Energy-optimized Scheduling Of Microgid Systems Based On Chaos Artificial Search Swarm Algorihm With Opposition Based Learning

With the development of modern power systems,the research of micro-grid systems has become a hot spot in recent years.As the basis of the development of smart grids,micro-grids provided an effective way to solve the two major problems of energy crisis and environmental degradation.As one of the core technologies of micro-grid system,microgrid economic optimization scheduling has an important engineering practical value and theoretical research significance.This thesis proposed Chaos artificial search swarm algorithm with opposition-based learning which regarding the micro-grid economic energy optimization scheduling as the research direction,and apply it to the optimal operation mathematical model to consider economic benefit and environmental costArtificial Search Swarm Algorithm(ASSA)is a kind of bionic intelligent algorithm,which inspired by the human communicating with each other in search.In this paper,detailed simulation experiments are carried out on various parameters of the artificial search swarm algorithm,and the influence of the adjustment parameters on the performance is deeply understood.However,ASSA still has some problems on local optimal and a slow convergence speed.This paper proposed a modified algorithm named Chaos artificial search swarm algorithm with opposition-based learning(CASSA-OBL).Opposition-based learning is applied in the field of intelligent algorithms recently and has a significant effect on optimal performance.Meanwhile,chaos theory and adaptive strategy are introduced in proposed algorithm.The opposition-based learning mechanism and chaos initialization increase the population diversity and enhance the exploration ability of the algorithm.Selfadaptive strategies can improve the exploitation ability of dominant populations at the later stage of convergence.To evaluate the performance of proposed algorithm,we compare CASSA-OBL with other ASSA variants and state-of-the-art algorithms basing on a set of CEC13 benchmark functions.The results of experiment show the proposed algorithm can effectively avoid falling into local optimal,and has better convergence speed and convergence precision than some other optimization algorithms significantly.In this thesis,a mathematical model with the lowest operating cost of the micro-grid system as the objective function is established,and the relevant constraints are set according to the actual situation.Two safety indicators of load shortage rate and energy waste rate are considered in the micro-grid system,and they are incorporated into the modelas a constraint.The chaos artificial search swam algorithm with opposition-based learning is applied to solve the micro-grid economic optimization model,and the micro-grid system in a certain region is taken as an example.The output of each period,part of the operating cost and total operating cost are calculated in the system,and finally the annual total cost is found.The chaos artificial search swarm algorithm with opposition-based learning is applied to solve the micro-grid economic optimization model,and the simulation results are compared and analyzed.In the optimization process of micro-grid,the artificial search swam algorithm with opposition-based learning can execute the energy scheduling strategy more accurately,realize the goal of lowest power generation and total running cost,which verify proposed algorithm can apply to field of grid energy optimization scheduling effectiveness and feasibility.