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A novel evolutionary algorithm for dynamic economic dispatch with energy saving and emission reduction in power system integrated wind power

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  • Liao, Gwo-Ching

Abstract

An optimization algorithm is proposed in this paper to solve the economic dispatch problem that includes wind farm using the Chaotic Quantum Genetic Algorithm (CQGA). In addition to the detailed models of economic dispatch introduction and their associated constraints, the wind power effect is also included in this paper. The chaotic quantum genetic algorithm used to solve the economic dispatch process and discussed with real scenarios used for the simulation tests. After comparing the proposed algorithm with several other algorithms commonly used to solve optimization problems, the results show that the proposed algorithm is able to find the optimal solution quickly and accurately (i.e. to obtain the minimum cost for power generation in the shortest time). At the end, the impact to the total cost savings for power generation after adding (or not adding) wind power generation is also discussed. The actual implementation results prove that the proposed algorithm is economical, fast and practical. They are quite valuable for further research.

Suggested Citation

  • Liao, Gwo-Ching, 2011. "A novel evolutionary algorithm for dynamic economic dispatch with energy saving and emission reduction in power system integrated wind power," Energy, Elsevier, vol. 36(2), pages 1018-1029.
    • Handle: RePEc:eee:energy:v:36:y:2011:i:2:p:1018-1029
    DOI: 10.1016/j.energy.2010.12.006
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    References listed on IDEAS

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    1. Vahidinasab, V. & Jadid, S., 2010. "Joint economic and emission dispatch in energy markets: A multiobjective mathematical programming approach," Energy, Elsevier, vol. 35(3), pages 1497-1504.
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    3. Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2009. "Chaotic artificial immune approach applied to economic dispatch of electric energy using thermal units," Chaos, Solitons & Fractals, Elsevier, vol. 40(5), pages 2376-2383.
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