Dynamic dispatch optimization of microgrid based on a QS-PSO algorithm

A microgrid (MG) has been regarded as an efficient way for integrating distributed generation sources (DGSs) into distribution systems, and the corresponding effective energy management is crucial to realize the benefits associated with MG. In general, most of the researches ignored the inherent coupling among the dispatch intervals. This study aims to establish a dynamic dispatch optimization model for reasonably scheduling the DGSs in the MG and to identify the optimal solution based on a swarm intelligence algorithm. With the introduction of the dynamic system theory, a dynamic multi-objective optimization model is established for minimizing the operation and maintenance cost of the MG, decreasing the pollutant emissions, and reducing the deprivation cost of the DGSs. In order to deal with this complex optimization problem with high-dimension variables and multiple constraints, an enhanced quorum sensing based particle swarm optimization (QS-PSO) algorithm, whose competitiveness has been verified, is successively applied for determining the optimal dispatch solution of the whole period, instead of dividing the entire dispatch period into a number of small time intervals via a static optimization approach. The results based on a series of numerical simulations validate the effectiveness of the proposed dispatching model and QS-PSO application and demonstrate the superiority of dynamic operation optimization over the static optimization.