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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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    1. Ghasemi, Mojtaba & Aghaei, Jamshid & Akbari, Ebrahim & Ghavidel, Sahand & Li, Li, 2016. "A differential evolution particle swarm optimizer for various types of multi-area economic dispatch problems," Energy, Elsevier, vol. 107(C), pages 182-195.
    2. Hong, Ying-Yi & Chang, Huei-Lin & Chiu, Ching-Sheng, 2010. "Hour-ahead wind power and speed forecasting using simultaneous perturbation stochastic approximation (SPSA) algorithm and neural network with fuzzy inputs," Energy, Elsevier, vol. 35(9), pages 3870-3876.
    3. Hong, Ying-Yi & Lin, Jie-Kai, 2013. "Interactive multi-objective active power scheduling considering uncertain renewable energies using adaptive chaos clonal evolutionary programming," Energy, Elsevier, vol. 53(C), pages 212-220.
    4. Li, Y.Z. & Wu, Q.H. & Li, M.S. & Zhan, J.P., 2014. "Mean-variance model for power system economic dispatch with wind power integrated," Energy, Elsevier, vol. 72(C), pages 510-520.
    5. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    6. Jin, Jingliang & Zhou, Dequn & Zhou, Peng & Qian, Shuqu & Zhang, Mingming, 2016. "Dispatching strategies for coordinating environmental awareness and risk perception in wind power integrated system," Energy, Elsevier, vol. 106(C), pages 453-463.
    7. Yusif Simaan, 1997. "Estimation Risk in Portfolio Selection: The Mean Variance Model Versus the Mean Absolute Deviation Model," Management Science, INFORMS, vol. 43(10), pages 1437-1446, October.
    8. Zhao, M. & Chen, Z. & Blaabjerg, F., 2006. "Probabilistic capacity of a grid connected wind farm based on optimization method," Renewable Energy, Elsevier, vol. 31(13), pages 2171-2187.
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    Cited by:

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    3. 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.
    4. Yongqi Zhao & Jiajia Chen, 2021. "A Quantitative Risk-Averse Model for Optimal Management of Multi-Source Standalone Microgrid with Demand Response and Pumped Hydro Storage," Energies, MDPI, vol. 14(9), pages 1-17, May.
    5. 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.
    6. 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).
    7. 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.
    8. 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.
    9. 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.
    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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