Representing Inflow Uncertainty for the Development of Monthly Reservoir Operations using Genetic Algorithms

Abstract Genetic Algorithms (GAs) have been commonly applied in the last two decades as a substitute for the traditional mathematical programming algorithms in searching for optimal operating rules of reservoir systems. However, only a few GA studies related to reservoir operations addressed the inflow uncertainty. In this study, the reservoir operation rules were developed and evaluated by reflecting the uncertainty of reservoir inflows using the non-dominated sorting GA II algorithm (NSGA-II) through the following procedure: (1) the historical inflow data were classified into four clusters using the self-organizing map (SOM) clustering technique; (2) NSGA-II searched for an optimal release rule in each cluster with all the inflow data of the corresponding cluster; (3) a release response function was then derived for each cluster by regressing the calculated optimal release data against the storage at the beginning of the month and the inflow during the month; and (4) finally, the derived release rules for each cluster were tested with three performance indices, namely, reliability, resilience, and vulnerability. The proposed procedure was applied to the monthly operations of the Lake Tana multi-reservoir system in Ethiopia, which has six upstream irrigation reservoirs and one natural lake that has three release outlets for agriculture, hydropower, and instream requirement. Using the NSGA-II, SOM, and a seasonal ARIMA forecasting model in this study, it is concluded that: 1) the overall performance of the proposed optimization procedure with ARIMA forecasts and cluster approach reaches 84% of the perfect information case across multiple performance measures; 2) the use of the one-month-ahead ARIMA forecasts improves the overall system performance by 8% over simply optimizing to the mean flow; and 3) the use of the four clusters and the consequent RRFs further improves the overall system performance by 14%. Furthermore, this study recommends that, in future, significant efforts should be focused on improving the operation performance of the upstream irrigation reservoirs in coordination with the Lake Tana system.