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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
043
Publicações

2022

Identification of Typical and Anomalous Patterns in Electricity Consumption

Autores
Fidalgo, JN; Macedo, P;

Publicação
APPLIED SCIENCES-BASEL

Abstract
Nontechnical losses in electricity distribution networks are often associated with a countries’ socioeconomic situation. Although the amount of global losses is usually known, the separation between technical and commercial (nontechnical) losses will remain one of the main challenges for DSO until smart grids become fully implemented and operational. The most common origins of commercial losses are energy theft and deliberate or accidental failures of energy measuring equipment. In any case, the consequences can be regarded as consumption anomalies. The work described in this paper aims to answer a request from a DSO, for the development of tools to detect consumption anomalies at end-customer facilities (HV, MV and LV), invoking two types of assessment. The first consists of the identification of typical patterns in the set of consumption profiles of a given group or zone and the detection of atypical consumers (outliers) within it. The second assessment involves the exploration of the load diagram evolution of each specific consumer to detect changes in the consumption pattern that could represent situations of probable irregularities. After a representative period, typically 12 months, these assessments are repeated, and the results are compared to the initial ones. The eventual changes in the typical classes or consumption scales are used to build a classifier indicating the risk of anomaly.

2022

Decision support system for long-term reinforcement planning of distribution networks

Autores
Fidalgo, JN; Azevedo, F;

Publicação
ELECTRIC POWER SYSTEMS RESEARCH

Abstract
The last decade has witnessed a growing tendency to promote deeper exploitation of power systems infrastructure, postponing investments in networks reinforcement. In particular, the literature on smart grids research often emphasizes their potential to defer investments. The study reported in this paper analyses the impact of reinforcement decisions, comparing the long-term costs associated with different network conditions and economic analysis parameters. The results support the conclusion that network reinforcement deferral is not a panacea, as it often generates costly situations in the long-term. The challenge is not to find new ways to postpone investments, but to find the most beneficial criterion to trigger the grid reinforcements actions. Another contribution of the present work is a decision support system to identify the most economical network reinforcement criterion in terms of the peak to capacity ratio.

2022

The Value of Investments in Network Efficiency in Systems with a Large Integration of Distributed Renewable Generation

Autores
Fidalgo, JN; Paulos, JP; MacEdo, P;

Publicação
International Conference on the European Energy Market, EEM

Abstract
This article analyzes the effects of the current policy trends - high levels of distributed generation (DG) and grid load/capacity ratio - on network efficiency. It starts by illustrating the network losses performance under different DG and load/capacity conditions. The second part concerns the simulation of network investments with the purpose of loss reduction for diverse system circumstances, including the impact of DG levels, energy cost, and discount rate. The attained results showed that DG, particularly large parks, have a negative impact on network efficiency: network losses tend to intensify with DG growth, under the current regulation. Furthermore, network investments in loss reduction would have a small global impact on network efficiency if the DG parks' connection lines are not included in the grid concession (not subjected to upgrade). Finally, the study determines that it is preferable to invest sooner, rather than to postpone the grid reinforcement for certain conditions, namely for low discount rates. © 2022 IEEE.

2022

Comparison Among National Energy Community Policies in Brazil, Germany, Portugal, and Spain

Autores
Castro, LFC; Carvalho, PCM; Fidalgo, JN; Saraiva, JT;

Publicação
International Conference on the European Energy Market, EEM

Abstract
Energy communities (ECs) are emerging as a promising step to mitigate energy poverty and climate changes, since their main objective is to obtain environmental, economic, and social benefits for the participants, namely in terms of increasing local production using primary renewable resources. In the European Union (EU), Directives D2018 and D944 established a common regime for the promotion of ECs. Given the relevance of the topic, comparing regulations in force in Brazil, Germany, Portugal, and Spain, can contribute to mitigate risks, as well as save time and energy resources. Among the assessed aspects, this work analyzes requirements to access to the activity and measurement issues, which are already well and clearly defined. As for business models and remuneration, focus is given to energy cooperatives and feed-in payments. In turn, the main barriers include financing, end of incentives, need to develop new business models, and issues related to peer-to-peer (P2P) transactions. © 2022 IEEE.

2021

Non-Intrusive Load Monitoring for Household Disaggregated Energy Sensing

Autores
Paulos, JP; Fidalgo, JN; Gama, J;

Publicação
2021 IEEE MADRID POWERTECH

Abstract

Teses
supervisionadas

2022

Planeamento de Investimentos na Rede de Distribuição com Base na Técnica Spike and Slab

Autor
Hugo Francisco Rocha Costa

Instituição
UP-FEUP

2022

Estudo do Impacto das Alterações Climáticas no Consumo de Energia Elétrica

Autor
Anabela Garcês de Aguiar

Instituição
UP-FEUP

2021

Previsão de preços de mercado baseada em Deep Learning

Autor
Ana Rita Martins Cruz e Silva

Instituição
UP-FEUP

2021

Previsão de consumo de médio e longo prazo

Autor
André Marques Rodrigues

Instituição
UP-FEUP

2021

Previsão de investimentos com base em informação esparsa

Autor
João Pedro Espírito Santo Almeida

Instituição
UP-FEUP