Research On Power Economic Dispatch Based On Biogeography-based Learning Particle Swarm Optimization

Economic dispatch(ED)is an important optimization issue in the power system operation.The current research of economic dispatch has evolved from the traditional static economic dispatch(SED)to more complex dynamic economic dispatch(DED)and dynamic environmental economic dispatch(DEED).DED considers different time periods with different power demand,and puts forward higher requirements for the for power system dispatch.DEED takes environmental issue into account in the dynamic economic dispatch,and minimizes the pollutant emissions and economic costs simultaneously.This paper proposes a biogeography-based learning particle swarm optimization algorithm(BLPSO)to solve the static ED problem.In addition,a local search based on onlooker bee strategy(OBS)is introduced into BLPSO,and the BLPSO-OBS algorithm is proposed to solve the DED problem and the DEED problem.In summary,the main research results of this thesis are as follows:1.Introduce PSO and some improved PSO algorithms,including comprehensive learning PSO(CLPSO),social learning PSO(SLPSO),backbone PSO(BBPSO),and Gaussian quantum PSO(GQPSO).On this basis,a novel biogeography-based learning PSO(BLPSO)is proposed.In addition,a local search based onlooker bee strategy is introduced into BLPSO,and the BLPSO-OBS algorithm is proposed.2.The BLPSO algorithm is ultilized to solve the static economic dispatch(SED)problem.The SED problem takes power generation cost as the objective function.The goal is to efficiently arrange the power output of the generators by satisfying various constraints,so as to minimize the fuel cost of the power generation system.In order to verify the performance of BLPSO,this paper conducts tests on five SED systems.These five SED systems consider different constraints,including transmission loss,prohibited operation zone,ramp rate limit,power balance constraint,and power generation capacity constraint.Through comparison with other optimization algorithms,the superiority of the BLPSO algorithm in solving SED problems is demonstrated.3.The BLPSO-OBS algorithm is applied to solve the dynamic economic dispatch(DED)problem with electric vehicles.Different from the SED problem,the DED considers the power demand in multiple time periods,which makes the problem more complicated.The BLPSO-OBS algorithm is used to solve three DED systems.These three DED systems take into account different constraints,such as transmission loss,ramp rate limit,power balance constraint and power generation capacity constraint.The comparison with other optimization algorithms shows the superiority of the BLPSO-OBS algorithm in solving DED problems.4.The BLPSO-OBS algorithm is implemented to solve the dynamic environmental economic dispatch(DEED)problem.The DEED problem considers both environmental benefits and economic benefits,which further complicates the economic dispatch problem.This paper uses the penalty factor based multi-objective method to transform the multi-objective problem into a single-objective problem.BLPSO-OBS is used to the 6-unit DEED system and compared with other optimization algorithms.The simulation results show the superiority of the BLPSO-OBS algorithm in solving DEED problems.