: Hydrosystems, Optimization