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About

About

Associate Professor since 2011 at the Faculty of Engineering of the University of Porto (FEUP).

PhD obtained in 1995 in Electrical Engineering and Computers at FEUP.

Licenciado in 1984 in Electrical Engineering and Computers at FEUP.

Researcher at INESC TEC since 1985.

Interest
Topics
Details

Details

  • Name

    José Nuno Fidalgo
  • Cluster

    Power and Energy
  • Role

    Senior Researcher
  • Since

    25th June 1985
026
Publications

2018

The Use of Smart Grids to Increase the Resilience of Brazilian Power Sector to Climate Change Effects

Authors
Jose, DD; Nuno Fidalgo, JN;

Publication
TECHNOLOGICAL INNOVATION FOR RESILIENT SYSTEMS (DOCEIS 2018)

Abstract
Climate change has been a much-commented subject in the last years. The energy sector is a major responsible for this event and one of the most affected by it. Increasing the participation of renewable is a way to mitigate these effects. However, a system with large share of renewables (like Brazil) is more vulnerable to climate phenomena. This article analyzes the implementation of smart grids as a strategy to mitigate and adapt the electricity sector to climate change. Different climate and energy sector scenarios were simulated using a bottom-up approach with an accounting model. The results show that smart grids can help save energy, increase network resilience to natural hazards and reduce operational, maintenance costs and investments in new utilities. It would also allow tariffs diminution because of generation and losses costs reductions.

2018

Load and electricity prices forecasting using Generalized Regression Neural Networks

Authors
Paulos, JP; Fidalgo, JN;

Publication
2018 INTERNATIONAL CONFERENCE ON SMART ENERGY SYSTEMS AND TECHNOLOGIES (SEST)

Abstract
Over time, the electricity price and energy consumption are increasingly growing their weight as prime foundations of the electrical sector, with their analysis and forecasts being targeted as key elements for the stable maintenance of electricity markets. The advent of smart grids is escalating the importance of forecasting because of the expected ubiquitous monitoring and growing complexity of a data-rich ever-changing milieu. So, the increasing data volatility will require forecasting tools able to rapidly readjust to a dynamic environment. The Generalized Regression Neural Network (GRNN) approach is a solution that has recently re-emerged, emphasizing good performance, fast run-times and ease of parameterization. The merging of this model with more conventional methods allows us to obtain more sturdy solutions with shortened training times, when compared to conventional Artificial Neural Networks (ANN). Overall, the performance of the GRNN, although slightly inferior to that of the ANN, is suitable, but linked to much lower training times. Ultimately, the GRNN would be a proper solution to blend with the latest smart grids features, which may require much reduced forecasting training times.

2018

The Use of Smart Grids to Increase the Resilience of Brazilian Power Sector to Climate Change Effects

Authors
de São José, D; Fidalgo, JN;

Publication
IFIP Advances in Information and Communication Technology - Technological Innovation for Resilient Systems

Abstract

2018

Improving electricity price forecasting trough data segmentation based on artificial immune systems

Authors
Fidalgo, JN; Da Rocha, EFNR;

Publication
International Conference on the European Energy Market, EEM

Abstract
The price evolution in electricity market with large share of renewables often exhibits a deep volatility, triggered by external factors such as wind and water availability, load level and also by business strategies of market agents. Consequently, in many real applications, the performance of electricity price is not appropriate. The goal of this article is to analyze the available market data and characterize circumstances that affect the evolution of prices, in order to allow the identification of states that promote price instability and to confirm that class segmentation allows increasing forecast performance. A regression technique (based on Artificial Neural Networks) was applied first to the whole set and then to each class individually. Performances results showed a clear advantage (above 20%) of the second approach when compared to the first one. © 2018 IEEE.

2017

Analysis of Storage and Microgeneration Impacts on LV Network Losses

Authors
Fidalgo, JN; Moura, EMF;

Publication
2017 IEEE MANCHESTER POWERTECH

Abstract
Last decade has witnessed the birth and dissemination of microgeneration (MG) in most EU countries. MG growth and diffusion in LV networks are expected to continue in the next decade. At the same time, the interest on energy storage systems (ESS) applications to power systems has been intensifying in the last years, following some major technological achievements that improved ESS abilities and decreased their price. This article analyzes the impacts of MG and ESS dissemination in LV networks' losses. The central goal is to estimate the global impact on the Portuguese LV distribution system. For that purpose, a set of empirical studies was carried out over a set of representative networks, in which different MG and ESS scenarios were considered. The extrapolation of the results to the global LV points out to a loss reduction potential of more than 15%.

Supervised
thesis

2017

Impacto do desequilíbrio trifásico nas perdas de redes BT

Author
Rafael Cavalheiro

Institution
UP-FEUP

2017

Deteção e caracterização de perturbações de preços de mercado com base em sistemas imunológicos artificiais

Author
Eduardo Fernando Nogueira Rodrigues da Rocha

Institution
UP-FEUP

2017

Análise do impacto de adiamento do investimento em smart grids

Author
Helder José Branco Pedrosa

Institution
UP-FEUP

2017

Teoria do Caos aplicada à previsão de preços

Author
Joana Sofia Alves Lopes

Institution
UP-FEUP

2017

Previsão de Consumo de Energia Elétrica e do Preço da Eletricidade através de Redes Neuronais de Regressão Generalizada

Author
José Pedro Ferreira Pelicano Paulos

Institution
UP-FEUP