4.7 Article

A Bound Strengthening Method for Optimal Transmission Switching in Power Systems

期刊

IEEE TRANSACTIONS ON POWER SYSTEMS
卷 34, 期 1, 页码 280-291

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2018.2867999

关键词

Optimal transmission switching; mixed-integer optimization; algorithms; global optimization; economic dispatch

资金

  1. ONR YIP Award
  2. DARPA YFA Award
  3. AFOSR YIP Award
  4. NSF CAREER Award
  5. ARL Grant
  6. Office of the Assistant Secretary of Defense for Research and Engineering [FA9550-10-1-0168]

向作者/读者索取更多资源

This paper studies the optimal transmission switching (OTS) problem for power systems, where certain lines are fixed (uncontrollable) and the remaining ones are controllable via ON/OFF switches. The goal is to identify a topology of the power grid that minimizes the cost of the system operation while satisfying the physical and operational constraints. Most of the existing methods for the problem are based on first converting the OTS into a mixed-integer linear program (MILP) or mixed-integer quadratic program (MIQP), and then iteratively solving a series of its convex relaxations. The performance of these methods depends heavily on the strength of the MILP or MIQP formulations. In this paper, it is shown that finding the strongest variable upper and lower bounds to be used in an MILP or MIQP formulation of the OTS based on the big-M or McCormick inequalities is NP-hard. Furthermore, it is proven that unless P = NP, there is no constant-factor approximation algorithm for constructing these variable bounds. Despite the inherent difficulty of obtaining the strongest bounds in general, a simple bound strengthening method is presented to strengthen the convex relaxation of the problem when there exists a connected spanning subnetwork of the system with fixed lines. With the proposed bound strengthening method, remarkable improvements in the runtime of the mixed-integer solvers and the optimality gaps of the solutions are achieved for medium- and large-scale real-world systems.

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