基于AG-MOPSO的含风电配电网无功优化

Reactive Power Optimization of Wind Power Distribution Network Based on AG-MOPSO

针对风电机组并网出力的不确定性,采用基于概率发生的场景分析法将不确定性模型转换为不同发生概率的多场景问题,建立以有功网损和电压偏差最小为目标的无功优化模型。针对传统方法得到的Pareto前沿多样性较差的问题,提出基于自适应网格的多目标粒子群优化AG-MOPSO(adaptive grid multi-objective particle swarm optimization)算法。该算法采用自适应网格得到外部档案库中粒子的密度,并根据密度信息以轮盘赌机制选取全局最优粒子和维护外部存储库的规模,有效地保证了Pareto前沿分布的均匀性和多样性。运用该算法对含风电的IEEE 33节点系统进行无功优化计算,并与已有NSGA-Ⅱ算法进行比较,结果表明所提算法得到的Pareto前沿较好,验证了该模型和算法的可行性。

Aimed at uncertainties in the output from grid-connected wind turbine,the scenario analysis method based on probability occurrence is adopted to transform the uncertainty model into a multi-scenario problem with different occurrence probabilities,and a reactive power optimization model with the goal of minimizing the active power loss and voltage deviation is established.In view of the poor diversity of Pareto frontiers obtained using the traditional methods,an adaptive grid multi-objective particle swarm optimization( AG-MOPSO) algorithm is proposed,which uses adaptive grids to obtain the density of particles in external archives,selects the global optimal particles and maintains the scale of the external storage library according to the density information as well as the betting mechanism,thus effectively ensuring the uniformity and diversity of the Pareto frontier distribution.This algorithm is used to perform reactive power optimization calculations on an IEEE 33-bus system with wind power,and it is also compared with the existing NSGA-Ⅱ algorithm.Results show that the Pareto frontier obtained using this algorithm is better,which verifies the feasibility of the proposed model and algorithm.