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An unequivocal normalization-based paradigm to solve dynamic economic and emission active-reactive OPF (optimal power flow)

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  • Pourakbari-Kasmaei, Mahdi
  • Rider, Marcos J.
  • Mantovani, José R.S.

Abstract

This paper presents a straightforward compromising methodology of dynamic economic and emission AROPF (active-reactive optimal power flow). AROPF considering environmental effects is a highly nonlinear problem, and the dynamic consideration of such problems makes it even more complicated and extra-high nonlinear; find an appropriate compromising solution for such problems is considered as a complicated task. In one hand the traditional compromising methodologies cannot find an acceptable compromise point for large-scale systems, and on the other hand metaheuristic methods are time consuming. In this paper an UNBP (unequivocal normalization-based paradigm) is proposed, while instead of maximum output-based pollution control cost, an adaptive pollution control cost is used to consider the system topology in dynamic scheduling and under various system conditions such as normal, outage, and critical conditions. By using a normalization process and adaptive pollution control cost, a uniform compromising procedure is obtained. Three case studies such as 14-bus, 30-bus, and 118-bus IEEE test systems are conducted and results are compared to those reported in literature. Results confirm the potential, effectiveness, and superiority of the proposed UNBP compared to traditional and heuristic-based multi-objective optimization techniques.

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  • Pourakbari-Kasmaei, Mahdi & Rider, Marcos J. & Mantovani, José R.S., 2014. "An unequivocal normalization-based paradigm to solve dynamic economic and emission active-reactive OPF (optimal power flow)," Energy, Elsevier, vol. 73(C), pages 554-566.
    • Handle: RePEc:eee:energy:v:73:y:2014:i:c:p:554-566
    DOI: 10.1016/j.energy.2014.06.057
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    1. Niknam, Taher & Azizipanah-Abarghooee, Rasoul & Narimani, Mohammad Rasoul, 2012. "Reserve constrained dynamic optimal power flow subject to valve-point effects, prohibited zones and multi-fuel constraints," Energy, Elsevier, vol. 47(1), pages 451-464.
    2. Salgado, R.S. & Rangel, E.L., 2012. "Optimal power flow solutions through multi-objective programming," Energy, Elsevier, vol. 42(1), pages 35-45.
    3. Niknam, Taher & Narimani, Mohammad rasoul & Jabbari, Masoud & Malekpour, Ahmad Reza, 2011. "A modified shuffle frog leaping algorithm for multi-objective optimal power flow," Energy, Elsevier, vol. 36(11), pages 6420-6432.
    4. Al-Muhawesh, Tareq A. & Qamber, Isa S., 2008. "The established mega watt linear programming-based optimal power flow model applied to the real power 56-bus system in eastern province of Saudi Arabia," Energy, Elsevier, vol. 33(1), pages 12-21.
    5. Reddy, S. Surender & Abhyankar, A.R. & Bijwe, P.R., 2011. "Reactive power price clearing using multi-objective optimization," Energy, Elsevier, vol. 36(5), pages 3579-3589.
    6. Saraswat, Amit & Saini, Ashish & Saxena, Ajay Kumar, 2013. "A novel multi-zone reactive power market settlement model: A pareto-optimization approach," Energy, Elsevier, vol. 51(C), pages 85-100.
    7. Niknam, Taher & Mojarrad, Hasan Doagou & Meymand, Hamed Zeinoddini & Firouzi, Bahman Bahmani, 2011. "A new honey bee mating optimization algorithm for non-smooth economic dispatch," Energy, Elsevier, vol. 36(2), pages 896-908.
    8. Vaisakh, K. & Srinivas, L.R., 2010. "A genetic evolving ant direction DE for OPF with non-smooth cost functions and statistical analysis," Energy, Elsevier, vol. 35(8), pages 3155-3171.
    9. Vahidinasab, V. & Jadid, S., 2010. "Joint economic and emission dispatch in energy markets: A multiobjective mathematical programming approach," Energy, Elsevier, vol. 35(3), pages 1497-1504.
    10. 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.
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    2. Li, Yang & Li, Yahui & Li, Guoqing & Zhao, Dongbo & Chen, Chen, 2018. "Two-stage multi-objective OPF for AC/DC grids with VSC-HVDC: Incorporating decisions analysis into optimization process," Energy, Elsevier, vol. 147(C), pages 286-296.
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    4. 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.

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