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    Sobre

    Sobre

    Ricardo Bessa (IEEE Fellow) nasceu em 1983, em Viseu, Portugal. Obteve o grau de Licenciado (cinco anos) em Engenharia Electrotécnica e de Computadores pela Faculdade de Engenharia da Universidade do Porto (FEUP) em 2006. Em 2008, concluiu o grau de Mestre em Análise de Dados e Sistemas de Apoio à Decisão pela Faculdade de Economia da Universidade do Porto (FEP). Obteve o grau de Doutor em 2013 no âmbito do Programa Doutoral em Sistemas de Energia Sustentáveis (MIT Portugal), na FEUP. Atualmente, é coordenador do Centro de Sistemas de Energia do INESC TEC. As suas atividades de investigação e inovação centram-se na aplicação de inteligência artificial e de métodos baseados em dados à operação de sistemas elétricos de energia, à integração de energias renováveis e às redes inteligentes.


    Ricardo Bessa tem participado ativamente em vários projetos internacionais de investigação e inovação, incluindo o projeto europeu FP6 ANEMOS.plus, FP7 SuSTAINABLE, FP7 evolvDSO, H2020 UPGRID, H2020 InteGrid, H2020 Smart4RES, H2020 InterConnect, Horizon Europe ENERSHARE, ENFIELD e AI4REALNET (Coordenador). Liderou igualmente colaborações internacionais com o Argonne National Laboratory, no âmbito do Departamento de Energia dos Estados Unidos. A nível nacional, contribuiu para o desenvolvimento de sistemas operacionais de previsão de produção de energia renovável e prestou serviços de consultoria nas áreas de analítica de energia e redes elétricas inteligentes.


    Exerceu funções de Editor das revistas IEEE Transactions on Sustainable Energy, Modern Power Systems and Clean Energy, International Journal of Forecasting e IEEE Data Descriptions. Em 2022, recebeu o Energy Systems Integration Group (ESIG) Excellence Award. Ricardo Bessa é coautor de mais de 80 artigos em revistas científicas e de mais de 140 publicações em conferências internacionais.

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    Detalhes

    Detalhes

    • Nome

      Ricardo Jorge Bessa

    • Cargo

      Coordenador de Centro

    • Desde

      01 fevereiro 2006

    • Nacionalidade

      Portugal

    • Centro

      Sistemas de Energia

    • Contactos

      +351222094230
      ricardo.j.bessa@inesctec.pt
    072
    Publicações
    Ler todas as publicações

    2026

    Evolving power system operator rules for real-time congestion management

    Autores
    Moaidi, F; Bessa, J;

    Publicação
    Energy and AI

    Abstract
    The growing integration of renewable energy sources and the widespread electrification of the energy demand have significantly reduced the capacity margin of the electrical grid. This demands a more flexible approach to grid operation, for instance, combining real-time topology optimization and redispatching. Traditional expert-driven decision-making rules may become insufficient to manage the increasing complexity of real-time grid operations and derive remedial actions under the N-1 contingency. This work proposes a novel hybrid AI framework for power grid topology control that integrates genetic network programming (GNP), reinforcement learning, and decision trees. A new variant of GNP is introduced that is capable of evolving the decision-making rules by learning from data in a reinforcement learning framework. The graph-based evolutionary structure of GNP and decision trees enables transparent, traceable reasoning. The proposed method outperforms both a baseline expert system and a state-of-the-art deep reinforcement learning agent on the IEEE 118-bus system, achieving up to an 28% improvement in a key performance metric used in the Learning to Run a Power Network (L2RPN) competition. © 2025

    FecharLer Abstract

    2025

    Dynamic incentives for electric vehicles charging at supermarket stations: Causal insights on demand flexibility

    Autores
    Silva, CAM; Andrade, JR; Ferreira, A; Gomes, A; Bessa, RJ;

    Publicação
    ENERGY

    Abstract
    Electric vehicles (EVs) are crucial in achieving a low-carbon transportation sector and can inherently offer demand-side flexibility by responding to price signals and incentives, yet real-world strategies to influence charging behavior remain limited. This paper combines bilevel optimization and causal machine learning as complementary tools to design and evaluate dynamic incentive schemes as part of a pilot project using a supermarket's EV charging station network. The bilevel model determines discount levels, while double machine learning quantifies the causal impact of these incentives on charging demand. The results indicate a marginal increase of 1.16 kW in charging demand for each one-percentage-point increase in discount. User response varies by hour and weekday, revealing treatment effect heterogeneity, insights that can inform business decision-making. While the two methods are applied independently, their combined use provides a framework for connecting optimization-based incentive design with data-driven causal evaluation. By isolating the impact of incentives from other drivers, the study sheds light on the potential of incentives to enhance demand-side flexibility in the electric mobility ecosystem.

    FecharLer Abstract

    2025

    On the Definition of Robustness and Resilience of AI Agents for Real-time Congestion Management

    Autores
    Tjhay T.; Bessa R.J.; Paulos J.;

    Publicação
    2025 IEEE Kiel Powertech Powertech 2025

    Abstract
    The European Union's Artificial Intelligence (AI) Act defines robustness, resilience, and security requirements for high-risk sectors but lacks detailed methodologies for assessment. This paper introduces a novel framework for quantitatively evaluating the robustness and resilience of reinforcement learning agents in congestion management. Using the AI-friendly digital environment Grid2Op, perturbation agents simulate natural and adversarial disruptions by perturbing the input of AI systems without altering the actual state of the environment, enabling the assessment of AI performance under various scenarios. Robustness is measured through stability and reward impact metrics, while resilience quantifies recovery from performance degradation. The results demonstrate the framework's effectiveness in identifying vulnerabilities and improving AI robustness and resilience for critical applications.

    FecharLer Abstract

    2025

    Graph Neural Networks for Fault Location in Large Photovoltaic Power Plants

    Autores
    Klyagina O.; Silva C.G.; Silva A.S.; Guedes T.; Andrade J.R.; Bessa R.J.;

    Publicação
    2025 IEEE Kiel Powertech Powertech 2025

    Abstract
    A fast response to faults in large-scale photovoltaic power plants (PVPPs), which can occur on hundreds of components like photovoltaic panels and inverters, is fundamental for maximizing energy generation and reliable system operation. This work proposes using a Graph Neural Network (GNN) combined with a digital twin for synthetic fault data scenario generation for fault location in PVPPs. It shows that GNN can adapt to system changes without requiring model retraining, thus offering a scalable solution for the real operating PVPPs, where some parts of the system may be disconnected for maintenance. The results for a real PVPP show the GNN outperforms baseline models, especially in larger topologies, achieving up to twice the accuracy in a fault location task. The GNN's adaptability to topology changes was tested on the simulated reconfigured systems. A decrease in performance was observed, and its value depends on the complexity of the original training topology. It can be mitigated by using several system reconfigurations in the training set.

    FecharLer Abstract

    2025

    Generation of Power Network Operating Scenarios for an AI-friendly Digital Environment

    Autores
    Paulos J.; Silva P.R.; Bessa R.J.; Marot A.; Dejaegher J.; Donnot B.;

    Publicação
    2025 IEEE Kiel Powertech Powertech 2025

    Abstract
    With the growing need for AI-driven solutions in power grid management, this work addresses the challenge of creating realistic synthetic operating scenarios essential for developing, testing, and validating AI-based decision-making systems. It uses spatial-temporal noise functions, predefined patterns, and optimal power flow to model renewable energy and conventional power plant generation, load, and losses. Quantitative and visual key performance indicators are proposed to evaluate the quality of the generated operating scenarios, and the validation highlights the framework's ability to emulate diverse and practical operating scenarios, bridging gaps in AI-driven power system research and real-world applications.

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