Abstract
A hybrid method combining classical deterministic programming technique and evolutionary algorithm (EA)-enhanced stochastic optimization strategy is proposed for more effective and flexible computation of transient stability-constrained optimal power flow (TSC-OPF). The method consists of searching the maximum TSC-feasible solution region with EA and deterministically optimizing an operating point in the found region via a conventional OPF solution. Stability constraints are exactly treated by a rigorous transient stability assessment (TSA) procedure, where complex system model and multi-contingency can be readily considered and stability margin is applied as the transient stability index (TSI) to avoid over-stabilizing. Benefiting from both the EA and the deterministic programming, the proposed method can continuously approach the global optima in a robust, reliable and fast-convergent way. The method is verified on the New England test system and a dynamic equivalent system of a real-world large power grid. Comparing with existing methods, it has provided more economic solutions and better adaptability to multi-swing instability and multi-contingency stabilizing. It can also be a unified approach for solving other stability constrained-OPFs. © 1969-2012 IEEE.
| Original language | English |
|---|---|
| Article number | 6179362 |
| Pages (from-to) | 1769-1777 |
| Journal | IEEE Transactions on Power Systems |
| Volume | 27 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2012 |
| Externally published | Yes |
Bibliographical note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].Research Keywords
- Deterministic programming
- evolutionary algorithm
- optimal power flow
- stability margin
- transient stability
RGC Funding Information
- RGC-funded
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