Power and Energy

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


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.


Forecasting production from wind and solar power plants, and making effective decisions under forecast uncertainty, are essential capabilities in low-carbon energy systems. This competition invites participants to develop state-of-the-art forecasting and energy trading techniques to accelerate the global transition to net-zero and to win a share of $21,000 in prize money. It aims to bridge the gap between academic and industry practice, introduce energy forecasting challenges to new communities, and promote energy analytics and data science education.

Last Updated On: 
Sun, 06/16/2024 - 11:50
Citation Author(s): 
Jethro Browell, Sebastian Haglund, Henrik Kälvegren, Edoardo Simioni, Ricardo Bessa, Yi Wang

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.


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.


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.


Electric power systems are comprised of cyber and physical components that are crucial to grid resiliency. Data from both components should be collected when modeling power systems: data from communication networks and intrusion detection systems; physical telemetry from sensors and field devices.


This paper presents a cost-effective approach for building energy usage management through energy usage optimization of available building energy sources. An energy cost reduction model is developed considering grid energy usage and cost, generator energy usage and cost as well as carbon emissions tax penalties associated with scope 1 emissions. The model selects optimal times to use either Grid energy, generator energy, or a combination of the two to minimize the overall building energy usage cost.


In order to obtain the ex-ante least-cost schedule of energy generation and reserves for online generating units, the system operator addresses a dynamic decision-making process known as the economic dispatch (ED) problem. Current industry practice involves adopting a deterministic two-stage optimization framework that relies on a one-day-ahead horizon and a forecast of uncertain parameters. The optimal solution to the resulting problem thus yields a generation schedule for the entire day ahead.