Optimal distributed generation planning considering reliability, cost of energy and power loss

This paper suggests a Pareto based Multi-objective Optimization Algorithm (MOA) called Strength Pareto Evolutionary Algorithm (SPEA) for Distributed Generation (DG) planning in distribution networks. As opposed to conventional multi-objective optimization techniques that correlate different objective functions by utilizing of weighting coefficients and create one single objective function, in SPEA, each objective function is optimized separately. Since the objective functions are in conflict with each other, the SPEA produces a set of optimum solutions instead of one single optimum one. Three different objective functions are considered in this study: (1) minimization of power generation cost (2) minimization of active power loss (3) maximization of reliability level. The goal is to optimize each objective function. The site and size of DG units are assumed as design variables. The results are discussed and compared with those of traditional distribution planning and also with Partial Swarm Optimization (PSO).   Key words: Distributed generation, distribution network planning, multi-objective optimization.

[1]  H Lee Willis,et al.  Power distribution planning reference book , 2000 .

[2]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[3]  M. Gandomkar,et al.  A Genetic–Based Tabu Search Algorithm for Optimal DG Allocation in Distribution Networks , 2005 .

[4]  Seyed Ali Arefifar,et al.  Optimal allocation of distributed generation and reactive sources considering tap positions of voltage regulators as control variables , 2007 .

[5]  Jeffrey Horn,et al.  Multiobjective Optimization Using the Niched Pareto Genetic Algorithm , 1993 .

[6]  Roy Billinton,et al.  Reliability evaluation of power systems , 1984 .

[7]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

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

[9]  Ulas Eminoglu,et al.  Distribution Systems Forward/Backward Sweep-based Power Flow Algorithms: A Review and Comparison Study , 2008 .

[10]  Gilbert Syswerda,et al.  The Application of Genetic Algorithms to Resource Scheduling , 1991, International Conference on Genetic Algorithms.

[11]  R. Billinton,et al.  A Canadian customer survey to assess power system reliability worth , 1994 .

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

[13]  Prabhat Hajela,et al.  Genetic search strategies in multicriterion optimal design , 1991 .

[14]  Kyung Bin Song,et al.  Multiobjective distributed generation placement using fuzzy goal programming with genetic algorithm , 2008 .

[15]  Pola Kishore Kumar,et al.  Selection of Optimal Location and Size of Multiple Distributed Generations by Using Kalman Filter Algorithm , 2013 .

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

[17]  G. Joós,et al.  Models for Quantifying the Economic Benefits of Distributed Generation , 2008, IEEE Transactions on Power Systems.

[18]  W. Short,et al.  A manual for the economic evaluation of energy efficiency and renewable energy technologies , 1995 .

[19]  Taher Niknam,et al.  Impact of distributed generation on volt/Var control in distribution networks , 2003, 2003 IEEE Bologna Power Tech Conference Proceedings,.

[20]  H. Shayeghi,et al.  Discrete PSO algorithm based optimization of transmission lines loading in TNEP problem , 2010 .

[21]  Caisheng Wang,et al.  Analytical approaches for optimal placement of distributed generation sources in power systems , 2004, IEEE Transactions on Power Systems.

[22]  Martina Gorges-Schleuter,et al.  Application of Genetic Algorithms to Task Planning and Learning , 1992, Parallel Problem Solving from Nature.

[23]  Peter J. Fleming,et al.  An Overview of Evolutionary Algorithms in Multiobjective Optimization , 1995, Evolutionary Computation.

[24]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[25]  R. Billinton,et al.  Determination and use of sector and composite customer damage functions , 1999, Engineering Solutions for the Next Millennium. 1999 IEEE Canadian Conference on Electrical and Computer Engineering (Cat. No.99TH8411).