Abstract
This paper proposes a robust method for solving the Multi-Constrained Optimal Power Flow (MCOPF) problem based on an opposition-based differential evolution (ODE) algorithm. The MCOPF problem, which considers transient stability, valve-point effects, prohibited operating zones, and branch flow thermal constraints, is a nonlinear, nonconvex, and nondifferentiable optimization problem in power system planning and operation, and is very difficult for conventional optimization methods to handle. The proposed ODE is an enhanced differential evolution (DE) method and employs the Opposition-Based Learning (OBL) for population initialization, production of new generations and also improving population's best fitness value. Numerical tests comparing conventional DE and ODE methods on the New England 10-generator, 39-bus system have validated the effectiveness and robustness of the proposed approach both in convergence speed and solution accuracy.
| Original language | English |
|---|---|
| Title of host publication | 2012 IEEE Power and Energy Society General Meeting, PES 2012 |
| DOIs | |
| Publication status | Published - Jul 2012 |
| Event | 2012 IEEE Power and Energy Society General Meeting, PES 2012 - San Diego, CA, United States Duration: 22 Jul 2012 → 26 Jul 2012 |
Publication series
| Name | IEEE Power and Energy Society General Meeting |
|---|---|
| ISSN (Print) | 1944-9925 |
| ISSN (Electronic) | 1944-9933 |
Conference
| Conference | 2012 IEEE Power and Energy Society General Meeting, PES 2012 |
|---|---|
| Country/Territory | United States |
| City | San Diego, CA |
| Period | 22/07/12 → 26/07/12 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- opposition-based differential evolution
- Optimal power flow
- prohibited operating zones
- transient stability
- valve-point effects
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Nuclear Energy and Engineering
- Renewable Energy, Sustainability and the Environment
- Electrical and Electronic Engineering
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