基于数字网系方法的概率最优潮流计算

风电场和光伏电站的大规模接入使得在进行电力系统最优潮流计算时需要考虑风电场和光伏电站出力的随机性。传统的蒙特卡洛法耗时长、占用内存大,文中提出一种利用数字网系(DN)的采样值具有等分布这一特性来改善输入随机变量分布空间覆盖程度的方法,并将该方法用于含风电场和光伏电站的电力系统概率最优潮流计算中。以IEEE 30节点系统对所提方法的准确性与有效性进行了验证,仿真结果表明:DN方法可以较好地估计输出随机变量的概率分布,能有效地处理电力市场中的不确定性问题。将该方法用于IEEE 300节点系统,研究了系统接入不同容量光伏电站对节点电价的影响。同时,还将风电场和光伏混合系统与单独风电场系统进行对比,得到前者的节点电价、网损和支路功率波动更小的结论。

Probabilistic Optimal Power Flow Calculation Based on Digital Nets Method

With large-scale integration of wind farms and photovoltaic plants into the power system, it becomes necessary to take the wind power and photovoltaic power uncertainty into consideration during optimal power flow (OPF) calculation. The traditional Monte Carlo simulation method cannot be used for the thousands of stochastic simulations required to achieve a rational result. Hence the digital nets (DN) method with equivalent distribution sample values is proposed to improve the sample value coverage of random variable input spaces. And the method is used in the OPF calculation of power system containing wind farms and photovoltaic plants. The accuracy and validity of the proposed method tested on an IEEE 30-bus system and the simulation results show that the proposed method has the advantages of fast computation and high accuracy while capable of estimating the probability distribution of the output random variables and dealing with the uncertainties in the electricity market. Then the DN method is applied to an IEEE 300-bus system to study the influence of different capacities of photovoltaic plants on the location marginal price. Furthermore, a comparison between the wind farm/photovoltaic plant hybrid system and single wind farm system has led to the conclusion that the location marginal price, net loss and branch active power of the former are less volatile.