OASYS REPOSITORIES

This page lists all the repositories where the OASYS group shares data and results of their research. Visit the OASYS group GitHub page to explore all our repositories. These repositories are publicly accessible and aim to promote transparency, collaboration, and reproducibility in research.

SweetSpotBT (GitHub)

This repository contains data and files for the work The Sweet Spot of Bound Tightening for Topology Optimization, including the IEEE-118 test system data, demand profiles, and the initial large enough constants used in the study.

storage_valid_inequalities (GitHub)

This repository provides data sets and code for the research titled Linear and Second-order-cone Valid Inequalities for Problems with Storage. It includes battery parameters, PV and wind profiles, demand data, price scenarios, and Python code for the case studies.

RobustContextualSE (GitHub)

This repository contains data sets for the research titled Robust Contextual State Estimation with Limited Measurement Data, including voltage and measurement data for the 33-bus and 39-bus systems under various variability conditions.

BeyondNeuralFog (GitHub)

This repository provides datasets for the research titled Beyond the Neural Fog: Interpretable Learning for AC Optimal Power Flow. It includes MATPOWER files for IEEE power systems (14-bus, 30-bus, 57-bus, and 118-bus) and datasets sourced from the OPFLearnData project. The data enables reproducibility and further research in AC optimal power flow.

LearningStateEstimation (GitHub)

This repository provides datasets for the research titled Learning-based state estimation in distribution systems with limited real-time measurements. It includes voltage magnitude and angle data for 33-bus and 136-bus systems under various conditions.

TightBigM_OTS (GitHub)

This repository provides datasets for the research titled Tight big-Ms for optimal transmission switching. It includes network data for the 118-bus system and node demand data for 100 instances, detailing the switchable status of transmission lines.

RPP_VNS_data (GitHub)

This repository provides datasets used in the paper An enhanced heuristic framework for solving the Rank Pricing Problem by A. Jiménez-Cordero, S. Pineda, and J.M. Morales. Developed by OASYS group members, it includes data for computational experiments to demonstrate the efficiency of the proposed framework.

AGC_and_Manual_Reserve_CC (GitHub)

This repository provides data for the 118-bus and 300-bus power systems used in the paper Unifying Chance-Constrained and Robust Optimal Power Flow for Resilient Network Operations. The data includes information extracted from a repository of power grids and details about the wind farms added to the system. Developed by OASYS group members and funded by the Flexanalytics project.

Learning_Assisted_Optimization_for_Transmission_Switching (GitHub)

This repository provides details of the datasets used in the paper Learning-Assisted Optimization for Transmission Switching. Developed by OASYS group members and funded by the Flexanalytics project.

TC_SAA_JCC-OPF (GitHub)

This repository provides the power systems dataset and code used in the paper Tight and Compact Model of the SAA-based Joint Chance-constrained OPF. Developed by OASYS group members and funded by the Flexanalytics project, the data is sourced from the Library of IEEE PES Power Grid Benchmarks.

CMCFL (GitHub)

This repository contains instances for the computational experiments in the paper The cooperative maximum capture facility location problem. It includes synthetic data for approach comparisons and real data for EV charging station location in Trois-Rivières, Québec.

Warm_starting_CG_for_MIO_ML (GitHub)

This repository provides datasets and code used in the paper Warm-starting Column Generation for Mixed-Integer Optimization in Machine Learning. Developed by OASYS group members and funded by the Flexanalytics project.

smartOASYS (GitHub)

This package implements methodologies for analyzing smart meter data. The data is used in the paper A high dimensional functional time series approach to evolution outlier detection for grouped smart meters.

DRO_DCOPF_CONTEXTUAL (GitHub)

This repository provides data for numerical experiments in the paper Distributionally Robust Optimal Power Flow with Contextual Information. It includes datasets for a 118-bus case study and a 3-bus illustrative example.

Cost-driven_Screening_Method_Data (GitHub)

This repository provides data for power systems, including a 5-node illustrative system and a 2000-node Texas system with net demands, generating unit data, and transmission line information. The data is used in the paper Cost-driven Screening of Network Constraints for the Unit Commitment Problem.

DRO_CONDITIONAL_TRIMMINGS (GitHub)

This repository provides data for numerical experiments in the paper Distributionally robust stochastic programs with side information based on trimmings. It includes datasets for the single-item Newsvendor problem and the portfolio allocation problem.

segisoreg (GitHub)

This repository provides supplementary material for the paper An exact dynamic programming approach to segmented isotonic regression. It includes synthetic and realistic datasets used in the study.

118TN33DN (GitHub)

This repository provides input data for a case study on the coordination of transmission and distribution operations. The database includes network parameters, generator data, consumer flexibility, and renewable capacity factors. The data is used in the paper Learning the price response of active distribution networks for TSO-DSO coordination.

Medical_data (GitHub)

This repository provides details of the medical datasets used in the paper A novel embedded min-max approach for feature selection in nonlinear support vector machine classification, developed by OASYS group members and funded by the Flexanalytics project. Visit the related links to learn more about the research.

Aggregated-EV-data (GitHub)

This repository provides input data for all cases of the inverse optimization methodology described in the work Inverse Optimization with Kernel Regression: Application to the Power Forecasting and Bidding of a Fleet of Electric Vehicles. The paper is authored by R. Fernandez-Blanco, J. M. Morales, S. Pineda, and A. Porras, members of the OASYS group, and funded by the Flexanalytics project.

2020_cournot_producer (GitHub)

This repository contains power systems data (2018–2019) from ENTSO-e and OMIE (Spain), including renewable production forecasts and inverse demand function parameters for Spain’s day-ahead market. The data is used in the paper A bilevel framework for decision-making under uncertainty with contextual information.

Data_EV_Driving_Patterns (GitHub)

This repository provides data on EV driving patterns, including availability profiles and daily energy demand for a fleet of 1000 EVs. The dataset is synthetically derived from the National Household Travel Survey. The data is used in the paper An Efficient Robust Approach to the Day-ahead Operation of an Aggregator of Electric Vehicles.

homothetic (GitHub)

This repository provides supplementary information for the work Forecasting the Price-Response of a Pool of Buildings via Homothetic Inverse Optimization. It includes a heuristic estimation procedure and a synthetic database of smart buildings.

Comparison_non_linear_solvers (GitHub)

This repository compares several non-linear solvers in terms of objective value and computational time. Non-linear optimization problems of different types are modeled using Pyomo and tested with various solvers. The results are analyzed to provide guidance on solver selection, though the conclusions are not universally applicable.

data_ieee96 (GitHub)

This repository provides data from the IEEE RTS-96 test system, modified to include 19 wind farms. It contains information on generators, transmission lines, electricity demand, wind power generation, and congestion scenarios. The data is used in the paper Data-Driven Network Screening of Network Constraints for Unit Commitment.

Data_LOW_CARBON_LONDON (GitHub)

This repository contains data from the UK Power Networks-led Low Carbon London project (2011–2014), which studied consumer responses to dynamic electricity pricing. The datasets include energy consumption, pricing, temperature, and demand data.

2019_wind_producer (GitHub)

This repository provides power systems data (2015–2019) used in the numerical case study of the paper Feature-driven Improvement of Renewable Energy Forecasting and Trading, published on IEEE Transactions on Power Systems. The data includes relevant forecasts of the DK1 bidding zone and wind power production forecasts of nearby areas. Day-ahead market prices of DK1 are also included.