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• W2890101752 abstract "Along with the emerging development of demand side management applications, it is still a challenge to exploit flexibility realistically to resolve or prevent specific geographical network issues due to limited situational awareness of the (unbalanced low-voltage) network as well as complex time dependent constraints. To overcome these problems, this paper presents a time-horizon three-phase grid-supportive demand side management methodology for low voltage networks by using a universal interface that is established between the demand side management application and the monitoring and network analysis tools of the network operator. Using time-horizon predictions of the system states that the probability of operational limit violations is identified. Since this analysis is computationally intensive, a data driven approach is adopted by using machine learning. Time-horizon flexibility is procured, which effectively prevents operation limit violation from occurring independent of the objective that the demand side management application has. A practical example featuring fair power sharing demonstrates the effectiveness of the presented method for resolving over-voltages and under-voltages. This is followed by conclusions and recommendations for future work." @default.
• W2890101752 created "2018-09-27" @default.
• W2890101752 creator A5004920556 @default.
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• W2890101752 date "2018-09-21" @default.
• W2890101752 modified "2023-09-26" @default.
• W2890101752 title "Data-Driven Risk Analysis for Probabilistic Three-Phase Grid-Supportive Demand Side Management" @default.
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• W2890101752 doi "https://doi.org/10.3390/en11102514" @default.
• W2890101752 hasPublicationYear "2018" @default.
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