Investigating the Effect of Load Modeling on Network Reconfiguration of a Distribution System

In order to minimize the losses of the distribution network, solving the reconfiguration problem is a vitally established issue. Modification in the bus connection reduces the system loss and improves the bus voltages as well. Major intricacy for reconfiguring distribution system lies in the development of a convex optimization model constituting voltage-dependent loads. This paper models mixed integer second order conic program for Network Reconfiguration of a Distribution System (NRDS), where binary variables depict the branch connections between the buses. A ZIP load model composed of constant impedance, constant current, and constant power type loads, has been deployed for voltage-dependent load study. Proposed ZIP load model based NRDS has been tested on 33-bus, and 69-bus distribution system. Results are compared with the existing model given in literature incorporating only constant power loads that establishes the case for adaptation of the proposed model. Simulated results of constant power and voltage-dependent load model are also validated by performing load flow for evaluated branch connections.

[1]  Matti Lehtonen,et al.  Stochastic Operation Framework for Distribution Networks Hosting High Wind Penetrations , 2019, IEEE Transactions on Sustainable Energy.

[2]  Abhijit R. Abhyankar,et al.  Multi-Objective Day-Ahead Real Power Market Clearing with Voltage Dependent Load Models , 2011 .

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

[4]  J. Z. Zhu,et al.  Optimal reconfiguration of electrical distribution network using the refined genetic algorithm , 2002 .

[5]  M. Kitagawa,et al.  Implementation of genetic algorithm for distribution systems loss minimum re-configuration , 1992 .

[6]  Mohammed H. Haque,et al.  Load flow solution of distribution systems with voltage dependent load models , 1996 .

[7]  K. Ravindra,et al.  Power Loss Minimization in Distribution System Using Network Reconfiguration in the Presence of Distributed Generation , 2013, IEEE Transactions on Power Systems.

[8]  J. Riquelme-Santos,et al.  A simpler and exact mathematical model for the computation of the minimal power losses tree , 2010 .

[9]  K. Prasad,et al.  Optimal reconfiguration of radial distribution systems using a fuzzy mutated genetic algorithm , 2005, IEEE Transactions on Power Delivery.

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

[11]  Kwang Y. Lee,et al.  Determining PV Penetration for Distribution Systems With Time-Varying Load Models , 2014, IEEE Transactions on Power Systems.

[12]  Francisco de Leon,et al.  Experimental Determination of the ZIP Coefficients for Modern Residential, Commercial, and Industrial Loads , 2014, IEEE Transactions on Power Delivery.

[13]  F. S. Hover,et al.  Convex Models of Distribution System Reconfiguration , 2012, IEEE Transactions on Power Systems.

[14]  D. Das,et al.  Impact of Network Reconfiguration on Loss Allocation of Radial Distribution Systems , 2007, IEEE Transactions on Power Delivery.

[15]  B. Lesieutre,et al.  Approximate Representation of ZIP Loads in a Semidefinite Relaxation of the OPF Problem , 2014, IEEE Transactions on Power Systems.

[16]  Roohollah Fadaeinedjad,et al.  Energy Loss Minimization in Distribution Systems Utilizing an Enhanced Reconfiguration Method Integrating Distributed Generation , 2015, IEEE Systems Journal.

[17]  M. J. Rider,et al.  Optimal Conductor Size Selection and Reconductoring in Radial Distribution Systems Using a Mixed-Integer LP Approach , 2013, IEEE Transactions on Power Systems.