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

Ricardo Bessa nasceu em 1983 em Viseu, e obteve o grau de Licenciado (5 anos) pela Faculdade de Engenharia da Universidade do Porto, Portugal (FEUP) em 2006 em Engenharia Eletrotécnica e de Computadores. Em 2008, obteve 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 no Programa Doutoral em Sistemas Sustentáveis de Energia (MIT Portugal) na FEUP em 2013. Atualmente, é Coordenador do Centro de Sistemas de Energia do INESC TEC. Trabalhou em vários projetos internacionais como os projetos europeus FP6 ANEMOS.plus, FP7 SuSTAINABLE, FP7 evolvDSO, Horizon 2020 UPGRID, Horizon 2020 InteGrid, H2020 Smart4RES, H2020 InterConnect, HORIZON ENERSHARE, e uma colaboração internacional com o Argonne National Laboratory para o U.S. Department of Energy. A nível nacional, participou no desenvolvimento de sistemas de previsão de energias renováveis e serviços de consultoria sobre armazenamento de energia e IA. Editor Associado da IEEE Transactions on Sustainable Energy e recebeu o Prémio de Excelência ESIG em 2022. É coautor de mais de 60 artigos em revistas e 120 artigos em conferências, e Membro Sénior do IEEE.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Ricardo Jorge Bessa
  • Cargo

    Coordenador de Centro
  • Desde

    01 fevereiro 2006
058
Publicações

2024

A review on the decarbonization of high-performance computing centers

Autores
Silva, CA; Vilaça, R; Pereira, A; Bessa, RJ;

Publicação
RENEWABLE & SUSTAINABLE ENERGY REVIEWS

Abstract
High-performance computing relies on performance-oriented infrastructures with access to powerful computing resources to complete tasks that contribute to solve complex problems in society. The intensive use of resources and the increase in service demand due to emerging fields of science, combined with the exascale paradigm, climate change concerns, and rising energy costs, ultimately means that the decarbonization of these centers is key to improve their environmental and financial performance. Therefore, a review on the main opportunities and challenges for the decarbonization of high-performance computing centers is essential to help decision-makers, operators and users contribute to a more sustainable computing ecosystem. It was found that state-of-the-art supercomputers are growing in computing power, but are combining different measures to meet sustainability concerns, namely going beyond energy efficiency measures and evolving simultaneously in terms of energy and information technology infrastructure. It was also shown that policy and multiple entities are now targeting specifically HPC, and that identifying synergies with the energy sector can reveal new revenue streams, but also enable a smoother integration of these centers in energy systems. Computing-intensive users can continue to pursue their scientific research, but participating more actively in the decarbonization process, in cooperation with computing service providers. Overall, many opportunities, but also challenges, were identified, to decrease carbon emissions in a sector mostly concerned with improving hardware performance.

2023

Uncertainty-Aware Procurement of Flexibilities for Electrical Grid Operational Planning

Autores
Bessa R.J.; Moaidi F.; Viana J.; Andrade J.R.;

Publicação
IEEE Transactions on Sustainable Energy

Abstract
In the power system decarbonization roadmap, novel grid management tools and market mechanisms are fundamental to solving technical problems concerning renewable energy forecast uncertainty. This work proposes a predictive algorithm for procurement of grid flexibility by the system operator (SO), which combines the SO flexible assets with active and reactive power short-term flexibility markets. The goal is to reduce the cognitive load of the human operator when analyzing multiple flexibility options and trajectories for the forecasted load/RES and create a human-in-the-loop approach for balancing risk, stakes, and cost. This work also formulates the decision problem into several steps where the operator must decide to book flexibility now or wait for the next forecast update (time-to-decide method), considering that flexibility (availability) price may increase with a lower notification time. Numerical results obtained for a public MV grid (Oberrhein) show that the time-to-decide method improves up to 22% a performance indicator related to a cost-loss matrix, compared to the option of booking the flexibility now at a lower price and without waiting for a forecast update.

2023

Data-driven Assessment of the DER Flexibility Impact on the LV Grid Management

Autores
Fritz, B; Sampaio, G; Bessa, RJ;

Publicação
2023 IEEE BELGRADE POWERTECH

Abstract
Low voltage (LV) grids face a challenge of effectively managing the growing presence of new loads like electric vehicles and heat pumps, along with the equally growing installation of rooftop photovoltaic panels. This paper describes practical applications of sensitivity factors, extracted from smart meter data (i.e., without resorting to grid models), to i) link voltage problems to different costumers/devices and their location in the grid, ii) manage the flexibility provided by distributed energy resources (DERs) to regulate voltage, and iii) assess favorable locations for DER capacity extensions, all with the aim of supporting the decision-making process of distribution system operators (DSOs) and the design of incentives for customers to invest in DERs. The methods are tested by running simulations based on historical meter data on six grid models provided by the EU-Joint Research Center. The results prove that it is feasible to implement advanced LV grid analysis and management tools despite the typical limitations in its electrical and topological characterisation, while avoiding the use of computationally heavy tools such as optimal power flows.

2023

PV Inverter Fault Classification using Machine Learning and Clarke Transformation

Autores
Costa, L; Silva, A; Bessa, RJ; Araújo, RE;

Publicação
2023 IEEE BELGRADE POWERTECH

Abstract
In a photovoltaic power plant (PVPP), the DC-AC converter (inverter) is one of the components most prone to faults. Even though they are key equipment in such installations, their fault detection techniques are not as much explored as PV module-related issues, for instance. In that sense, this paper is motivated to find novel tools for detection focused on the inverter, employing machine learning (ML) algorithms trained using a hybrid dataset. The hybrid dataset is composed of real and synthetic data for fault-free and faulty conditions. A dataset is built based on fault-free data from the PVPP and faulty data generated by a digital twin (DT). The combination DT and ML is employed using a Clarke/space vector representation of the inverter electrical variables, thus resulting in a novel feature engineering method to extract the most relevant features that can properly represent the operating condition of the PVPP. The solution that was developed can classify multiple operation conditions of the inverter with high accuracy.

2023

Analysis of Flexibility-centric Energy and Cross-sector Business Models

Autores
Rodrigues, L; Faria, D; Coelho, F; Mello, J; Saraiva, JT; Villar, J; Bessa, RJ;

Publicação
2023 19TH INTERNATIONAL CONFERENCE ON THE EUROPEAN ENERGY MARKET, EEM

Abstract
The new energy policies adopted by the European Union are set to help in the decarbonization of the energy system. In this context, the share of Variable Renewable Energy Sources is growing, affecting electricity markets, and increasing the need for system flexibility to accommodate their volatility. For this reason, legislation and incentives are being developed to engage consumers in the power sector activities and in providing their potential flexibility in the scope of grid system services. This work identifies energy and cross-sector Business Models (BM) centered on or linked to the provision of distributed flexibility to the DSO and TSO, building on those found in previous research projects or from companies' commercial proposals. These BM are described and classified according to the main actor. The remaining actors, their roles, the interactions among them, how value is created by the BM activities and their value propositions are also described.

Teses
supervisionadas

2022

Impact of smart meter data availability in data-driven low voltage management

Autor
Inês Barroso Ferreira Marques

Instituição
UP-FEUP

2022

Communicating Forecast Uncertainty in Predictive Management of Power System

Autor
Ferinar Moaidi

Instituição
UP-FEUP

2022

Improving renewable energy predictability via weather stations' location determination

Autor
Olga Klyagina

Instituição
IES_Outra

2022

Development and Analysis of A Local Energy Market Using Blockchain

Autor
Tiago Manuel Massano Tavares

Instituição
UP-FEUP

2022

Data markets for single buyer and multiple data owners in the energy sector

Autor
Luís Carlos de Vasconcelos Negrão Cyrne de Noronha

Instituição
UP-FEP