Research And Comparison Of Power System Optimization In Power System

Power system active power optimization is a very important part of power system economic dispatch.The classical algorithm represented by classical method has the characteristics of reliable calculation,fast calculation speed and high precision.The intelligent algorithm represented by ant colony algorithm is robust features.In this paper,the classical method and ant colony algorithm are studied and compared,and the optimization process is carried out.The improved classical method active optimization algorithm and improved ant colony algorithm are proposed.In the first part of this paper,the active power optimization of the classical power system is studied.The power flow calculation and the network loss micro-increment rate calculation are the main components of the classical method.The power system power flow calculation process is roughly divided into three parts.The paper stores and reads the admittance matrix.The method of solving the linear equations is improved,and the improved PQ decomposition method for Cartesian coordinates/polar coordinate power flow is proposed.Cartesian coordinate impedance matrix method,polar coordinate impedance matrix method and Jacobian matrix method are very representative three kinds of network loss micro-increment rate algorithms.This paper introduces and deduces these three algorithms in detail,and proposes on the basis of three kinds of network loss micro-increasing rate improvement algorithms and finally proposed to improve the classical method active power optimization algorithm.The second part of this paper introduces the basic principle of ant colony algorithm and three traditional continuous domain ant colony algorithms.Based on three traditional continuous domain ant colony algorithms,an improved ant colony algorithm is proposed and applied to the power system optimized.In the third part of the thesis,the feasibility and comparison of the classical method and the improved ant colony algorithm are verified by examples.The feasibility of applying the improved ant colony algorithm to power system active power optimization is verified.