Bayesian Model-based Generation of Synthetic Unbalanced Distribution Networks Incorporating Reliability Indices

TL;DR

This paper introduces a Bayesian Hierarchical Model that generates realistic, phase-consistent, unbalanced power distribution networks incorporating reliability indices, validated on Brazilian systems, enabling privacy-preserving analysis and planning.

Contribution

The work presents a novel Bayesian approach that jointly models phase configuration, demand, and reliability, ensuring realistic and electrically feasible synthetic distribution networks.

Findings

Accurately reproduces phase and demand distributions

Predicts reliability indices effectively on unseen data

Generates electrically feasible networks with convergent power flows

Abstract

Real-world power distribution data are often inaccessible due to privacy and security concerns, highlighting the need for tools for generating realistic synthetic networks. Existing methods typically overlook critical reliability metrics such as the Customer Average Interruption Frequency Index (CAIFI) and the Customer Average Interruption Duration Index (CAIDI). Moreover, these methods often neglect phase consistency during the design stage, necessitating the use of a separate phase assignment algorithm. This work proposes a Bayesian Hierarchical Model (BHM) that generates phase-consistent unbalanced three-phase distribution systems, and incorporates reliability indices. The BHM learns the joint distribution of phase configuration, power demand, and reliability indices from a reference network, conditioning these attributes on topological features. We apply the proposed methodology to generate synthetic power distribution networks in Brazil, and validated it on known Brazilian networks. The results show that the BHM accurately reproduces the distributions of phase allocation, power demand, and reliability metrics on the training system. Furthermore, in out-of-sample validation on unseen data, the model generates phase-consistent networks and accurately predicts the reliability indices for the synthetic systems. The generated networks are also electrically feasible: three-phase power flows converge and voltages remain within typical operating limits, enabling studies of planning, reliability, and resilience.