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Stochastic operation framework for distribution networks hosting High wind penetrations

Dorostkar Ghamsari, M. R ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1109/TSTE.2017.2761179
  3. Abstract:
  4. In this paper, a stochastic framework including two hierarchical stages is presented for the operation of distribution systems with high penetrations of wind power. In the first stage, termed Day Ahead Market Stage (DAMS), power purchases from day-ahead (DA) market and commitment of distributed generations (DGs) are determined. The DAMS model is formulated as a mixed integer linear programming (MILP) optimization problem. The uncertainty in predictions of wind generation, real time prices, and load profile are included in the optimization problem according to a scenario-based stochastic programming approach. The risk encountered due to the uncertainties is also taken into account. The objective is to minimize the expected operation cost while satisfying the acceptable level of risk. In the second stage, named Real Time Market Stage (RTMS), power purchases from the real time (RT) market, dispatch of committed DGs, load curtailment invocations, and hourly reconfigurations are determined. In each hour, the RTMS problem is solved based on the information of that hour and the next few hours. To prevent large number of switching operations during a day, switching cost of reconfiguration is considered. The RTMS is modeled as a mixed integer conic programming (MICP) problem. IEEE
  5. Keywords:
  6. Cone programming ; Distribution network ; Stochastic optimization ; Stochastic processes ; Wind power generation ; Commerce ; Costs ; Electric load management ; Electric power distribution ; Electric power generation ; Hierarchical systems ; Hydraulic models ; Optimization ; Random processes ; Stochastic programming ; Stochastic systems ; Switches ; Uncertainty analysis ; Weather forecasting ; Wind power ; Load modeling ; reconfiguration ; Stochastic optimizations ; Uncertainty ; Wind forecasting ; Integer programming
  7. Source: IEEE Transactions on Sustainable Energy ; 2017 ; 19493029 (ISSN)
  8. URL: https://ieeexplore.ieee.org/abstract/document/8064667