Smart grids are considered as one of the most effective answers to the need of reliable, economic, and sustainable electricity services. The smart grids are conceived as a fusion of the energy, information, and communication infrastructures, which is obtained by designing integrated management and protection tools, able to handle heterogeneous and complex problems ranging from network optimization to security issues. In particular, issues such as grid efficiency improvement, flexible load supply, demand side management, emission control, and optimal network regulation can be addressed by a smart management system, which aims at acquiring and processing the available set of information describing the actual smart grid operation state. As easily understandable, this computing process is a very complex and time-intensive task, since it requires the periodic estimation of the power system state, the analysis of the massive data streams generated by the grid sensors and the repetitive solution of large-scale optimization problems, which are complex, nonlinear, and NP-hard problems. Moreover, in order to provide the grid operators with updated information to better understand and reduce the impact of system uncertainties associated with load and generation variations (e.g., in solar and wind power sources), the required computation times should be fast enough [1].

A fuzzy-based data mining paradigm for on-line optimal power flow analysis

Vaccaro A.
2016

Abstract

Smart grids are considered as one of the most effective answers to the need of reliable, economic, and sustainable electricity services. The smart grids are conceived as a fusion of the energy, information, and communication infrastructures, which is obtained by designing integrated management and protection tools, able to handle heterogeneous and complex problems ranging from network optimization to security issues. In particular, issues such as grid efficiency improvement, flexible load supply, demand side management, emission control, and optimal network regulation can be addressed by a smart management system, which aims at acquiring and processing the available set of information describing the actual smart grid operation state. As easily understandable, this computing process is a very complex and time-intensive task, since it requires the periodic estimation of the power system state, the analysis of the massive data streams generated by the grid sensors and the repetitive solution of large-scale optimization problems, which are complex, nonlinear, and NP-hard problems. Moreover, in order to provide the grid operators with updated information to better understand and reduce the impact of system uncertainties associated with load and generation variations (e.g., in solar and wind power sources), the required computation times should be fast enough [1].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12070/53419
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