Cookies
O website necessita de alguns cookies e outros recursos semelhantes para funcionar. Caso o permita, o INESC TEC irá utilizar cookies para recolher dados sobre as suas visitas, contribuindo, assim, para estatísticas agregadas que permitem melhorar o nosso serviço. Ver mais
Aceitar Rejeitar
  • Menu
Sobre

Sobre

Professr Associado desde 2011 na Faculda de de Engenhgaria da Universidade do Porto (FEUP).

Doutorado em 1995 em Engenharia Eletrotécnica e Computadores na FEUP.

Licenciado em 1984 em Engenharia Eletrotécnica e Computadores na FEUP.

Investigador do INESC TEC desde 1985.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    José Nuno Fidalgo
  • Cluster

    Energia
  • Cargo

    Investigador Sénior
  • Desde

    25 junho 1985
038
Publicações

2020

Assessing the Impact of Investments in Distribution Planning

Autores
MacEdo, P; Fidalgo, JN; Tome Saraiva, J;

Publicação
2020 17th International Conference on the European Energy Market (EEM)

Abstract

2020

Cost-benefit Analysis on a New Access Tariff: Case Study on the Portuguese System

Autores
Vilaca, P; Saraiva, JT; Fidalgo, JN;

Publicação
International Conference on the European Energy Market, EEM

Abstract
This paper reports the main results that were obtained in the scope of a consultancy study that was developed for EDP Distribuição, the main Portuguese distribution company, to evaluate the impact of a number of changes to be introduced in the Tariff System. These changes were proposed by ERSE, the Portuguese Regulatory Agency for the Energy Services, and included the redesign of the tariff periods and the possible introduction of a geographic differentiation on the Access Tariff to reflect different daily and yearly demand and flow patterns along the country. This work involved the development of a Cost Benefit Analysis, CBA, as well as a Pilot Project that included 82 MV and HV consumers to evaluate several Key Performance Indices, KPI, used to characterize the proposed changes on the tariff system. © 2020 IEEE.

2020

Predicting Long-Term Wind Speed in Wind Farms of Northeast Brazil: A Comparative Analysis Through Machine Learning Models

Autores
de Paula, M; Colnago, M; Fidalgo, J; Casaca, W;

Publicação
IEEE LATIN AMERICA TRANSACTIONS

Abstract
The rapid growth of wind generation in northeast Brazil has led to multiple benefits to many different stakeholders of energy industry, especially because the wind is a renewable resource - an abundant and ubiquitous power source present in almost every state in the northeast region of Brazil. Despite the several benefits of wind power, forecasting the wind speed becomes a challenging task in practice, as it is highly volatile over time, especially when one has to deal with long-term predictions. Therefore, this paper focuses on applying different Machine Learning strategies such as Random Forest, Neural Networks and Gradient Boosting to perform regression on wind data for long periods of time. Three wind farms in the northeast Brazil have been investigated, whose data sets were constructed from the wind farms data collections and the National Institute of Meteorology (INMET). Statistical analyses of the wind data and the optimization of the trained predictors were conducted, as well as several quantitative assessments of the obtained forecast results.

2019

Impact of load unbalance on low voltage network losses

Autores
Nuno Fidalgo, JN; Moreira, C; Cavalheiro, R;

Publicação
2019 IEEE Milan PowerTech, PowerTech 2019

Abstract
The total losses volume represents a substantial amount of energy and, consequently, a large cost that is often included in the tariffs structure. Uneven connection of single-phase loads is a major cause for three-phase unbalance and a fundamental cause for active power losses, particularly in Low Voltage (LV) networks. This paper analyzes the impact of load unbalance on LV network losses. In the first phase, several load scenarios per phase are considered to characterize how losses depend on load unbalance. The second phase examines the data collected per phase on a set of real networks, aiming at illustrating real-world cases. The third phase analyzes the effect that public lighting and microgeneration may have in the load unbalance and on the subsequent energy losses. The results of this work clearly demonstrate that it is possible to reduce three-phase unbalance (and losses) through a judicious distribution of loads and microgeneration. © 2019 IEEE.

2019

Impact of Climate Changes on the Portuguese Energy Generation Mix

Autores
Nuno Fidalgo, JN; Jose, DD; Silva, C;

Publicação
2019 16TH INTERNATIONAL CONFERENCE ON THE EUROPEAN ENERGY MARKET (EEM)

Abstract
Global climate change is currently a focus issue because of its impacts on the most diverse natural systems and, consequently, the development of humanity. The electricity sector is a major contributor to climate change because of its long-standing dependence on fossil fuels. However, the energy paradigm is changing, and renewable sources tend to play an increasingly important role in the energy mix in Portugal. Due to the strong relationship between renewable energies and climate-related natural resources, the climate change phenomenon could have considerable effects on the electricity sector. This paper analyzes the effects of climate change on the energy mix in Portugal in the medium / long term (up to 2050). The proposed methodology is based on the simulation of climate scenarios and projections of installed power by type and consumption. The combinations of these conditions are inputted to an energy accounting simulation tool, able to combine all information and provide a characterization of the system state for each case. The most favorable forecasted scenarios indicate that a fully renewable electricity system is achievable in the medium term, in line with the objectives of the European Union, as long as investments in renewable sources continue to be stimulated in the coming years.

Teses
supervisionadas

2020

Previsão de investimentos com base em informação esparsa

Autor
João Pedro Espírito Santo Almeida

Instituição
UP-FEUP

2020

Determinação de perfis de consumo baseada em mapas de Kohonen modificados

Autor
Rui Manuel Proença Bidarra

Instituição
UP-FEUP

2020

Estudo de Modelos de Previsão Aplicados à Produção Renovável

Autor
João Carlos Marques Moreira

Instituição
UP-FEUP

2020

Deteção e mitigação de perdas extremas em redes AT

Autor
Nuno Miguel da Cunha Barbosa

Instituição
UP-FEUP

2019

Identificação de perfis típicos e de anomalias de consumo

Autor
Eduardo Miguel Reis Gonçalves Moreira

Instituição
UP-FEUP