基于粒子群算法的配电网储能优质优化研究

随着经济发展由粗放式增长向集约型增长转变,对配电网储能进行优化成为了学术界的前瞻性研究,这对于我国加速发展意义重大。此次研究利用粒子群优化算法以及双层多目标优化配置数学方法,构建了基于粒子群优化算法的配电网储能优质优化研究模型,该模型在对主动配电网概率潮流进行计算的基础上,达到了提高广义电源运行效率的效果,从而为相关人员进行含广义电源的配电网管理提供了方法参考。以PG&E-69节点配电系统作为研究对象,对该模型进行验证。研究结果表明,智能算法类型对广义电源优化配置的影响并不大。同时较上层模型,下层模型对广义电源优化配置的效率更高,因而,要实现广义电源的高效运行,进而提高配电网的储能效果,必须实现双层模型的合理运用。所提研究可以为配电网储能优质研究提供一定的指导价值。

Research on high quality energy storage optimization of distribution network based on particle swarm optimization

With the transformation of economic development from extensive growth to intensive growth, the optimization of distribution network energy storage has become a forward-looking research in the academic community, which is of great significance to the accelerated development of China. In this study, using particle swarm optimization algorithm and the mathematical method of double-layer and multi-objective optimization configuration, a research model of high-quality optimization of distribution network energy storage based on particle swarm optimization algorithm is constructed. Based on the calculation of probability power flow of active distribution network, the model achieves the effect of improving the operation efficiency of Guangyi power supply, so as to carry out distribution network management with generalized power supply for relevant personnel The method reference is provided. The PG & e-69 node distribution system is taken as the research object to verify the model. The results show that the type of intelligent algorithm has little influence on the optimal configuration of generalized power supply. At the same time, compared with the upper model, the lower model is more efficient for the optimal allocation of generalized power supply. Therefore, to achieve the efficient operation of generalized power supply and improve the energy storage effect of distribution network, it is necessary to realize the reasonable use of the double-layer model. This study can provide some guidance value for the research of energy storage quality of distribution network.