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Wu, J. (author), Li, S. (author), Fu, A. (author), Cvetkovic, M. (author), Palensky, P. (author), Vasquez, Juan C. (author), Guerrero, Josep M. (author)
The increasing proportion of renewable energy introduces both long-term and short-term uncertainty to power systems, which restricts the implementation of energy management systems (EMSs) with high dependency on accurate prediction techniques. A hierarchical online EMS (HEMS) is proposed in this paper to economically operate the Hybrid...
journal article 2024
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Shengren, H. (author), Salazar, Edgar Mauricio (author), Vergara Barrios, P.P. (author), Palensky, P. (author)
Taking advantage of their data-driven and model-free features, Deep Reinforcement Learning (DRL) algorithms have the potential to deal with the increasing level of uncertainty due to the introduction of renewable-based generation. To deal simultaneously with the energy systems’ operational cost and technical constraints (e.g, generation-demand...
conference paper 2022
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Pan, K. (author), Herdeiro Teixeira, A.M. (author), López, Claudio (author), Palensky, P. (author)
It is challenging to assess the vulnerability of a cyber-physical power system to data attacks from an integral perspective. In order to support vulnerability assessment except analytic analysis, suitable platform for security tests needs to be developed. In this paper we analyze the cyber security of energy management system (EMS) against data...
conference paper 2018