Details
Name
Cláudio MonteiroCluster
Power and EnergyRole
External Research CollaboratorSince
01st January 1997
Nationality
PortugalCentre
Power and Energy SystemsContacts
+351222094230
claudio.monteiro@inesctec.pt
2020
Authors
Lotfi, M; Javadi, M; Osorio, GJ; Monteiro, C; Catalao, JPS;
Publication
ENERGIES
Abstract
2020
Authors
Monteiro, C; Ramirez Rosado, IJ; Fernandez Jimenez, LA;
Publication
E3S Web of Conferences
Abstract
2020
Authors
Monteiro, C; Alfredo Fernandez Jimenez, LA; Ramirez Rosado, IJ;
Publication
ENERGIES
Abstract
This article presents an original predictive strategy, based on a new mid-term forecasting model, to be used for trading physical electricity futures. The forecasting model is used to predict the average spot price, which is used to estimate the Risk Premium corresponding to electricity futures trade operations with a physical delivery. A feed-forward neural network trained with the extreme learning machine algorithm is used as the initial implementation of the forecasting model. The predictive strategy and the forecasting model only need information available from electricity derivatives and spot markets at the time of negotiation. In this paper, the predictive trading strategy has been applied successfully to the Iberian Electricity Market (MIBEL). The forecasting model was applied for the six types of maturities available for monthly futures in the MIBEL, from 1 to 6 months ahead. The forecasting model was trained with MIBEL price data corresponding to 44 months and the performances of the forecasting model and of the predictive strategy were tested with data corresponding to a further 12 months. Furthermore, a simpler forecasting model and three benchmark trading strategies are also presented and evaluated using the Risk Premium in the testing period, for comparative purposes. The results prove the advantages of the predictive strategy, even using the simpler forecasting model, which showed improvements over the conventional benchmark trading strategy, evincing an interesting hedging potential for electricity futures trading.
2020
Authors
Lotfi, M; Monteiro, C; Shafie-khah, M; Catalão, JP;
Publication
Blockchain-based Smart Grids
Abstract
2019
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.
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