Image: NASA Earth Observatory
RunKPG.jl Demo
#tutorial#RunKPG#Julia#large-scale
What You’ll Learn
- Introduction to RunKPG.jl and the Julia ecosystem for power systems
- Setting up your Julia environment and installing RunKPG.jl
- Loading and preprocessing power system data
- Running large-scale optimal power flow (OPF) problems
- Unit commitment (UC) optimization for day-ahead scheduling
- Analyzing and visualizing optimization results
- Performance tuning and best practices for large systems
Quick Start Example
Step 1: Install RunKPG.jl
julia> using Pkg
julia> Pkg.add(“RunKPG”)Step 2: Load Package and Data
julia> using RunKPG
julia> network = load_network(“KPG193.raw”)Step 3: Run Optimization
julia> result = run_dcopf(network)
julia> println(“Optimal Cost: $(result.objective)“)Why Julia and RunKPG.jl?
⚡ High Performance
Julia’s just-in-time compilation provides C/Fortran-like speed with Python-like syntax
📈 Scalability
Efficiently handles large-scale Korean power system (1000+ buses)
🔬 Research-Ready
Easy prototyping of new algorithms and optimization formulations
🔗 Interoperability
Seamless integration with Python, R, MATLAB, and C/C++
Demo Cases Covered
Case 1: DC Optimal Power Flow (DCOPF)
Solve economic dispatch with transmission constraints for KPG193 system
Duration: ~15 minutes | Difficulty: Beginner
Case 2: Unit Commitment (UC)
Day-ahead scheduling with generator commitment decisions and ramp constraints
Duration: ~20 minutes | Difficulty: Intermediate
Case 3: Security-Constrained OPF (SCOPF)
Multi-period optimization with N-1 contingency analysis
Duration: ~25 minutes | Difficulty: Advanced
Downloadable Resources
Resources will be available soon.