Author Topic: Distributed Generation Planning Optimization  (Read 2541 times)

0 Members and 1 Guest are viewing this topic.

content.writer

  • Newbie
  • *
  • Posts: 48
  • Karma: +0/-0
    • View Profile
Distributed Generation Planning Optimization
« on: April 23, 2011, 04:30:51 pm »
Distributed Generation Planning Optimization Using Multiobjective Evolutionary Algorithms
Quote
Author : Mahmood Sheidaee, Mohsen Kalantar
International Journal of Scientific & Engineering Research, IJSER - Volume 2, Issue 4, April-2011
ISSN 2229-5518
Download Full Paper - http://www.ijser.org/onlineResearchPaperViewer.aspx?Distributed_Generation_Planning_Optimization_Using_Multiobjective_Evolutionary_Algorithms.pdf

Abstractó In this paper, a method to determine the size - location of Distributed Generations (DGs) in distribution systems based on multi objective performance index is provided considering load models. We will see that load models affect the location and the optimized size of Distributed Generations in distributed systems significantly. The simulation studies are also done based on a new multi objective evolutionary algorithm. The proposed method has a mechanism to keep the diversity to overcome the premature convergence and the other problems. A hierarchical clustering algorithm is used to provide a manageable and representative Pareto set for decision maker. In addition, fuzzy set theory is used to extract the best solution. Comparing this method with the other methods shows the superiority of proposed method. Furthermore, this method can easily satisfy other purposes with little development and extension.

Index TermsóDistributed generation, Distribution systems, Load models, Strength Pareto Evolutionary Algorithm. 

Introduction                                                                     
Optimization was used to reconstruct electricity industry and looked for the best location for distributed products. Development in technology and client requirements to have cheap electric power and reliable one caused more motivation in distributed generation .Discussion about reliability and maintaining prevent the penetration of DG resources in the distribution systems.
In [1] one approach was described based on genetic algo-rithm for multistage planning of distribution systems optimizations. In this work, it's expressed as a mathematical model and algorithmic one and also tested with real systems. In [2] Ė [5], it was studied on load models that are usable for power flow and dynamic studies. This study was done on load models depended on frequency or voltage. During the recent years, studies on evolutionary algorithm have shown that these methods donít have the difficulties of classical methods. In principle, multiple Pareto optimal solutions can be found in one single run.
This paper has discussed on load model effects in location and size planning and distributed generation optimization. We can see that the load models affect on location and size planning of DGs in distribution network. For the purpose of studying on load models, its delivered location and size planning for single DG, its assumed that the regarded DG has enough capacity. The proposed method is general and it can be used for case of multiple DG in the network with increasing some variables.
This paper also suggested a new Strength Pareto Evolu-tionary Algorithm (SPEA) based approach for solving the problem. The diversity-preserving mechanism embedded in the search algorithm makes it effective in exploring the problem space and capable of finding widely different solutions. A hierarchical clustering technique is implemented to provide a representative and manageable    Parto-optimal set. Also, a fuzzy-based mechanism has used the best solution for extraction.

2 LOAD MODELS AND IMPACT INDICES
To determine different load model effects on distributed generation planning, 37-bus distribution system will be studied (appendix 1)[7]. The effect of load models depends on voltage, means residential, industrial and commercial, will be studied in different planning scenarios. Load model defined as followed.
                (1)                 (Download Full Paper for Equation )
Where Pi and Qi are active and reactive power at bus i, P0i and Q0i are active and reactive power operating point in bus I, Vi is voltage in bus i and α and β are active and reactive power exponents. In a constant power model conventionally used in power flow studied α = β = 0 is assumed. The values of the real and reactive exponents used in the present paper for industrial, residential and commercial loads are given in Table 1 [7].

TABLE 1 (Download Full Paper for Table View )
EXPONENT VALUES
Load Type   α   β
Constant   0   0
Industrial   0.18   6.00
Residential   0.92   4.04
Commercial   1.51   3.40

During studying residential, itís assumed that 38-bus systems just has residential load. It's assumed that for industrial and commercial load, all loads are a kind of industrial and commercial. In real situations, loads arenít exactly residential, commercial and industrial, so the mixture load class should be foreseen for distribution system. There are different ideas for studying DG effects in distribution systems .One of this idea is different indices evaluation on the purpose of effect description on distribution system because of DG during maximum power production.

Read More: http://www.ijser.org/onlineResearchPaperViewer.aspx?Distributed_Generation_Planning_Optimization_Using_Multiobjective_Evolutionary_Algorithms.pdf