Research On Power Environmental Economic Dispatch Based On Improved Quantum Particle Swarm Optimization Algorithm

With the rapid development of the global economy and the acceleration of industrialization,"power" has played an irreplaceable role as the driving force behind the development of the real economy of various countries.The stability and safe operation of the power system has been related to the development of national economic security.However,in the operation and control process of the power system,there are very complicated characteristics and some constraints often exist.So,it is necessary to study the economic dispatch of the power system.But in fact,today is developing rapidly,the past research on environmental economic dispatch has been unable to meet the demand for power in different industries.In the past,swarm intelligence algorithms were mainly used,and some numerical calculation methods were used to solve environmental economic dispatch problems.However,due to the shortcomings of the swarm intelligence algorithm,it is easy to fall into the local optimum when solving the global optimal value of the environmental economic dispatch problem.Therefore,it is of great significance to control the cost and pollution of the power industry by studying the swarm intelligence algorithm to improve the application efficiency of the algorithm in the power system.In this paper,differential evolution operator and crossover operator in genetic algorithms are introduced on the basis of the quantum particle swarm algorithm.An adaptive method is adopted in the update of the cross probability,and an improved quantum particle swarm algorithm is given.This algorithm is applied to the problem of environmental economic dispatch.In this paper,differential evolution operator is introduced on the basis of the quantum particle swarm optimization algorithm to improve the global search ability of the algorithm.At the same time,crossover operator of the genetic algorithm is introduced to ensure the diversity of the particle population of QPSO(Quantum Particle Swarm Optimization)and maintain the overall particle.And the instability of convergence of the QPSO algorithm in special circumstances and the contingency of falling into a local optimum;We need a suitable crossover probability in order to achieve the best optimization effect in the process of introducing crossover operator.In order to obtain suitable values,this paper proposes an improved cross-probability repair method based on the adaptive method,and the shrinkage-expansion factor adopts a linear decrease method as the number of iterations increases.Finally,we made the detailed calculation steps and flowchart of the improved algorithm(Differential Evolution-Quantum Particle Swarm Optimization,DE-CQPSO)with the above content,and the effectiveness of the improved algorithm is verified by using several standard test functions.In this paper,the improved quantum particle swarm optimization algorithm is applied to the environmental economic dispatching problem.Firstly,by selecting three cases of environmental economic dispatching problems,including two static dispatching and one dynamic dispatching case;secondly,comparing the effects of different methods to solve the EED(Environmental Economic Dispatch)problem,it is divided into single-objective optimization comparison of fuel cost and emissions,combined static environmental economic dispatch optimization comparison,and combined dynamic environmental economic dispatch optimization comparison to obtain the best compromise value;Finally,the convergence efficiency of each method in the process of optimizing power environment economic dispatch is analyzed,which further proves the effectiveness of the improved method.The experimental results show that on the basis of considering fuel cost and emissions at the same time,whether in single-objective optimization or multi-objective optimization,each evaluation index and convergence speed of the DE-CQPSO algorithm are better than QPSO and other algorithms,and get better A compromise value,which verifies the effectiveness and robustness of the DE-CQPSO algorithm in the economic dispatch of coal-fired power generation emissions.