Small hydropower station group short-term robust optimization scheduling method considering prediction error

Abstract

The invention provides a small hydropower station group short-term robust optimization scheduling method considering prediction errors. The method comprises the following steps: acquiring samples of uncertain factors after the prediction errors are considered by using a large amount of historical data, wherein the samples are subjected to numerical optimization processing, and an output set of the runoff type small hydropower stations is constructed; and constructing a short-term robust optimization scheduling model, and substituting the output set into the short-term robust optimization scheduling model for solving to obtain final output of the small hydropower station. According to the optimization scheduling method, samples of prediction errors of the runoff type small hydropower stations are included in the uncertain set, the prediction errors are considered while short-term optimal scheduling of the small hydropower station group is achieved, model solution conservative property is reduced, and robustness of the uncertain set and power generation benefits of the power stations in the area are improved.

Description

technical field [0001] The invention relates to the field of short-term robust optimization of small hydropower groups, in particular to a short-term robust optimization scheduling method for small hydropower groups considering prediction errors. Background technique [0002] In regions rich in hydropower resources, how to significantly reduce water abandonment, realize the optimal economic dispatch of small hydropower station groups, increase power generation, and solve "blind operation" and "blind adjustment" has become a key issue for the coordinated operation of multiple hydropower stations. Since hydropower is an intermittent power source, its power generation capacity has strong randomness and uncertainty, especially run-of-the-river small hydropower, which is limited by storage capacity adjustment capabilities, has strong seasonality, is greatly affected by climate, and fluctuates. More obvious. Although many studies have carried out short-term power prediction for s...

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Problems solved by technology

The worse the robustness of the set, the more conservative the optimization result is. When calculating large-scale systems, this method will occupy more computing resources and consume a lot of running time. Therefore, the robustness of the uncertain set affects the model solution. s efficiency

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