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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 de Centro
  • Desde

    01 fevereiro 2006
044
Publicações

2020

Distributed multi-period three-phase optimal power flow using temporal neighbors

Autores
Pinto, R; Bessa, RJ; Sumaili, J; Matos, MA;

Publicação
Electric Power Systems Research

Abstract
The penetration of distributed generation in medium (MV) and low (LV) voltage distribution grids has been steadily increasing every year in multiple countries, thus creating new technical challenges in grid operation and motivating developments in distributed optimization for flexibility management. The traditional centralized optimal power flow (OPF) algorithm can solve technical constraints violation. However, computational efficiency, new technologies (e.g., edge computing) and control architectures (e.g., web-of-cells) are demanding for distributed approaches. This work formulates a novel distributed multi-period OPF for three-phase unbalanced grids that is essential when integrating energy storage units in operational planning (e.g., day-ahead) of LV or local energy community grids. The decentralized constrained optimization problem is solved with the alternating direction method of multipliers (ADMM) adapted for unbalanced LV grids and multi-period optimization problems. A 33-bus LV distribution grid is used as a case-study in order to define optimal battery storage scheduling along a finite time horizon that minimizes overall grid operational costs, while complying with technical constraints of the grid (e.g., voltage and current limits) and battery state-of-charge constraints. © 2020

2020

The future of forecasting for renewable energy

Autores
Sweeney, C; Bessa, RJ; Browell, J; Pinson, P;

Publicação
WILEY INTERDISCIPLINARY REVIEWS-ENERGY AND ENVIRONMENT

Abstract
Forecasting for wind and solar renewable energy is becoming more important as the amount of energy generated from these sources increases. Forecast skill is improving, but so too is the way forecasts are being used. In this paper, we present a brief overview of the state-of-the-art of forecasting wind and solar energy. We describe approaches in statistical and physical modeling for time scales from minutes to days ahead, for both deterministic and probabilistic forecasting. Our focus changes then to consider the future of forecasting for renewable energy. We discuss recent advances which show potential for great improvement in forecast skill. Beyond the forecast itself, we consider new products which will be required to aid decision making subject to risk constraints. Future forecast products will need to include probabilistic information, but deliver it in a way tailored to the end user and their specific decision making problems. Businesses operating in this sector may see a change in business models as more people compete in this space, with different combinations of skills, data and modeling being required for different products. The transaction of data itself may change with the adoption of blockchain technology, which could allow providers and end users to interact in a trusted, yet decentralized way. Finally, we discuss new industry requirements and challenges for scenarios with high amounts of renewable energy. New forecasting products have the potential to model the impact of renewables on the power system, and aid dispatch tools in guaranteeing system security. This article is categorized under: Energy Infrastructure > Systems and Infrastructure Wind Power > Systems and Infrastructure Photovoltaics > Systems and Infrastructure

2020

Extreme Quantiles Dynamic Line Rating Forecasts and Application on Network Operation

Autores
Dupin, R; Cavalcante, L; Bessa, RJ; Kariniotakis, G; Michiorri, A;

Publicação
Energies

Abstract
This paper presents a study on dynamic line rating (DLR) forecasting procedure aimed at developing a new methodology able to forecast future ampacity values for rare and extreme events. This is motivated by the belief that to apply DLR network operators must be able to forecast their values and this must be based on conservative approaches able to guarantee the safe operation of the network. The proposed methodology can be summarised as follows: firstly, probabilistic forecasts of conductors’ ampacity are calculated with a non-parametric model, secondly, the lower part of the distribution is replaced with a new distribution calculated with a parametric model. The paper presents also an evaluation of the proposed methodology in network operation, suggesting an application method and highlighting the advantages. The proposed forecasting methodology delivers a high improvement of the lowest quantiles’ reliability, allowing perfect reliability for the 1% quantile and a reduction of roughly 75% in overconfidence for the 0.1% quantile.

2020

Reactive power provision by the DSO to the TSO considering renewable energy sources uncertainty

Autores
Soares, T; Carvalho, L; Moris, H; Bessa, RJ; Abreu, T; Lambert, E;

Publicação
SUSTAINABLE ENERGY GRIDS & NETWORKS

Abstract
The current coordination between the transmission system operator (TSO) and the distribution system operator (DSO) is changing mainly due to the continuous integration of distributed energy resources (DER) in the distribution system. The DER technologies are able to provide reactive power services helping the DSOs and TSOs in the network operation. This paper follows this trend by proposing a methodology for the reactive power management by the DSO under renewable energy sources (RES) forecast uncertainty, allowing the DSO to coordinate and supply reactive power services to the TSO. The proposed methodology entails the use of a stochastic AC-OPF, ensuring reliable solutions for the DSO. RES forecast uncertainty is modeled by a set of probabilistic spatiotemporal trajectories. A 37-bus distribution grid considering realistic generation and consumption data is used to validate the proposed methodology. An important conclusion is that the methodology allows the DSO to leverage the DER full capabilities to provide a new service to the TSO.

2020

Simulating Tariff Impact in Electrical Energy Consumption Profiles With Conditional Variational Autoencoders

Autores
Bregere, M; Bessa, RJ;

Publicação
IEEE Access

Abstract

Teses
supervisionadas

2018

Artificial intelligence techniques applied for the predictive control of stationary storage

Autor
Ricardo Emanuel Gomes Fernandes da Silva

Instituição
UP-FEUP

2017

Forecasting high-dimensional electrical energy time-series

Autor
Carla Sofia da Silva Gonçalves

Instituição
UP-FCUP

2017

Optimization and Control of Virtual Power Plants

Autor
Jorge Miguel Pérola Filipe

Instituição
UP-FEUP

2017

Big Data techniques for Solar Power Forecasting

Autor
Rui Miguel da Cunha Nunes

Instituição
UP-FEP

2017

Training autoencoders for state estimation in smart grids

Autor
Rui Miguel Machado Oliveira

Instituição
UP-FEUP