Real-time optimal water allocation for daily hydropower generation from the Vanderkloof dam, South Africa

The adverse effects of power shortages resulting from escalating energy demands, due to rapid global urbanization and industrial developments, have driven efforts worldwide in search for improved techniques for sustainable reservoir operations and optimization for hydropower generation. Recent studies have shown that, combining accurate reservoir inflow forecasting procedures with efficient optimization techniques can produce more efficient and balanced solutions, for operation of multipurpose reservoir systems to improve on the economy of hydropower production. This study presents the coupling of a data driven artificial neural network (ANN) model and a novel combined Pareto multi-objective differential evolution (CPMDE), for hydrological simulation and multi-objective numerical optimization of hydropower production, from the Vanderkloof dam in real-time. Results from the application of the real-time strategy, indicate a significant improvement in performance over the current practice. Therefore, the hybrid ANN-CPMDE real-time reservoir operation model suggested herein provides a low cost solution methodology, suitable for sustainable operation of the Vanderkloof reservoir in South Africa.

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