Multi-objective micro-grid planning by NSGA-II in primary distribution system

The paper proposes a two-stage multi-objective micro-grid (MG) planning in a primary distribution system. In the first stage, the optimal region for micro-grid is identified by the loss sensitivity factor. In the second stage, a Pareto-based non-dominated sorting genetic algorithm II (NSGA-II) is proposed to determine locations and sizes of a specified number of distributed generator units (DGs) within MG. A fuzzy decision making analysis is used to obtain the final trade off optimal solution. The proposed methodology is tested on 33-bus radial and 69-bus closed-loop distribution systems. Test results indicate that NSGA-II is a viable planning tool for practical MG system and useful contribution of MG in improving distribution system performance. Copyright (C) 2010 John Wiley & Sons, Ltd.

[1]  P.W. Lehn,et al.  Micro-grid autonomous operation during and subsequent to islanding process , 2005, IEEE Transactions on Power Delivery.

[2]  G. B. Jasmon,et al.  A novel method for loss minimization in distribution networks , 2000, DRPT2000. International Conference on Electric Utility Deregulation and Restructuring and Power Technologies. Proceedings (Cat. No.00EX382).

[3]  D. Kirschen,et al.  Quantifying the Effect of Demand Response on Electricity Markets , 2007, IEEE Transactions on Power Systems.

[4]  J.R. Abbad,et al.  Assessment of energy distribution losses for increasing penetration of distributed generation , 2006, IEEE Transactions on Power Systems.

[5]  Robert H. Lasseter.  Microgrids and Distributed Generation , 2007 .

[6]  F. Pilo,et al.  Optimal reconfiguration of distribution networks according to the microgrid paradigm , 2005, 2005 International Conference on Future Power Systems.

[7]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[8]  Chin E. Lin,et al.  Distribution system load flow calculation with microcomputer implementation , 1987 .

[9]  J. Mitra,et al.  Siting and sizing of distributed generation for optimal microgrid architecture , 2005, Proceedings of the 37th Annual North American Power Symposium, 2005..

[10]  Masatoshi Sakawa,et al.  An interactive fuzzy satisficing method for multiobjective nonlinear programming problems with fuzzy parameters , 1987 .

[11]  Nadarajah Mithulananthan,et al.  AN ANALYTICAL APPROACH FOR DG ALLOCATION IN PRIMARY DISTRIBUTION NETWORK , 2006 .

[12]  E. Bompard,et al.  Convergence of the backward/forward sweep method for the load-flow analysis of radial distribution systems , 2000 .

[13]  D. P. Kothari,et al.  Stochastic economic emission load dispatch , 1993 .

[14]  M.R. Iravani,et al.  Power Management Strategies for a Microgrid With Multiple Distributed Generation Units , 2006, IEEE Transactions on Power Systems.

[15]  D. Shirmohammadi,et al.  A compensation-based power flow method for weakly meshed distribution and transmission networks , 1988 .

[16]  Hans B. Puttgen,et al.  Distributed generation: Semantic hype or the dawn of a new era? , 2003 .

[17]  Jung-Wook Park,et al.  Selection of Optimal Location and Size of Multiple Distributed Generations by Using Kalman Filter Algorithm , 2009, IEEE Transactions on Power Systems.

[18]  Vladimiro Miranda,et al.  Evolutionary computation in power systems , 1998 .

[19]  R.H. Lasseter,et al.  Extended Microgrid Using (DER) Distributed Energy Resources , 2007, 2007 IEEE Power Engineering Society General Meeting.

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