Data-Driven Power Flow for Radial Distribution Networks with Sparse Real-Time Data

TL;DR

This paper introduces a data-driven power flow method for radial distribution networks that accurately predicts voltages using limited real-time measurements and optimized sensor placement.

Contribution

It develops a novel framework combining behavioral modeling with the DistFlow model, enabling power flow estimation with sparse measurements and optimized sensor locations.

Findings

Achieves voltage prediction errors below 0.001 p.u.

Operates effectively with only 25% sensor coverage.

Demonstrates applicability on multiple test cases.

Abstract

Real-time control of distribution networks requires accurate information about the system state. In practice, however, such information is difficult to obtain because real-time measurements are available only at a limited number of locations. This paper proposes a novel data-driven power flow (DDPF) framework for balanced radial distribution networks. The proposed algorithm combines the behavioral approach with the DistFlow model and leverages offline historical data to solve power flow problems using only a limited set of real-time measurements. To design DDPF under sparse measurement conditions, we develop a sensor placement problem based on optimal network reductions. This allows us to determine sensor locations subject to a predefined sensor budget and to explicitly account for the radial nature of distribution networks. Unlike approaches that rely on full observability, the proposed framework is designed for practical distribution grids with sparse measurement availability. This enables data-driven power flow for real-time operation while reducing the number of required sensors. On several test cases, the proposed DDPF algorithm could demonstrate accurate voltage magnitude predictions, with a maximum error less than 0.001 p.u., with as little as 25% of total locations equipped with sensors.