Gbest guided artificial bee colony algorithm for environmental/economic dispatch considering wind power
•Wind power is incorporated in economic emission dispatch problem.•The stochastic nature of wind power is model using weibull probability function.•Gbest guided artificial bee colony algorithm is proposed to find optimal solutions.•The proposed algorithm is applied to IEEE-30 bus, 6, 15 and 40 unit...
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Veröffentlicht in: | Expert systems with applications 2013-11, Vol.40 (16), p.6385-6399 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | •Wind power is incorporated in economic emission dispatch problem.•The stochastic nature of wind power is model using weibull probability function.•Gbest guided artificial bee colony algorithm is proposed to find optimal solutions.•The proposed algorithm is applied to IEEE-30 bus, 6, 15 and 40 unit test systems.•The proposed algorithm is compared with several recently proposed algorithms.
The current energy consumption in most of the countries is weighing heavily on fossil fuels, which account for about 70–90% of total energy used. The ecological concerns about air pollution and global warming are encouraging wider use of clean renewable technologies such as wind and solar energy. In this paper, Gbest guided artificial bee colony algorithm (GABC) is applied to optimize the emission and overall cost of operation of wind–thermal power system. The random nature of wind power is modeled using weibull probability distribution function (PDF). Moreover, the uncertainty in wind power is considered in the cost model by including the power imbalance terms such as overestimation and underestimation costs of available wind power. To validate the effectiveness of proposed method, it is first applied to three standard test systems considering different technical constraints such as valve loading effect, prohibited zones, ramp rate limits, etc. In second part, the effect of wind power generation on dispatch cost and emission is analyzed for IEEE-30 bus test system. A comparative analysis with other similar optimization techniques reveals that the proposed technique has better solution accuracy and convergence results. |
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ISSN: | 0957-4174 1873-6793 |
DOI: | 10.1016/j.eswa.2013.05.048 |