| With the gradual depletion of energy, environmental deterioration, and thedrawbacks of large-scale power systems, distributed generation has been used widely inrecent years. Microgrid, as the effective integration of distributed generation, with theability of run flexibility in grid-connected mode or isolated mode, improves thereliability and security of system, and makes full use of the advantages of distributedenergy, which provides an effective means to solve the connection of distributedgeneration to large grid. However, the diversity and flexibility of distributed generationsmake the microgrid scheduling operation, energy management, operation control andprotection very complicated. The optimal operation of the microgrid can improveenergy efficiency, reduce generation costs and pollutant emission. Therefore, theresearch of microgrid optimal operation has important theoretical and practical value.The problem of microgrid optimal operation is a complex non-linear optimizationproblem with characteristics of multi-objective, multi-constrained, and multi-variable.Intelligent optimization algorithm has drawn extensive application due to its superiorperformance, such as Genetic Algorithm, Particle Swarm Optimization (PSO) andImmune Algorithm, in which, PSO has the advantages of simplicity, strong robustness,high precision and fast convergence rate. In this paper, an Improved MultiobjectiveParticle Swarm Optimization Algorithm Based on Global Best Adaptive Selection andMutative Scale Chaotic Local Search(IMOPSO-GL) is proposed, and using both ZDTseries of standard test functions and reactive power optimization of IEEE30nodepower system to test the performance of the IMOPSO-GL algorithm. It is show thatIMOPSO-GL has better in convergence to Pareto optimal front and in uniformdistribution. The main improved strategies of IMOPSO-GL are:①Position of the best particle of the entire swarm(gbest) has a great influence onthe convergence and diversity of multiobjective particle swarm optimization. The gbestadaptive selection strategy is raised in this paper, by using the theory of Sigma method,dynamic zero technology and crowding distance mechanism. This strategy can make theswarm close to the present Pareto optimal front uniformly and quickly;②Archive retains non-dominated solutions found in evolutionary process. In thispaper, archive has a minimum size to avoid populations excessive clustering in certainareas when the size of archive is too small, moreover, the circular crowded sorting isapplied to sort the crowding distance of all non-dominated solutions with same rank, and the non-dominated solution with least crowding distance is eliminated, while thesize of archive is larger than the maximum size. thus archive can maintain diversityduring the entire evolutionary process;③The mutative scale chaotic local search strategy is introduced to improve theability of local search and the convergence of multiobjective particle swarmoptimization, which searches some archive members when the search ability ofIMOPSO-GL is weak.A microgrid contains photovoltaic cells, wind turbines, fuel cells, micro turbines,diesel generators and energy storage system has been studied in this paper, and theenergy storage system is using hybrid energy storage system, which is composed by thebattery and super capacitor. Under the conditions of grid-connected mode, amultiobjective optimal operation model for microgrid is proposed, which objectivefunction includes the economic benefits and environmental costs. And themultiobjective optimal operation model for microgrid under isolated mode is proposed,which objective function includes the generation costs and environmental costs, theconstraints of up spinning reserve and down spinning reserve are introduced. Fuzzyexpert system and IMOPSO-GL is used to solve the model under constraints of powerbalance, output of distributed power, the state of charge and output of battery and thepower transmission with distribution network. Simulation results demonstrate theeffectiveness of the model. |
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