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Sobre

Sobre

Nasci no Porto, em 1992. Tenho licenciatura em matemática e mestrado em matemática aplicada. Atualmente estou no doutoramento em matemática aplicada (FCUP). Trabalho em machine learning, mais concretamente em previsão probabilística de energias renováveis.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Carla Silva Gonçalves
  • Cluster

    Energia
  • Cargo

    Assistente de Investigação
  • Desde

    12 outubro 2015
001
Publicações

2019

A methodology to evaluate the uncertainties used to perform security assessment for branch overloads

Autores
Vasconcelos, MH; Goncalves, C; Meirinhos, J; Omont, N; Pitto, A; Ceresa, G;

Publicação
International Journal of Electrical Power and Energy Systems

Abstract
This paper presents a generic framework to evaluate and compare the quality of the uncertainties provided by probabilistic forecasts of power system state when used to perform security assessment for branch overloads. Besides exploiting advanced univariate and multivariate metrics that are traditionally used in weather prediction, the evaluation is complemented by assessing the benefits from exploiting probabilistic forecasts over the current practices of using deterministic forecasts of the system operating conditions. Another important feature of this framework is the provision of parameters tuning when applying flow probabilistic forecasts to perform security assessment for branch overloads. The quality and scalability of this framework is demonstrated and validated on recent historical data of the French transmission system. Although being developed to address branch overload problems, with proper adaptations, this work can be extended to other power system security problems. © 2019 Elsevier Ltd

2018

Data economy for prosumers in a smart grid ecosystem

Autores
Bessa, RJ; Rua, D; Abreu, C; Machado, P; Andrade, JR; Pinto, R; Goncalves, C; Reis, M;

Publicação
e-Energy 2018 - Proceedings of the 9th ACM International Conference on Future Energy Systems

Abstract
Smart grids technologies are enablers of new business models for domestic consumers with local flexibility (generation, loads, storage) and where access to data is a key requirement in the value stream. However, legislation on personal data privacy and protection imposes the need to develop local models for flexibility modeling and forecasting and exchange models instead of personal data. This paper describes the functional architecture of an home energy management system (HEMS) and its optimization functions. A set of data-driven models, embedded in the HEMS, are discussed for improving renewable energy forecasting skill and modeling multi-period flexibility of distributed energy resources. © 2018 Copyright held by the owner/author(s).

2016

Setting the Maximum Import Net Transfer Capacity under Extreme RES Integration Scenarios

Autores
Matos, MA; Bessa, RJ; Goncalves, C; Cavalcante, L; Miranda, V; Machado, N; Marques, P; Matos, F;

Publicação
2016 INTERNATIONAL CONFERENCE ON PROBABILISTIC METHODS APPLIED TO POWER SYSTEMS (PMAPS)

Abstract
In order to reduce the curtailment of renewable generation in periods of low load, operators can limit the import net transfer capacity (NTC) of interconnections. This paper presents a probabilistic approach to support the operator in setting the maximum import NTC value in a way that the risk of curtailment remains below a pre-specified threshold. Main inputs are the probabilistic forecasts of wind power and solar PV generation, and special care is taken regarding the tails of the global margin distribution (all generation all loads and pumping), since the accepted thresholds are generally very low. Two techniques are used for this purpose: interpolation with exponential functions and nonparametric estimation of extreme conditional quantiles using extreme value theory. The methodology is applied to five representative days, where situations ranging from high maximum NTC values to NTC=0 are addressed. Comparison of the two techniques for modeling tails is also comprised.