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GPNBI inspired MOSDE for electric power dispatch considering wind energy penetration

Author

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  • Zhang, Xian
  • Wang, Huaizhi
  • Peng, Jian-chun
  • Liu, Yitao
  • Wang, Guibin
  • Jiang, Hui

Abstract

This paper aims to solve wind energy integrated electric power dispatch (EPD) problem from the perspectives of stochastic modelling and multiobjective optimization. At first, the competing objectives in modern electric power and energy system is analyzed and a new interval optimization model for each objective is proposed based on the uncertainties with respect to wind power. Then, a novel strength differential evolution (SDE) algorithm is developed to address the interval optimization model. The SDE adopts a population selection process based on chaotic sequence and Boltzmann distribution to balance the tradeoff between local exploitation and global exploration. Afterwards, a multiobjective EPD model is established and a novel generalized piecewise normal boundary intersection (GPNBI) method is mooted to transform multiobjective EPD into a series of single-objective sub-problems which can be effectively solved by SDE. In order to deal with the highly constrains in GPNBI, a new heuristic constraint handling strategy is proposed accordingly to accelerate the convergence speed. At last, a hyper-plane based decision-making strategy is originally developed to identify the best compromise solution in the obtained Pareto frontiers (PFs). The feasibility and effectiveness of the interval optimization model and GPNBI inspired multiobjective SDE (MOSDE) have been comprehensively evaluated on a modified IEEE 30-bus system and a 118-bus system. The statistical results confirm that the proposed interval optimization model can approximately quantify the potential uncertainty in each objective and also demonstrate that the proposed MOSDE exhibits better performance than the algorithms of the state. Therefore, it is convinced that the proposed optimization model and method have high potentials to address the practical implementation problems in electric power and energy systems with wind energy penetration.

Suggested Citation

  • Zhang, Xian & Wang, Huaizhi & Peng, Jian-chun & Liu, Yitao & Wang, Guibin & Jiang, Hui, 2018. "GPNBI inspired MOSDE for electric power dispatch considering wind energy penetration," Energy, Elsevier, vol. 144(C), pages 404-419.
    • Handle: RePEc:eee:energy:v:144:y:2018:i:c:p:404-419
    DOI: 10.1016/j.energy.2017.12.005
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    as
    1. Lotfan, S. & Ghiasi, R. Akbarpour & Fallah, M. & Sadeghi, M.H., 2016. "ANN-based modeling and reducing dual-fuel engine’s challenging emissions by multi-objective evolutionary algorithm NSGA-II," Applied Energy, Elsevier, vol. 175(C), pages 91-99.
    2. Jiang, Yibo & Xu, Jian & Sun, Yuanzhang & Wei, Congying & Wang, Jing & Ke, Deping & Li, Xiong & Yang, Jun & Peng, Xiaotao & Tang, Bowen, 2017. "Day-ahead stochastic economic dispatch of wind integrated power system considering demand response of residential hybrid energy system," Applied Energy, Elsevier, vol. 190(C), pages 1126-1137.
    3. de Jong, Pieter & Kiperstok, Asher & Sánchez, Antonio Santos & Dargaville, Roger & Torres, Ednildo Andrade, 2016. "Integrating large scale wind power into the electricity grid in the Northeast of Brazil," Energy, Elsevier, vol. 100(C), pages 401-415.
    4. 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.
    5. Adarsh, B.R. & Raghunathan, T. & Jayabarathi, T. & Yang, Xin-She, 2016. "Economic dispatch using chaotic bat algorithm," Energy, Elsevier, vol. 96(C), pages 666-675.
    6. Wei, F. & Wu, Q.H. & Jing, Z.X. & Chen, J.J. & Zhou, X.X., 2016. "Optimal unit sizing for small-scale integrated energy systems using multi-objective interval optimization and evidential reasoning approach," Energy, Elsevier, vol. 111(C), pages 933-946.
    7. Jubril, A.M. & Olaniyan, O.A. & Komolafe, O.A. & Ogunbona, P.O., 2014. "Economic-emission dispatch problem: A semi-definite programming approach," Applied Energy, Elsevier, vol. 134(C), pages 446-455.
    8. Wang, H.Z. & Wang, G.B. & Li, G.Q. & Peng, J.C. & Liu, Y.T., 2016. "Deep belief network based deterministic and probabilistic wind speed forecasting approach," Applied Energy, Elsevier, vol. 182(C), pages 80-93.
    9. Secui, Dinu Calin, 2016. "A modified Symbiotic Organisms Search algorithm for large scale economic dispatch problem with valve-point effects," Energy, Elsevier, vol. 113(C), pages 366-384.
    10. Wang, Huai-zhi & Li, Gang-qiang & Wang, Gui-bin & Peng, Jian-chun & Jiang, Hui & Liu, Yi-tao, 2017. "Deep learning based ensemble approach for probabilistic wind power forecasting," Applied Energy, Elsevier, vol. 188(C), pages 56-70.
    11. Subbaraj, P. & Rengaraj, R. & Salivahanan, S., 2009. "Enhancement of combined heat and power economic dispatch using self adaptive real-coded genetic algorithm," Applied Energy, Elsevier, vol. 86(6), pages 915-921, June.
    12. Chen, Fang & Zhou, Jianzhong & Wang, Chao & Li, Chunlong & Lu, Peng, 2017. "A modified gravitational search algorithm based on a non-dominated sorting genetic approach for hydro-thermal-wind economic emission dispatching," Energy, Elsevier, vol. 121(C), pages 276-291.
    13. Abdelaziz, A.Y. & Ali, E.S. & Abd Elazim, S.M., 2016. "Implementation of flower pollination algorithm for solving economic load dispatch and combined economic emission dispatch problems in power systems," Energy, Elsevier, vol. 101(C), pages 506-518.
    14. Jayabarathi, T. & Raghunathan, T. & Adarsh, B.R. & Suganthan, Ponnuthurai Nagaratnam, 2016. "Economic dispatch using hybrid grey wolf optimizer," Energy, Elsevier, vol. 111(C), pages 630-641.
    15. Younes, Mimoun & Khodja, Fouad & Kherfane, Riad Lakhdar, 2014. "Multi-objective economic emission dispatch solution using hybrid FFA (firefly algorithm) and considering wind power penetration," Energy, Elsevier, vol. 67(C), pages 595-606.
    16. Alsumait, J.S. & Sykulski, J.K. & Al-Othman, A.K., 2010. "A hybrid GA-PS-SQP method to solve power system valve-point economic dispatch problems," Applied Energy, Elsevier, vol. 87(5), pages 1773-1781, May.
    17. Modiri-Delshad, Mostafa & Aghay Kaboli, S. Hr. & Taslimi-Renani, Ehsan & Rahim, Nasrudin Abd, 2016. "Backtracking search algorithm for solving economic dispatch problems with valve-point effects and multiple fuel options," Energy, Elsevier, vol. 116(P1), pages 637-649.
    18. Gherbi, Yamina Ahlem & Bouzeboudja, Hamid & Gherbi, Fatima Zohra, 2016. "The combined economic environmental dispatch using new hybrid metaheuristic," Energy, Elsevier, vol. 115(P1), pages 468-477.
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    2. Andréa Camila dos Santos Martins & Antonio Roberto Balbo & Dylan Jones & Leonardo Nepomuceno & Edilaine Martins Soler & Edméa Cássia Baptista, 2020. "A Hybrid Multi-Criteria Methodology for Solving the Sustainable Dispatch Problem," Sustainability, MDPI, vol. 12(17), pages 1-20, August.

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