This newspaper proposes a new approach to decide the optimal location and sizing of Distributed Generation (DG) and Distribution STATic COMpensator (DSTATCOM) simultaneously in the distribution network. The objective part is formulated to minimize the total power losses of the system subjected to equality and inequality constraints. Loss sensitivity factor (LSF) and Voltage Stability Alphabetize (VSI) are used to predetermine the optimal location of DG and DSTATCOM, respectively. Recently developed nature-inspired cuckoo search algorithm (CSA) has been used to determine the optimal size of both DG and DSTATCOM. In the nowadays work, five different cases accept been considered during DG and DSTATCOM placement to admission the performance of the proposed technique. To bank check the feasibility, the proposed method is tested on IEEE 12-charabanc, 34-bus, and 69-omnibus radial distribution arrangement and the results were compared with other existing techniques.
Generally, the majority of the distribution network loads are anterior in nature. So the network power factor volition be lagging in nature. It leads to increasing the power losses, causes poor voltage profile, and creates network security problems in the distribution networks. The distribution arrangement total power losses can exist divided into real and reactive power losses. Compared to the effect of reactive ability losses in the system, the real power losses () touch the efficiency of the power transfer and lead to poor voltage profile [ane].
Studies indicated that ten–13% of the total power generation is consumed as
losses (existent power loss) at the distribution arrangement [2–5]. Hence, information technology is necessary to identify the compensating devices in the distribution organization to reduce power losses and improve the voltages between the buses. In this work, DG and DSTATCOM units are placed simultaneously in the distribution for bounty. In that location are different benefits of simultaneous allotment of DG and DSTATCOM in the distribution arrangement including reducing system power loss, voltage profile enhancement, power factor correction, load balancing, power quality improvement, on-peak operating costs reduction, releasing the overloading of distribution lines, system stability comeback, pollutant emission reduction, and increased overall energy efficiency.
In recent years Distributed Generation integration plays an of import part in distribution system planning which results in major system upgrade, power loss reduction, and voltage profile enhancement and, finally, improving overall system reliability. DG is defined equally electricity generation with express size generator connected to the distribution arrangement. Several factors have been responsible for the appearance of DG in radial distribution organisation. The environment problems such equally reducing the greenhouse effect, reduction of fossil fuel, and current scenario of deregulation of electricity market recommend the requirement for more flexible electric systems .
STATCOM was initially developed for transmission systems to regulate the voltage contour so as to provide reactive power compensation and ability factor control; so similar concept has been started to be applied to distribution systems [vii,
8]. DSTATCOM is used to improve the voltage profile, power gene, and voltage stability of the distribution organisation. DSTATCOM is a shunt connected voltage source converter (VSC) that can be used to compensate power quality issues [nine]. The DSTATCOM is a fast and rapid compensating device which enhances voltage contour and power losses reduction through injection of compensating current into the organisation [ten]. It is brash to place the DG and DSTATCOM units at optimal place with optimal size to reach maximum benefits of the system. Improper placement of DG and DSTATCOM units volition lead to collapse and even endanger the unabridged system operation [eleven]. The master objective of DG placement is to compensate the existent ability, whereas the DSTATCOM placement is to compensate reactive power in the distribution organization.
In the recent past, several population based metaheuristic techniques such as Genetic Algorithm (GA), Pismire Colony Optimization (ACO), Immune Algorithm (IA), Differential Development Algorithm (DEA), Firefly Algorithm (FA), Particle Swarm Optimization (PSO), Pedagogy Learning Based Optimization (TLBO), Artificial Bee Colony (ABC), Harmony Search Algorithm (HSA), and Bat Algorithm (BA) accept shown their potential to solve optimal DSTATCOM placement problem [13–eighteen] or optimal DG placement problem [nineteen–27].
A lot of research work has been carried out to successfully optimize the siting and sizing problems of DG and DSTATCOM devices when allocated separately. Though, simply a single research work has been washed in simultaneous allocation of DG and DSTATCOM in the radial distribution networks. The authors have used particle swarm optimization algorithm for the problem of simultaneous placement of DG and DSTACOM with an objective of full power loss minimization .
Cuckoo Search Algorithm (CSA)  is 1 of the new nature-inspired algorithms that has been proposed recently to solve complex optimization issues. CSA can exist used to efficiently solve global optimization bug  likewise as NP-hard issues that cannot be solved by exact solution methods . The most powerful characteristic of CS is its use of Levy flights to update the search space for generating new candidate solutions. This mechanism allows the candidate solutions to be modified by applying many small changes during the iteration of the algorithm. This in turn makes a compromised relationship between exploration and exploitation which enhance the search adequacy . To this terminate, contempo studies proved that CSA is potentially far more efficient than GA and PSO . In addition, it is a elementary and population based stochastic optimization algorithm. Moreover, it requires less control parameters to be tuned. Also, it is a uniform optimization tool for ability system controller design. Such characteristic has motivated the use of CSA to solve different kinds of technology bug such equally multiobjective scheduling trouble , reliability optimization problems , DG allocation in distribution network , economic acceleration , network reconfiguration, and Distributed Generation allocation in distribution network .
The nowadays piece of work is aimed at developing a fast and new technique to decide the optimal location and sizing of DG and DSTATCOM for minimize the power losses and enhance voltage profile. The optimal location of the DG and DSTATCOM can be identified using LSF and VSI, respectively. The optimal size of the DG and DSTATCOM can exist determined past using cuckoo search algorithm. The novelty of this work is implementing an integrated approach of LSF and VSI with CSA to determine the optimal location and sizing of DG and DSTATCOM for the sake of power loss minimization and voltage profile enhancement. Another advantage of this piece of work is that multiple DG and DSTATCOM are placed simultaneously in the radial distribution system. The comparison over single and multiple DG and DSTATCOM placement has been analyzed and information technology gives encouraging results. In addition to that, the total operating cost (TOC) of the DG and DSTATCOM simultaneously has been considered for all the cases which is not considered before in the literature. To show the effectiveness of the proposed method, it has been applied on standard IEEE test radial distribution organization and the obtained results are compared with other techniques.
2. Problem Formulation
2.1. Load Period Analysis
The directly approach for distribution load flow is used to find the power losses and also the voltage at each branch . The single line diagram of a sample distribution system is shown in Figure
The voltage at node
is given past
is the voltage magnitude of the coach
is the voltage magnitude of the bus
is the resistance of the line betwixt
is the reactance of the line between
is the co-operative electric current. BIBC is the motorcoach current injection to branch current matrix.
is the real power load at bus
is the reactive power load at double-decker
is the electric current injected at node
The real and reactive power losses of the system are calculated by using the post-obit equation:
is the real power period in the line between
is the reactive power flow in the line between
The total real and reactive power losses of the arrangement can be easily found past summing all the branch power losses and it is expressed in
is the number of the branches.
ii.2. Objective Function
The objective function () of the proposed piece of work is formulated to minimize the ability losses of the system.
The mathematical formulation of the objective function is given by
is the total power loss of the radial distribution arrangement.
Constraints. The optimal allotment of DG and DSTATCOM in distribution system is subjected to the following constraints.
(a) Power Balance. Power generation is equal to the power demand and power losses.
are the minimum and maximum voltage limits at motorbus
(b) Existent Power Compensation
are the minimum and maximum real ability limits of compensated motorcoach
(c) Reactive Power Compensation
are the minimum and maximum reactive power limits of compensated bus
three. Optimal Location
The loss sensitivity factor is used to preidentify the optimal location of the DG and the voltage stability alphabetize is used to preidentify the optimal location of the DSTATCOM. The optimal size of the DG and DSTATCOM will be obtained using cuckoo search algorithm. Another advantage of preidentifying the optimal location is that it has to reduce the search space of the optimization process.
three.1. Loss Sensitivity Factor
The loss sensitivity factor is used to identify the optimal location for DG placement. The node which has the highest value of LSF with respect to the existent ability has more adventure to place DG [39,
40]. The LSF values of all buses are calculated, and and then they are arranged in descending society. The top most LSF value has more hazard to exist selected as a candidate location of DG.
Equation (4) is partially differentiated with respect to real power and it is given by
iii.ii. Voltage Stability Alphabetize
At that place are many indices used to bank check the power organisation security level. In this section, a new steady state voltage stability index is used in order to identify the node, which has more risk of voltage collapse and it is expressed in (12) [2,
42]. In order to attain the stable operation of the radial distribution system, the VSI should be
. The voltage stability at each node is calculated from the power flow using (12). The node which has the low value of VSI has more take a chance to install DSTATCOM.
4. Cuckoo Search Algorithm
Cuckoo search algorithm is introduced past Yang and Deb [28,
29]. CSA accept two main operators. One is direct search based on Levy flights and some other one is random search based on the probability for a host bird to discover an alien egg in its nest. The parameters used in cuckoo search algorithm are every bit follows:
N: number of nests or unlike solutions (25).
Pa: discovery charge per unit of alien eggs/solutions (0.25).
Nd: dimension search space (1 or 3).
Lb and Ub: the lower and upper bounds limits.
CSA consists of three steps. They are as follows:
Every cuckoo lays 1 egg at a time and dumps its egg in a randomly called nest.
The best nests with loftier quality of eggs volition carry over to the next generation.
The number of available host nests is fixed, and the egg laid past a cuckoo is discovered past the host bird.
Cuckoos are bonny birds; they not simply make beautiful sounds but also have fantastic reproduction strategy. Some of the species in cuckoo similar
lay their eggs in common nests, though they may remove other’s eggs to rise the hatching probability of their own eggs. The cuckoo eggs may hatch before than that of their host eggs. When the beginning cuckoo eggs is hatched, the first action is to remove the host eggs by blindly pushing out the egg from the nest. The cuckoo chick may also mimic the call of host chick to increase the feeding opportunity.
The term Levy flying was introduced by Benoit Mandelbrot, who used this term for 1 specific definition of the distribution of stride size. Naturally most of the animals search for (cuckoo bird will search for host nest) their food in the random fashion (the next pace is always based on the electric current location and the probability of moving to the next location). It can be modeled with a Levy distribution (a continuous probability distribution for nonnegative random variables) know as Levy flights.
The cuckoo bird volition find the best nest to lay their egg (solution) to maximize their eggs survival rate. Actually every cuckoo lays merely one egg at a fourth dimension. The loftier quality eggs (optimal value) which are more like to the host bird’due south eggs accept more chance to develop (side by side generation) and get a mature cuckoo. Unhealthy eggs (not optimal value) are identified by host bird with a probability Pa
and these eggs are thrown away or the nest is discarded, and the new nest is congenital at a new location. A randomly distributed initial population of host nest is generated and then the population of solutions is subjected to repeated cycles of the search process of the cuckoo birds. The cuckoo randomly chooses the nest position to lay egg using
is abiding (),
is a random number generated betwixt ,
is gamma office, and
, which is step size.
The step size can be obtained using
are randomly chosen indexes and
is chosen randomly but its value must exist different from
The host bird volition place the cuckoo egg and cull the high quality egg with probability of using
is the fitness value of the solution and
is the proportional to the quality of egg in the nest position
If the host bird identifies the cuckoo egg, then the host bird may throw the egg away or leave that nest and built a new nest using (16). Otherwise the egg will grow and is live for the next generation.
four.1. Steps to Be Followed for Optimization
Run load menstruum analysis.
Obtain the base power losses and voltage at each omnibus.
Run the LSF and VSI to find the candidate location for DG and DSTATCOM.
Gear up the lower and upper limits for the constraints.
Initiate random population of
, for amount of kW or kVAr that will exist injected inside constraints.
Obtain cuckoo randomly using Levy flights,
Evaluate its fitness () according to objective function.
Go a nest randomly from population
be the solution.
every bit the new solution.
If a fraction of nest is replaced by new nests, then create a new nest at new location with the assistance of Levy flights.
Choose the best current nests.
Allow the electric current all-time solution to the next generation.
If maximum iteration is non reached, then go to Step
6; otherwise it is the best nest (optimal solution).
Display optimal solution.
These are the steps involved to minimize
v. Test Upshot and Discussion
In order to analyze the functioning of the proposed method, it has been tested on IEEE 12-autobus system, 34-bus system, and 69-bus system. The direct load flow analysis is used to detect the power losses, voltage magnitude, and phase bending at various buses. For all the exam systems, the substation voltage is considered as 1 p.u. The load is assumed to exist constant power load. The DG that is used in the test system is capable of delivering merely real power. The maximum limit of the DG unit of measurement is 60% of the total kW loading of the network. The maximum limit of the DSTATCOM unit is 100% of the full kVAr loading of the network. Regarding multiple DG and DSTATCOM, the maximum number of DG and DSTATCOM placement is express to iii, since, beyond this limit, at that place is no pregnant comeback in power loss reduction. The total operating toll (TOC) of DG and DSTATCOM is given by [xi]
Let us assume that
are the price coefficient and their values are four$/kW or kVAr and 5$/kW or kVAr, respectively.
The five different cases are considered to clarify the effectiveness of the proposed method.
Example I. The system is without DG and DSTATCOM units (base instance).
|Cases||PSO ||Proposed method|
|Only DG (Case II)||DG size in MW (location)||0.0378 ()||
|Power loss (kW)||17.68||ten.77|
|Average computation time (south)||NA||12.12|
|Just DSTATCOM (Case III)||DSTATCOM (size and location)||0.0321 ()||
|Power loss (kW)||18.40||12.58|
|Average ciphering time (s)||NA||12.51|
|Both DG and DSTATCOM placed simultaneously (Case 4)||DG & DSTATCOM (size and location)||0.0390 (),
|Power loss (kW)||eleven.05||three.17|
|Average ciphering time (s)||NA||11.92|
|Cases||PSO ||Proposed method|
|Only DG case (2)||DG (size and location)||0.1996 (21)||2.3278 (23)|
|Power loss (kW)||203.98||98.42|
|Average computation fourth dimension (s)||NA||xi.41|
|Only DSTATCOM (Instance III)||DSTATCOM (size and location)||0.1606 (21)||i.3705 (23)|
|Power loss (kW)||212.96||175.01|
|Average computation time (due south)||NA||eleven.52|
|Both DG and DSTATCOM placed simultaneously (Instance IV)||DG & DSTATCOM (size and location)||0.1371 (21),
|Power loss (kW)||177.ten||55.03|
|Average computation time (due south)||NA||11.69|
Effect of 69-autobus system
Upshot of multiple DG and DSTATCOM placement (Example V)
Case V. The system is with multiple DG and DSTATCOM. Results are presented in Tabular array
5.ane. 12-Bus System
The IEEE 12-jitney radial distribution system consists of 12 buses and xi branches. The line information and bus information of this system are taken from . The base values are 100 MVA and 11 KV and the total real and reactive ability loads of this organization are 0.435 MW and 0.405 MVAr, respectively. The loss sensitivity factor is calculated for all the nodes in order to find the optimal placement of DG for the cases II, Iv, and V. As shortly equally the values of LSF are calculated, then the next step is to adapt all the values in descending order. The meridian most three values which are more sensitive are selected to install the DSTATCOM units in the arrangement. The VSI are calculated for all the buses and and so they are sorted in ascending gild. The jitney which has more sensitivity to voltage plummet is chosen to identify the DSTATCOM units. These steps are to be followed for the cases III, IV, and Five. In order to avoid incongruity in values, the existing method results are obtained using our load flow assay.
Case I. The total ability loss, minimum voltage, and minimum VSI of this case are 20.7 kW, 0.9431 p.u., and 0.7912 p.u., respectively.
Example 2. In this instance the DG units are optimally placed at 9th motorcoach with the optimal size of 0.2355 MW. Because of this the power losses of this case have been reduced to 10.77 kW from 20.seven kW. The minimum voltage and minimum VSI of this case are found to be 0.9830 p.u. and 0.9340 p.u., respectively.
Case 3. The power losses of this case are reduced to 12.58 kW from 20.7 kW later on placement of DSTATCOM units of 0.2102 MW at bus ix. The voltage profile and minimum VSI of this case have been improved every bit 0.9562 p.u. and 0.8235 p.u., respectively.
Instance Iv. In this case the single DG and DSTATCOM are placed simultaneously at ninth bus and the size of the DG and DSTATCOM units is 0.2324 MW and 0.2121 MVAr, respectively. The voltage profile and minimum VSI have been improved to 0.9908 p.u. and 0.9636 p.u. and they are 0.9431 p.u. and 0.7912 p.u. before placement of DG and DSTATCOM. Equally a result the total power losses of the system have been reduced to 3.17 kW from twenty.vii kW.
Case Five. Regarding this case, the multiple DG and DSTATCOM are placed simultaneously at seventh bus ( MVA), 9th motorcoach ( MVA), and twelfth bus ( MVA), respectively, so that the total power losses of this case are reduced to 1.34 kW from 20.seven kW.
5.two. 34-Bus Organisation
This is a medium scale radial distribution system with 34 buses and 33 branches. The line data and load data are taken from . The base values are 100 MVA and xi KV and the total real and reactive power loads of the system are three.715 MW and 2.3 MVAr, respectively.
Case I. The total power loss, minimum voltage, and minimum VSI of this case are 0.2213 MW, 0.9420 p.u., and 0.7875 p.u., respectively.
Case II. In this case the DG units are optimally placed at 23rd motorcoach with the size of two.3278 MW. Because of this the power losses in this case accept been reduced to 98.42 kW from 221.2860 kW. The minimum voltage and minimum VSI of this example are institute to be 0.9740 p.u. and 0.8999 p.u., respectively.
Instance Iii. The power losses of this case are reduced to 175.01 kW from 221.2860 kW after placement of DSTATCOM units of 1.3705 MW at bus 23. The voltage contour and minimum VSI of this case take been improved as 0.9488 p.u. and 0.8105 p.u., respectively.
Instance 4. In this case the single DG and DSTATCOM are placed simultaneously at 23rd bus and the optimal size of the DG and DSTATCOM units is 2.3905 MW and 1.3419 MVAr, respectively. The voltage profile and minimum VSI have been improved to 0.9771 p.u. and 0.9115 p.u. and they are 0.9420 p.u. and 0.7875 p.u. before placement of DG and DSTATCOM. The full real ability losses of this case are 55.03 kW.
Case V. Regarding this case, the multiple DG and DSTATCOM are placed simultaneously at 23rd passenger vehicle ( MVA), 32nd bus ( MVA), and 34th coach ( MVA), respectively. The total power losses are reduced to xix.32 kW later the placement of multiple DG and DSTATCOM.
5.3. 69-Motorcoach Organization
This is a large scale radial distribution system with 69 buses and 68 branches. The line and autobus data of this system are taken from . The base values are 100 MVA and 12.66 KV and the total real and reactive power loads are three.80 MW and 2.69 MVAr, respectively.
Case I. The total power loss, minimum voltage, and minimum VSI of the organisation are 0.255 MW, 0.9090 p.u., and 0.6822 p.u., respectively.
Case Ii. In this case the DG units are optimally placed at 61st bus with the optimal size of i.8727 MW. Because of this the power losses in this example are reduced to 83.21 kW from 225 kW. The minimum voltage and minimum VSI of this case are plant to be 0.9682 p.u. and 0.8788 p.u., respectively.
Example 3. The power losses of this case are reduced to 152.95 kW from 225 kW after placement of DSTATCOM units of 1.200 MW at bus 61. The voltage profile and minimum VSI of this case have been improved every bit 0.9285 p.u. and 0.7375 p.u., respectively.
Case IV. In this case the single DG and DSTATCOM are placed simultaneously at 61st jitney and the size of the DG and DSTATCOM units is ane.75 MW and 1.15 MVAr, respectively. The voltage contour and minimum VSI have been improved to 0.9715 p.u. and 0.8908 p.u. and information technology is 0.9090 p.u. and 0.6822 p.u. earlier placement of DG and DSTATCOM. The total real ability losses of this case are reduced to 24.xv kW.
Case V. Regarding this case, the multiple DG and DSTATCOM are placed simultaneously at 17th bus ( MVA), 61st coach ( MVA), and 63rd jitney ( MVA), respectively. The full power losses are reduced to eight.07 kW afterward the placement of multiple DG and DSTATCOM.
Overall Analysis. When compared with all the cases, it is very clear that the prodigious improvement in the voltage profile and satisfactory ability losses reduction was accomplished using case 5 (i.e., simultaneous placement of multiple DG and DSTATCOM units) every bit presented in Table
3(b). Hence information technology is recommend to install simultaneous placement of multiple DG and DSTATCOM units in distribution organisation to accomplish maximum benefits of the system. The simulation results are compared with PSO method and it was found that the result obtained by the CSA method gives encouraging results. Since the existing method’southward computational time is not available, the computational efficiency in terms of CPU fourth dimension of the CSA method could not be compared with other methods.
Simultaneous allocation of DG and DSTATCOM in the radial distribution system is used to compensate the real and reactive power which leads to reducing system power loss, voltage profile enhancement, power gene correction, load balancing, power quality improvement, on-meridian operating costs reduction, arrangement stability improvement, pollutant emission reduction, and increased overall energy efficiency. Information technology is essential to place the DGs and DSTATCOMs at candidate locations with optimal kW and kVAr to ensure the maximum benefits of the organisation. In this work, an integrated approach has been used to notice the optimal locations of DG and DSTATCOM in the RDS. The sizing of the both compensating devices can be obtained past using cuckoo search algorithm. The principal advantage of using CSA is that it does not demand to spend more try in tuning the command parameters, as in the instance of GA, PGS, MINLP, DSA, and other evolutionary algorithms. The proposed method is applied to IEEE 12-bus, 34-double-decker, and 69-autobus radial distribution arrangement with unlike cases. The simulated results obtained using CSA are compared with the other existing techniques, and the results show that the performance of the proposed method for minimization of power loss and maximization of voltage profile is institute to be better than the other existing methods. From the above discussion information technology can be concluded that the proposed method can be easily applied to any big scale and existent fourth dimension distribution system.
The authors declare that at that place is no conflict of interests regarding the publication of this paper.
The authors gratefully acknowledge support from the management at VIT University, Vellore, India.
Copyright © 2017 T. Yuvaraj et al. This is an open access commodity distributed under the
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