Uncertainty management is a challenging task in decision making of the operators of the power systems. Prediction of the system state is vital for the operation of a system with stochastic behavior especially in a power system with a significant amount of renewable energies such as wind power. Predictable power systems are in more interest of operators, of course. This paper proposes a multiobjective framework for optimal placement and parameters setting of a unified power flow controller (UPFC) considering system predictability. The well-known multiobjective nondominated sorting genetic algorithm is implemented to handle various objective functions such as active power losses and predictability of system in the presence of operational constraints and uncertainties. The point estimate method is used for modeling probabilistic nature of the wind power. Using the proposed method, statistical information of voltage magnitude and apparent power of converters of UPFCs can be obtained, which are very useful in making decision on the sizing of UPFCs. Comprehensive discussions are provided using the simulations on the IEEE 57-bus test system. Also, in order to validate the obtained results, a multiobjective particle swarm optimization algorithm is implemented and the results of two algorithms are compared with each other.

中文翻译：

---

含风电系统中基于多目标可预测性的UPFC优化布局及参数设置


不确定性管理是电力系统运营商决策中的一项具有挑战性的任务。系统状态的预测对于具有随机行为的系统的运行至关重要，尤其是在具有大量可再生能源（例如风电）的电力系统中。当然，可预测的电力系统更符合运营商的利益。本文提出了一种多目标框架，用于考虑系统可预测性的统一潮流控制器（UPFC）的最佳布局和参数设置。著名的多目标非支配排序遗传算法被用来处理各种目标函数，例如在存在操作约束和不确定性的情况下的有功功率损耗和系统的可预测性。点估计方法用于对风电的概率性质进行建模。使用所提出的方法，可以获得UPFC转换器的电压幅度和视在功率的统计信息，这对于UPFC的尺寸决策非常有用。使用 IEEE 57 总线测试系统上的模拟进行了全面的讨论。此外，为了验证所获得的结果，实现了多目标粒子群优化算法，并对两种算法的结果进行了比较。