Considerations Regarding the Negative Prices on the Electricity Market

Atănăsoae, Pavel; Pentiuc, Radu Dumitru; Hopulele, Eugen

doi:10.3390/proceedings2020063026

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…Various smart metrics for measuring, communication, synchronization, forecasting and control are being developed [9]. Among the economic instruments, it is possible to name, for example, the real-time pricing [10] or the Adaptive Billing Mechanism [11], which can work, with negative energy prices and thus flatten the oscillation of the overall load diagram. A whole new market with new entrants can be expected.…”

Section: Smart Gridmentioning

confidence: 99%

Machine Learning-Based Node Characterization for Smart Grid Demand Response Flexibility Assessment

et al. 2021

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As energy distribution systems evolve from a traditional hierarchical load structure towards distributed smart grids, flexibility is increasingly investigated as both a key measure and core challenge of grid balancing. This paper contributes to the theoretical framework for quantifying network flexibility potential by introducing a machine learning based node characterization. In particular, artificial neural networks are considered for classification of historic demand data from several network substations. Performance of the resulting classifiers is evaluated with respect to clustering analysis and parameter space of the models considered, while the bootstrapping based statistical evaluation is reported in terms of mean confusion matrices. The resulting meta-models of individual nodes can be further utilized on a network level to mitigate the difficulties associated with identifying, implementing and actuating many small sources of energy flexibility, compared to the few large ones traditionally acknowledged.

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Section: Smart Gridmentioning

confidence: 99%