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Considering forecasting errors in flexibility-oriented distribution network expansion planning using the spherical simplex unscented transformation

Karimi Arpanahi, S ; Sharif University of Technology | 2020

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  1. Type of Document: Article
  2. DOI: 10.1049/iet-gtd.2020.0702
  3. Publisher: Institution of Engineering and Technology , 2020
  4. Abstract:
  5. The rapid rise in the grid integration of low-carbon technologies, e.g. renewable energy sources (RESs) and plug-in electric vehicles (PEVs), has led to several challenges in distribution networks (DNs). This is due to the intermittent generation of RESs and uncertain loads of PEVs, both of which necessitate enhancing the flexibility requirements at the distribution level so as to accommodate the high penetration of these clean technologies in the future. To address such issues, this study proposes a mixed-integer linear programming-based expansion planning model for DNs considering the impact of high RES and PEV penetration, the associated uncertainties, and providing flexibility requirements at the distribution level. In this respect, the spherical simplex unscented transformation, an analytical uncertainty modelling method, is implemented in the planning model to take into account the forecasting errors of the uncertain green technologies. Also, in order to estimate the electric vehicle parking lot demand at each load node of the network, a new approach for PEV-charging model is suggested. To investigate the effectiveness and efficiency of the proposed probabilistic planning model, it is implemented on two test DNs, and the obtained results are thoroughly discussed. © The Institution of Engineering and Technology 2020
  6. Keywords:
  7. Electric automobiles ; Electromagnetic wave emission ; Forecasting ; Integer programming ; Internet protocols ; Mathematical transformations ; Renewable energy resources ; Uncertainty analysis ; Vehicle-to-grid ; Analytical uncertainty ; Effectiveness and efficiencies ; Low-carbon technologies ; Mixed integer linear programming ; Oriented distributions ; Probabilistic planning ; Renewable energy source ; Unscented transformations ; Plug-in electric vehicles
  8. Source: IET Generation, Transmission and Distribution ; Volume 14, Issue 24 , 2020 , Pages 5816-5822
  9. URL: https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/iet-gtd.2020.0702