Iterative Decomposition of Joint Chance Constraints in OPF

TL;DR

This paper introduces an iterative, non-parametric framework with adaptive risk allocation to decompose joint chance constraints in OPF, significantly reducing conservativeness and lowering generation costs.

Contribution

It presents a novel iterative decomposition method for joint chance constraints in OPF that minimizes conservativeness compared to traditional approaches.

Findings

Conservativeness is nearly eliminated using the proposed framework.

Generation costs are reduced significantly in IEEE test cases.

The adaptive risk allocation improves the efficiency of the decomposition.

Abstract

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.