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About

Dr. Leonel Carvalho was born in Espinho, Portugal, in 1985. He received his B.Sc., M.Sc., and Ph.D. degrees in Electrical Engineering from the Faculty of Engineering of the University of Porto (FEUP), Portugal, in 2006, 2008, and 2013, respectively. Currently, he is a senior researcher at INESC TEC. In 2011, he was a Visiting Researcher at the Institute of Electric Systems and Energy of the Federal University of Itajubá (UNIFEI), Minas Gerais, Brazil, where he was engaged in research activities related with the use of the Cross-Entropy Method for improving the Reliability Assessment of large-scale power systems. In 2014, he was the winner of the IEEE International Competition on the Application of Modern Heuristic Optimization Algorithms for Solving Optimal Power Flow Problems organized by the IEEE PES Working Group on Modern Heuristic Optimization, with the algorithm entitled “DEEPSO as a successful blend of evolutionary and swarm search strategies in the OPF challenge”. In 2015, he held the Auxiliary Professor position at the Universidade Lusíada of Vila Nova de Famalicão where he was responsible of several courses of the Licenciatura degree in Eletronic and Computer Engineering. Dr. Leonel has co-supervised several M.Sc. theses, one of which was granted the first place in 2015 edition of the prestigious REN Prize, which is an award aiming at distinguishing the best M.Sc. theses completed in Portuguese higher education institutions in the fields of Engineering, Economics, Mathematics, Physics, Chemistry, Information Systems and Computer Science. As a researcher in INESC TEC, he has been involved in several national and international R&D projects amongst which is worth highlighting the RESERVE project with the Portuguese TSO, the ARGUS project with the Argonne National Laboratory in the USA, the FP7 projects MERGE, STABALID, evolvDSO, and iTESLA and the H2020 project SENSIBLE. He has authored and co-authored several papers in peer-reviewed journals as well as in international conferences. His current research interests include power system Reliability Assessment and the application of Computational Intelligence algorithms to power system optimization problems.

Interest
Topics
Details

Details

  • Name

    Leonel Magalhães Carvalho
  • Cluster

    Power and Energy
  • Role

    Area Manager
  • Since

    18th February 2008
021
Publications

2020

Aggregated dynamic model of active distribution networks for large voltage disturbances

Authors
Fulgencio, N; Moreira, C; Carvalho, L; Lopes, JP;

Publication
Electric Power Systems Research

Abstract

2020

A Hierarchical Optimization Strategy for Energy Scheduling and Volt/var Control in Autonomous Clusters of Microgrids

Authors
Castro, MV; Moreira, C; Carvalho, LM;

Publication
IET Renewable Power Generation

Abstract

2020

Reactive power provision by the DSO to the TSO considering renewable energy sources uncertainty

Authors
Soares, T; Carvalho, L; Moris, H; Bessa, RJ; Abreu, T; Lambert, E;

Publication
SUSTAINABLE ENERGY GRIDS & NETWORKS

Abstract
The current coordination between the transmission system operator (TSO) and the distribution system operator (DSO) is changing mainly due to the continuous integration of distributed energy resources (DER) in the distribution system. The DER technologies are able to provide reactive power services helping the DSOs and TSOs in the network operation. This paper follows this trend by proposing a methodology for the reactive power management by the DSO under renewable energy sources (RES) forecast uncertainty, allowing the DSO to coordinate and supply reactive power services to the TSO. The proposed methodology entails the use of a stochastic AC-OPF, ensuring reliable solutions for the DSO. RES forecast uncertainty is modeled by a set of probabilistic spatiotemporal trajectories. A 37-bus distribution grid considering realistic generation and consumption data is used to validate the proposed methodology. An important conclusion is that the methodology allows the DSO to leverage the DER full capabilities to provide a new service to the TSO.

2020

Planning of distribution networks islanded operation: from simulation to live demonstration

Authors
Gouveia, J; Gouveia, C; Rodrigues, J; Carvalho, L; Moreira, CL; Lopes, JAP;

Publication
Electric Power Systems Research

Abstract
The integration of distributed Battery Energy Storage Systems (BESS) at the Medium Voltage (MV) and Low Voltage (LV) networks increases the distribution grid flexibility to deal with high penetration of Renewable Energy Sources (RES). In addition, it also enables the deployment of key self-healing functionalities, which allow the islanded operation of small sections of the distribution network. However, new planning and real-time operation strategies are required to allow the BESS coordinated control, as well as a cost-effective and stable operation. This paper presents new tools developed for the planning and real-time operation of distribution networks integrating BESS, particularly when operating islanding. For real-time operation, a short-term emergency operation-planning tool assesses the feasibility of islanded operation of a small section of the distribution network. The long-term impact of a BESS control strategy for islanded operation is assessed through a Life Cycle Analysis (LCA) tool. The results and implementation experience in real distribution network are also discussed. © 2020

2020

A combined optimisation and decision-making approach for battery-supported HMGS

Authors
Marcelino, C; Baumann, M; Carvalho, L; Chibeles Martins, N; Weil, M; Almeida, P; Wanner, E;

Publication
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY

Abstract
Hybrid micro-grid systems (HMGS) are gaining increasing attention worldwide. The balance between electricity load and generation based on fluctuating renewable energy sources is a main challenge in the operation and design of HMGS. Battery energy storage systems are considered essential components for integrating high shares of renewable energy into a HMGS. Currently, there are very few studies in the field of mathematical optimisation and multi-criteria decision analysis that focus on the evaluation of different battery technologies and their impact on the HMGS design. The model proposed in this paper aims at optimising three different criteria: minimising electricity costs, reducing the loss of load probability, and maximising the use of locally available renewable energy. The model is applied in a case study in southern Germany. The optimisation is carried out using the C-DEEPSO algorithm. Its results are used as input for an AHP-TOPSIS model to identify the most suitable alternative out of five different battery technologies using expert weights. Lithium batteries are considered the best solution with regard to the given group preferences and the optimisation results.

Supervised
thesis

2019

MOCAPIRA - Monte Carlo parallel implementation for reliability assessment

Author
Inês Maria Afonso Trigo de Freitas Alves

Institution
UP-FEUP

2015

Impacto do Erro da Previsão Eólica nas Necessidades a Longo-Prazo de Reserva Operacional

Author
João Teixeira

Institution
UP-FEUP