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KPG Run Overview

KPG Run is a cross-platform desktop application for solving various power system optimization problems. It provides a modern UI for running production-grade solvers and reviewing optimization results. KPG Run also exports result files compatible with KPG View.

flowchart LR
    A["Test System Data"] --> C["KPG Run GUI"]
    B["User Configuration"] --> C
    C --> D["Solver Selection"]
    D --> |Simple| E["Economic Dispatch"]
    D --> |Scheduling| F["Unit Commitment"]
    D --> |"Network (DC)"| G["DC-OPF"]
    D --> |"Network (AC)"| H["AC-OPF"]
    E --> I["Results"]
    F --> I
    G --> I
    H --> I
    I --> J["Visualization"]

Test System Data

KPG Run uses the KPG 193 test system dataset and visualizes key optimization results. KPG 193 is a synthetic Korean transmission grid model constructed from publicly available data and intended as a benchmark for studies of the Korean power system.

→ KPG Test System Details

Available Optimization Models

KPG Run includes four optimization models commonly used in power system operations and planning.

  1. Economic Dispatch (ED): Simplest model for cost-minimized generation dispatch without network constraints. → ED Formulation Details
  2. Unit Commitment (UC): Day-ahead scheduling with generator commitment decisions and temporal constraints. → UC Formulation Details
  3. DC Optimal Power Flow (DC-OPF): Network-constrained dispatch using linearized power flow equations. → DC-OPF Formulation Details
  4. AC Optimal Power Flow (AC-OPF): Nonlinear network-constrained dispatch using AC power flow, capturing voltage magnitudes and reactive power. → AC-OPF Formulation Details

Custom Solvers

KPG Run supports custom solver scripts written in Julia.

To create a custom solver, use the existing .jl solver scripts as templates. Implement your own optimization model in Julia using JuMP.jl, and run it through KPG Run.

For details on using Julia and JuMP, refer to the resources listed below.

Model Comparison

FeatureEDUCDC-OPFAC-OPF
Problem TypeLP/QPMILPLP/QPNLP
Network ModelNoneNoneDCAC
Time PeriodsSingleMultipleSingleSingle
Commitment DecisionsNoneBinaryNoneNone
AccuracyLowMediumMediumHigh
Solving SpeedFastestSlowFastMedium

→ Detailed Solver Comparison

Key Features

User-Friendly Interface

  • Intuitive GUI with clean, modern design
  • Real-time solver output streaming to console
  • Parameter validation before solving
  • Status indicators for running optimizations
  • Result summarization with key metrics

→ Detailed Interface Guide

Configuration

  • Data Source: Test system folder selection for the run.
  • Problem Setup: Problem type configuration and optimization horizon settings.
  • Solver: Solver engine selection with solver options (e.g., time limit and output level).

Solver Engine Support

  • Ipopt (open source)
  • Gurobi (commercial)
  • Knitro (commercial)
ModelIpoptGurobiKnitro
ED✓✓✓
UC✗✓✓
DC-OPF✓✓✓
AC-OPF✓✓✓
  • Each optimization model must be solved with a solver engine that supports its problem type.
  • Solver choice may affect runtime and convergence.
  • Academic users may obtain a free Gurobi academic license.

Workflow Integration

  • Chain solvers (UC → OPF) using stored results (planned)
  • Batch processing for multiple scenarios (planned)
  • CSV export for external analysis
  • GeoJSON generation for KPG View visualization

Installation Guide

System Requirements

  • Operating System: macOS 10.15+, Windows 10+ (Linux support planned)
  • Julia: 1.11.2+
  • Solvers: Ipopt, Gurobi, Knitro
  • Memory: 8GB RAM minimum, 16GB recommended
  • Storage: 2GB for application and data

→ Detailed Installation Guide

Getting Started

  1. Launch KPG Run
  2. Choose “KPG 193 v1.5” as the data source
  3. Select “DC-OPF” for the problem type
  4. Set Day to 1
  5. Click Solve button and monitor the console output
  6. View results in the Results tab

→ Getting Started with KPG Run