Impact of an ML-Based Demand Response Mechanism on the Electrical Distribution Network: A Case Study in Terni-Reference-Cited by-同舟云学术

Impact of an ML-Based Demand Response Mechanism on the Electrical Distribution Network: A Case Study in Terni

Published:2023-09-19 Issue:18 Volume:12 Page:3948
ISSN:2079-9292
Container-title:Electronics
language:en
Short-container-title:Electronics

Author:

Bucarelli Marco Antonio¹^ORCID,Ghoreishi Mohammad¹^ORCID,Santori Francesca¹,Mira Jorge²,Gorroñogoitia Jesús²

Affiliation:

1. ASM Terni S.p.A., 05100 Terni, Italy

2. Atos Research & Innovation, 28042 Madrid, Spain

Abstract

The development of smart grids requires the active participation of end users through demand response mechanisms to provide technical benefits to the distribution network and receive economic savings. Integrating advanced machine learning tools makes it possible to optimise the network and manage the mechanism to maximise the benefits. This paper proceeds by forecasting consumption for the next 24 h using a recurrent neural network and by processing these data using a reinforcement learning-based optimisation model to identify the best demand response policy. The model is tested in a real environment: a portion of the Terni electrical distribution network. Several scenarios were identified, considering users’ participation at different levels and limiting the potential with various constraints.

Funder

H2020 European Commission project IoT-NGIN

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

Link

https://www.mdpi.com/2079-9292/12/18/3948/pdf

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