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Multi-objective stochastic model for joint optimal allocation of DG units and network reconfiguration from DG owner’s and DisCo’s perspectives

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  • kianmehr, Ehsan
  • Nikkhah, Saman
  • Rabiee, Abbas

Abstract

Optimal distribution system reconfiguration (DSR) and distribution generation (DG) allocation are viable solutions for improvement of technical and economic aspects of distribution systems. This paper proposes a stochastic multi-objective DSR (SMO-DSR) model, aims to maximize the DG owner’s profit and minimizes the distribution company’s (DisCo’s) costs. The uncertainties of wind power generation, electricity price, and demand are handled via scenario based approach. The proposed SMO-DSR model is solved via ε-constraint method and the best compromise solution is selected by fuzzy satisfying criterion. The model is a mixed integer non-linear programing (MINLP) problem which is implemented on IEEE 33-bus distribution system in General Algebraic Modeling System (GAMS) environment. To show the effectiveness of the proposed SMO-DSR approach, it is studied in different cases. A sensitivity analysis is also performed to show the effect of contract price of wind energy on the objectives of DisCo and DG owner. The obtained results substantiate the interaction between the DSR and DG allocation problems. Also, it is shown that the contract price of wind energy considerably influences both DG owner and DisCo schedules. Besides, when a compromise is made between the DG owner’s profit and DisCo’s cost, the power losses of the network is reduced.

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  • kianmehr, Ehsan & Nikkhah, Saman & Rabiee, Abbas, 2019. "Multi-objective stochastic model for joint optimal allocation of DG units and network reconfiguration from DG owner’s and DisCo’s perspectives," Renewable Energy, Elsevier, vol. 132(C), pages 471-485.
  • Handle: RePEc:eee:renene:v:132:y:2019:i:c:p:471-485
    DOI: 10.1016/j.renene.2018.08.032
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    1. Kavousi-Fard, Abdollah & Niknam, Taher, 2014. "Multi-objective stochastic Distribution Feeder Reconfiguration from the reliability point of view," Energy, Elsevier, vol. 64(C), pages 342-354.
    2. Mohseni-Bonab, Seyed Masoud & Rabiee, Abbas & Mohammadi-Ivatloo, Behnam, 2016. "Voltage stability constrained multi-objective optimal reactive power dispatch under load and wind power uncertainties: A stochastic approach," Renewable Energy, Elsevier, vol. 85(C), pages 598-609.
    3. Das, Sangeeta & Das, Debapriya & Patra, Amit, 2017. "Reconfiguration of distribution networks with optimal placement of distributed generations in the presence of remote voltage controlled bus," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 772-781.
    4. Antonio J. Conejo & Miguel Carrión & Juan M. Morales, 2010. "Decision Making Under Uncertainty in Electricity Markets," International Series in Operations Research and Management Science, Springer, number 978-1-4419-7421-1, December.
    5. Niknam, Taher & Fard, Abdollah Kavousi & Seifi, Alireza, 2012. "Distribution feeder reconfiguration considering fuel cell/wind/photovoltaic power plants," Renewable Energy, Elsevier, vol. 37(1), pages 213-225.
    6. Nikkhah, Saman & Rabiee, Abbas, 2018. "Optimal wind power generation investment, considering voltage stability of power systems," Renewable Energy, Elsevier, vol. 115(C), pages 308-325.
    7. Zidan, Aboelsood & El-Saadany, Ehab F., 2013. "Distribution system reconfiguration for energy loss reduction considering the variability of load and local renewable generation," Energy, Elsevier, vol. 59(C), pages 698-707.
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