Power and Energy
The Integrated Energy Management and Forecasting Dataset is a comprehensive data collection specifically designed for advanced algorithmic modeling in energy management. It combines two distinct yet complementary datasets - the Energy Forecasting Data and the Energy Grid Status Data - each tailored for different but related purposes in the energy sector.
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Each sheet in this document meticulously details the electrical parameters of the grounding networks. Among all these parameters, touch voltage stands out as the most critical factor to consider in each evaluated conductor configuration.
The determination of these physical configurations is based on meticulous measurement of the distances between conductors on one side of the grid. It's important to note that in this context, square grids are being employed for grounding, which implies a specific arrangement of conductors.
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This dataset proposes a new method of modelling dynamic loads based on instantaneous p-q theory, to be employed in large power system networks in a digital real time environment. In order to decrease the computational burden associated to the dynamic load modelling, a p-q- theory-based approach for load modelling is proposed in this dataset. This approach is based on the well-known p-q- instantaneous theory developed for power electronics converters, and it consts only of linear controllers and of a minimal usage of control loops, reducing the required computational power.
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This dataset contains scenarios for the assessment of Flexible Distributed Energy Resources (DERs) in new districts in Spain. It contains energy performance indicators, a summary of thermal and electricity generators and energy carrier tariffs
The following Tables are provided:
Table I References for energy performance indicators
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This dataset has been compiled using publicly available data about the substation loads, generation capacities, transmission line lengths and voltages of the Bangladesh Power Grid, incorporating typical electrical parameters for power factor, transmission line impedance, generator impedances etc. It is tailored for power flow studies as a test case within the PSSE software. However, the dataset can be easily converted to a format compatible with alternative power system analysis software.
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In order to study the role and mechanism of the Cu surface during the generation of SF6 decomposition products inside GIS, we established the interfacial reaction model of low-fluorine sulfides with oxygen on the Cu(111) surface. The influence of the Cu metal on the reaction is analyzed from the perspective of reaction kinetics. Thermodynamic properties and structural files of the reactions can be found in Supplementary Materials. We have supplemented the thermodynamic properties of reactions, and also how we calculated them in Supporting information file.
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This dataset encompasses cycling experiments conducted on lithium-ion batteries, involving 5 distinct batches, each originating from a different manufacturer. Within each batch, there were 6 batteries subjected to testing. The battery types employed for testing included cylindrical 21700 cells and 18650 type and nickel manganese cobalt or nickel cobalt aluminum.
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The two distribution systems provided in this dataset are based on the data provided in [1]. The dataset was used in the analysis made in [2], where it was developed a model that can operate the distribution system considering wildfire-prone climate conditions. In this work, we consider that part of the grid is vulnerable to the ignition of a wildfire, which can be influenced by the levels of power flows passing through the line segments within the region.
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Different faults are experienced by a power system, particulary in transmission lines. In this dataset, the IEEE 5-Bus Model was used to different types of transmission line faults.
Indication of the label of the faults come from the time that the fault has been induced in the simulation.
This dataset aims to be utilized for machine learning algorithms, particularly in multi-class classification of the transmission line fault. In this simulation, each fault was induced at each transmission line one instance at a time during a certain period.
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