Journal of
Engineering and Technology Research

  • Abbreviation: J. Eng. Technol. Res.
  • Language: English
  • ISSN: 2006-9790
  • DOI: 10.5897/JETR
  • Start Year: 2009
  • Published Articles: 184

Full Length Research Paper

Relocation of source of sporadic production using genetic algorithm in distribution network

Shirin Farhadi1 and Reza Dashti2*
1Azad University of Jasb, Iran. 2Iran University of Science and Technology, Iran.
Email: [email protected]

  •  Received: 29 March 2014
  •  Accepted: 29 October 2014
  •  Published: 28 December 2014

 ABSTRACT

Today, due to the development of distribution systems and increase of demand and load, the use of the source of scattered production has developed. The establishment of the installation place and the size of the source of scattered production decrease network loss, lead to best action of network as well as the recovery of the voltage profile. In this article, we use the genetic algorithm for relocating and finding the best sources of scattered production with active power production and reactive power production by arranging and using them. Also in this article, in addition to decreased loss, balanced voltage, stabilization and recovery of profile have purpose function. The results show that the system of 33 bass is the power and effectiveness of the method.

 

Key words: Scattered production, optimization, genetic logs. 


 INTRODUCTION

The main shave of loss in one power system is related to the distribution system. The study shows that distribution system loses because of the high relation of R/X and high decrease of voltage in this system.
 
Today, increased demand and load leads to the development of the distribution system and its aspects; and this agent causes more loss of voltage and increases casualty. As a result, there is decreased voltage stability of knots and load imbalance.
 
Different methods created for determining the capacity of decomposed granite (DG) are presented in this work. Willis (2000) showed the famous legal 2/3 methods used for determining the optimum place of condenser, which in turn determines the optimum place of DG. DG  of  2/3  length  lower  than  the  post  was installed.
 
Kashem et al. (2008) offered a number of methods for determining the optimum size of DG based on the sensation of the loss of power. The method based on the minimum casualty power in the presence of DG was established. The methods chosen were tested on the practical network in Tasmania and Australia. Acharya et al. (2007) used the increasing change in casualty power related to the change of the sensation factor of the real power injected; it was developed by Elgerd (1970). The factor this article was used to determine the bass that caused the casualty at the time the DG was installed on it, by arranging the offered method based on factor sensation.
 
The problem of this method is the length used in determining  the  place  of  optimum  installation  of DG in distribution network. It was also the only place to optimize the DG, and the only way to install a DG in the distribution network is proposed. Kean and Omalley (2006) also solve the DG optimum size on one of the line program (LP) on the Ireland network. In the literature, Rosehart and Nowicki (2007) presented a new model to determine location of DGs for economical distribution system and voltage improvement.
 
The purpose function was solved by using interpolation method (IP). The later output is the other rank of bass for DG installation.
 
The size of the optimum DG in this installation is not studied in this investigation. Hedayati et al. (2009) presented the method of load distribution for bass that is sensitive to reversal. The sensitive bass in this article is an appropriate place for DG installation.
 
The repeated method used for determining the place of DG optimum installation was used at first for one of the distinct capacity of DG that connected to the network the program of load distribution and the casualty of real power; voltage profile and the capacity of passing power of line are calculated. In the literature, MIthulananthan et al. (2006) determined the decreased casualty of real power of distribution network by using genetic algorithm (GA) containing the size of DG without considering any condition.
 
NR was used to calculate the casualty. Only one DG is investigated in this work. In the literature, Nara et al. (2001) hypothesized clearly the place of DG installation and used the method of Taboo to determine the size of DGs for the purpose of designing system network casualty.
 
Load is the source of fixed current generated by the coefficient of fixed power. Golshan and Arefifar (2007) have propounded the Taboo method for the size recovery of DG, where the source of reactive power like condenser and reactor or both in the distribution system is considered.
 
The purpose function decreases the cost of the reactive power, line load and the cost of the added reactive source.
In this investigation, the place of DG and source of reactive are not optimal.


 PURPOSE FUNCTION

The aim of this study is to recover the technical function of network formulated in the form of recovery of two terms of technical network that include casualty and arrangement of the voltage of network distribution. So the presented purpose function is introduced in the form of one of the two purpose functions. In addition, the problem has equal condition, protection and functional condition which will be introduced later.
Relation 1 is description of the presented purpose function.
 
 
 
EQUAL CONDITIONS
 
The presentation of DG in the network should be in one way, in which all of the control and system variables in the equations of load distribution is applied. The active and reactive power based on the famous equation of load distribution in Relations 4 and 5 is shown thus (MIthulananthan et al., 2006),
 
 
Operational and protective constraints
 
Voltage condition
 
For the purpose of the observation of the quality of delivery to consumer, the voltage of every shin in distribution   network should be in the minimum and defined maximum limit.
 
So in every condition, in the DG installation, the condition of voltage in accordance to the Relation 6 should be checked until the voltage of the bass place is in their allowed field.
 
 
The condition of passing power line in distribution
 
Systems use the conductor by different segment surfaces, where the limit of the passing power is different. So during the time of installing DG in the network, subsidiary feeder should be investigated, that the power of every angle according to Relation 7 should not be more than the allowed amount (that is based on the kind of connector used).
 
 
In Relation 7,   is the passing power of branch   is the maximum passing power of branch    is the amount of the branch of the tested systems.
 
MODELING OF THE SPORADIC PRODUCTION
 
Source of sporadic production generally can be divided into four groups as follows:
 
1) The source of sporadic production that has the ability of producing real power (fotoveltaik). In recovery program, it is related to the limited power production, which in the form of DG, governs the condition on this kind of formulation.
 
 
2) Sporadic production source that only has the ability to produce of reactive power (synchronous condenser). Governing condition on this equation is written thus,
 
 
3) Sporadic production source that produces active power and consumes reactive power (induction generator and winding turbin). DG is the governing condition of this kind,
 
 
 
4) Sporadic production source that is known as the synchronous generator as follows:
 
a) Has the ability of fixing reactive or any kind of pY
b) Sets voltage or any kind of pV
 
In this article, we use four kinds of DGs (Yanjun and Yun, 2008; Vinothkumar and Selvan, 2009).

 


 GENETIC ALGORITHM (GA)

We can call the genetic algorithm as an explorer method. This is based on the specification and choosing of the children of sequential generation based on the principles of the best programmed genetic algorithm (from the answer to the problem in one step) This simulates the laws in genetic algorithm and their use leads to the production of children with the best quality.
 
In every generation, using appropriate choices in reproducing children, better approximated final answers are achieved. This process causes the new generation to be more compatible with the problem condition. This competition between generations, the victory of dominant generations and side tracking of beaten ones are effective methods for solving complex and hard problem (Golberg, 1989).
 
The methods of solving the problem GA are as follows,
 
Step 1: (the amount of first overall) we equal the counter to zero (t = 0) and produce the chromosome accidentally [xj(.), j = 1,…,n] where xj(.) = [xj1(0), xj2(0),…,xjm(0)]Xjk(0), in the form of accidental chromosome in the searching space, is produced.
 
Step 2: (evaluation) every chromosome in the first population is evaluated by the use of j purpose function, and to choose the best amount, we search for JBST and arrange the chromosome that is proportional to the JBEST for best amount of XBEST.
 
Step 3 (change of counter) = in this step t=t+1
 
Step 4: (production of new population) = by repeating the following step, we the complete the new population.
 
 
 
a) Choice: We took an amount of parent chromosomes based on their suitability (the ones with better suitability have better chance of being chosen).
b) Intersection: By one probability, we apply the intersection operator on the chosen parent to produce the new children. If the intersection is not doing well, every branch of population is precisely copy as a parent.
c) Mutation = by one probability, we change every gene in one chromosome.
d) Acceptance = we put the new chromosome in the population.
 
Step 5: (replacement) = We used the new population produced to do the algorithm again.
Step 6: (stop) if the amounts of repeats are the distinct amounts needed, the program will stop, but if not, we will go to the next stop as shown in Figure 1. 
 
 
NUMBER STUDY
 
The network includes one network of 12.66 radial KW. As shown in Figure 2, the whole active load is 3.2 MVAR and active casualty is about 210 KW before the installation of DG (Chakravorty and Das, 2001; Abu-mouti, 2010).
 
 
The appropriate initial population has direct effect on the speed of answer convergence and can directly affectthe time of convergence.
 
The initial population DG created by 300 chromosomes and in every field shows the capacity and place of the installation of DG. Another information that is related to the genetic algorithm program is shown in Table 1.
 
 
In Table 2, there is a comparative between genetic method and movement algorithm of birds for installation of DG that has the ability to produce one active power.
 
 
In Table 2, the first column introduces the kinds of method, the second column introduces the casualty of the   active   power  according  to  KW,  the  third  column introduces the light reactive casualty according to KVAR, the fourth column shows the installation place of DG and the final column shows the capacity amount of DG installation on candidate bass. The place of DG installation in every three initial method is same; the only difference is in the DG capacity amount using the GA method in relation to the two other methods, which decrease the casualty a little more. Figure 3 shows the purpose function diagram.
 
 
Table 3 shows the comparison between the three methods used  for  DG  installation, with the ability to produce reactive power only (type two).
 
 
Table 4 shows the comparison between the three methods used for DG installation, with the ability to produce active power and consume reactive power (type three).
 
 
Table 5 shows the comparison between every DG 3 method used for the installation of synchronous generator by stabilizing power (type four).
 
 
Looking at the charts and graphs of voltage profile presented in the previous section, the following points may be noted:
GA method presented to determine location and installed capacity of the four types of distributed generation sources has similar results with the GA and particle swarm optimization (PSO) methods, whose installation places are the same. The only difference could be found in installed DG capacity that causes less calculated loss in this method than the three other methods. The point is: As the search space and unknown parameters increase, GA method can be more useful. Looking at the calculated active and reactive power losses in the tables, we can conclude that, installed DG of fourth type, that is, synchronous generators have greater role in reducing losses than the other types of DG. After this type of DG, distributed generation sources from the type of photo voltaic systems, induction generator (active power generation and reactive power consumption) and the synchronous condensers decrease losses in distribution companies. Another point about the system voltage profile diagrams (Figures 4  to  7)  on  the bass 33 system after installation of DG is that changes in bus   voltage  magnitude  can  be  seen   at  the  bass  33 system. As seen, the DG of the synchronous generator (PV), photo voltaic  systems (P), induction generator and synchronous condenser ultimately are effective in improving network profiles.
 
 
 
 
 

 


 CONCLUSION

GA approach was formulated to solve the problem of determining the size and location of DG and in this method, the location and capacity was nominated by GA. in spite of that, object function has two terms that improve Network voltage losses and profile. Also, DG impact of four models in the 33 bus network is evaluated and it could be seen that type of distributed generation sources will have a better impact to generate active and reactive power to reduce losses in the distribution network. Also, after installing the four types of distributed generation sources, loses reduced and the network voltage profile has been improved significantly.


 CONFLICT OF INTEREST

The author(s) have not declared any conflict of interest.



 REFERENCES

Abu-mouti FS (2010). A Priority-Ordered Constrained search technique for Optimal distributed generation Allocation In Radial Distribution Feeder systems.
 
Acharya N, Mahat P, Mithulananthan N (2007). An analytical approach for DG allocation in primary distribution network. Int. J. Elect. Power Energy Syst. 28(10):669-678.
CrossRef
 
Chakravorty M, Das D (2001). Voltage stability analysis of radial distribution networks. Int. J. Elect. Power Energy Syst. 23(2):129-135.
CrossRef
 
Elgerd OI (2007). Electric Energy Systems Theory: An Introduction McGraw-Hill Inc.
 
Golberg DE (1989). Genetic Algorithms in search, optimization and machine learning. Longman.
 

Golshan MEH, Arefifar SA (2007). Optimal allocation of distributed generation and reactive sources considering tap positions of voltage regulators as control variables. European Transaction on Electrical Power, p. 17.

View

 
Hedayati H, Nabaviniaki SA, Akbarmajid A (2006). A method for placement of DG units in distribution networks. IEEE Transactions on Power Delivery, p. 23.
 
Installation, operation, and maintenance costs for distributed generation technologies, EPRI, Palo Alto, CA, 2003.
 
Kashem MA, Le ADT, Negnevitsky M, Ledwich G (2008). Distributed generation generation for minimization of power losses in distribution systems. IEEE Power Engineering Society General Meeting, p. 8.
 
Kean A, Omalley M (2006). Optimal allocation of embedded generation on distribution networks. IEEE Transactions on Power Systems.
 

Li Y, Yao D, Chen W (2005). Adaptive Particle Swarm Optimizer for Beam Angle Selection in Radiotherapy Planning. IEEE International Conference on Mechatronics & Automation, July2005, pp. 421-425.

View

 
MIthulananthan N, Oo T, Phu LV (2006). Distributed generator placement in power distribution system using genetic algorithm to reduce losses. Thammasat Int. J. Sci. Technol. 9(3):55-62.
 
Nara K, Hayashi Y, Ikeda K, Ashizawa T (2001). Application of tabu search to optimal placement of distributed generators. IEEE Power Engineering Society Winter Meeting.
 
Rosehart W, Nowicki E (2007). Optimal placement of distributed generation. 14th Power System Computation Conference, Sevilla, Spain.
 
Vinothkumar K, Selvan MP (2009). Planning and Operation of Distributed Generations in Distribution Systems for Improved Voltage Profile. IEEE Power Engineering Society.
 

Willis HL (2000). Analytical methods and rules of thumb for modeling DG-distribution interaction. IEEE Power Eng. Soc. Summer Meet. 3(3):143-1644.

View

 

Yanjun W, Yun Z (2008). Optimal Algorithm of Distribution Network Planning Including Distributed Generation. Nanjing China, DRPT, pp. 6-9.

View

 

 




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