DISTRIBUTED GENERATION
ALLOCATION AND VOLTAGE
IMPROVEMENT IN DISTRIBUTION
SYSTEM USING CUCKOO SEARCH
ALGORITHM
ANUPAMA KUMARI
ME student, Department of Electrical Engineering, Jabalpur Engineering College Jabalpur, Madhya Pradesh, India
[email protected] SHAILJA SHUKLA
Professor (Electrical Engineering) & H.O.D Computer Science & Engineering Department Jabalpur Engineering College, Jabalpur, Madhya Pradesh, India
Abstract
Distributed Generation (DGs) penetration in distributed system improves voltage quality and minimizes power loss. In recent time DGs allocation in distribution system has been matter of research. In this paper optimal allocation of DG has been performed by using Cuckoo Search Algorithm.Minimization of the total real power losses within the system is the objective of the present work.Voltage profile improvement is another parameter compared before and after allocation of DG. The performance of the Cuckoo Search is examined and tested on IEEE 30-bus distribution system in MATLAB environment.
Keywords: Distributed Generation;cuckoo search algorithm; voltage profile; power loss 1. Introduction
Distributed generation units are small renewable sources of energy (10 to 10,000 kW) located at or near the point of use in the distribution system. They are environment friendly and easy to install nearer to customers. A large number of DG installations in distribution system may increases the cost. As a result optimal allocation of DG in distribution system is a matter of interest. Many optimization techniques has been proposed for optimal allocation and sizing of the DG in distribution system.Ant Colony Optimization Algorithm has been used for optimal allocation and sizing of DG in 69-bus feeder standard test system by Farhat[1]. They concluded that real power losses reduced to 14.351 KW from 221.436 KW by installing 3 DG units at bus no. 20, 61 and 64.Artificial Bee Colony Algorithmwas used on IEEE 34 bus radial distribution system for optimal allocation and sizing of DGs by Selve[2].Before and after allocation of DG the loss was found to be 0.09770 MW and 0.0022 MW respectively accordingly.
concluded that the power losses reduction in case of one DG, two DGs and three DGs installation compared with no DG will be 93.11% 97.06% and 97.85% respectively. The minimization of fuel cost is considered as objective function by Rao and Babu [11] and Cuckoo Searchalgorithm is applied for demonstrated on IEEE 14 bus system.
In thepresent work cuckoo Search Algorithm has been used for optimal allocation of DG. Implementation of the proposed algorithm has been demonstrated on IEEE 30-bus distribution system in MATLAB environment. Power loss in distribution system before and after the penetration of DG is compared.
2.Problem Formulation
Optimal allocation of DG in distributed system is necessarily a compromise between reduction of power loss and cost. With increasing number of DGs power loss reduced but cost increased simultaneously. As a result optimal sizing and allocation of DGs in distributed system is matter of concern. In this paper minimization of power loss is the objective function. It is formulated as a constrained non-linear integer optimization problem.
2.1Objective Functions
The positive DG impact is improving the voltage profile while increase in the system losses is the negative impact. The goal of this paper is to minimize the negative impact by maximizing the positive one. The total power loss is considered as an objective function in this study, which is expressed as the sum of all nodes injections of power in the distribution system.
The objective function proposed in this study is defined as
OF = Min (PL) ... (1)
Where, PL is total power loss in distribution system.
PL = ∑ ∑ Aij( PiPj+ QiQj) + Bij ( QiPj– PiQj) ... (2) 2.2. Constraints
The operating constraints used herefor study is defined as follows:
Voltage constraints
The occurrence of the voltage rise in distribution system due to reverse flow of power after DG implementation is considered as constraints for calculations.
Vmin≤ Vi≤Vmax ... (3)
Where Vminand Vmaxare the minimum and maximum allowed voltageand Vi is the voltage at bus i. Power balance constraints
∑ PDGi =∑ PDi + PL ...(4)
Where PDGiis the real power generation with DG at bus i, PDi is the power demand at bus i and PL is the real
power loss in the system.
3. Cuckoo Search Algorithm
Cuckoo search (CS) was inspired by the obligate brood parasitism of some cuckoo species which lay their eggs in the nests of birds of other species (host birds). Host birds engagedin direct contest with the infringing cuckoos. If a host bird discovers the eggs are not their own, it will either throw these alien eggs away or simply abandon its nest and build a new nest elsewhere. Cuckoos choose a nest where the host bird has just laid its eggs andgenerally the host eggs hatch slightly later than cuckoo eggs. The first instinct action is to evict the host eggs as the first cuckoo chick is hatched, by blindly propelling the eggs out of the nest. Cuckoo chick’s share of food provided by its host bird increases. Cuckoo chick can also mimic the call of host chicks to gain access to more feeding opportunity.In nature, animalssearch for food in a random manner. The next move is based on the current location/state and thetransition probability to the next location. The flight behaviour of many animals and insects has demonstrated the typical characteristics of Levy flights.
CS is based on three idealized rules[12]:
Each cuckoo lays one egg at a time, and dumps its egg in arandomly chosen nest.
The best nests with high quality of eggs will be carried to thenext generation by the algorithm.
The egg laidby a cuckoo is discovered by the host bird with a probabilityPa(0, 1), from fixed number of
available host nests.In this case, the host bird can either simply throw the eggs away or abandon its nest and build a completely new nest elsewhere.
= + α⊕Levy (λ) ... (5)
Where α> 0 is the step size (In most cases, α = 1)which is related to the scales ofthe problem of interests. The random step length inlevyflight is drawn from a Levy distribution which has an infinite variance with an infinite mean.
Levy ~ U = , (1≤λ≤3) ...(6)
Some of the new solutions should be generated by Levy walk around the best solution obtained so far, this will speed up the local search. However, a substantial fraction of the new solutions should be generated by far field randomization and whose locations should be far enough from the current best solution, this will make sure the system will not be trapped in a local optimum.
4. Proposed Methodology
The proposed methodology is based on Cuckoo Search Algorithm and assumes a solution by each egg in a nest and a cuckoo egg represents a new solution. To replace a worse solution in the nests by the new better solutions is the goal. The implementation of CS for optimal allocation of DG in distribution system involves several steps of procedure as presented in Fig. 1. For the CS parameters setting, number of nests, n=25, step size, α=1, and the probability to discover foreign eggs, Pa=0.25have been applied in this study.
Fig.1. Flow chart of Cuckoo Search Algorithm
5. Result and Discussion
Fig.2. Single line diagram of IEEE 30-bus distribution network
CS algorithm has been used for optimal allocation of DGs in 30-bus distribution system in this study.All the buses of the network have been considered as candidate for installation of DGs for the problem optimization, except the first bus. The optimal number of DGs to be connected in the system identified is found to be 5, each of 3 MW rating. Table.1 shows the optimal allocation of DG at different buses.
Table.1 Allocation of DG on different buses
S.No. Bus No. Capacity
1 7 3 MW
2 10 3 MW
3 14 3 MW
4 16 3 MW
Fig.3 shows the voltage profile improvement due to penetration of DG in distribution system. It is observed from the figure that there is improvement in bus voltage and overall voltage profile of the system is improved after allocation of DGs.
6. Conclusion
In this paper the optimal DG allocation with application of a meta heuristic approach called cuckoo search was studied for IEEE 30-bus distribution system. DGs are allocated at bus no. 4, 8, 10, 13 and 18.Power loss before and after DG placement is 18.040 MW and 6.475 MW respectively. This study also concludes that allocationof DGs at respective positions improves voltage profile and power loss decreased by 64%.
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