Overview
This ongoing study moves from component-level converter control to system-level planning and stability. An IEEE 39-bus New England model was built and validated in PSS®E, then used for steady-state and dynamic studies with inverter-based renewable generation.
A 250 MW renewable plant was integrated with standard renewable dynamic models, while Python scripts using PSS®E APIs automate model changes, contingency screening, P-Q analysis, and results extraction. The study is being extended to more severe contingency sequences, loading scenarios, and voltage/VAr support using shunt and dynamic reactive-power devices.
My Contribution
Built and validated the IEEE 39-bus test system in PSS®E.
Performed AC load-flow, N-1 contingency, and voltage-profile studies with Python automation.
Integrated renewable generation using steady-state and REGCA1/REECA1/REPCA1 dynamic models.
Extended the workflow toward N-2/N-1-1 events, dynamic disturbances, and voltage-support alternatives.
Methods & Methodology
Steady-State Planning
Base cases and renewable-dispatch scenarios are checked for voltage, thermal loading, reactive-power capability, and operating margins.
Security Assessment
Contingency screening is automated in Python so large sets of N-1 and more severe events can be evaluated consistently.
Dynamic Studies
Generator and renewable dynamic models are exercised under faults and network disturbances to connect planning decisions with transient performance.
Key Findings
- The workflow links operational planning, renewable integration, contingency analysis, and dynamic security rather than treating them as separate exercises.
- Python automation makes repeatable screening and data extraction practical as the number of cases and contingencies grows.
- The study provides a software-only platform for research on system strength, converter-dominated grids, and security-constrained planning.