The Multi-Objective Optimization Operation of Microsources in Microgrid based on an Improved Particle Swarm Algorithm

Peng Li, Tao Li, Yuwei Li, and Duo Xu


microgrid, microsources, mathematical model, improved particle swarm algorithm


As an important part of smart grid, microgrid(MG) is a new form of smart grid in the future. Microgrid(MG) technology can effectively integrate the advantages of new energy and renewable energy generation and provide a novel way for large-scale applications of new energy and renewable energy connecting to grid. This paper deals with the problem of economic operation of microsources in the microgrid, such as micro-turbine(MT), fuel cell(FC), diesel generator(DG), photovoltaic cell(PV), wind turbine(WT), and battery storage. The proposed problem is formulated as a nonlinear constrained optimization problem. The paper takes into consideration the operation cost as well as the emission reduction of NOx, SO2, and CO2. So a mathematical optimal model is built to optimize operation of microgrid(MG) system, based on the characteristics of various microsources, the restraint of microgrid system and the predicting output of the next 24-hours’ wind turbine and photovoltaic cell and load demand. An improved particle swarm algorithm is employed to minimum the comprehensive benefit cost of microgrid operation including economic and environmental benefits which realizes the multi-objective optimization operation. Besides, this paper focuses on the effect of electricity price between microgrid and the main grid on system operation costs. The results demonstrate the efficiency of the proposed approach to satisfy the load and to reduce the operation cost and the emissions.

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