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Details

  • Name

    Cláudio Monteiro
  • Cluster

    Power and Energy
  • Role

    External Research Collaborator
  • Since

    01st January 1997
001
Publications

2018

Antibacterial coatings produced by chemoselective grafting of Dhvar-5 onto chitosan

Authors
Barbosa, M; Monteiro, C; Costa, F; Teixeira, C; Martins, C; Gomes, P;

Publication
JOURNAL OF PEPTIDE SCIENCE

Abstract

2018

Analysis of spinning reserves in systems with variable power sources

Authors
Fonte, PM; Monteiro, C; Barbosa, FM;

Publication
International Conference on the European Energy Market, EEM

Abstract
In this paper is studied an approach based on risk assessment to solve the scheduling of a power production system with variable power sources. The spinning reserves resulting from the unit commitment are analyzed too. In this methodology there are no infeasible solutions, only more or less costly solutions associated to the operation risks, such as, load or renewable production curtailment. The uncertainty of forecasted production and load demand are defined by probability distribution functions. The methodology is tested in a real case study, an island with high penetration of renewable power production. Finally, forecasted and measured reserves are compared, once the reserves are strongly linked with the forecasting quality. The results of a real case study are presented and discussed. They show the difficulty to achieve complete robust solutions. © 2018 IEEE.

2018

Evolution of Demand Response: A Historical Analysis of Legislation and Research Trends

Authors
Lotfi, M; Monteiro, C; Shafie Khah, M; Catalao, JPS;

Publication
2018 20th International Middle East Power Systems Conference, MEPCON 2018 - Proceedings

Abstract
In the past two decades, interest in demand response (DR) schemes has grown exponentially. The need for DR has been driven by sustainability (environmental and socioeconomic) and cost-efficiency. The main premise of DR is to influence the timing and magnitude of consumption to match energy supply by sharing the benefits with consumers, ultimately aiming to optimize generation cost. As such, the first and primary enabler to DR was the establishment of contemporary electricity markets. Increased proliferation of Distributed Energy Resources (DER) and microgeneration further motivated the participation of consumers as active players in the market, popularizing DR and the wider category of Demand-Side Management (DSM) programs. Smart Grids (SG) have been an enabler to modern DR schemes, with smart metering data providing input to the underlying optimization and forecasting tools. The more recent emergence of the Internet of Energy (IoE), seen as the evolution of SG, is driven by increased Internet of Things (IoT)-enabling and high penetration of scalable and distributed energy resources. In this IoE paradigm being a fully decentralized network of energy prosumers, DR will continue to be a vital aspect of the grid in future Transactive Energy (TE) schemes, aiming for a more user-centered, energy-efficient, cost-saving, energy management approach. This paper investigates original motives and identifies the first mentions of DR in the legislative and scientific literature. Afterwards, the evolution of DR is tracked over the past four decades, attempting to study the co-influence of legislation and research by performing a thorough statistical analysis of research trends on the IEEE Xplore digital library. Finally, conclusions are made as to the current state of DR and future prospects of DR are discussed. © 2018 IEEE.

2018

New probabilistic price forecasting models: Application to the Iberian electricity market

Authors
Monteiro, C; Ramirez Rosado, IJ; Alfredo Fernandez Jimenez, LA; Ribeiro, M;

Publication
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS

Abstract
This article presents original Probabilistic Price Forecasting Models, for day-ahead hourly price forecasts in electricity markets, based on a Nadaraya-Watson Kernel Density Estimator approach. A Gaussian Kernel Density Estimator function is used for each input variable, which allows to calculate the parameters of the probability density function (PDF) of a Beta distribution for the hourly price variable. Thus, valuable information is obtained from PDFs such as point forecasts, variance values, quantiles, probabilities of prices, and time series representations of forecast uncertainty. A Reliability Indicator is also introduced to give a measure of "reliability" of forecasts. The Probabilistic Price Forecasting Models were satisfactorily applied to the real-world case study of the Iberian Electricity Market. Input variables of these models include recent prices, power demands and power generations in the previous day, power demands in the previous week, forecasts of demand, wind power generation and weather for the day-ahead, and chronological data. The best model, corresponding to the best combination of input variables that achieves the lowest MAE, obtains one of the highest Reliability Indicator values. A systematic analysis of MAE values of the Probabilistic Price Forecasting Models for different combinations of input variables showed that as more types of input variables were considered in these models, MAE values improved and Reliability Indicator values usually increased.

2016

Unit Commitment Based on Risk Assessment to Systems with variable Power Sources

Authors
Fonte, PM; Monteiro, C; Barbosa, FM;

Publication
PROCEEDINGS OF THE IECON 2016 - 42ND ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY

Abstract
This paper presents the development of a complete methodology for power systems scheduling with highly variable sources based on a risk assessment model. The methodology is tested in a real case study, namely an island with high penetration of renewable energy production. The uncertainty of renewable power production forecasts and load demand are defined by the probability distribution function, which can be a good alternative to the scenarios approach. The production mix chosen for each hour results from the costs associated to the operation risks, such as load shed and renewable production curtailment. The results to a seven days case study allow concluding about the difficulty to achieve a complete robust solution.