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Optimal scheduling of dispatchable distributed generation in smart environment with the aim of energy loss minimization

Author

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  • Rahiminejad, A.
  • Vahidi, B.
  • Hejazi, M.A.
  • Shahrooyan, S.

Abstract

It is obvious that the high accurate information of network conditions in smart grids definitely leads to high efficient performance of the network. This paper discusses how much is the effect of smart grid compared to conventional networks to the daily energy loss minimization. In other words, the question of “is it worth to move towards the smart environment?” is discussed from only an aspect point of view in the paper. For this purpose, an optimal management of Dispatchable Distributed Generation (DDG) in smart grid with the aim of daily energy loss minimization is performed and fairly compared to DDG optimal management in conventional distribution networks. The effect of suboptimal performance of the system in conventional networks is economically analyzed. A 3-level load profile which is forecasted in advance is taken into account as the load profile of the conventional network. This load profile is investigated in 5 different scenarios from prediction points of view. On contrary, the load profile of the network in smart environment is considered as a 24-h load profile which is achieved using smart metering devices. To show how the smart grid impressively affects the network performance regards to conventional network, the DDGs are also programed in order to minimize the voltage deviation of the network. The economic analysis and yearly benefit of loss reduction are also conducted in both situations (smart grid and conventional network). In addition, the performances of the conventional network and smart grid are evaluated in two other phases i.e., in the presence of renewable energy resources and encountering with disturbances. The study is applied on 69-bus radial test system which is used in many previous studies. The results show the detrimental effects of suboptimal operation of the system on network performance in the case of conventional networks. Moreover, the impressive impacts of smart environment on energy loss reduction and voltage profile improvement in distribution systems can be concluded from the results. Furthermore, the study shows how the smart environment can be useful for utilization of renewable energy resources and managing the disturbances.

Suggested Citation

  • Rahiminejad, A. & Vahidi, B. & Hejazi, M.A. & Shahrooyan, S., 2016. "Optimal scheduling of dispatchable distributed generation in smart environment with the aim of energy loss minimization," Energy, Elsevier, vol. 116(P1), pages 190-201.
    • Handle: RePEc:eee:energy:v:116:y:2016:i:p1:p:190-201
    DOI: 10.1016/j.energy.2016.09.111
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    References listed on IDEAS

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    1. Arasteh, Hamidreza & Sepasian, Mohammad Sadegh & Vahidinasab, Vahid, 2016. "An aggregated model for coordinated planning and reconfiguration of electric distribution networks," Energy, Elsevier, vol. 94(C), pages 786-798.
    2. Maleki, Akbar & Pourfayaz, Fathollah & Rosen, Marc A., 2016. "A novel framework for optimal design of hybrid renewable energy-based autonomous energy systems: A case study for Namin, Iran," Energy, Elsevier, vol. 98(C), pages 168-180.
    3. Doagou-Mojarrad, Hasan & Gharehpetian, G.B. & Rastegar, H. & Olamaei, Javad, 2013. "Optimal placement and sizing of DG (distributed generation) units in distribution networks by novel hybrid evolutionary algorithm," Energy, Elsevier, vol. 54(C), pages 129-138.
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