含风电场的配电网无功优化策略研究

Reactive power optimization strategy in distribution network with wind farm

针对传统的配电网无功优化调节手段离散化、难以实现电压的连续调节等问题,研究了含风电场的配电网无功优化模型和算法,分析了双馈感应电机的无功发生能力,将风电场作为连续的无功调节手段参与配电网无功优化。并针对风电出力随机性的特点,用场景功率描述风电的随机出力,使之更具代表性。考虑了配电网的网损、电压偏差以及电压稳定性指标,建立了多目标无功优化模型。提出了基于量子粒子群算法(QPSO)的无功优化方法,该算法通过波函数描述粒子的状态,增加了种群的多样性,有效地避免了种群早熟等问题。用该算法对改进的IEEE33节点进行无功优化计算,并和粒子群算法(PSO)进行了比较,结果表明量子粒子群算法能更好地达到全局最优解,收敛速度更快,证明了优化模型和算法的有效性。

Considering the traditional methods of voltage adjusting in distribution network reactive power optimization is discretized, and difficult to realize the continuous voltage adjustment, this paper studies the reactive power optimization model and algorithm in distribution network with wind farm. The limitation of reactive power capacity of doubly-fed induction generator is considered and the wind farm is taken as a continuous reactive power adjustment means to participate in the optimization. Scenario power is used to describe the random power of wind farm in respect to the random characteristic of wind power. The network loss, deviation of voltage and stability of voltage are included in the multi-objective reactive power optimization model. Reactive power optimization based on quantum particle swarm optimization（QPSO） is proposed. The algorithm describes particle state by wave function, which not only increases the diversity of population, but also avoids premature of population. The comparison of results between QPSO with PSO on the modified IEEE 33-bus system demonstrates the effectiveness and advantage of quantum particle swarm optimization model, which can achieve a better global optimal solution and shows a faster convergence speed.