Various kinds of new engineering technologies have been studied to realize the low-carbon and sustainable power supply systems all over the world.In actual implementation of these technologies,mostly,there are multipl...Various kinds of new engineering technologies have been studied to realize the low-carbon and sustainable power supply systems all over the world.In actual implementation of these technologies,mostly,there are multiple objectives with trade off relationships among each other,and also various constraints in the achievement of these objectives.Therefore,it should be essential to solve multiobjective optimization problems effectively in the applications of these new technologies in power systems.This paper proposes an improved method to realize multiobjective optimization for critical challenges in advanced power systems.To realize that,in an optimal dispersed generation installation problem,that is,one of effective measures for low-carbon power systems,various optimization methods and their combination methods are evaluated and a hybrid method for evolutionary algorithms was developed.The method can provide improved results compared with other state-of-the-art multi-objective optimization methods.展开更多
文摘Various kinds of new engineering technologies have been studied to realize the low-carbon and sustainable power supply systems all over the world.In actual implementation of these technologies,mostly,there are multiple objectives with trade off relationships among each other,and also various constraints in the achievement of these objectives.Therefore,it should be essential to solve multiobjective optimization problems effectively in the applications of these new technologies in power systems.This paper proposes an improved method to realize multiobjective optimization for critical challenges in advanced power systems.To realize that,in an optimal dispersed generation installation problem,that is,one of effective measures for low-carbon power systems,various optimization methods and their combination methods are evaluated and a hybrid method for evolutionary algorithms was developed.The method can provide improved results compared with other state-of-the-art multi-objective optimization methods.
