Decentralized optimization for multi-area optimal transmission switching via iterative ADMM
Release time:2018-12-23
Hits:
- Indexed by:
- Essay collection
- Document Code:
- 18364224
- First Author:
- Mingyu Yan
- Correspondence Author:
- Xiaomeng AI
- Co-author:
- Jinyu Wen,Yubin He,Yining Zhang
- Journal:
- 2018 IEEE Power & Energy Society General Meeting (PESGM)
- Included Journals:
- EI
- Place of Publication:
- United States
- Discipline:
- Engineering
- First-Level Discipline:
- Electrical Engineering
- Document Type:
- C
- Page Number:
- 1-5
- ISSN No.:
- 1944-9933
- Key Words:
- Multi-area power system, optimal transmission switching, decentralized optimization, iterative ADMM.
- DOI number:
- 10.1109/PESGM.2018.8586669
- Date of Publication:
- 2018-12-23
- Abstract:
- This paper proposes a decentralized strategy for the multi-area optimal transmission switching (OTS) problem. We illustrate the decentralized framework for this problem, which is able to help reduce model scale and communication burden. Each area operator (AO) governs its subsystem and decides the OTS independently. Then each AO coordinates with the adjacent areas by sharing the corresponding information. The augmented Lagrange function (ALF) is utilized to relax and decompose the primal centralized OTS problem into decentralized subproblems for each subsystem. A two-level iterative alternating direction method of multipliers (ADMM) is adopted to co-optimize these subproblems. The upper level is used to fix the binary variables, while the lower level is the standard ADMM method, which guarantees the accuracy of the relaxed subproblem. Numerical tests on a two-area 12-bus system and a modified IEEE RTS-96 system show the effectiveness of the proposed strategy and solution technique.
- Links to published journals:
- https://ieeexplore.ieee.org/abstract/document/8586669/authors#authors