Multi-objective optimization of multi-purpose multi-reservoir systems under high reliability constraints

Abstract

The supply of municipal water in Germany is often governed by policies that warrant occurrence-based reliabilities of 99.5 % or more. Such reliabilities have to be considered in the optimization of reservoir operation. A reliability-based optimization requires sufficiently long simulation periods of several thousand years of time series of inflow and demand. However, long simulation periods lead, especially for multi-objective parameterization–simulation–optimization (MOPSO), to unacceptable computational burden. Therefore, techniques need to be developed that increase the computational efficiency of MOPSO for such optimization problems. In this paper, a novel Monte Carlo recombination method (MCR) is proposed. MCR reduces the length of inflow time series significantly while preserving critical statistics, i.e., characteristics of probability distributions and variability of wet and dry conditions. It could be shown that simulations based on these shortened time series allow for highly efficient MOPSO and yield comparable Pareto-fronts and reliabilities. For the demonstration of the capabilities of MCR, it is integrated into a MOPSO framework for the optimization of a multi-purpose multi-reservoir system in the Eastern Ore Mountains, Germany. For this real-world application, synthetic time series of a length of 10,000 years are generated and reduced to 882 years, which results in a reduction of the computational burden by a factor of eleven. A validation of the results shows that the MOPSO framework allows for optimization of operational policies that yield reliabilities over 99.95 % on a monthly scale and up to 99.7 % on an annual timescale.