“Outage Forecast” – A Real Application of Machine Learning on Grid Operation Management Strategies

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Paper number
1209
Working Group Number
Conference name
CIRED 2019
Conference date
3-6 June 2019
Conference location
Madrid, Spain
Peer-reviewed
Yes
Short title
Convener
Authors
Almeida, Bernardo, EDP Distribuição, Portugal
Faria, Gonçalo, EDP Distribuição, Portugal
Soares, Tiago, EDP Distribuição, Portugal
Santos, Ricardo Jorge, EDP Distribuição, Portugal
Ferreira Pinto, José, EDP Distribuição, Portugal
Santos, Tiago, Smartwatt, Portugal
Preto, Isabel, Smartwatt, Portugal
Monteiro, Cláudio, FEUP, Portugal
Abstract
The energy sector is under one of the biggest transformations ever. Driven by the current digitalization process being held in all sectors, energy utilities are embracing data analytics and artificial intelligence to face current challenges and create new opportunities. Utilities need to act fast to unlock the true potential of the digital grid. The use of real-time data to monitor and operate grid infrastructure under efforts to improve the efficiency and reliability of energy networks is increasing at a global scale.A lot of utilities experience challenges associated with extreme weather conditions and its implications in quality of service, so a special focus on storm analytics, reliability, resiliency and outage management is required. With that in mind and being work force management (WFM) an extremely value-driving activity, EDP Distribuição (EDPD), the Portuguese DSO, started investing on new methodologies based on data analytics and machine learning to aid in the decision-making process of operational planning activities like the ones presented in this paper.This paper presents the newly developed tool that uses machine learning algorithms to predict the number and location of outages on EDPD’s high and medium voltage grid based on weather forecast. The paper shows in detail the used methodology and presents the project’s first results.
Table of content
Keywords
Publisher
AIM
Date
2019-06-03
Permanent link to this record
https://cired-repository.org/handle/20.500.12455/344
http://dx.doi.org/10.34890/570
ISSN
2032-9644
ISBN
978-2-9602415-0-1