Abstract
Challenge remains to find the optimal carryover storage to balance the immediate and carryover utilities for long-term hydropower reservoir operation due to high uncertainties of long-term forecasts. Thus, this paper develops a two-stage stochastic optimal operation model to dynamically decide the optimal carryover storage. First, a successive iteration method based on periodic Markov characteristics of reservoir operation is proposed to obtain the approximate utility function of the carryover stage. Then, three two-stage stochastic optimal operation models based on different forecast accuracy (no forecasts, perfect forecasts, and uncertainty forecasts) are developed to guide the long-term hydropower reservoir operation. The applications shows that: 1) the back propagation neural network can approximate the utility function of the carryover stage with a high accuracy and avoid the need to predetermine the function type; 2) the approximate utility function of the carryover stage increases with the carryover storage and current inflow, and it changes gradually from a nearly linear surface to an approximate concave surface with the shift from the dry season to the flood season; 3) two-stage stochastic optimal operation models outperform the conventional operating rules and conventional optimization method in guiding the long-term hydropower operation.
Published Version
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