Smart Grid

The work starts with a short overview of grid requirements for photovoltaic (PV) systems and control structures of grid-connected PV power systems. Advanced control strategies for PV power systems are presented next, to enhance the integration of this technology. The aim of this work is to investigate the response of the three-phase PV systems during symmetrical and asymmetrical grid faults.

This is the snapshot of the directories included in this dataset, which are load, price, pv, and wind. Within each directory, there is an .xlsx file that list the detailed data in that category. These data are used in an article titled "Consensus-based distributed reinforcement learning with primal-dual update for networked microgrids on-line coordination."

This dataset originates from a wind farm and a photovoltaic (PV) power station located in a region of western Inner Mongolia. It includes meteorological and power output data from the entire year of 2022, with a temporal resolution of 15-minute intervals. The wind farm data comprises meteorological parameters such as wind speed, wind direction, temperature, and humidity, as well as the power output of the wind turbines. The PV station data includes irradiation, temperature, and other weather-related parameters relevant to solar power generation, along with the corresponding output data.

This repository contains the datasets produced using different data generation strategies to train data driven models (e.g., decision trees, gradient tree boosting, and deep neural networks), and to evaluate their performances. The data generation strategies are described, and the results are presented in the conference paper: "Training Data Generation Strategies for Data-driven Security Assessment of Low Voltage Smart Grids" J. Cuenca, E. Aldea, E. Le Guern-Dall'o, R. Féraud, G. Camilleri, and A. Blavette. IEEE ISGT EU 2024, Dubrovnik, Croatia, Oct 2024.