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Economically optimal uncertainty set characterization for power system operational flexibility

Pourahmadi, F ; Sharif University of Technology | 2019

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  1. Type of Document: Article
  2. DOI: 10.1109/TII.2019.2906058
  3. Publisher: IEEE Computer Society , 2019
  4. Abstract:
  5. Assessing the operational flexibility of the grid with high penetration of renewable resources remains an issue of critical importance. Operational flexibility insufficiency may bring about two major problems: 1) there may exist no feasible solution for operation under uncertain conditions due to insufficient available flexibility capacity; 2) even if the solution feasibility criterion is ensured, dispatch limitations of the flexible resources may force the system operating point to deviate from the optimal economic point with increased redispatch. In this paper, an optimal uncertainty set at the unit commitment time scale is proposed and characterized as a reliable operational metric. A novel robust-based framework is developed to quantify the economically optimal uncertainty set such that both feasibility and optimality robustness are guaranteed. Furthermore, the flexible capabilities of storage units, nonquick-start, and quick-start generating units are formulated to illustrate the effectiveness of the proposed metrics. Two proposed algorithms based on column and constraint generation are adopted to solve the proposed formulations. The performance of the suggested formulations is compared with the scenarios, where only the feasibility robustness is taken into account. Numerical simulations on the modified IEEE 73-bus system will verify the effectiveness and efficiency of the suggested framework. © 2005-2012 IEEE
  6. Keywords:
  7. Optimality robustness ; Robust optimization (RO) ; Electrical engineering ; Industry ; Constraint generation ; Effectiveness and efficiencies ; Feasibility robustness ; Flexibility metrics ; Operational flexibility ; Optimality ; Robust optimization ; System operating points ; Optimization
  8. Source: IEEE Transactions on Industrial Informatics ; Volume 15, Issue 10 , 2019 , Pages 5456-5465 ; 15513203 (ISSN)
  9. URL: https://ieeexplore.ieee.org/document/8669743