Work

Recurrent neural networks in distribution network planning: enhanced decision-making under uncertainty

Year: 2025

Type: conference-paper

Abstract: The electrical distribution networks are rapidly growing due to the integration of new consumer profiles and distributed generation, creating challenges in predicting the fluctuating demand. This paper presents a distribution network planning method to address these challenges, which considers a Recurrent Neural Network (RNN) model, combined with a confidence interval threshold, to forecast time-series demand scenarios while considering the aggregated PV generation from smart meters. A case study in a radial medium voltage distribution network in Spain was used to test the proposed method, ensuring the operational thermal limits of transformers. This validation confirms its practical applicability in real-world scenarios, essential for maintaining network integrity and efficiency.

Source: IET conference proceedings.

DOI: 10.1049/icp.2024.1925

Authors: Antonio E. Saldaña-Gonzalez, Mònica Aragüés-Peñalba, Marc Carrillo-Muñoz, Andreas Sumper

Institution: Universitat Politècnica de Catalunya

Language: English

Topic: Optimal Power Flow Distribution

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: green

Funder: European Commission