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A multi-objective optimization of the active and reactive resource scheduling at a distribution level in a smart grid context

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  • Sousa, Tiago
  • Morais, Hugo
  • Vale, Zita
  • Castro, Rui

Abstract

In the traditional paradigm, the large power plants supply the reactive power required at a transmission level and the capacitors and transformer tap changer were also used at a distribution level. However, in a near future will be necessary to schedule both active and reactive power at a distribution level, due to the high number of resources connected in distribution levels. This paper proposes a new multi-objective methodology to deal with the optimal resource scheduling considering the distributed generation, electric vehicles and capacitor banks for the joint active and reactive power scheduling. The proposed methodology considers the minimization of the cost (economic perspective) of all distributed resources, and the minimization of the voltage magnitude difference (technical perspective) in all buses. The Pareto front is determined and a fuzzy-based mechanism is applied to present the best compromise solution. The proposed methodology has been tested in the 33-bus distribution network. The case study shows the results of three different scenarios for the economic, technical, and multi-objective perspectives, and the results demonstrated the importance of incorporating the reactive scheduling in the distribution network using the multi-objective perspective to obtain the best compromise solution for the economic and technical perspectives.

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  • 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.
  • Handle: RePEc:eee:energy:v:85:y:2015:i:c:p:236-250
    DOI: 10.1016/j.energy.2015.03.077
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    6. Mallol-Poyato, R. & Jiménez-Fernández, S. & Díaz-Villar, P. & Salcedo-Sanz, S., 2016. "Joint optimization of a Microgrid's structure design and its operation using a two-steps evolutionary algorithm," Energy, Elsevier, vol. 94(C), pages 775-785.
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    8. Thomas, Dimitrios & D’Hoop, Gaspard & Deblecker, Olivier & Genikomsakis, Konstantinos N. & Ioakimidis, Christos S., 2020. "An integrated tool for optimal energy scheduling and power quality improvement of a microgrid under multiple demand response schemes," Applied Energy, Elsevier, vol. 260(C).
    9. Rongxiang Yuan & Timing Li & Xiangtian Deng & Jun Ye, 2016. "Optimal Day-Ahead Scheduling of a Smart Distribution Grid Considering Reactive Power Capability of Distributed Generation," Energies, MDPI, vol. 9(5), pages 1-17, April.
    10. Zamani, Ali Ghahgharaee & Zakariazadeh, Alireza & Jadid, Shahram, 2016. "Day-ahead resource scheduling of a renewable energy based virtual power plant," Applied Energy, Elsevier, vol. 169(C), pages 324-340.
    11. Ju, Liwei & Zhao, Rui & Tan, Qinliang & Lu, Yan & Tan, Qingkun & Wang, Wei, 2019. "A multi-objective robust scheduling model and solution algorithm for a novel virtual power plant connected with power-to-gas and gas storage tank considering uncertainty and demand response," Applied Energy, Elsevier, vol. 250(C), pages 1336-1355.
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