Reservoir optimization scheduling method based on dynamic optimization algorithm

Abstract

The reservoir optimization scheduling method based on the dynamic optimization algorithm comprises the following steps: acquiring reservoir operation parameters, and setting the reservoir operation parameters as an initialized population popt; mathematical modeling is carried out; setting constraint conditions of the reservoir; detecting an environmental change; performing non-dominated sorting and layering on the reservoir population, and selecting a first layer as a non-dominated solution set popNon; edge individuals and lines or planes formed by the edge individuals are found and stored in several B; calculating the distance from the point to a straight line or a plane; uniformly dividing the mth target value into k regions, and selecting a point with the maximum distance to the line or the plane in each region as a Knee point of the first target value; calculating a boundary reference point; the calculated boundary reference points are added into the KN and recorded as updated Knee points NKN, congestion degree distance calculation is carried out, sorting is carried out, and the point with the minimum congestion degree distance is deleted; predicting a new position of the Knee point after the environment changes; calculating the position of the Knee point in the decision space; predicting the non-dominated solution set after the environment change to obtain a predicted new population; using an optimization algorithm to optimize the overall; and ending iteration.

Description

technical field [0001] The invention relates to a reservoir optimization scheduling method based on dynamic optimization algorithm Background technique [0002] With the development of economy, the optimal dispatching strategy of large-scale reservoir groups has been gradually improved and updated. Reservoir dispatching integrates various target factors such as power generation, reservoir water supply, flood discharge, and user water demand. Objective decision-making is constantly changing. In order to plan and coordinate various objectives and satisfy multiple constraints at the same time, it is necessary to summarize the reservoir optimization problem as a dynamic optimization problem. Different from static scheduling, the static scheduling problem is often the optimal solution in an ideal state. However, the reservoir environment changes dynamically, so the reservoir optimal scheduling problem belongs to a typical multi-stage decision-making problem. In real problems, mu...

AI Technical Summary

Problems solved by technology

[0003] In order to solve the problem of tracking the Pareto front and the Pareto solution set, it is usually devoted to finding a well-distributed Pareto front, but this method undoubtedly increases the computational burden

Therefore, the challenge of this problem is to use appropriate diversity and convergence strategies to maintain the search ability of the algorithm when the environment changes

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