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About

About

I was born in Porto in 1992. I have an undergraduate's degree in mathematics and a master's degree in applied mathematics. I am currently a PhD candidate in applied mathematics (FCUP). I work in machine learning, more concretely in probabilistic forecast of renewable energies.

Interest
Topics
Details

Details

  • Name

    Carla Silva Gonçalves
  • Cluster

    Power and Energy
  • Role

    Research Assistant
  • Since

    12th October 2015
Publications

2018

Data economy for prosumers in a smart grid ecosystem

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

Publication
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

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

Publication
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.

Supervised
thesis

2016

Forecasting high-dimensional electrical energy time-series

Author
Carla Sofia da Silva Gonçalves

Institution
UM