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A Wasserstein Distributionally Robust Model for Transmission Expansion Planning with renewable-based Microgrid Penetration
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  • Sahar Rahim,
  • Zhen Wang,
  • Ke SUN,
  • Hangcheng Chen
Sahar Rahim
COMSATS University Islamabad - Wah Campus
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Zhen Wang
Zhejiang University

Corresponding Author:eezwang@ieee.org

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Ke SUN
State Grid Zhejiang Electric Power Co
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Hangcheng Chen
Zhejiang University
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Abstract

This article introduces a Wasserstein distance-based distributionally robust optimization model to address the transmission expansion planning considering wind turbine-powered microgrids (MGs) under the impact of uncertainties. The primary objective of the presented methodology is to devise a robust expansion strategy that accounts for both short-term variability and long-term uncertainty over the planning horizon from the perspective of a central planner. In this framework, the central planner fosters the construction of appropriate transmission lines and the deployment of optimal MG-based generating units among profit-driven private investors. Short-term uncertainties, stemming from variations in load demands and production levels of stochastic units, are modeled through operating conditions. The Wasserstein distance uncertainty set is used to characterize the long-term uncertainty about the future load demand. To ensure the tractability of the proposed planning model, the authors introduce a decomposition framework embedded with a modified application of Bender’s method. To validate the efficiency and highlight the potential benefits of the proposed expansion planning methodology, two case studies based on simplified IEEE 6-bus and IEEE 118-bus systems are included. These case studies assess the effectiveness of the presented approach, its ability to navigate uncertainties, and its capacity to effectively optimize expansion decisions.
16 Apr 2024Submitted to IET Generation, Transmission & Distribution
18 Apr 2024Submission Checks Completed
18 Apr 2024Assigned to Editor
18 Apr 2024Review(s) Completed, Editorial Evaluation Pending
19 May 2024Editorial Decision: Revise Major
29 May 20241st Revision Received
04 Jun 2024Submission Checks Completed
04 Jun 2024Assigned to Editor
04 Jun 2024Review(s) Completed, Editorial Evaluation Pending
04 Jun 2024Reviewer(s) Assigned
30 Jun 20242nd Revision Received
01 Jul 2024Review(s) Completed, Editorial Evaluation Pending
13 Jul 2024Editorial Decision: Accept