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Details

  • Name

    Cláudio Monteiro
  • Cluster

    Power and Energy
  • Role

    External Research Collaborator
  • Since

    01st January 1997
001
Publications

2020

A strategy for electricity buyers in futures markets

Authors
Monteiro, C; Ramirez Rosado, IJ; Fernandez Jimenez, LA;

Publication
E3S Web of Conferences

Abstract
This paper presents an original trading strategy for electricity buyers in futures markets. The strategy applies a medium-term electricity price forecasting model to predict the monthly average spot price which is used to evaluate the Risk Premium for a physical delivery under a monthly electricity futures contract. The proposed trading strategy aims to provide an advantage relatively to the traditional strategy of electricity buyers (used as benchmark), anticipating the good/wrong decision of buying electricity in the futures market instead in the day-ahead market. The mid-term monthly average spot price forecasting model, which supports the trading strategy, uses only information available from futures and spot markets at the decision moment. Both the new trading strategy and the monthly average spot price forecasting model, proposed in this paper, have been successfully tested with historical data of the Iberian Electricity Market (MIBEL), although they could be applied to other electricity markets.

2020

A Novel Ensemble Algorithm for Solar Power Forecasting Based on Kernel Density Estimation

Authors
Lotfi, M; Javadi, M; Osorio, GJ; Monteiro, C; Catalao, JPS;

Publication
Energies

Abstract
A novel ensemble algorithm based on kernel density estimation (KDE) is proposed to forecast distributed generation (DG) from renewable energy sources (RES). The proposed method relies solely on publicly available historical input variables (e.g., meteorological forecasts) and the corresponding local output (e.g., recorded power generation). Given a new case (with forecasted meteorological variables), the resulting power generation is forecasted. This is performed by calculating a KDE-based similarity index to determine a set of most similar cases from the historical dataset. Then, the outputs of the most similar cases are used to calculate an ensemble prediction. The method is tested using historical weather forecasts and recorded generation of a PV installation in Portugal. Despite only being given averaged data as input, the algorithm is shown to be capable of predicting uncertainties associated with high frequency weather variations, outperforming deterministic predictions based on solar irradiance forecasts. Moreover, the algorithm is shown to outperform a neural network (NN) in most test cases while being exceptionally faster (32 times). Given that the proposed model only relies on public locally-metered data, it is a convenient tool for DG owners/operators to effectively forecast their expected generation without depending on private/proprietary data or divulging their own.

2019

Optimal Prosumer Scheduling in Transactive Energy Networks Based on Energy Value Signals

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

Publication
SEST 2019 - 2nd International Conference on Smart Energy Systems and Technologies

Abstract
We present a novel fully distributed strategy for joint scheduling of consumption and trading within transactive energy networks. The aim is maximizing social welfare, which itself is redefined and adapted for peer-to-peer prosumer-based markets. In the proposed scheme, hourly energy values are calculated to coordinate the joint scheduling of consumption and trading, taking into consideration both preferences and needs of all network participants. Electricity market prices are scaled locally based on hourly energy values of each prosumer. This creates a system where energy consumption and trading are coordinated based on the value of energy use throughout the day, rather than only the market price. For each prosumer, scheduling is done by allocating load (consumption) and supply (trading) blocks, maximizing the energy value globally and locally within the network. The proposed strategy was tested using a case study of typical residential prosumers. It was shown that the proposed model could provide potential benefits for both prosumers and the grid, albeit with a user-centered, fully distributed management model which relies solely on local scheduling in transactive energy networks. © 2019 IEEE.

2019

Energy performance of buildings with on-site energy generation and storage - An integrated assessment using dynamic simulation

Authors
Bot, K; Ramos, NMM; Almeida, RMSF; Pereira, PF; Monteiro, C;

Publication
JOURNAL OF BUILDING ENGINEERING

Abstract
The European Union aims to achieve a nearly zero energy balance in buildings by 2020. The present study takes into consideration the passive systems of the building, energy demand, and energy generated by the on-site photovoltaic and storage system, and how they interact in different scenarios. The study also considers the energy demand from the grid and the surplus of renewable energy. The software EnergyPlus was used and the parametric sensitivity simulation method was applied, taking into account blinds operation, ventilation strategies, HVAC operation schemes and battery storage capacity, in 96 scenarios. The results highlight that there is great variability between the considered scenarios, highlighting the importance of sizing methodologies for the passive systems and the use of optimized home management algorithms. It was found that the use of batteries with higher storage capacity increases the demand-supply from the on-site PV energy but decreases the amount of energy injected into the grid. The design of the PV and battery system based on yearly integrated simulations allows for an optimized solution. This study also emphasizes the importance of knowing the expected occupancy during the design phase, as a significant input to the sizing methodologies of the storage capacity and on-site generation.

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.