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Wind energy dispatch considering environmental and economic factors

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  • Kuo, Cheng-Chien

Abstract

Renewable energy sources, especially wind energy, are widely applied as a mean to reach emission reduction with the increasing concern of environmental protection. Although wind generation does not produce harmful emissions, its effect on the thermal generation dispatch can actually cause an increase of emissions, especially during low or medium power demand periods of a day. A multi-objective energy dispatch that considers environment and fuel cost under large wind energy is proposed. An efficient encoding/decoding scheme that could effectively prevent unviable solutions during the application of stochastic search methods is applied; thereby dramatically improving search efficiency and solution quality. The non-linear characteristics of power generators, and their operational constraints, such as generation limitations, ramp rate limits, prohibited operating zones, and transmission loss, could be considered for practical operation. The effectiveness and feasibility of the proposed approach are demonstrated by a TAI-POWER and IEEE 30-bus test systems study. The experiment shows encouraging results, suggesting that the proposed approach is capable of providing higher quality and a wider range of Pareto-optimal solutions so that the decision makers can have a more flexible and reasonable choice.

Suggested Citation

  • Kuo, Cheng-Chien, 2010. "Wind energy dispatch considering environmental and economic factors," Renewable Energy, Elsevier, vol. 35(10), pages 2217-2227.
    • Handle: RePEc:eee:renene:v:35:y:2010:i:10:p:2217-2227
    DOI: 10.1016/j.renene.2010.02.023
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    References listed on IDEAS

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    1. Maddaloni, Jesse D. & Rowe, Andrew M. & van Kooten, G. Cornelis, 2009. "Wind integration into various generation mixtures," Renewable Energy, Elsevier, vol. 34(3), pages 807-814.
    2. Göransson, Lisa & Johnsson, Filip, 2009. "Dispatch modeling of a regional power generation system – Integrating wind power," Renewable Energy, Elsevier, vol. 34(4), pages 1040-1049.
    3. Vahidinasab, V. & Jadid, S., 2009. "Multiobjective environmental/techno-economic approach for strategic bidding in energy markets," Applied Energy, Elsevier, vol. 86(4), pages 496-504, April.
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    2. Qingbin Yu & Yuliang Dong & Yanjun Du & Jiahai Yuan & Fang Fang, 2022. "Optimizing Operation Strategy in a Simulated High-Proportion Wind Power Wind–Coal Combined Base Load Power Generation System under Multiple Scenes," Energies, MDPI, vol. 15(21), pages 1-21, October.
    3. Baños, R. & Manzano-Agugliaro, F. & Montoya, F.G. & Gil, C. & Alcayde, A. & Gómez, J., 2011. "Optimization methods applied to renewable and sustainable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(4), pages 1753-1766, May.
    4. Wang, K.Y. & Luo, X.J. & Wu, L. & Liu, X.C., 2013. "Optimal coordination of wind-hydro-thermal based on water complementing wind," Renewable Energy, Elsevier, vol. 60(C), pages 169-178.
    5. Rodrigues, R.B. & Mendes, V.M.F. & Catalão, J.P.S., 2011. "Protection of wind energy systems against the indirect effects of lightning," Renewable Energy, Elsevier, vol. 36(11), pages 2888-2896.
    6. Dubey, Hari Mohan & Pandit, Manjaree & Panigrahi, B.K., 2015. "Hybrid flower pollination algorithm with time-varying fuzzy selection mechanism for wind integrated multi-objective dynamic economic dispatch," Renewable Energy, Elsevier, vol. 83(C), pages 188-202.

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