论文标题
OPF中联合机会限制的迭代分解
Iterative Decomposition of Joint Chance Constraints in OPF
论文作者
论文摘要
在偶然约束的OPF模型中，与单个机会限制（SCC）相比，联合机会限制（JCC）为安全提供了更强的保证。使用Boole的不平等或改进版本将JCC分解为SCCS是很受欢迎的，但是所引入的保守性仍然很重要。在这封信中，提出了一个非参数迭代框架，以实现JCC的分解，而保守性可忽略不计。还提出了自适应风险分配策略，并将其嵌入到框架中。 IEEE测试案例的结果表明，使用该框架的保守性几乎消除了，从而大大降低了一代成本。
In chance-constrained OPF models, joint chance constraints (JCCs) offer a stronger guarantee on security compared to single chance constraints (SCCs). Using Boole's inequality or its improved versions to decompose JCCs into SCCs is popular, yet the conservativeness introduced is still significant. In this letter, a non-parametric iterative framework is proposed to achieve the decomposition of JCCs with negligible conservativeness. An adaptive risk allocation strategy is also proposed and embedded in the framework. Results on an IEEE test case show that the conservativeness using the framework is nearly eliminated, thereby reducing the generation cost considerably.
