Multi-target moth algorithm-based small hydropower station optimal scheduling method

Abstract

The invention relates to a multi-target moth algorithm-based small hydropower station optimal scheduling method. The method comprises the following steps of: firstly collecting target small hydropower station, and combining a storage capacity, a water yield, power generation scheduling, water supply and a boundary condition constraint to establish a mathematic model with targets of maximum power generation capacity and minimum ecological water deficit; and secondly taking the established model as a target function and substituting the target function into a multi-target moth algorithm to carry out optimal computation, and after carrying out the optimal computation through the algorithm, finally returning a set with optimal scheduling schemes so that decision makers can finally make a scheduling scheme through referencing the given optimal scheduling scheme set. The method provided by the invention emphasizes on improving the correctness and high efficiency of optimal scheduling of small hydropower stations and solving the problems existing on models and methods in the prior art, and has significance for pushing the development of the optimal scheduling of the small hydropower stations and improving the economic benefit.

Description

technical field [0001] The invention relates to the field of operation and optimal scheduling of small-scale water conservancy and hydropower projects, in particular to an optimal scheduling method for small-scale hydropower stations based on a multi-objective moth algorithm. Background technique [0002] With the continuous development of my country's economy, people's living standards are gradually improving, and the demand for electricity is also increasing, which also drives the rapid development of various power industries, such as hydropower, thermal power, nuclear power, photovoltaic industry, wind power and even tidal power. All have made great progress and achieved world-renowned achievements. By the end of 2010, the national hydropower installed capacity reached 210 million kW, ranking first in the world, and the annual power generation reached 563.3 billion kW·h, accounting for 21.6% and 16.4% of the national power installed capacity and annual power generation res...

AI Technical Summary

Problems solved by technology

[0005] In recent years, although many gratifying research results have been obtained in the research and practice of reservoir optimal scheduling at home and abroad, the research target has developed from a single reservoir to cascade and cross-basin reservoir groups, and the runoff description has developed from a deterministic description to a random description. This optimization theory, algorithm and optimal dispatching model are also constantly developing, and some aspects are also applied in practice—but as far as the optimal dispatching of rural small hydropower is concerned, since rural small hydropower has different characteristics from those of large rivers and large river basins, whether it is The coverage area of ​​the watershed or the characteristics of hydrology and water conservancy, and rural small hydropower have their own unique aspects. Therefore, some theories and calculation methods of conventional optimal dispatch cannot be applied to rural small hydropower, and it is necessary to explore a set of suitable theories to deal with it; on the other hand, Due to the complexity and diversity of the reservoir system, various intelligent optimization methods and scheduling mathematical models often have the disadvantages of dimensionality disaster, which makes it difficult to solve, and incomplete consideration of the problem. The problem of optimal scheduling of reservoir groups is far from a complete solution. , there is still a large gap between optimization theory and practical application, and we need to make further efforts to make theory better serve practice

Images