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A WAY TO COMPLEMENT ENERGY DEMAND MANAGEMENT
Abstract
Increasing the share of renewable energy sources for the generation of electricity in the public electricity network can have an important impact on the performance of the electricity system. An important problem in the electric power system is ensuring the continuity of the supply. The aim of the paper is to present a way to complement energy demand management and smart grids with models that follow changing energy production. This can be achieved by a holistic management system for diagnosis and risk assessment that allows the identification of parts of the electrical network that are affected by a fault, in order to isolate them and restore a new operational scheme. In order to exemplify in practice the effectiveness of the approach of advanced prediction algorithms, a model is made for the analysis of the installation of each primary, storage and management system aiming at the establishment of scenarios with increased probability in the case of the variation of some parameters of the proposed holistic system. This algorithm is based on the Bayesian probabilistic model which is based on the use of the conditional probabilities of specific events in the presence of the production of other events. The results obtained are: simultaneous processing of simultaneous faults in the electrical network; streamlining the refueling configuration by analyzing the information obtained through advanced prediction algorithms; improving the detection of faults in the low voltage network; remote verification and communication of faults to diagnose and solve them in real time.
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