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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

    Centre Coordinator
  • Since

    01st February 2006
044
Publications

2020

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

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

Publication
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

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

Publication
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

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

Publication
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

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

Publication
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

Authors
Bregere, M; Bessa, RJ;

Publication
IEEE Access

Abstract

Supervised
thesis

2018

Artificial intelligence techniques applied for the predictive control of stationary storage

Author
Ricardo Emanuel Gomes Fernandes da Silva

Institution
UP-FEUP

2017

Deep Learning Applied to PMU Data in Power Systems

Author
Pedro Emanuel Almeida Cardoso

Institution
UP-FEUP

2017

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

Author
Cézar Vicente Cerciari

Institution
UP-FCUP

2017

Forecasting high-dimensional electrical energy time-series

Author
Carla Sofia da Silva Gonçalves

Institution
UP-FCUP

2017

Optimization and Control of Virtual Power Plants

Author
Jorge Miguel Pérola Filipe

Institution
UP-FEUP