Abstract
Energy aggregation business models play a crucial role in maximizing the benefits of renewable energy generation and consumption. In this context, this paper presents an examination of energy system models and their correlation with aggregation business models, emphasizing algorithmic approaches. The distinct characteristics of energy system models in comparison to traditional business models are elucidated. The study encompasses diverse model types employed in the energy sector, including optimization, classification, clustering, and integrated assessment models. Additionally, it investigates the algorithms utilized in aggregation business models, such as demand response, virtual power plants, and peer-to-peer energy trading, highlighting both their advantages and challenges. The paper concludes that the advancement of energy aggregation business models holds significant promise in addressing the intricate complexities of contemporary power systems and the ongoing energy transition. Future research endeavors involve the exploration of Electroencephalography (EEG) techniques within energy aggregation models, inspired by the information processing mechanisms of the human brain.
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This work is part of the IoTalentum project, which has received funding from the EU H2020 research and innovation programme under the MSCA grant agreement No 953442.
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Bwalya, D., Azevedo, M., Corchado, E.S. (2023). Exploring the Cutting-Edge of Energy Aggregation Approaches and Business Models. In: Mehmood, R., et al. Distributed Computing and Artificial Intelligence, Special Sessions I, 20th International Conference. DCAI 2023. Lecture Notes in Networks and Systems, vol 741. Springer, Cham. https://doi.org/10.1007/978-3-031-38318-2_50
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