PyOPF – Advanced AC Optimal Power Flow Modeling
PyOPF is an open-source Python-based AC Optimal Power Flow solver designed to improve knowledge and access of grid optimization models.
Challenge
Existing open-source ACOPF solvers lacked clear, standardized implementations, making it difficult for researchers to understand and apply current-voltage formulation effectively.
Approach (Methodology & Analysis)
1. Development of a Python-Based AC Optimal Power Flow (ACOPF) Solver
Designed an optimization framework using Pyomo, a Python-based algebraic modeling language, to solve ACOPF problems.
Implemented Interior Point Optimization (IPOPT) as the primary solver for nonlinear programming,
Integrated C2DataUtilities for RAW file parsing, enabling direct compatibility with power system test cases from RTS-GMLC and IEEE standard networks.
2. Handling Large-Scale Power System Optimization
Modeled power flow equations using a current-voltage formulation, improving numerical stability in cases where traditional polar-form power flow models fail.
Tested solver performance across IEEE-14, IEEE-118, and Texas7k test cases, evaluating solution accuracy, feasibility, and execution time.
Key Findings & Insights
PyOPF Successfully Solved ACOPF Problems Across Multiple Test Networks
Achieved feasible power dispatch solutions in all test cases, validating its ability to handle complex power flow scenarios.
Ensured adherence to system constraints, including voltage limits, line loading, and generation dispatch bounds.
Future Enhancements Could Expand Functionality
Integration with renewable energy uncertainty models could enhance grid reliability and economic dispatch strategies.
Adaptive optimization techniques could be introduced to improve solver efficiency under changing grid conditions.