Optimization-based distribution system reliability evaluation: An enhanced MILP model

Jooshaki, M ; Sharif University of Technology | 2021

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  1. Type of Document: Article
  2. DOI: 10.1109/SEST50973.2021.9543121
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2021
  4. Abstract:
  5. Standard mathematical-programming-based models have attracted considerable attention for optimizing distribution system planning and operation due to their salient advantages. More specifically, mixed-integer linear programming (MILP) has proven very effective in modeling such problems. The availability of optimization software for efficiently solving MILP problems with guaranteed convergence to optimality while providing a measure of the distance to the optimal solution has made MILP models more popular. Although a plethora of efficient MILP formulations have been proposed for planning and operating studies of distribution grids, incorporating reliability into such models is still challenging. Recently, several innovative techniques have been proposed in the literature for optimization-based reliability evaluation of distribution networks. However, either oversimplifications or high computation costs are featured, thereby limiting the applicability of such approaches in practical problems. To overcome this issue, this paper presents an enhanced MILP model for distribution system reliability evaluation. The proposed model boosts the computational effectiveness without sacrificing solution accuracy. Numerical experience with the proposed model demonstrates its superior performance over the state of the art. © 2021 IEEE
  6. Keywords:
  7. Electric utilities ; Reliability ; Distribution system planning ; Distribution system reliability ; Electricity distribution systems ; Integer Linear Programming ; Mixed integer linear ; Mixed integer linear programming model ; Mixed-integer linear programming ; Optimisations ; Reliability assessments ; System reliability evaluation ; Integer programming
  8. Source: 4th International Conference on Smart Energy Systems and Technologies, SEST 2021, 6 September 2021 through 8 September 2021 ; 2021 ; 9781728176604 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/9543121