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Multi-objective probabilistic reactive power and voltage control with wind site correlations

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  • Zare, Mohsen
  • Niknam, Taher
  • Azizipanah-Abarghooee, Rasoul
  • Amiri, Babak

Abstract

This paper proposes a multi-objective probabilistic reactive power and voltage control in distribution networks using wind turbines, hydro turbines, fuel cells, static compensators and load tap changing transforms. The objective functions are total electrical energy costs, the electrical energy losses, total emissions produced, and voltage deviations during the next day. Since the wind sources and load demand have intermittent characteristics, a probabilistic load flow based on 2m + 1 point estimated method is used to investigate the objective functions. The correlation in wind speed is considered as the distances between WTs are not large in distribution systems. Furthermore, a multi-objective modified bee swarm optimization is proposed to solve the optimization problem by defining a set of non-dominated points as the solutions. A fuzzy based clustering is used to control the size of the repository and a niching method is utilized to choose the best solution during the optimization process. Performance of the proposed algorithm is tested on a 69-bus distribution feeder. The results confirm the necessity of modeling the reactive power and voltage control problem in a stochastic framework. Also, the effects of wind site correlations on different objective functions are discussed completely.

Suggested Citation

  • Zare, Mohsen & Niknam, Taher & Azizipanah-Abarghooee, Rasoul & Amiri, Babak, 2014. "Multi-objective probabilistic reactive power and voltage control with wind site correlations," Energy, Elsevier, vol. 66(C), pages 810-822.
    • Handle: RePEc:eee:energy:v:66:y:2014:i:c:p:810-822
    DOI: 10.1016/j.energy.2014.01.034
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    References listed on IDEAS

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    Cited by:

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    2. Gandhi, Oktoviano & Rodríguez-Gallegos, Carlos D. & Zhang, Wenjie & Srinivasan, Dipti & Reindl, Thomas, 2018. "Economic and technical analysis of reactive power provision from distributed energy resources in microgrids," Applied Energy, Elsevier, vol. 210(C), pages 827-841.
    3. Azizipanah-Abarghooee, Rasoul & Niknam, Taher & Bina, Mohammad Amin & Zare, Mohsen, 2015. "Coordination of combined heat and power-thermal-wind-photovoltaic units in economic load dispatch using chance-constrained and jointly distributed random variables methods," Energy, Elsevier, vol. 79(C), pages 50-67.
    4. Zhou, Bin & Xu, Da & Chan, Ka Wing & Li, Canbing & Cao, Yijia & Bu, Siqi, 2017. "A two-stage framework for multiobjective energy management in distribution networks with a high penetration of wind energy," Energy, Elsevier, vol. 135(C), pages 754-766.
    5. Sousa, Tiago & Morais, Hugo & Vale, Zita & Castro, Rui, 2015. "A multi-objective optimization of the active and reactive resource scheduling at a distribution level in a smart grid context," Energy, Elsevier, vol. 85(C), pages 236-250.
    6. Yu, L. & Li, Y.P. & Huang, G.H., 2016. "A fuzzy-stochastic simulation-optimization model for planning electric power systems with considering peak-electricity demand: A case study of Qingdao, China," Energy, Elsevier, vol. 98(C), pages 190-203.
    7. David Raz & Yuval Beck, 2020. "An Operational Approach to Multi-Objective Optimization for Volt-VAr Control," Energies, MDPI, vol. 13(22), pages 1-14, November.
    8. Ghasemi, Mojtaba & Ghavidel, Sahand & Akbari, Ebrahim & Vahed, Ali Azizi, 2014. "Solving non-linear, non-smooth and non-convex optimal power flow problems using chaotic invasive weed optimization algorithms based on chaos," Energy, Elsevier, vol. 73(C), pages 340-353.
    9. Jin, Hongyang & Li, Zhengshuo & Sun, Hongbin & Guo, Qinglai & Wang, Bin, 2018. "A two-stage reactive power optimization in transmission network incorporating reserves from voltage-dependent loads," Energy, Elsevier, vol. 157(C), pages 752-763.
    10. Taghavi, Reza & Seifi, Ali Reza & Samet, Haidar, 2015. "Stochastic reactive power dispatch in hybrid power system with intermittent wind power generation," Energy, Elsevier, vol. 89(C), pages 511-518.

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