Multiobjective optimisation on a budget: Exploring surrogate modelling for robust multi-reservoir rules generation under hydrological uncertainty. (July 2015)

• Record Type: Journal Article
• Authors: Tsoukalas, Ioannis; Makropoulos, Christos
• Is Part Of: Environmental modelling & software. Volume 69(2015:Jul.)
• Page Start: 396
• Page End: 413
• Publication Date: 2015-07
• Subjects: Multi-objective surrogate based optimisation -- Uncertainty -- Expensive simulation -- Performance assessment -- Parameterization-simulation-optimisation -- Hydro-energy; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2014.09.023
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Developing long term operation rules for multi-reservoir systems is complicated due to the number of decision variables, the non-linearity of system dynamics and the hydrological uncertainty. This uncertainty can be addressed by coupling simulation models with multi-objective optimisation algorithms driven by stochastically generated hydrological timeseries but the computational effort required imposes barriers to the exploration of the solution space. The paper addresses this by (a) employing a parsimonious multi-objective parameterization-simulation-optimization (PSO) framework, which incorporates hydrological uncertainty through stochastic simulation and allows the use of probabilistic objective functions and (b) by investigating the potential of multi-objective surrogate based optimisation (MOSBO) to significantly reduce the resulting computational effort. Three MOSBO algorithms are compared against two multi-objective evolutionary algorithms. Results suggest that MOSBOs are indeed able to provide robust, uncertainty-aware operation rules much faster, without significant loss of neither the generality of evolutionary algorithms nor of the knowledge embedded in domain-specific models. Highlights: Extended multi-objective parameterization-simulation-optimisation framework. Development of uncertainty-aware reservoir operation rules. Benchmarking of multi-objective surrogate based optimisation algorithms. Coupling WEAP21 simulation model with MATLAB.