Simulation approach to improve power analysis network for integration of distributed generation

The capability of the classical network to support the level penetration of Distribution Generation (DG) has been present to analysis how to achieve a better integration of flexible demand of Electrical Networks(Mesh and Radial systems). Based on this analysis, two simulation algorithms have been proposed to reduce CPU time and memory, can help the grid operator to assess power situation and increase penetration level of DG. Firstly, It deals with an algorithm developed for power load flow studies in electrical power systems using Schur complement method. By dividing Jacobian matrix into two separated matrices with reasonable computation time, It could be avoided divergence of the solution. A next step that was applied concerned Data Compression for the Simulated algorithms conceived to study load flow calculation in electrical power systems. Real time monitor of grids required less computation time in calculation of power system analysis. It proposed an algorithm based on Run Length Encoding (RLE) method where no zero values are included. Matlab results obtained by applying these algorithms match the analytical approaches. Results have been compared with Newton Raphson and Fast Decoupled methods in term of influence of convergence properties and it's efficiency. These methods have been implemented and tested on several IEEE bus test systems.

[1]  David Guibert,et al.  A Schur Complement Method for DAE/ODE Systems in Multi-Domain Mechanical Design , 2008 .

[2]  Johan Driesen,et al.  Optimal placement and sizing of distributed generator units using genetic optimization algorithms , 2005 .

[3]  M. Ashari,et al.  Reconfiguration of distribution network with DG using fuzzy multi-objective method , 2012, 2012 International Conference on Innovation Management and Technology Research.

[4]  T. Lantharthong,et al.  Network Reconfiguration for Load Balancing in Distribution System with Distributed Generation and Capacitor Placement , 2012 .

[5]  J. M. Yusta,et al.  Optimal Location of Small Generators in Weak Networks with Optimal Operation , 2005 .

[6]  Gianni Celli,et al.  Distribution network interconnection for facilitating the diffusion of Distributed Generation , 2005 .

[7]  Jae-Chul Kim,et al.  Integration operation of dispersed generations to automated distribution networks for network reconfiguration , 2003, 2003 IEEE Bologna Power Tech Conference Proceedings,.

[8]  Z Q Bo,et al.  Research of the impact of distribution generation on distribution network loss , 2010, 45th International Universities Power Engineering Conference UPEC2010.

[9]  Davide Fabozzi,et al.  Dynamic Simulation of Large-Scale Power Systems Using a Parallel Schur-Complement-Based Decomposition Method , 2014, IEEE Transactions on Parallel and Distributed Systems.

[10]  N. Hadjsaid,et al.  Novel architectures and operation modes of distribution network to increase DG integration , 2010, IEEE PES General Meeting.

[11]  M. Matos,et al.  Loss allocation in distribution networks with embedded generation , 2004, IEEE Transactions on Power Systems.

[12]  P. Srikanth,et al.  Load Flow Analysis Of Ieee14 Bus System Using MATLAB , 2013 .

[13]  T. Ackermann,et al.  Interaction between distributed generation and the distribution network: operation aspects , 2002, IEEE/PES Transmission and Distribution Conference and Exhibition.

[14]  H. Bludszuweit,et al.  Probabilistic model for distributed generation expansion in distribution power network , 2011 .

[15]  M.S. Rios,et al.  Sequential Optimization for Siting and Sizing Distributed Generation (DG) in Medium Voltage (MV) Distribution Networks , 2007, 2007 IEEE Lausanne Power Tech.