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About

I was born in Porto (Portugal) in 8 April 1955, graduated in Electrical Engineering in the Faculty of Engineering of the University of Porto - FEUP (1977), and completed my PhD in Power Systems in 1988, also from the University of Porto. In November 1996 I got the aggregation title from the University of Porto. In 1978 I joined the Department of Electrical Engineering of FEUP, where I am Full Professor since 2000. In the period 1990-98, I was also the Director of the Library of FEUP. In the period 2001-08, I was the director of the MSc program on Information Management, and I am presently the director of the PhD program in Sustainable Energy Systems, integrated in the MIT-Portugal program. I lectured several courses in graduation, MSc and PhD courses in Electrical Engineering and Power Systems and supervised the research activities of many graduation, MSc and PhD students. I also collaborated with the Management School of the University of Porto, lecturing MSc courses on Decision-Aid. I am a member of the Senate of the University of Porto since October 2009.

In 1985 I joined INESC (now INESC TEC). Since 1996 I coordinate the Centre for Power and Energy Systems of INESC TEC, leading 60+ researchers (including 20+ PhD). I am presently the President of the Scientific Council of INESC TEC. I have been involved in several national, EU and international RTD projects and in development contracts and consultancy for utilities, TSO, DSO, industrial partners, government agencies and for the Regulatory Authority for the Energy Services of Portugal. In particular, I was or am the responsible for the INESC TEC research team in the EU financed projects “CARE” (Advanced Control Advice for power systems with large-scale integration of Renewable Energy sources), “MORE CARE” (More Advanced Control Advice for Secure Operation of Isolated Power Systems with Increased Renewable Energy Penetration and Storage), “ANEMOS.PLUS” (Advanced Tools for the Management of Electricity Grids with Large-Scale Wind Generation) and “evolvDSO” (Development of methodologies and tools for new and evolving DSO roles for efficient DRES integration in distribution networks). I am presently the Principal Investigator of project “SusCity” (Urban data driven models for creative and resourceful urban transitions), financed by FCT (MPP-Testbed).

I also coordinated the research teams of the contracts “CCR” (Load Profiling and Distribution Network Characterization), with EDP Distribution (the Portuguese DSO), Study on the Impact of Large Renewable Deployment on European Electricity Higher Voltage Systems (JRC-Institute for Energy), “RESERVES” (Mid and Long Term Evaluation of the adequacy of Operational Reserve levels in the Iberian Electric Power Systems), with the TSO of Portugal (REN) and Spain (REE), “RECEP” (Development and testing of methodologies to determine the hosting capacity in the nodes of the National Grid of Portugal) (REN), “ReservaProb” (Software Module for helping setting the operational reserve of the National Electric System) (REN) and “MORA” (Long-term adequacy evaluation of reserves in a multi-area context) (REN). I was also involved in consultancy actions regarding the design of public tenders, namely the call for tenders for new wind power generation in Portugal mainland.

My research interests include classic and fuzzy modeling of power systems, reliability and optimization and decision-aid, with application to renewables’ integration, electric vehicles’ deployment and smart grids. I was involved in the organization of the international conferences PMAPS’2000, IEEE PPT’2001, ISAP’2015 and EEM’2016. I am a member of the Editorial board of EPSR (Top Reviewer in 2010) and of Int J of Multicriteria Decision Making. I am a senior member of IEEE.

Interest
Topics
Details

Details

  • Name

    Manuel Matos
  • Cluster

    Power and Energy
  • Role

    Centre Coordinator
  • Since

    01st April 1985
035
Publications

2021

An improved version of the Continuous Newton's method for efficiently solving the Power-Flow in Ill-conditioned systems

Authors
Tostado Veliz, M; Matos, MA; Lopes, JAP; Jurado, F;

Publication
International Journal of Electrical Power and Energy Systems

Abstract
This paper tackles the efficient Power-Flow solution of ill-conditioned cases. In that sense, those methods based on the Continuous Newton's philosophy look very promising, however, these methodologies still present some issues mainly related with the computational efficiency or the robustness properties. In order to overcome these drawbacks, we suggest several modifications about the standard structure of the Continuous Newton's method. Thus, the standard Continuous Newton's paradigm is firstly modified with a frozen Jacobian scheme for reducing its computational burden; secondly, it is extended for being used with High-order Newton-like method for achieving higher convergence rate and, finally, a regularization scheme is introduced for improving its robustness features. On the basis of the suggested improvements, a Power-Flow solution paradigm is developed. As example, a novel Power-Flow solver based on the introduced solution framework and the 4th order Runge-Kutta formula is developed. The novel technique is validated in several realistic large-scale ill-conditioned systems. Results show that the suggested modifications allow to overcome the drawbacks presented by those methodologies based on the Continuous Newton's method. On the light of the results obtained it can be also claimed, that the developed solution paradigm constitutes a promising framework for developing robust and efficient Power-Flow solution techniques. © 2020 Elsevier Ltd

2021

An unsupervised approach for fault diagnosis of power transformers

Authors
Dias, L; Ribeiro, M; Leitao, A; Guimaraes, L; Carvalho, L; Matos, MA; Bessa, RJ;

Publication
QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL

Abstract
Electrical utilities apply condition monitoring on power transformers (PTs) to prevent unplanned outages and detect incipient faults. This monitoring is often done using dissolved gas analysis (DGA) coupled with engineering methods to interpret the data, however the obtained results lack accuracy and reproducibility. In order to improve accuracy, various advanced analytical methods have been proposed in the literature. Nonetheless, these methods are often hard to interpret by the decision-maker and require a substantial amount of failure records to be trained. In the context of the PTs, failure data quality is recurrently questionable, and failure records are scarce when compared to nonfailure records. This work tackles these challenges by proposing a novel unsupervised methodology for diagnosing PT condition. Differently from the supervised approaches in the literature, our method does not require the labeling of DGA records and incorporates a visual representation of the results in a 2D scatter plot to assist in interpretation. A modified clustering technique is used to classify the condition of different PTs using historical DGA data. Finally, well-known engineering methods are applied to interpret each of the obtained clusters. The approach was validated using data from two different real-world data sets provided by a generation company and a distribution system operator. The results highlight the advantages of the proposed approach and outperformed engineering methods (from IEC and IEEE standards) and companies legacy method. The approach was also validated on the public IEC TC10 database, showing the capability to achieve comparable accuracy with supervised learning methods from the literature. As a result of the methodology performance, both companies are currently using it in their daily DGA diagnosis.

2020

The future of power systems: Challenges, trends, and upcoming paradigms

Authors
Lopes, JAP; Madureira, AG; Matos, M; Bessa, RJ; Monteiro, V; Afonso, JL; Santos, SF; Catalao, JPS; Antunes, CH; Magalhaes, P;

Publication
Wiley Interdisciplinary Reviews: Energy and Environment

Abstract

2020

Distributed multi-period three-phase optimal power flow using temporal neighbors

Authors
Pinto, R; Bessa, RJ; Sumaili, J; Matos, MA;

Publication
Electric Power Systems Research

Abstract
The penetration of distributed generation in medium (MV) and low (LV) voltage distribution grids has been steadily increasing every year in multiple countries, thus creating new technical challenges in grid operation and motivating developments in distributed optimization for flexibility management. The traditional centralized optimal power flow (OPF) algorithm can solve technical constraints violation. However, computational efficiency, new technologies (e.g., edge computing) and control architectures (e.g., web-of-cells) are demanding for distributed approaches. This work formulates a novel distributed multi-period OPF for three-phase unbalanced grids that is essential when integrating energy storage units in operational planning (e.g., day-ahead) of LV or local energy community grids. The decentralized constrained optimization problem is solved with the alternating direction method of multipliers (ADMM) adapted for unbalanced LV grids and multi-period optimization problems. A 33-bus LV distribution grid is used as a case-study in order to define optimal battery storage scheduling along a finite time horizon that minimizes overall grid operational costs, while complying with technical constraints of the grid (e.g., voltage and current limits) and battery state-of-charge constraints. © 2020

2020

Participation of an EV Aggregator in the Reserve Market through Chance-Constrained Optimization

Authors
Faria, AS; Soares, T; Sousa, T; Matos, MA;

Publication
ENERGIES

Abstract
The adoption of Electric Vehicles (EVs) will revolutionize the storage capacity in the power system and, therefore, will contribute to mitigate the uncertainty of renewable generation. In addition, EVs have fast response capabilities and are suitable for frequency regulation, which is essential for the proliferation of intermittent renewable sources. To this end, EV aggregators will arise as a market representative party on behalf of EVs. Thus, this player will be responsible for supplying the power needed to charge EVs, as well as offering their flexibility to support the system. The main goal of EV aggregators is to manage the potential participation of EVs in the reserve market, accounting for their charging and travel needs. This work follows this trend by conceiving a chance-constrained model able to optimize EVs participation in the reserve market, taking into account the uncertain behavior of EVs and their charging needs. The proposed model, includes penalties in the event of a failure in the provision of upward or downward reserve. Therefore, stochastic and chance-constrained programming are used to handle the uncertainty of a small fleet of EVs and the risk profile of the EV aggregator. Two different relaxation approaches, i.e., Big-M and McCormick, of the chance-constrained model are tested and validated for different number of scenarios and risk levels, based on an actual test case in Denmark with actual driving patterns. As a final remark, the McCormick relaxation presents better performance when the uncertainty budget increases, which is appropriated for large-scale problems.

Supervised
thesis

2021

models and algorithms for network reinforcement planning in a smart grid environment

Author
Ricardo Jorge Duque Fernandes da Costa Ferreira

Institution
UP-FEUP

2021

Modeling energy sector integration using green hydrogen to define public policies and new regulatory support schemes to accelerate energy transition

Author
Bruno Henrique Martins Santos

Institution
UP-FEUP

2020

Cost Allocation Model for Distribution Networks Considering Flexibility from Distributed Energy Resources

Author
Miguel Ângelo Pereira da Cruz

Institution
UP-FEUP

2020

Modelling Stochastic Optimization to Energy and Reserve Market in a Microgrid Environment

Author
Diogo Castro

Institution
UP-FEUP

2020

OPF robusto

Author
Pedro Gonçalo Oliveira Campos Ferreira da Silva

Institution
UP-FEUP