Pinned Repositories
AdaptiveRateCompressiveForegroundExtraction
Code to replicate experiments in paper
Area_Coverage
commercial_storage_analysis
Project to explore & optimize dispatch of a commercial-scale battery storage system
cs-with-prior-information
This repository provides a Matlab implementation of the ADMM-based solvers for L1-L1 and L1-L2 minimization.
electricity_load_forecast
Electricity Load and Price Forecasting
ev_chargingcoordination2017
Optimal Scheduling of Electric Vehicle Charging in Distribution Networks
Gurobi-Python
Learning how to use gurobi with python (in chinese)
MultiStageCVAR
TransportationNetworks
Transportation Networks for Research
Two_Layer_EMS
Code for IEEE Transactions: A Two-Layer Energy Management System for Microgrids With Hybrid Energy Storage Considering Degradation Costs.
fjdhkyf3423's Repositories
fjdhkyf3423/ev_chargingcoordination2017
Optimal Scheduling of Electric Vehicle Charging in Distribution Networks
fjdhkyf3423/TransportationNetworks
Transportation Networks for Research
fjdhkyf3423/Gurobi-Python
Learning how to use gurobi with python (in chinese)
fjdhkyf3423/MultiStageCVAR
fjdhkyf3423/Two_Layer_EMS
Code for IEEE Transactions: A Two-Layer Energy Management System for Microgrids With Hybrid Energy Storage Considering Degradation Costs.
fjdhkyf3423/AdaptiveRateCompressiveForegroundExtraction
Code to replicate experiments in paper
fjdhkyf3423/Area_Coverage
fjdhkyf3423/commercial_storage_analysis
Project to explore & optimize dispatch of a commercial-scale battery storage system
fjdhkyf3423/cs-with-prior-information
This repository provides a Matlab implementation of the ADMM-based solvers for L1-L1 and L1-L2 minimization.
fjdhkyf3423/electricity_load_forecast
Electricity Load and Price Forecasting
fjdhkyf3423/energy-market-deep-learning
Experiments in using deep learning to model competition in liberalised electricity markets.
fjdhkyf3423/energyDS
fjdhkyf3423/GridLimitsforDERs
fjdhkyf3423/gurobi
for gurobi
fjdhkyf3423/Hybrid-Learning-Aided-Inactive-Constraints-Filtering-Algorithm-to-Enhance-AC-OPF-Solution-Time
The Optimal power flow (OPF) problem contains many constraints. However, equality constraints along with a limited set of active inequality constraints encompass sufficient information to determine the problem’s feasible space. In this paper, a hybrid supervised regression-classification learning-based algorithm is proposed to identify active and inactive sets of inequality constraints of AC OPF solely based on nodal power demand information. The proposed algorithm is structured using several classifiers and regression learners. The combination of classifiers with regression learners enhances the accuracy of active/inactive constraints identification procedure. The proposed algorithm modifies the OPF feasible space rather than a direct mapping of OPF results from demand. Inactive constraints are removed from the design space to construct a truncated AC OPF. This truncated optimization problem can be solved faster than the original problem with less computational resources. Numerical results on several test systems show the effectiveness of the proposed algorithm for predicting active and inactive constraints and constructing a truncated AC OPF. We have posted our code for all simulations on arxiv and have uploaded the data used in numerical studies to IEEE DataPort as an open access dataset.
fjdhkyf3423/learning-cbfs
Code needed to reproduce the examples found in "Learning Control Barrier Functions from Expert Demonstrations," by A. Robey, H. Hu, L. Lindemann, H. Zhang, D. V. Dimarogonas, S. Tu, and N. Matni, https://arxiv.org/abs/2004.03315
fjdhkyf3423/multimodal-transportation-optimization
A project on using mathematical programming to solve multi-modal transportation cost minimization in goods delivery and supply chain management.
fjdhkyf3423/nkOperationalSecurity
fjdhkyf3423/optimizatingelectricitytransmission
Using Python (Gurobi) to optimize the Transportation Problem of Electricity Transmission.
fjdhkyf3423/Power-Systems-Optimization
Implementation of Optimization Techniques for Economic Dispatch of Power Systems
fjdhkyf3423/PowerSystemCaseBuilder.jl
Package to build Cases for Power Systems Modeling
fjdhkyf3423/problem1
This repository consists of illustrative examples of mixed integer linear programming problems arising in battery-based energy storage systems, economic dispatch and unit commitment problems.
fjdhkyf3423/Publications
fjdhkyf3423/RTS-GMLC
Reliability Test System - Grid Modernization Lab Consortium
fjdhkyf3423/solar-net-metering-research-based-
Net metering (or net energy metering, NEM) is an electricity billing mechanism that allows consumers who generate some or all of their own electricity to use that electricity anytime, instead of when it is generated. This is particularly important with renewable energy sources like wind and solar, which are non-dispatchable (when not coupled to storage). Monthly net metering allows consumers to use solar power generated during the day at night, or wind from a windy day later in the month.
fjdhkyf3423/spotless
A fork of the Systems Polynomial Optimization Toolbox.
fjdhkyf3423/stanford-cs-229-machine-learning
VIP cheatsheets for Stanford's CS 229 Machine Learning
fjdhkyf3423/thesis
Examples, experiments, algorithms and any code needed to reproduce the results in my PhD Thesis
fjdhkyf3423/Topology-Aware-Learning-Assisted-Branch-and-Ramp-Constraints-Screening-for-Dynamic-Economic-Dispatch
keywords—Dynamic economic dispatch, branch and ramp constraints, topology change, machine learning, constraint classification.
fjdhkyf3423/udrc-summerschool
Code to replicate experiments in lecture in UDRC-EURASIP Summer School