Iterative Decomposition of Joint Chance Constraints in OPF.

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 IEEE test cases show that the conservativeness using the framework is nearly eliminated, thereby reducing the generation cost considerably. [ABSTRACT FROM AUTHOR]