With the large-scale integration of new energy sources

the distribution network becomes increasingly complex and variable.In order to deal with the reactive power problem caused by the high proportion of renewable energy connected to the distribution network

it proposes a reactive power optimization scheduling model based on the improved particle swarm optimization(PSO) algorithm.This model aims to optimize the operating cost of multi-energy systems

reduce network losses.Through reactive power compensation

energy storage regulation and energy conversion

it ensures the economy and operation safety of the power grid.Firstly

it improves the traditional PSO algorithm

applies chaotic initialization in conjunction with a reverse learning strategy to generate the initial population

optimizing it to accelerate convergence.Secondly

it uses nonlinear strategies and a golden sine algorithm search strategy to enhance the inertia weight formula

learning factors

and position update formulas

thereby improving the search efficiency of the algorithm.Finally

the traditional PSO algorithm and the improved PSO algorithm are applied to simulation comparisons on the IEEE 33-node system.The simulation result verifies the effectiveness of the improvements.