A method for optimal configuration of receiving-end power grid energy storage based on improved multi-objective particle swarm algorithm

Abstract

The invention discloses a method for optimizing the configuration of energy storage in the receiving end power grid based on an improved multi-objective particle swarm algorithm. The steps include: adding an energy storage device to the original receiving end power grid, establishing system static voltage stability, generator power fluctuation and system stability. An energy storage optimization configuration model with cost as the optimization goal; using the optimization goal in the above model as the fitness function of the improved multi-objective particle swarm algorithm, solve the model and generate a set of Pareto solution sets; use the TOPSIS method based on information entropy to The Pareto solution generated in the last iteration centrally establishes the best solution for the energy storage optimization configuration model. The energy storage optimization configuration model proposed by the present invention comprehensively considers the economy and safety of the system, and provides certain theoretical support for the application of energy storage power stations in the receiving end power grid. It can be seen from the final example analysis that the proposed algorithm It can improve the solving efficiency of the model and find a more comprehensive and reasonable configuration plan.

Description

technical field [0001] The invention relates to the planning problem of energy storage in a receiving-end power grid, in particular to an optimal configuration method for receiving-end power grid energy storage based on an improved multi-objective particle swarm algorithm. Background technique [0002] As the total amount of non-renewable and highly polluting energy such as coal is decreasing day by day, my country's power structure is changing from thermal power generation to low-carbon clean energy power generation. However, the distribution of clean energy reserves is uneven. The northwest region represented by Xinjiang is rich in renewable resources such as wind and light. The central and western regions represented by the Three Gorges have a large amount of water energy resources, which have exceeded their own energy consumption capacity; The eastern region represented by the Jiangsu and Zhejiang regions has small reserves of clean energy and a large demand for electric...

AI Technical Summary

Problems solved by technology

However, at the current stage, the configuration cost of the energy storage system is still relatively high, and the number of cycles is relatively limited. It is necessary to optimize the site selection and capacity configuration scheme of the energy storage system, so that its configuration, operation economy and adjustment ability can be comprehensively optimized.

Images