Multi-objective Optimal Scheduling Considering FACTS Devices and Renewable Energy
With the penetration of high proportion of renewable energy into power grid,the difficulty in the scheduling and operation of power grid rises.In this paper,a multi-objective competitive particle swarm optimization algorithm based on an adaptive penalty function is proposed to address the multi-objective optimization problems involving sto-chastic renewable energy and various flexible AC transmission system(FACTS)devices.First,a complete AC power flow model is constructed under various continuous/discrete control variable constraints,with optimization objectives of reducing the fuel costs,voltage deviation,transmission losses and emissions.Then,the trade-off among these objec-tives is explored under different configurations of FACTS devices.The analysis results of an IEEE 30-bus system indi-cate that with the comprehensive consideration of optimal configuration of renewable energy and FACTS devices,the emissions and fuel costs can be significantly reduced,and the grid reliability and transmission efficiency can also be im-proved.Compared with the existing methods,the proposed algorithm demonstrates higher solution quality and stability in solving multi-objective optimization problems.
AC optimal power flowrenewable energymulti-objective optimizationconstraint handling techniqueflexible AC transmission system(FACTS)device
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维普
