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Manuel Zamudio López, Hamidreza Zareipour and Mike Quashie
This research proposes an investigative experiment employing binary classification for short-term electricity price spike forecasting. Numerical definitions for price spikes are derived from economic and statistical thresholds. The predictive task employ...
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Nhung Nguyen Hong and Huy Nguyen Duc
In recent years, with the rapid increase in renewable energy sources (RESs), a Virtual Power Plant (VPP) concept has been developed to integrate many small-scale RESs, energy storage systems (ESSs), and customers into a unified agent in the electricity m...
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Yun-Sheng Tsai, Chi-Wen Chen, Cheng-Chien Kuo and Hung-Cheng Chen
In recent years, the escalating electricity demand in Taiwan has heightened the prominence and discourse surrounding the issue of power supply. With the enactment of the European climate law, global commitment to achieving net-zero emissions has gained m...
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Sylvain Rodat and Richard Thonig
Solar energy is not only the most abundant energy on earth but it is also renewable. The use of this energy is expanding very rapidly mainly through photovoltaic technology. However, electricity storage remains a bottleneck in tackling solar resource var...
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Denis E. Baskan, Daniel Meyer, Sebastian Mieck, Leonhard Faubel, Benjamin Klöpper, Nika Strem, Johannes A. Wagner and Jan J. Koltermann
In recent years, energy prices have become increasingly volatile, making it more challenging to predict them accurately. This uncertain market trend behavior makes it harder for market participants, e.g., power plant dispatchers, to make reliable decisio...
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Nika Nizharadze, Arash Farokhi Soofi and Saeed Manshadi
Predicting the price gap between the day-ahead Market (DAM) and the real-time Market (RTM) plays a vital role in the convergence bidding mechanism of Independent System Operators (ISOs) in wholesale electricity markets. This paper presents a model to pre...
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Daniel Manfre Jaimes, Manuel Zamudio López, Hamidreza Zareipour and Mike Quashie
This paper proposes a new hybrid model to forecast electricity market prices up to four days ahead. The components of the proposed model are combined in two dimensions. First, on the ?vertical? dimension, long short-term memory (LSTM) neural networks and...
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Silvia Golia, Luigi Grossi and Matteo Pelagatti
In this paper we assess how intra-day electricity prices can improve the prediction of zonal day-ahead wholesale electricity prices in Italy. We consider linear autoregressive models with exogenous variables (ARX) with and without interactions among pred...
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Oana Marin, Tudor Cioara and Ionut Anghel
Buildings can become a significant contributor to an energy system?s resilience if they are operated in a coordinated manner to exploit their flexibility in multi-carrier energy networks. However, research and innovation activities are focused on single-...
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Tugçin Kirant Mitic and Karsten Voss
Electricity generation from renewable energy reduces greenhouse gas emissions and, in the long term, the cost of electricity in power grids. However, there is currently no strong positive correlation between greenhouse gas intensity and electricity spot ...
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