基于多种群萤火虫算法的车载燃料电池直流微电网能量管理优化

绿色高效的新能源系统已广泛应用于汽车、有轨电车和智能建筑等领域。为了降低燃料电池(fuelcell,FC)有轨电车直流微电网系统的运行成本,提出一种基于多种群萤火虫算法的车载燃料电池直流微电网能量管理优化方法。该方法引入以初始投资成本、设备运行成本和更换维护成本为框架的系统运行成本模型。基于成本模型,设计一种融合多种群遗传算法与萤火虫算法的多种群萤火虫算法,对有轨电车状态机控制策略进行优化。在3种运行工况下,将所提方法与状态机控制策略、基于遗传算法的策略、基于多种群遗传算法的策略、基于粒子群算法的策略和基于萤火虫算法的策略进行对比。结果表明,所提方法能合理分配各能量源的输出功率,并获得最低的系统运行成本。

Multiple-population Firefly Algorithm-based Energy Management Strategy for Vehicle-mounted Fuel Cell DC Microgrid

Green and efficient new energy systems have been widely used in automobiles, trams, and smart buildings. To reduce the operating cost of DC microgrid of fuel cell(FC) tram, the paper proposed an energy management optimization method for FC tram DC microgrid system based on multiplepopulation firefly algorithm. The method introduced the operation cost model based on the initial cost, operation cost, and maintenance cost. Aiming at the model, a multiplepopulation firefly algorithm combining multiple-population genetic algorithm and firefly algorithm was established to optimize the state machine control strategy for FC tram. Under the three test cycles, the proposed method was compared with the state machine control strategy, the genetic algorithm-based strategy, the multiple-population genetic algorithm-based strategy, the particle swarm optimization-based strategy, and the firefly algorithm-based strategy. The results show that the proposed method can reasonably allocate the power output of the three energy sources and obtain the lowest operating cost.