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A new multi-objective for environmental and economic management of Volt/Var Control considering renewable energy resources

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  • Zare, Mohsen
  • Niknam, Taher

Abstract

Environmental and economical aspects of Volt/Var Control problem as well as technical issues of the networks, transform the classical problem into multiobjective one. In this paper, a Multiobjective θ-Smart Bacterial Foraging Algorithm (Mθ-SBFA) is proposed to solve the VVC problem in distribution networks including Renewable Energy Sources (RESs) involving the conflicting objectives; i.e. the electrical energy losses, the voltages deviations, the total electrical energy costs and the total emissions of RESs and substations. The proposed algorithm goes through the search space in the polar coordinates instead of the Cartesian one; whereby the feasible space is more compact. Also, several modifications are applied to restraint the premature convergence of the solutions. Hence, the bacteria in chemotaxis process are moved in short or long steps as well as swimming movement. Furthermore, replacement the least healthy bacteria are done by the strong dominated solutions reserved along the iterative search process. A new approach based on the variance of the objective functions is utilized to select the population; thereby lead to uniformly distributed the Pareto Optimal Front (POF) as well as reach to extreme points of the solutions. Niching mechanism, besides the smart population, leads the bacteria towards the lesser covered space of the POF. A fuzzy clustering technique controls the size of the repository when it gets filled. Finally, two test distribution feeders are used to assess the performance of the proposed modifications.

Suggested Citation

  • Zare, Mohsen & Niknam, Taher, 2013. "A new multi-objective for environmental and economic management of Volt/Var Control considering renewable energy resources," Energy, Elsevier, vol. 55(C), pages 236-252.
    • Handle: RePEc:eee:energy:v:55:y:2013:i:c:p:236-252
    DOI: 10.1016/j.energy.2013.03.058
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    References listed on IDEAS

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    1. Niknam, Taher & Azizipanah-Abarghooee, Rasoul & Roosta, Alireza & Amiri, Babak, 2012. "A new multi-objective reserve constrained combined heat and power dynamic economic emission dispatch," Energy, Elsevier, vol. 42(1), pages 530-545.
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    3. 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.
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    5. Kamankesh, Hamidreza & Agelidis, Vassilios G. & Kavousi-Fard, Abdollah, 2016. "Optimal scheduling of renewable micro-grids considering plug-in hybrid electric vehicle charging demand," Energy, Elsevier, vol. 100(C), pages 285-297.
    6. Li, Ximei & Gao, Jianmin & You, Shi & Zheng, Yi & Zhang, Yu & Du, Qian & Xie, Min & Qin, Yukun, 2022. "Optimal design and techno-economic analysis of renewable-based multi-carrier energy systems for industries: A case study of a food factory in China," Energy, Elsevier, vol. 244(PB).
    7. 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.
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    9. Suganthi, L. & Iniyan, S. & Samuel, Anand A., 2015. "Applications of fuzzy logic in renewable energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 585-607.
    10. Ahmed M. Nassef & Mohammad Ali Abdelkareem & Hussein M. Maghrabie & Ahmad Baroutaji, 2023. "Review of Metaheuristic Optimization Algorithms for Power Systems Problems," Sustainability, MDPI, vol. 15(12), pages 1-27, June.
    11. Azad-Farsani, Ehsan & Agah, S.M.M. & Askarian-Abyaneh, Hossein & Abedi, Mehrdad & Hosseinian, S.H., 2016. "Stochastic LMP (Locational marginal price) calculation method in distribution systems to minimize loss and emission based on Shapley value and two-point estimate method," Energy, Elsevier, vol. 107(C), pages 396-408.
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