A heuristic trade off model for integration of distributed generations in deregulated power systems considering technical, economical and environmental issues

With the introduction of restructuring concepts to traditional power systems, a great deal of attention is given to the utilization of distributed generation. Since the integration of DG units has been known as an alternative for main grid as a resource for energy supply, the determination of optimal sizing and sitting is an important issue in the planning procedure of DG. This work presents a comprehensive multi-objective model for integration of distributed generations into a distribution network, regarding various technical, economical and environmental issues such as reduction of carbon dioxide emissions and investment & running costs while the bus voltages shall be kept within acceptable limits. A genetic algorithm and a fuzzy decision making method has been proposed to solve this problem. The method is applied on IEEE-34 feeder test system and the results are presented and discussed.

[1]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[2]  M. E. Hamedani Golshan,et al.  Distributed generation, reactive sources and network-configuration planning for power and energy-loss reduction , 2006 .

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

[4]  R. Ramakumar,et al.  Voltage profile improvement with distributed generation , 2005, IEEE Power Engineering Society General Meeting, 2005.

[5]  M. Ehsan,et al.  Multi objective distributed generation planning in liberalized electricity markets , 2008, 2008 IEEE/PES Transmission and Distribution Conference and Exposition.

[6]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[7]  J. A. Domínguez-Navarro,et al.  NSGA and SPEA Applied to Multiobjective Design of Power Distribution Systems , 2006, IEEE Transactions on Power Systems.

[8]  M. Ehsan,et al.  The effects of enviromental parameters on wind turbine power PDF curve , 2008, 2008 Canadian Conference on Electrical and Computer Engineering.

[9]  Pierluigi Siano,et al.  Distributed Generation Capacity Evaluation Using Combined Genetic Algorithm and OPF , 2007 .

[10]  Jen-Hao Teng,et al.  Value-based distributed generator placements for service quality improvements , 2007 .

[11]  Walter G. Scott,et al.  Distributed Power Generation Planning and Evaluation , 2000 .

[12]  Ronnie Belmans,et al.  Distributed generation: definition, benefits and issues , 2005 .

[13]  M. Trovato,et al.  Embedding distributed generation in a national energy planning procedure , 2004, 2004 IEEE Africon. 7th Africon Conference in Africa (IEEE Cat. No.04CH37590).

[14]  I. Kim,et al.  Adaptive weighted sum method for multiobjective optimization: a new method for Pareto front generation , 2006 .

[15]  Mehdi Ehsan,et al.  A Life Cycle Model for Optimal DG Placement on Distribution Networks to Reduce Active Losses and Investment Costs , 2007, GEM.

[16]  Lennart Söder,et al.  Distributed generation : a definition , 2001 .

[17]  S.A. Khaparde,et al.  Evaluation of configuration plans for DGs in developing countries using advanced planning techniques , 2006, IEEE Transactions on Power Systems.

[18]  W. El-khattam,et al.  Optimal investment planning for distributed generation in a competitive electricity market , 2004, IEEE Transactions on Power Systems.

[19]  D. Zhu,et al.  Impact of DG placement on reliability and efficiency with time-varying loads , 2006, IEEE Transactions on Power Systems.

[20]  A. Selvakumar,et al.  A New Particle Swarm Optimization Solution to Nonconvex Economic Dispatch Problems , 2007, IEEE Transactions on Power Systems.