Recent Researches in Communications, Electrical & Computer Engineering
DG Allocation Using Clonal Selection Algorithm(CSA)to Minimize Losses and Improve Voltage Security Amir Khanjanzadeh Maryam Khoddam Electrical and Computer Engineering Faculty Shahid Beheshti University Tehran, Iran
[email protected] [email protected]
Abstract- The purpose of this paper is to minimize losses and to improve voltage security by DG allocation based on Clonal Selection Algorithm (CSA). The exigency for flexible electric system, Energy saving, Loss minimizing and environmental effects are providing motivation to the development of distributed generation. These days Scientists research shows that 30% of Generation of electrical energy is distributed. This subject caused to perform more studies about DG. The allocation of DG has the main effect of voltage improve and loss minimizing on the system. Finally the results are compared to Genetic Algorithm. Key- Words: -Distributed Generation, Voltage Security, DG Allocation, Clonal Selection Algorithm, Genetic Algorithm. up in distribution networks.another benefits are show below: i. improve voltage security ii. Improved power quality iii. developed productivity iv. stability index and etc
1 INTRODUCTION LOSSES are an important consideration when designing and planning the distribution network. Losses are inevitable on any network, however the amount can vary considerably depending on the design of the network. In the past the distribution network was a purely passive system, used only for the delivery of electricity to the consumer. With the introduction of distributed generation, the network is being utilized in a different way with more variable and be directional power flows. The level of losses is closely linked to the power flows, therefore the allocation of DG provides an opportunity to ameliorate losses. Large amounts of distributed generation are being connected to distribution networks. In Ireland at the end of 2004, applications received by the system operators concerned the connection of approximately 2,500MW of wind generation. A significant amount of this capacity is to be distribution connected. This is in addition to 920MW of previously contracted wind farm capacity and approximately 100MW of other forms of DG such as landfill gas (LFG) and hydro. In a country with a peak load of approximately 4,500MW this is an extremely large amount of DG to integrate into the distribution network [1].
Distributed Generations be able to reduce the electrical network loss for the reason that they produce the power in the nearness of load, so it is better to allocate DG units in places that they can supply a higher loss reduction. For the reason that DGs are so costly, loss reduction is a very essential object for DGs allocation. Power losses in distribution systems vary with various factors dependent on configuration of the system. Power losses be able to be divided into two parts: reactive power and real power. the reactive elements causes the reactive losses and real power loss is produced due to the resistanse of lines.
3 CLONAL SELECTION The Clonal Selection theory proposed by Burnet is used to describe the basic features of an immune response to an antigenic stimulus [2]. It establishes the idea that only thosecells that recognize the antigen proliferate, thus being selected against those that do not [3]. Attracted by the biologic characters such as learning, memory and antibody diversity which represented in the immune clonal process, some algorithms based on clonal selection theory are proposed and the idea has been widely
2 DG DESCRIPTON DG is a small generator which be able to operate separate or in connection with distribution networks. Energy loss reduction and reliability development are two most important advantages of distributed generation setting
ISBN: 978-960-474-286-8
71
Recent Researches in Communications, Electrical & Computer Engineering
app plied in some fields like in ntrusion detecction [4], sysstem m control [5], optimization o [6, 7], etc. Nevvertheless, thiis algorithms founded on clonal c selectionn Mechanismss are few annd simple. Thee calculation consequences c s are ungratifiied after solv-ing complicated problem. In this t paper willl be tried to study s power loss l and voltagee security fouunded on the analysis of im mmunology and d theory of clonal selectioon and finallyy compare to the Genetic Algoorithm. Fig 2.Fraame work of CSA C
4 CLO ONAL SE ELECTION N AL LGORITH HM The block diagram off our Clonal Selection S Aalgorithmbased opptimization aalgorithm is shown in Fiig. 3, in which thee Related stepps are explainned like as folllows. 1. Initialize the antibbody pool (Pinit i ) together with the Sub set of o memory cellls (M). 2. Evaluaate the fitnesss of all the perrsons in popuulation P. The fitneess here passees on to the afffinity measure . 3. Selectt the best appplicants (Pr) from populaation pinit relation to t their fitnesss (the affinityy with the antiigen) 4. Clone these best aantibodies intto a momentary pool (C). mutation 5. Producce a mutated aantibody pool (C1). The m rate of each person is contrary inn comparison with its fitness. 6. Evaluaate all the gennes in C1. 7. Eliminnate the antiboodies those arre like to oness in C, andd inform C1. 8. Re-sellect the persoons with bettter fitness froom C1 to create th he memory seet M. Other improved Peersons of C1 can replace r somee members inn Pinit to presserve the antibody variety.
Fig1.Clonal Selection
Thee main idea off clonal selecction theory liies in that thee antiibodies can selectively s reeact to the anntigens, which are the native prroduction and d spared on thhe cell surfacce in the form of peeptides. Whenn exposed to antigens, thhe mune cells thhat recognize and eliminatte the antigenns imm willl be selected and arouse ann effective ressponse againsst them m. The reacttion leads to cell proliferrating clonallly and d the colony has h the samee antibodies. Consequentlyy, the process of cllonal selectio on actually coonsists of threee i cellls maiin steps: clonne: descend a group of identical from m a single coommon ancesstor through asexual a propaagatiion. Mutation n: gain higheer affinity mainly m through hyp per mutation.[[8] Asssuming the ob bjective function and restrraining condiitionns of optimizaation are the antigens invaading the boddy and d candidate so olutions are the antibodiees recognizinng antiigens, then th he process of optimizationn can be consiidereed as the reaaction betweeen antigens annd antibodiess, and d the affinity between b the antigens a and the antibodiees are the matchingg degree betw ween objectivee function annd utions. The optimization process inccludes seconnd solu step ps: firstly, thhe antibodies are gained. secondly, acccord ding to the cllonal selectio on theory, thee most capablle antiibodies are prroduced. The framework of o clone selecctionn optimizationn algorithms is i showed in Fig.2 F [9].
ISBN: 978-960-474-286-8
72
Recent Researches in Communications, Electrical & Computer Engineering
Fig4. Power flow is derivedd for the two nodess equivalent syystem
6 TES ST SYSTE EM AND CLONAL C L TOOL LS The singlle line diagram m of the netw work is illustraated in Fig. 5. It is a MV feedder with 13 buuses. Tables 1 and 2 provide the t data of linnes and buses:: Fig3. The block diagram d of CSA A
5 POWER R FLOW To assess netwoork capability to absorb avvailable Distriibuteed Generatio on carefully, a steady-staate symbol of o disttribution systeem in the form m of Power flow f equationns sho ould be used. The Sensitiv vity Factor meethod helps tto deccrease the seaarch space by y the linearizaation of nonliineaar equations around a the firrst point .Thiss appliance on Disstributed Genneration allocaation is new and very im mporttant.
Fig5. A part of Tehran netw work
In TABL LE I. The lines data wiill be showeedand in TABLE II I the buses ddata will be shhowed.
L Lines Data From
To
Rohm
X ohm
1
2
0.176
0.138
2
3
0.176
0.138
3
4
0.045
0.035
4
5
0.089
0.069
5
6
0.045
0.035
5
7
0.116
0.091
7
8
0.073
0.073
8
9
0.074
0.058
8
10
0.093
0.093
7
11
0.063
0.05
11
12
0.068
0.053
7
13
0.062
0.053
TABLE I
ISBN: 978-960-474-286-8
73
Recent Researches in Communications, Electrical & Computer Engineering Buses Data kw
0.7 Q
kvar
0.6
Bus Number
P
1
0
0
0.5
2
890
468
0.4
3
628
470
0.3 0.2
4
1112
764
5
636
378
6
474
344
7
1342
1078
8
920
292
9
766
498
10
662
480
11
690
186
12
1292
554
Series1
0.1 0 1 2 3 4 5 6 7 8 9 10111213
Fig6. Losses without DG 0.5 0.4 0.3
TABLE II
0.2
Series1
0.1 0
7 RESULTS OF POWER FLOW
1 2 3 4 5 6 7 8 9 10111213
According to resolved method, optimal location and sizes of Distribution Generations are analyzed for improving the voltage security and minimizing the losses. In this section specially fig 7 it will be seen that for each DG which we are adding to the network, the losses would be less than before. In so far as one location is concerned in a distribution test system, the Clonal selection algorithm would give the value of DG size to include a possible least total loss.
Fig7.a. Losses in branches with one DG 0.25 0.2 0.15 0.1
Series1
0.05
The best location which is selected by Clonal Algorithm is checked for voltage security in each iteration. The sizes of allocated DGs are in ranged 0.5MW to 50 MW and are checked in the algorithm for optimal sizing of DG to achieve to the minimum Loss and improving the voltage security profile. By using the CSA, optimal location and size of the first DG unit in IEEE 13 bus distribution test system are bus 8 and 4.92 MW respectively. Installing DG unit in this location reduces the system losses to 0.168 MW.
ISBN: 978-960-474-286-8
0 1 2 3 4 5 6 7 8 9 10111213
Fig7.b. Losses in branches with two DGs
74
Recent Researches in Communications, Electrical & Computer Engineering
0.2
9 COMPARISON OF CSA AND GA RESULT
0.15 0.1
In this part the result of CSA for DG allocation are compared with the GA. Table 3,4 and 5 show the optimal location, optimal DG size and real power loss obtained from the CSA and GA for one, two and three installed DGs in 13 bus distribution test system. It is obvious the GA cannot present the optimal location for DG placement but the optimal location can be presented by the CSA. GA and CSA was run 20 times. The best solution choosed. And presented at Tables(III, IV, V).
Series1 0.05 0 1 2 3 4 5 6 7 8 9 10111213
Fig7.c. Losses in branches with three DGs Fig 7.line losses in branches with one, two and three DG
Bus number
DG Capacity
By CSA
8
By GA
5
8 Voltage security In this paper the voltage security of system is considered with CSA which it was not in pervious methods. The system voltage security is the standard norm of bus voltages that is given in: …
Real Power Loss
Fitness Function
4.9987
0.4
4.2549
0.63
6.3416e004 9.6521 e-004
Table(III)
Where V₁,…V are the voltages in per bus and V = 1.It is obvious that, by lessening the deviation of voltage from 1perunit, the voltage security profile for distribution test system are improved. In this paper, the voltage security profiles of the test system with three DG placement is plotted in fig 8.
Bus number
DG Capacity
Real Power Loss
5
4.9968
0.182 9.8828e006
By CSA 8
4.9975
0.220
4
4.043
0.293
By GA
12.59238e006 8
4.342
0.315
Fig8.Voltage Security with CSA and GA Table (IV)
ISBN: 978-960-474-286-8
75
Fitness Function
Recent Researches in Communications, Electrical & Computer Engineering
Bus number
DG Capacity
Real Power Loss
2.3832
0.12
4.5793
0.132
4.9296
0.168
3.4537
0.196
3.7392
0.278
3.5138
0.212
ideas from the clonal selection principle”, Lecture Notes in Comput. Sci., vol.2723, 2003, pp. 158–170. [8]. C. Berek and M. Ziegner, “The maturation of the immune response”, Immune Today, vol. 14, pp. 400– 402, August 1993. [9]. Na Wang1, Haifeng Du1, 2, Sun’an Wang1 1. School of Mechanical Engineering; 2. School of Public Policy and Administration Xi’an Jiaotong University Xi’an, China
Fitness Function
3 By CSA
6 1.3852e009
8 1 By GA
8 4.0634e009
12
Table (V)
10 CONCLUSION In this paper a new method has been proposed for DG placement. This paper shows that location and size of DGs are crucial factors in the application of DG for loss minimization ,in addition the optimal location for DG placement is an important factor in network voltage security. An algorithm based on CSA is presented to find the optimal location for DG placement for loss minimization and voltage security improvement. At the end the results was compared to GA. It is obvious that CSA is a better method than GA. REFERENCES
[1]. Commission for Energy Regulation , “CER annual report 2004,” 2004.[Online]. Available: http://www.cer.ie/ [2]. G. L. Ada and G. Nossal, “The clonal selection theory”, Sci. Am., vol. 257, pp. 50–57, 1987. [3]. D. Dasgupta, “Advances in artificial immune systems”, IEEE Comput. Intell. M., vol. 1, pp. 40–49, November 2006. [4]. J. Kim and P. Bentley, “Toward an artificial immune system for network intrusion detection: An investigation of dynamic clonal selection”, In: Proc. CEC2002, 2002, pp. 1244–1252. [5]. H. K. Dong, H. J. Jae and H. Lee, “Robust power plant control using clonal selection of immune algorithm based multiobjective”, In: Proc. HIS’04, 2004, pp. 450–455. [6]. L. N. de Castro and F. J. Von Zuben, “Learning and optimization using the clonal selection principle”, IEEE T. Evol. Comp., vol. 6, pp. 239– 251, 2002. [7]. N. C. Cortes and C. A. C. Coello, “Multiobjective optimization using
ISBN: 978-960-474-286-8
76
