Particle Swarm Optimization Algorithm Based On Multi-swarm Coordination And Its Application In Power System Economic Dispatch

At present,all countries in the world are facing the dual problems of depletion of fossil energy and gradual deterioration of the ecological environment,which seriously restricts the sustainable development of human society.Especially for China,the world’s largest energy-consuming country,due to factors such as resource endowment,consumption habits,and technological dependence,coal,oil,natural gas and other fossil energy have long played a dominant role in the primary energy consumption structure.Electric energy,as a clean and efficient energy source,is widely used in many countries.All countries have also pointed out that to promote sustainable economic development,it is necessary to improve energy utilization and accelerate the development of power systems.Therefore,in order to meet the economic and environmental protection,it is of great significance to study the power system dispatching problem for my country’s sustainable development and energy conservation and emission reduction.For the dispatching problem of power system,there have been many literature studies at home and abroad.On the basis of these studies,this paper starts with an evolutionary algorithm and aims to propose a new algorithm to minimize the operating cost of the power grid;Environmental indicators are introduced into the problem.While meeting the low cost,an improved evolutionary algorithm is proposed to make the environmental indicators of the power grid more excellent.Based on the above questions,the main research results of this paper are as follows:First of all,based on the traditional particle swarm optimization algorithm,the strategy of competitive co-evolution is introduced,and particle swarm optimization based on multi-population competition coevolution with nonlinear weights is proposed.The algorithm consists of a master population and multiple slave populations.The slave group can provide more "excellent" individuals to the master group through search.The master group updates and iterates according to the slave group and the optimal particles it has found so far,which strengthens the global search ability and diversity,and uses nonlinear inertia weights.,which is beneficial for the algorithm to perform global and local search smoothly.The effectiveness of the proposed new algorithm is verified by functional tests.Secondly,a Multi-subpopulation coevolutionary algorithm with adaptive Cauchy-Polynomial mutation is proposed for multi-objective optimization problems.The strategy of cooperative coevolution is introduced,multi-objective problems are dealt with separately by setting up multiple populations,and the strategy of information exchange between populations is used to prevent the algorithm from falling into local optimum;combined with adaptive Cauchy mutation and polynomial mutation technology,in some cases,The particles are mutated to ensure the diversity of the algorithm;a diversity evaluation index is introduced,and the convergence of the algorithm is judged by the value of the index.Through a series of simulation experiments and comparison with other algorithms,the superior performance of the proposed algorithm for solving multi-objective problems is verified.Finally,a dynamic economic dispatch model of the power system is established,taking the generator output cost considering the valve point effect as the objective function,and considering the load balance constraints,unit output constraints and nonlinear constraints such as ramp rate.Based on the above model,an objective function that takes the pollutants emitted by thermal power units as environmental indicators is introduced,and a dynamic economical emission scheduling model of the power system is constructed.Aiming at the above single-objective and multi-objective optimization models,simulation experiments are carried out on the multi-swarm competitive co-evolution particle swarm optimization algorithm based on nonlinear weights and the multi-swarm cooperative coevolution algorithm based on adaptive Cauchy mutation using real examples.The proposed algorithm has certain reference value for solving the dynamic dispatching problem of power system.