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A novel information exchange particle swarm optimization for microgrid multi-objective dynamic optimization control
Dynamic optimization is likely to play an important role in the reliability, quality, and efficiency of future power system, specifically for microgrid with renewable generation. This paper presents an improved hybrid optimization algorithm, called information exchange particle swarm optimization (IEPSO), to deal with the problem of dynamic optimal control in a microgrid with multiple distributed generations (DGs), such as wind power, small hydro, and energy storage. Based on the actual application of DG in Southwest China, we propose an optimization model for grid-connected microgrid that introduces a contribution rate of energy storage into the model and incorporates practical engineering constraints. The performance of different optimization control strategies for active and reactive power is investigated, and the results indicate that the IEPSO algorithm is efficient for dynamic optimization, and the dynamic contribution rate control strategy for energy storage is more effective than static strategy.
A novel information exchange particle swarm optimization for microgrid multi-objective dynamic optimization control
Dynamic optimization is likely to play an important role in the reliability, quality, and efficiency of future power system, specifically for microgrid with renewable generation. This paper presents an improved hybrid optimization algorithm, called information exchange particle swarm optimization (IEPSO), to deal with the problem of dynamic optimal control in a microgrid with multiple distributed generations (DGs), such as wind power, small hydro, and energy storage. Based on the actual application of DG in Southwest China, we propose an optimization model for grid-connected microgrid that introduces a contribution rate of energy storage into the model and incorporates practical engineering constraints. The performance of different optimization control strategies for active and reactive power is investigated, and the results indicate that the IEPSO algorithm is efficient for dynamic optimization, and the dynamic contribution rate control strategy for energy storage is more effective than static strategy.
A novel information exchange particle swarm optimization for microgrid multi-objective dynamic optimization control
Yu, Lei (Autor:in) / Chen, Minyou (Autor:in) / Yu, David C. (Autor:in) / Zhang, Liang (Autor:in) / Yang, Fan (Autor:in) / Zhai, Jinqian (Autor:in)
01.03.2014
12 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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