Energy Optimization Research Of Energy Internet Based On Intelligent Optimization Algorithm

As a product of the combination of information technology and all kinds of distributed energy,the energy Internet provides a feasible technical scheme for solving the adaption scheduling of multiple types of energy,the energy transmission control of open intelligence and the acquisition and efficient processing of energy information.Among them,the adaptive scheduling of multiple types of energy is a core problem that the energy Internet needs to solve.The main problem is to minimize the total operating cost by determining the output power of all distributed power sources in the energy Internet for each time period of the day.In essence,it is a NP difficult optimization problem.And users can also participate in the adaptation scheduling of distributed energy sources on the energy Internet,providing personalized energy support for all distributed power supply dispatching.Traditional mathematical methods have some problems in the efficiency and accuracy of energy adaptation in the energy Internet.Therefore,effective selection and improvement of intelligent optimization algorithm to solve the energy Internet energy adaptation problem has important theoretical significance and research prospects.In this thesis,the energy adaptation process in the energy Internet is modeled as an intelligent optimization problem,and the improved moth-flame optimization(MFO)is applied to optimize the energy adaptation problem of the energy Internet.(1)In view of the distributed energy model theory in the energy Internet,the energy adaptation model of the energy Internet is constructed.In addition,the local search strategy is introduced to MFO in order to realize the local search for the current solution.The improved algorithm is applied to the energy adaptation model of the energy Internet,and the simulation results show that the improved algorithm shows a certain advantage in the above model.(2)Based on the variation and cross operation of differential evolution algorithm,local search based moth-flame optimization(LSMFO)is further improved.LSMFO and differential evolution algorithm(DE)fusion,mutations in DE and crossover operation can be well applied to MFO based on local search,completing one iteration in MFO,mutation and crossover operation for iterative moth populations,then according to the search to calculate the new position of the moth the fitness value.(3)Considering the energy and economic adaptation model of energy and Internet,the environmental cost caused by various pollutants is not considered.Therefore,an energy Internet based energy adaptation model based on environmental cost is built,and the neighborhood search moth algorithm with differential evolution is applied to the above models.The experimental results show that the fusion algorithm has better optimization effect on the energy and Internet energy adaptation model based on the environmental cost.