Dynamic Reconfiguration and Fault Isolation for a Self-Healing Distribution System

This paper proposes a new mathematical model for network reconfiguration and fault isolation in a self-healing distribution network. The proposed model dynamically operates automatic switches in the distribution network. The model includes a new approach to ensure network radiality by combining spanning tree constraints with a virtual network framework. Multiple faults and their associated clearance and recovery are taken into account. The mathematical model is formulated as a mixed integer linear program (MILP) that can be efficiently solved using commercial solvers such as CPLEX. The model is tested on a modified IEEE 123-bus distribution system with automatic switches, distributed generators (DGs) and energy storage system (ESS).

[1]  Jianhui Wang,et al.  Resilient Distribution System by Microgrids Formation After Natural Disasters , 2016, IEEE Transactions on Smart Grid.

[2]  A. Borghetti.  A Mixed-Integer Linear Programming Approach for the Computation of the Minimum-Losses Radial Configuration of Electrical Distribution Networks , 2012, IEEE Transactions on Power Systems.

[3]  J. Martí,et al.  Mathematical representation of radiality constraint in distribution system reconfiguration problem , 2015 .

[4]  Matti Lehtonen,et al.  Value of Distribution Network Reconfiguration in Presence of Renewable Energy Resources , 2016, IEEE Transactions on Power Systems.

[5]  Ruben Romero,et al.  A New Mathematical Model for the Restoration Problem in Balanced Radial Distribution Systems , 2016, IEEE Transactions on Power Systems.

[6]  Lin Li,et al.  Fault Isolation and Reconfiguration of Multi-Voltage Level Distribution Network Based on PSO Algorithm , 2012, 2012 Asia-Pacific Power and Energy Engineering Conference.

[7]  Jianhui Wang,et al.  Power Distribution System Outage Management With Co-Optimization of Repairs, Reconfiguration, and DG Dispatch , 2018, IEEE Transactions on Smart Grid.

[8]  Zhaoyu Wang,et al.  Service restoration based on AMI and networked MGs under extreme weather events , 2016 .

[9]  Zhaoyu Wang,et al.  Resilience Enhancement Strategy for Distribution Systems Under Extreme Weather Events , 2018, IEEE Transactions on Smart Grid.

[10]  Ali Feliachi,et al.  A Multiagent Design for Power Distribution Systems Automation , 2016, IEEE Transactions on Smart Grid.

[11]  Jianhui Wang,et al.  Networked Microgrids for Self-Healing Power Systems , 2016, IEEE Transactions on Smart Grid.

[12]  R. Jabr,et al.  Minimum Loss Network Reconfiguration Using Mixed-Integer Convex Programming , 2012, IEEE Transactions on Power Systems.