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Sobre

Ricardo Bessa nasceu in 1983 em Viseu. Completou a licenciatura em Engenharia Eletrotécnica pela Faculdade de Engenharia da Universidade do Porto (FEUP) em 2006, o mestrado em Análise de Dados e Sistemas de Apoio à Decisão pela Faculdade de Economia da Universidade do Porto (FEP) em 2008 e o Doutoramento em Sistemas Sustentáveis de Energia pela FEUP em 2013.

É Investigador Sénior no INESC TEC desde 2006 no Centro de Sistemas de Energia. Foi investigador em diversos projetos relacionados com previsão eólica e sua integração na gestão do sistema elétrico de energia. Tem participado ativamente em projetos relacionados com redes elétricas inteligentes, nomeadamente os Projetos Europeus FP7 SusTAINABLE e evolvDSO e os projetos Horizonte 2020 UPGRID e InteGrid (onde é coordenador técnico).

Os seus interesses de I&D são energias renováveis, veículos elétricos, extração de conhecimento de dados e apoio à decisão.  

Tem publicado 32 artigos em revistas internacionais e 61 artigos em conferências internacionais.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Ricardo Jorge Bessa
  • Cluster

    Energia
  • Cargo

    Coordenador Adjunto de Centro
  • Desde

    01 fevereiro 2006
038
Publicações

2019

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

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

Publicação
Energy Systems

Abstract

2019

Through the looking glass: Seeing events in power systems dynamics

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

Publicação
International Journal of Electrical Power & Energy Systems

Abstract

2019

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

Autores
Soares, T; Bessa, RJ;

Publicação
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

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

Publicação
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

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

Publicação
Advances in Energy Systems

Abstract

Teses
supervisionadas

2018

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

Autor
Maria Inês Manero Koch

Instituição
UP-FEUP

2018

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

Autor
João Pedro Açoreira Teixeira

Instituição
UP-FEUP

2018

Planeamento de redes de distribuição com incerteza na produção distribuída

Autor
João Pedro Gomes Pina Marques

Instituição
UP-FEUP

2017

Big Data techniques for Solar Power Forecasting

Autor
Rui Miguel da Cunha Nunes

Instituição
UP-FEP

2017

Previsão Probabilística dos Desvios dos Agentes Comerciais e Produtores do Mercado de Eletricidade

Autor
Cézar Vicente Cerciari

Instituição
UP-FCUP