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About

Ricardo Bessa was born in 1983 in Viseu, received his Licenciado (five-year) 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 on 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 a Senior Researcher and Area Manager at INESC TEC in its Center for Power and Energy Systems. 

His research interests include renewable energy forecasting, electric vehicles, data mining and decision-making under risk. He worked in several international projects such as the European Projects FP6 ANEMOS.plus, FP7 SuSTAINABLE, FP7 EvolvDSO, Horizon 2020 UPGRID, Horizon 2020 InteGrid 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.

He is co-authors of more than 32 journal papers and 61 conference papers.

Interest
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Details

Details

  • Name

    Ricardo Jorge Bessa
  • Cluster

    Power and Energy
  • Role

    Assistant Centre Coordinator
  • Since

    01st February 2006
038
Publications

2019

Optimal bidding strategy for variable-speed pump storage in day-ahead and frequency restoration reserve markets

Authors
Filipe, J; Bessa, RJ; Moreira, C; Silva, B;

Publication
Energy Systems

Abstract

2019

Through the looking glass: Seeing events in power systems dynamics

Authors
Miranda, V; Cardoso, PA; Bessa, RJ; Decker, I;

Publication
International Journal of Electrical Power & Energy Systems

Abstract

2019

Proactive management of distribution grids with chance-constrained linearized AC OPF

Authors
Soares, T; Bessa, RJ;

Publication
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS

Abstract
Distribution system operators (DSO) are currently moving towards active distribution grid management. One goal is the development of tools for operational planning of flexibility from distributed energy resources (DER) in order to solve potential (predicted) congestion and voltage problems. This work proposes an innovative flexibility management function based on stochastic and chance-constrained optimization that copes with forecast uncertainty from renewable energy sources (RES). Furthermore, the model allows the decision-maker to integrate its attitude towards risk by considering a trade-off between operating costs and system reliability. RES forecast uncertainty is modeled through spatial-temporal trajectories or ensembles. An AC-OPF linearization that approximates the actual behavior of the system is included, ensuring complete convexity of the problem. McCormick and big-M relaxation methods are compared to reformulate the chance-constrained optimization problem. The discussion and comparison of the proposed models is carried out through a case study based on actual generation data, where operating costs, system reliability and computer performance are evaluated.

2019

On the use of causality inference in designing tariffs to implement more effective behavioral demand response programs

Authors
Ganesan, K; Saraiva, JT; Bessa, RJ;

Publication
Energies

Abstract
Providing a price tariff that matches the randomized behavior of residential consumers is one of the major barriers to demand response (DR) implementation. The current trend of DR products provided by aggregators or retailers are not consumer-specific, which poses additional barriers for the engagement of consumers in these programs. In order to address this issue, this paper describes a methodology based on causality inference between DR tariffs and observed residential electricity consumption to estimate consumers’ consumption elasticity. It determines the flexibility of each client under the considered DR program and identifies whether the tariffs offered by the DR program affect the consumers’ usual consumption or not. The aim of this approach is to aid aggregators and retailers to better tune DR offers to consumer needs and so to enlarge the response rate to their DR programs. We identify a set of critical clients who actively participate in DR events along with the most responsive and least responsive clients for the considered DR program. We find that the percentage of DR consumers who actively participate seem to be much less than expected by retailers, indicating that not all consumers’ elasticity is effectively utilized. © 2019 by the authors.

2019

Handling Renewable Energy Variability and Uncertainty in Power System Operation

Authors
Bessa, R; Moreira, C; Silva, B; Matos, M;

Publication
Advances in Energy Systems

Abstract

Supervised
thesis

2018

Planeamento de redes de distribuição com incerteza na produção distribuída

Author
João Pedro Gomes Pina Marques

Institution
UP-FEUP

2018

A methodology for controlling the consequences of demand variability in the design of manufacturing systems

Author
Maria Inês Manero Koch

Institution
UP-FEUP

2018

Comportamento dos Preços do MIBEL no ano de 2016 Tendo em Conta Cenários de Crescimento da Produção em Regime Especial

Author
João Pedro Açoreira Teixeira

Institution
UP-FEUP

2017

Deep Learning Applied to PMU Data in Power Systems

Author
Pedro Emanuel Almeida Cardoso

Institution
UP-FEUP

2017

Training autoencoders for state estimation in smart grids

Author
Rui Miguel Machado Oliveira

Institution
UP-FEUP