Mobbhans's starred repositories
Awesome_Imputation
Awesome Deep Learning for Time-Series Imputation, including a must-read paper list about applying neural networks to impute incomplete time series containing NaN missing values/data
Stable-DDPG-for-voltage-control
Official implementation for the paper
Voltage-regulation-using-SVM
This code is the implementation of the following paper: M. Jalali, V. Kekatos, N. Gatsis and D. Deka, "Designing Reactive Power Control Rules for Smart Inverters Using Support Vector Machines," in IEEE Transactions on Smart Grid, vol. 11, no. 2, pp. 1759-1770, March 2020, doi: 10.1109/TSG.2019.2942850. The main code 1) loads the data. However, the data is not included here. the code should be modified accordingly. 2) Finds the hyperparameters uding cross validation. While the main file includes the optimization using l2 loss function, the functions for l1 oprimization are included here with suffix "2". 3) Solves the volatge regulation optimization problem using the Mosek solver. 4) Solves the volatge regulation problem using the optimal power flow problem and the local control rules as well. The main function includes the following functions: 1) Preprocessing: the scaling, oversizing, centering and normalzing of the data. 2) KFCrossvalid_SVM: finds the hyperparameters using crossvalidation 3) mosek_crossValid (mosek2_crossValid): located inside the KFCrossvalid_SVM which solves theoptimization problem 4) SVM_gauss_mosek and SVM_lin_mosek: solve the ctual optimization problems for finding the parameters a abd b for reative power control rules. 5) localControl: finds the reactive power local control rules 6) eval_SVM_gauss, eval_SVM_lin: evaluates the reactive power control rules given the measurements and obtained parameters 7) optimalGlobal (SOCP): solves the central optimal power flow problem
RL_VVC_dataset
A Reinforcement Learning-based Volt-VAR Control Dataset
-RLlib-IMPALA
Scalable Volt-VAR Optimization using RLlib-IMPALA Framework: A Reinforcement Learning Approach
agent-based-modeling-in-electricity-market-using-DDPG-algorithm
Agent-Based Modeling in Electricity Market Using Deep Deterministic Policy Gradient Algorithm
ADMM_in_scheduling
ADMM算法在分布式调度中的应用
CCGAN_OPF_Journal2022
The code for the paper 'Fast Optimal Power Flow with Guarantees via an Unsupervised Generative Model', IEEE Transaction on Power Systems.
PyTorch_ML_models
PyTorch Linear Regression and CNN Implementations from Scratch
DeepOPF-Codes
This repository stores the main codes for the DeepOPF projects.
CNO-AC-OPF
CNO implementation for AC-OPF (pandapower based)
Physics-Informed-Neural-Network-for-DC-OPF
This repository contains the code for Physics-Informed Neural Network for DC Optimal Power Flow applications and the worst case guarantees
msdro_opf_public
Multi-source data-driven DRO OPF code supplement
MultiPeriod-DistOPF-AlgoTesting
Testing different temporal decomposition algorithms for the (already) spatially decomposed MPOPF problem. 3-Phase Balanced. MATLAB.
accelerated_ADMM
Accelerated variants of the ADMM algorithm.
aaai2016_changkyu
Fast ADMM Algorithm for Distributed Optimization with Adaptive Penalty (AAAI 2016) - Changkyu Song