Power Grid Decomposition Based on Vertex Cut Sets and Its Applications to Topology Control and Power Trading

It is well known that the reserves/redundancies built into the transmission grid in order to address a variety of contingencies over a long planning horizon may, in the short run, cause economic dispatch inefficiency. Accordingly, power grid optimization by means of short term line switching has been proposed and is typically formulated as a mixed integer programming problem by treating the state of the transmission lines as a binary decision variable, i.e. in-service or out-of-service, in the optimal power flow problem. To handle the combinatorial explosion, a number of heuristic approaches to grid topology reconfiguration have been proposed in the literature. This paper extends our recent results on the iterative heuristics and proposes a fast grid decomposition algorithm based on vertex cut sets with the purpose of further reducing the computational cost. The paper concludes with a discussion of the possible relationship between vertex cut sets in transmission networks and power trading.

[1]  Shuai Wang,et al.  The Kirchhoff-Braess paradox and its implications for smart microgrids , 2015, 2015 54th IEEE Conference on Decision and Control (CDC).

[2]  Mario Montagna,et al.  Optimal network reconfiguration for congestion management by deterministic and genetic algorithms , 2006 .

[3]  Paul S. Fischbeck,et al.  Quantifying siting difficulty : A case study of US transmission line siting , 2007 .

[4]  Florian Dörfler,et al.  Kron Reduction of Graphs With Applications to Electrical Networks , 2011, IEEE Transactions on Circuits and Systems I: Regular Papers.

[5]  Shuai Wang,et al.  Paradigm and Paradox in Topology Control of Power Grids , 2018, 2018 IEEE Conference on Decision and Control (CDC).

[6]  R.P. O'Neill,et al.  Optimal Transmission Switching With Contingency Analysis , 2010, IEEE Transactions on Power Systems.

[7]  A. Cha,et al.  Fast Heuristics for Transmission-Line Switching , 2012, IEEE Transactions on Power Systems.

[8]  R D Zimmerman,et al.  MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education , 2011, IEEE Transactions on Power Systems.

[9]  A. G. Bakirtzis,et al.  Incorporation of Switching Operations in Power System Corrective Control Computations , 1987, IEEE Transactions on Power Systems.

[10]  Shuai Wang,et al.  A Novel Decomposition for Control of DC Circuits and Grid Models with Heterogeneous Energy Sources , 2018, 2018 Annual American Control Conference (ACC).

[11]  K. W. Hedman,et al.  Real-Time Contingency Analysis With Transmission Switching on Real Power System Data , 2016, IEEE Transactions on Power Systems.

[12]  Roman Obermaisser,et al.  Composability and compositionality in CAN-based automotive systems based on bus and star topologies , 2013, 2013 11th IEEE International Conference on Industrial Informatics (INDIN).

[13]  M. Ferris,et al.  Optimal Transmission Switching , 2008, IEEE Transactions on Power Systems.

[14]  Shuai Wang,et al.  Kirchhoff-Braess phenomena in DC electric networks , 2016, 2016 IEEE 55th Conference on Decision and Control (CDC).

[15]  S. Blumsack The Braess Paradox in Electric Power Systems , 2022 .

[16]  Aleksandr Rudkevich,et al.  On fast transmission topology control heuristics , 2011, 2011 IEEE Power and Energy Society General Meeting.

[17]  Kory W. Hedman,et al.  A review of transmission switching and network topology optimization , 2011, 2011 IEEE Power and Energy Society General Meeting.

[18]  J. G. Rolim,et al.  A study of the use of corrective switching in transmission systems , 1999 .

[19]  H. Glavitsch,et al.  Power System Security Enhanced by Post-Contingency Switching and Rescheduling , 1993, Proceedings. Joint International Power Conference Athens Power Tech,.

[20]  B. F. Wollenberg,et al.  Corrective Control of Power System Flows by Line and Bus-Bar Switching , 1986, IEEE Transactions on Power Systems.