基于混沌粒子群算法的含风电配电网无功优化

发布时间：2018-06-28 08:20

本文选题：配电网 + 无功优化　；参考：《西安理工大学》2017年硕士论文

【摘要】：随着风电不断接入配电网,从而引起配电网潮流发生变化,研究含风电的配电网无功优化问题显得十分重要。在求解多目标优化的问题时,网损最小和无功补偿容量最小等多个目标函数相互矛盾,并不能同时达到最优,再考虑到风电功率具有随机性和间歇性,按照固定的风机出力进行无功优化并不能反映风电变化特点。因此,本文在场景分析的基础上,采用改进的混沌粒子群算法对含风电的配电网进行了无功优化。首先,分析了常用风电机组在配电网潮流计算时的等效处理方式,给出了含双馈风电机组的配电网潮流计算方法。其次,将粒子群算法和混沌算法相结合进行单目标的无功优化,在Pareto最优解的基础上,结合混沌粒子群算法,使其适用于求解多目标的优化问题,并用场景分析法中场景的确定性近似替代风机出力的不确定性,将场景分析法结合风机的不同工况,划分单一场景和全场景。最后,在IEEE33节点配电网选取固定节点接入风机及无功补偿堓置,建立了有功网损和无功补偿容量多口标最小的无功优化模型,用混沌粒子群算法求解无功补偿容量、有载变压器分接头位置。以网损最小为目标函数的单目标优化模型求解的过程中,某种场景下的方案用于其他场景会使网损明显高于最优值;以网损最小和无功补偿容量最小为目标函数的多目标优化求解的过程中,得出了一组最优解和Pareto最优前沿。结果表明,特定场景下的最优方案并不适用于其它场景,但全场景下的方案是一种整体最优的方案;改进的混沌粒子群多目标优化算法,可用于解决含风电配电网多目标无功优化的问题,并且效果优于粒子群的多目标优化方法。
[Abstract]:With the wind power being connected to the distribution network, the power flow of the distribution network changes, so it is very important to study the reactive power optimization problem of the distribution network with wind power. In solving the problem of multi-objective optimization, many objective functions, such as minimum network loss and minimum reactive power compensation capacity, are contradictory to each other and can not be optimized at the same time. Considering the randomness and intermittency of wind power, Reactive power optimization based on fixed fan output does not reflect the characteristics of wind power change. Therefore, based on the scene analysis, the improved chaotic particle swarm optimization algorithm is used to optimize the reactive power of the distribution network with wind power. Firstly, the equivalent treatment method of wind turbine in distribution network power flow calculation is analyzed, and the power flow calculation method of distribution network with doubly-fed wind turbine is presented. Secondly, the particle swarm optimization algorithm and chaos algorithm are combined to solve the multi-objective optimization problem. Based on the Pareto optimal solution and the chaotic particle swarm optimization algorithm, the particle swarm optimization algorithm is applied to solve the multi-objective optimization problem. The certainty of scene in scene analysis method is used to replace the uncertainty of fan output force, and the scene analysis method is combined with the different working conditions of the fan to divide the single scene and the whole scene. Finally, the fixed nodes of IEEE33 node distribution network are selected to access fan and reactive power compensation, and the reactive power optimization model with minimum reactive power loss and reactive power compensation capacity is established, and the reactive power compensation capacity is solved by chaotic particle swarm optimization algorithm. Tap position of loaded transformer. In the process of solving the single-objective optimization model with the minimum loss as the objective function, the scheme in one scenario will be used in other scenarios to make the loss significantly higher than the optimal value. A set of optimal solutions and Pareto optimal frontier are obtained in the process of solving multiobjective optimization with minimum network loss and minimum reactive power compensation capacity as objective functions. The results show that the optimal scheme is not suitable for other scenarios, but the scheme under the whole scenario is a global optimal scheme. It can be used to solve the problem of multi-objective reactive power optimization in wind power distribution network, and its effect is better than that of particle swarm optimization.
【学位授予单位】：西安理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM714.3

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