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About

About

Ricardo Bessa was born in 1983 in Viseu, and received his Licenciado (5-years) degree from the Faculty of Engineering of the University of Porto, Portugal (FEUP) in 2006 in Electrical and Computer Engineering. In 2008, he received the M.Sc. degree in Data Analysis and Decision Support Systems from the Faculty of Economics of the University of Porto (FEP). He obtained his Ph.D. degree in the Doctoral Program in Sustainable Energy Systems (MIT Portugal) at FEUP in 2013. Currently, he is Coordinator of the Center for Power and Energy Systems at INESC TEC. He worked on several international projects such as the European Projects FP6 ANEMOS.plus, FP7 SuSTAINABLE, FP7 evolvDSO, Horizon 2020 UPGRID, Horizon 2020 InteGrid, H2020 Smart4RES, H2020 InterConnect, HORIZON ENERSHARE, and an international collaboration with Argonne National Laboratory for the U.S. Department of Energy. At the national level, he participated in the development of renewable energy forecasting systems and consultant services about energy storage and AI. Associate Editor of IEEE Transactions on Sustainable Energy and received the ESIG Excellence Award in 2022. He is co-authors of more than 60 journal papers and 120 conference papers, and IEEE Senior Member.

Interest
Topics
Details

Details

  • Name

    Ricardo Jorge Bessa
  • Role

    Centre Coordinator
  • Since

    01st February 2006
059
Publications

2024

A review on the decarbonization of high-performance computing centers

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

Publication
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

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

Publication
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

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

Publication
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

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

Publication
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

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

Publication
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.

Supervised
thesis

2022

A new models editor for the IVY Workbench

Author
Rui Filipe Moreira Mendes

Institution
UM

2022

Classificação de Objetos BIM para Mapas de Trabalhos e Quantidades

Author
Eva Alexandra Machado Pinho Silva

Institution
UP-FEUP

2022

Provision of services for TSO through distribution resources in a local market

Author
Fábio Sester Retorta

Institution
UP-FEUP

2022

Development and Analysis of A Local Energy Market Using Blockchain

Author
Tiago Manuel Massano Tavares

Institution
UP-FEUP

2022

Automatic Eyetracking-Assisted Chest Radiography Pathology Screening

Author
Rui Manuel Azevedo dos Santos

Institution
UP-FEUP