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Risk constrained economic dispatch with integration of wind power by multi-objective optimization approach

Author

Listed:
  • Li, Y.Z.
  • Li, K.C.
  • Wang, P.
  • Liu, Y.
  • Lin, X.N.
  • Gooi, H.B.
  • Li, G.F.
  • Cai, D.L.
  • Luo, Y.

Abstract

With increasing wind energy integrated into power systems, the topic of economic dispatch (ED) becomes more important. In this paper, a risk constrained ED (RCED) model is proposed, which aims to obtain the optimal dispatch solution to balance the economic gain and the economic risk brought by uncertain wind power. Then Pareto-based multi-objective optimization approach is applied to optimize the gain and risk under the uncertain environment, simultaneously. Afterwards, an improved optimization algorithm, chaotic group search optimizer with multiple producers (CGSOMP) is used for solving this complex problem. Simulation studies are conducted on a modified IEEE 30-bus power system, and results verify outperformance of the RCED model, compared with the traditional ED approach.

Suggested Citation

  • Li, Y.Z. & Li, K.C. & Wang, P. & Liu, Y. & Lin, X.N. & Gooi, H.B. & Li, G.F. & Cai, D.L. & Luo, Y., 2017. "Risk constrained economic dispatch with integration of wind power by multi-objective optimization approach," Energy, Elsevier, vol. 126(C), pages 810-820.
    • Handle: RePEc:eee:energy:v:126:y:2017:i:c:p:810-820
    DOI: 10.1016/j.energy.2017.02.142
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    References listed on IDEAS

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    Cited by:

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    2. Chen, J.J. & Zhao, Y.L. & Peng, K. & Wu, P.Z., 2017. "Optimal trade-off planning for wind-solar power day-ahead scheduling under uncertainties," Energy, Elsevier, vol. 141(C), pages 1969-1981.
    3. Lin, Zhenjia & Chen, Haoyong & Wu, Qiuwei & Li, Weiwei & Li, Mengshi & Ji, Tianyao, 2020. "Mean-tracking model based stochastic economic dispatch for power systems with high penetration of wind power," Energy, Elsevier, vol. 193(C).
    4. Li, M.S. & Lin, Z.J. & Ji, T.Y. & Wu, Q.H., 2018. "Risk constrained stochastic economic dispatch considering dependence of multiple wind farms using pair-copula," Applied Energy, Elsevier, vol. 226(C), pages 967-978.
    5. Ji, Bin & Zhang, Binqiao & Yu, Samson S. & Zhang, Dezhi & Yuan, Xiaohui, 2021. "An enhanced Borg algorithmic framework for solving the hydro-thermal-wind Co-scheduling problem," Energy, Elsevier, vol. 218(C).
    6. Jin, Jingliang & Zhou, Peng & Li, Chenyu & Bai, Yang & Wen, Qinglan, 2020. "Optimization of power dispatching strategies integrating management attitudes with low carbon factors," Renewable Energy, Elsevier, vol. 155(C), pages 555-568.
    7. Motaeb Eid Alshammari & Makbul A. M. Ramli & Ibrahim M. Mehedi, 2021. "A New Chaotic Artificial Bee Colony for the Risk-Constrained Economic Emission Dispatch Problem Incorporating Wind Power," Energies, MDPI, vol. 14(13), pages 1-24, July.
    8. Jin, Jingliang & Zhou, Peng & Li, Chenyu & Guo, Xiaojun & Zhang, Mingming, 2019. "Low-carbon power dispatch with wind power based on carbon trading mechanism," Energy, Elsevier, vol. 170(C), pages 250-260.
    9. Rahmani, Shima & Amjady, Nima, 2017. "A new optimal power flow approach for wind energy integrated power systems," Energy, Elsevier, vol. 134(C), pages 349-359.
    10. Cong Dong & Xiucheng Dong & Joel Gehman & Lianne Lefsrud, 2017. "Using BP Neural Networks to Prioritize Risk Management Approaches for China’s Unconventional Shale Gas Industry," Sustainability, MDPI, vol. 9(6), pages 1-18, June.
    11. Moradijoz, M. & Moghaddam, M. Parsa & Haghifam, M.R., 2018. "A flexible active distribution system expansion planning model: A risk-based approach," Energy, Elsevier, vol. 145(C), pages 442-457.

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