A Cuckoo Search Based Co-ordination of Distributed Generation Units and Shunt Capacitor Bank in Radial Distribution Networks
Chapter One
Aim and Objectives
The aim of this work is to apply a Cuckoo search algorithm for the placement ofDistributed Generation (DG) units and shunt capacitor banks in radial distribution networks with the intent of minimizing power loss (active and reactive), improving the voltage profile and voltage stability. To achieve the set aim, theobjectives are:
- To Run power flow for base case power loss and voltage at eachbus
- To integrate the Cuckoo search algorithm to the power flowfor separate and simultaneous (combined)DG units and shunt capacitor bank location and sizing on radial distribution networks in MATLAB 2013a
- To compare the results obtained from the separate sizing and location of DG units and capacitor banks with that from the simultaneous DG units and shunt capacitor bank sizing and locationon the basis of power loss minimization, voltage profile andvoltage stability improvement using cuckoo search algorithm on IEEE 33 and 69 radial test buses and on 50-bus Canteen Feeder in Zaria distribution network.
CHAPTER TWO
LITERATURE REVIEW
This chapter is divided into two sections. The first section shows the review of fundamental concepts that will enable the understanding of the basic aspects of distribution systems, load flow analysis, etc. while the second presents the review of similar works published in the areas of separate DG and capacitor bank placement and sizing and a combined DG and capacitor bank placement and sizing.
Review of Fundamental Concepts
Some of the fundamental concepts regarding this research work are discussed below. These concepts served as background information to the study.
Radial Distribution Network (RDN)
Radial distribution systems are systems with only one power source for a group of customers. They are intended mainly to attend to customers in a reliable and quality manner thereby reducing investment costs and energy losses (Kumarasaswamy et al., 2014). In this system, separate feeders radiate from a single substation and feed the distributors at one end only. This is the simplest distribution circuit widely used in sparsely populated areas and has the lowest initial cost. Classical power flow techniques such as Gauss-Seidel, coupled and fast-decoupled Newton-Raphson are more suitable for transmission systems. These techniques, in most cases, fail to converge when used for distribution systems power flow. The failure of classical techniques in a distribution system as clearly attributed to its inability to accommodate features of RDNs such as High R X ratio, large number of buses and lines, uncertainties and imperfection of network parameters, dynamic nature of connected loads and dynamic change in imposed load (Singh and Ghose, 2013). In RDNs, the large R/X ratio causes problems in convergence of conventional load flow algorithms. Due to this reason, popularly used Newton
– Raphson and Fast Decoupled load flow algorithms may provide inaccurate results and may not converge. Hence, conventional load flow methods cannot be applied to obtain the load flow solution of radial distribution systems.
Power Flow Analysis
Power Flow Analysis is an important and basic tool for power system analysis which helps to determine the steady state behavior of the power system. A number of power flow algorithms were developed in order to solve unbalanced distribution system. Some of them were capable of finding solution even after including DG sources. The most popular method is the Backward-Forward sweep method (Maya and Jasmin, 2015). Backward-forward sweep power flow algorithm for Radial Distribution Systems (RDS) described by Alhaddad and El-Hawary (2014) is used in this research work. In this radial distribution power flow algorithm (RDPF) with backward forward sweep, two matrices namely [BIBC] and [BCBV] are formed to get the power flow solution. [BIBC] is the relationship matrix between bus current injections and branch currents. Whereas, [BCBV] represent the relationship between branch currents and bus voltages. In order to explain the backward-forward sweep method, a radial distribution system is considered having „n‟ buses. The single line diagram is presented in Figure 2.4 (Alhaddad and El-Hawary, 2014).
CHAPTER THREE
MATERIALS AND METHODS
Introduction
In this chapter, the detailed procedures, methods and materials used in achieving the aim of this research are discussed. This involves the application of the Cuckoo Search Algorithm in order to optimally site and size Micro-DGs and Shunt capacitor banks ina radial distribution network.
Materials
The materials employed for the actualization of this research are as follows:
Personal Computer
All simulation analyses were carried out using HP EliteBook 6930p with the following specifications:
- Intel(R) Core(TM) 2Duo CPU P8700;
- 5.3 GHz 64-based processor;
- 00GB installed memory (RAM)and;
- 32-bit windows 8 Operating system(OS)
MATLAB 2013a Software
Simulations were performed under virtual platform using MATLAB 2013a for analysis (Mathworks Corporation). The details of the programs developed are provided in the appendices.
Distribution Network Parameters
The standard IEEE 33 and 69- bus and a dedicated 50-bus Canteen Feeder in Zaria distribution network with the following network parameters: slack bus, active and reactive powers and bus voltages in Appendix A1, A2 and A3 have been adopted for this research.
Methods
The following steps which comprises the methods adopted for this research as follows
Acquisition of Relevant Data
Relevant network data for the standard IEEE 33 and 69- bus radial distribution network and the 50- bus Canteen Feeder in Zaria distribution network used are:
- Line data (resistance and reactance of lines inohms)
- Bus data (active and reactive power demand of lines in kW and kVAr respectively)
- Network base voltage
- Sending and receiving end bus numbers and voltages
CHAPTER FOUR
RESULTS AND DISCUSSION
In this chapter, results obtained are presented and discussed. The Cuckoo search algorithm was used for the optimal location and sizing of the DGs and Capacitor banks for the test buses. All simulations were carried out on Matlab R2013a environment and the times of simulation for each process were noted.
IEEE 33 Bus Test System
The bus and line data for the standard IEEE 33- bus system as shown in appendix A1 were used in modeling the system and the base case voltage for each bus were noted. The proposed cuckoo search algorithm described in section 3.8 was used in obtaining the optimal DG and Shunt capacitor bank location and size for the 33-bus network. The bus voltages, total power loss (real and reactive), voltage profile and voltage stability index before and after separate and combined DG and shunt capacitor bank placements were noted for comparison.
CHAPTER FIVE
CONCLUSION AND RECOMMENDATION
Conclusion
This research work has presented the application of DG units and Shunt capacitor banks in a simultaneous placement approach on IEEE 33 and 69 radial test systems with further implementation on 50 Bus Zaria Canteen Feeder. The cuckoo search algorithm was employed in obtaining the optimal sizes and locations of the DGs and CBs for total active and reactive power loss reduction. The voltage stability index was computed for each bus of the networks to ascertain the weakest voltage bus of the network before and after DG and CB allocation. The simultaneous placement approach of the DGs and CBs was applied on the IEEE test networks and Zaria Canteen Feeder network and the results obtained were validated by comparing with the results obtained from separate DGs and CBs allocation on the networks. For IEEE 33 bus system, the simultaneous allocation of DGs and of optimal sizes 515.69 kW, 214.01 kW and at locations of bus 25 and 32 and CBs of optimal size 572.27 kVAr and at locations of bus 30 respectively lead to a 63.29% and 59.38% reduction in active and reactive power loss and 6.32% improvement in voltage profile. The allocation of 3 DGs of sizes 501.89 kW, 100.73 kW, 179.47 kW and at locations of bus 25, 30 and 29 lead to a 31.65% and 31.25% reduction in active and reactive power loss and 5.11% improvement in voltage profile. The allocation of 3 CBs of optimal sizes 620.18 kVAr, 189.98 kVAr, 188.29 kVAr and at locations of bus 30,25 and 24 lead to a 53.16% and 53.13% reduction in active and reactive power loss and 3.95% improvement in voltage profile. For IEEE 69 bus system, the simultaneous placement of DGs and CBs of sizes 239.83 kW, 1408.79 kW, 885.58 kVAr and at locations of bus 53, 50 and 50 respectively lead to a 74.29% and 79.17% reduction in active and reactive power loss and 2.34% improvement in voltage profile. The allocation of 3 DGs of sizes 134.82 kW, 171.46 kW, 677.89 kW and at locations of bus 50, 53 and 39 lead to a 51.43% and 54.17% reduction in active and reactive power loss and 2.02% improvement in voltage profile. The allocation of 3 CBs of sizes 873.64 kVAr, 330.85 kVAr, 105.67 kVAr and at locations of bus 50, 39 and 12 lead to a 28.57% and 31.25% reduction in active and reactive power loss respectively and 1.65% improvement in voltage profile. For50- bus Canteen Feeder in Zaria distribution network, the simultaneous placement of DGs and CBs of sizes 247.19 kW, 137.82 kVAr, 131.78 kW and at locations of bus 23, 25 and 25 respectively lead to a 17.77% and 17.76% reduction in active and reactive power loss and 0.47% improvement in voltage profile. The allocation of 3 DGs of sizes 141.89 kW, 70.83 kW, 262.95 kW and at locations of bus 25, 47 and 23 lead to a 15.70% and 15.79% reduction in active and reactive power loss and 0.45% improvement in voltage profile. The allocation of 3 CBs of sizes 123.85 kVAr, 189.91 kVAr, 63.80 kVAr and at locations of bus 24, 25 and 23 lead to a 9.92% and 9.87% reduction in active and reactive power loss and 0.38% improvement in voltage profile. From results comparison, it is evident that the simultaneous allocation of DGs and Capacitor banksusing the best combination, gives a better performance in terms of power loss reduction, voltage profile and voltage stability improvements of networks when compared to their individual / separate allocation.
Significant Contribution
The significant contributions of this research work are as follows:
Three different types of non- linear complex optimization problems in the direction of optimal placement and sizing problem are proposed considering all the practical constraints. Theseare
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- Optimal placement and sizing of capacitors in RDS for minimization of power loss and improvement of voltage and VSI
- Optimal placement and sizing of DGs in RDS for minimization of power loss and improvement of voltage and VSI
- Optimal placement of DGs and CBs simultaneously in RDS for minimization of power loss and improvement of voltage and VSI
- Improvement of active power loss reduction by 63.29% for the 33 bus system, 74.29% for69 bus system and 17.77% for the 50 bus system over the active power loss reduction obtained from the individual placement of DGs and CBs and base case values.
- Voltage profile improvement of 6.32%, 2.34% and 0.47% for 33, 69 and 50 bus system respectively over base case
Limitations
The aim of this research work was successfully achieved, but some of the limitations of this research work are highlighted as follows:
- Recent line and bus data for the 50 bus canteen feeder in Zaria distribution network were not available for proper analysis of voltage
- System control and practical implementation of the DGs and capacitor banks was not considered.
- This research work does not consider decentralization of the micro-DGs
- This research work was carried out based on an offline study of a radial distribution system.
Recommendations
The following are recommended for future works that can be considered as an extension of this research work:
- As a future study, researchers can define an index for cost to evaluate the various cost associated with the allocation of the DG units and shunt capacitor banks, operation and maintenance cost of theDG units and shunt capacitor
- The simultaneous allocation method for the DG units and shunt capacitor banks can also be used for the optimal location and sizing of decentralised micro- DGs in an online
- Further analysis can also be done by using a hybridization of CSA with analytical optimization algorithms.
REFERENCES
- Abubakar, A. S. (2014). Developement of an Optimal Reconfiguration Algorithm for Radial Distribution Power Network (A Case Study of Zaria Distribution Network).(MSc Thesis), Ahmadu Bello University Zaria, Nigeria
- Akorede, M., (2010). “A review of strategies for optimal placement of distributed generation in power distribution systems.” Research Journal of Applied Sciences5(2),137-145.
- Alhaddad and El-Hawary, F.M., & El-Hawary, M. (2014 November). Optimal Filter Placemaent and sizing Using Ant Colony Optimization in Electrical Distribution System. In Electrical Power and Energy Conference (EPEC), 2014 IEEE (pp. 128- 133). IEEE
- Aman, M.M., Jasmon, G.B., A. H.A Bukar, Mokhlis, H. (2013). Optimal Simultaneous DG and Capacitor bank placement on the Basis of Minimization of Power Losses. IJCEE, Vol. 5(5), 516- 522
- Archarya, N., Mahat, P., & Mithulananthan, N. (2006). An analytical approach for DG allocation in primary distribution network. International Journal of Electrical Power & Energy systems, 28(10), 669-678.
